Modern Physics Letters B

The anti-reflective (AR) structure greatly reduces the light reflection. When it is applied on solar cells, it enables more light to be absorbed by the cells, increasing the energy of the incident light and improving the light-to-electricity conversion efficiency. In this study, the optical properties of AR microstructures are investigated followed by the performance evaluation of solar cells. The AR microstructure is arrayed in a uniform and periodic fashion. When it is applied on PMMA, only 1.0% of the light is reflected away while 2.6% of the light is reflected on glass. The angular dependence performance is also improved with AR structure with 9.4% more light absorption, which can increase the effective energy generation duration for the solar cell. The AR structure is applied to amorphous silicon thin film solar cells by nano-imprinting technology. The solar cell with AR structure gained 8.63% more power compared to the conventional solar cells.

Low-reflection polyethylene terephthalate (PET) films are fabricated with nano-imprinting method. The films are then used to cover polycrystalline silicon solar cells. The morphological and optical properties of films are investigated. The films have periodic cylinder-like nanostructures and relatively low reflectivity in light incident angle ranging from 30${}^{\circ }$ to 60${}^{\circ }$. The nanostructures are with a period of 600 nm and height of 90 nm. Besides, the polycrystalline Si solar cells covered with the films exhibit 12% more power generation than the cells covered with glass. Nano-imprinting method offers a cost-effective approach to fabricate omnidirectional anti-reflection films, which could boost the power generation of Si solar cells. Additionally, the films also have potential applications in different types of solar cells due to its facile fabricating process.

The submicron array was fabricated onto a cyclo olefin copolymer (COC) film by a hot embossing method. An amorphous silicon p-i-n junction and transparent conductive layers were then deposited onto it through a plasma enhanced chemical vapor deposition (PECVD) and magnetron sputtering. The efficiency of the fabricated COC-based solar cell was measured and the result demonstrated 18.6% increase of the solar cell efficiency when compared to the sample without array structure. The imprinted polymer solar cells with submicron array indeed increase their efficiency.

Implementation of Electrostatic Discharge (ESD) protection in Silicon on Insulator (SOI) technology is a challenge because of the inherent properties of poor heat conductor and heat trapping. In this paper, a novel device as ESD clamp is proposed as Fix-Base SOI FinFET clamp which addresses the troublesome problem of floating base. Moreover, its manufacturing process is compatible to the normal SOI process flow well. Finally, a detailed discussion including current density and thermal distribution are presented with the technique of 3D TCAD simulation.

The special using condition of high-power three-level explosion-proof inverter limits its cooling system within heat pipe and water-cooled cooling systems. How to calculate these two systems quantitatively to provide references for engineering application becomes one of the critical problems. In this paper, the principle of three-level explosion-proof was introduced first, and the power-loss generation theory was described and deduced into equations. Secondly, the heat pipe cooling system theory calculation was conducted based on the power losses of power devices, and the whole cooling system model was built by using finite element analysis. Finally, the temperature rise experiment was carried out on a 1 MW high-power three-level explosion-proof inverter, and the results proved the feasibility of this theory and its accuracy of analysis.

Filtered cathode vacuum arc deposition can remove the macroparticles produced from the cathode. Positively biasing the whole filter or inserting a biased plate in the filter can increase the plasma transport efficiency. We developed a curved magnetic filter with rectangular cross-section to improve the coating efficiency. In this study, the effect of biased plates at outer-wall and inner-wall on the transport efficiency of vacuum arc plasma through rectangular curved magnetic filter was investigated. A Langmuir probe system is used to measure the distribution properties of the filtered plasma at 15 places in the outlet plane of the filter. The results showed that a positively biased plate at inner-wall would increase the output ion current density and make the plasma concentrate to the middle of the outlet plane.

Channel mobility in the p-type polycrystalline silicon thin film transistors (poly-Si TFTs) is extracted using Hoffman method, linear region transconductance method and multi-frequency C-V method. Due to the non-negligible errors when neglecting the dependence of gate-source voltage on the effective mobility, the extracted mobility results are overestimated using linear region transconductance method and Hoffman method, especially in the lower gate-source voltage region. By considering of the distribution of localized states in the band-gap, the frequency independent capacitance due to localized charges in the sub-gap states and due to channel free electron charges in the conduction band were extracted using multi-frequency C-V method. Therefore, channel mobility was extracted accurately based on the charge transport theory. In addition, the effect of electrical field dependent mobility degradation was also considered in the higher gate-source voltage region. In the end, the extracted mobility results in the poly-Si TFTs using these three methods are compared and analyzed.

Based on the switched capacitor system theory, a new charge pump is designed as the driver of the H-bridge power circuits. The proposed circuit is added with the output feedback control module to realize the steady output, lower the ripple and power noise, and improve the transforming efficiency. Simulation based on 0.35 $\mu \mathrm{\text{m}}$ BCD350GE process demonstrates that the circuit has a ripple voltage as low as 200 mV and reaches a high efficiency up to 70% with a load as much as 20 mA when the supply voltage changes from 8 V to 36 V.

Substrate removing technique is proposed in silicon Mach–Zehnder modulator (MZM) to improve the electro-optic bandwidth. Based on this technique, a silicon MZM with 3 dB electro-optical bandwidth of 55 GHz is achieved at 5 V reverse bias for the first time. The V$\pi \cdot$ L of the modulator is 1.3 V$\cdot$cm with an on-chip insertion loss of 5.4 dB. The substrate removing technique reduces the electrode transmission loss, achieves the electro-optical group index matching and realizes 50 $\mathrm{\Omega }$ impedance matching, simultaneously. In this work, we experimentally demonstrate BPSK modulation based on this modulator at the baud rate up to 56 Gb/s.

Optoelectronic and structural properties of hydrogenated microcrystalline silicon–germanium ($\mu$c-Si${}_{1-x}$Ge${}_x$:H) alloys prepared by radio-frequency plasma-enhanced chemical vapor deposition (RF-PECVD) were investigated. When the Ge atoms were predominantly incorporated in amorphous matrix, the dark and photo-conductivity decreased due to the reduced crystalline volume fraction of the Si atoms (X${}_{\mathrm{\text{Si–Si}}}$) and the increased Ge dangling bond density. The photosensitivity decreased monotonously with Ge incorporation under higher hydrogen dilution condition, which was attributed to the increase in both crystallization of Ge and the defect density.

Based on the operating conditions of tablet PC, this paper presents the design of a long load life aluminum electrolytic capacitor. Due to the key technology breakthrough of electrolyte with low resistance and excellent temperature stability, the capacitor boasts low leakage current, low impedance, high frequency, high ripple resistance and high temperature resistance. In the meantime, it can pass 5000 h of durability test with load at 105${}^{\circ }$C. The aluminum electrolytic capacitor can be used in tablet PC with long load life.

In active magnetic bearing (AMB) system, the catcher bearings (CB) are indispensable to temporarily support the rotor from directly impacting the stators. In most cases, traditional CB cannot bear the ultra-high speed, vibrations and impacts after a rotor drop event. To address the shortcomings, a double-decker ball bearing (DDBB) with inner two face-to-face angular contact ball bearings are proposed to be used as CB in an AMB system, and the dynamic response of the rotor after a rotor drop event is experimentally analyzed. The results indicate that using a DDBB as a CB helps to reduce the following collision forces after a rotor drop. Larger ball initial contact angles and smaller pre-load force on the inner layer bearings, larger radial clearance of the outer layer bearing and choosing AISI 10AISI 1045 steel which has a larger density for the adapter ring can effectively reduce the maximum impact force after a rotor drop event.

A new sliding mode variable structure control algorithm suitable for active magnetic bearing is proposed, which is widely used for nonlinear control system. The model and controller is designed, simulation and experimental parts are also made, according to the switching function and the sliding mode control law. The current of electromagnet is adjusted to realize stable levitation of the rotor. The experimental result shows that the sliding mode variable structure controller is an effective way for magnetic bearing control, and the active magnetic bearing system is a highly nonlinear and advanced control method that can reduce the setting time and the cost.

Catcher bearings (CB) are required to support the rotor rotating for some time when a failure event of active magnetic bearing (AMB) system occurs. For this purpose, a new type zero-clearance catcher bearing (NTZCB) is proposed. The influences of different parameters of NTZCB on the rotor dynamic responses are theoretically and experimentally analyzed. The results indicate that choosing relatively soft spring and heavy moveable supporting pedestal can effectively buffer the rotor vibrations, which makes it possible for the rotor to keep rotating with the support of the CB system for a long time.

With the application of active magnetic levitation technology, flutter is a problem in the planar multi-point support system, which reduces the bearing capacity and the control precision, and it is difficult to apply advanced control strategies. Therefore, a new method called magnetic damping control is proposed to solve the flutter problem, which can make active magnetic levitation guide to run smoothly.

Because of the shortage of rule gap measurement for light guide plate (LGP) warpage degree, image definition criterion of quality indexing is present based on single CCD camera. The detection paper and LGP are respectively arranged on the camera detection device platform, located by X- and Y-axes driving unit and focused by Z-axis driving unit firstly, different image definition criterion peaks of edge detection evaluation function are obtained in the middle surface of LGP bottom and surface. Finally, different edge detection algorithms are compared under different situations. Results show that the algorithm has good repetition and can meet the online, untouched detection needs.

In order to improve the robustness of microfluidic networks in printed circuit board (PCB)-based microfluidic platforms, a new method was presented. A pattern in a PCB was formed using hollowed-out technology. Polydimethylsiloxane was partly filled in the hollowed-out fields after mounting an adhesive tape on the bottom of the PCB, and solidified in an oven. Then, microfluidic networks were built using soft lithography technology. Microfluidic transportation and dilution operations were demonstrated using the fabricated microfluidic platform. Results show that this method can embed microfluidic networks into a PCB, and microfluidic operations can be implemented in the microfluidic networks embedded into the PCB.

To study the ignition characteristics of semiconductor bridges (SCB) for automobile airbag igniters, a model was proposed to compute the electrical characteristics of SCB based on the structural characteristics, resistance properties, and the working process of the bridge. Numerical simulation was conducted to analyze the voltage and current trends at two ends of bridges with various geometric dimensions and those with identical geometric dimensions and oxide layers of different thicknesses. Voltage–current curves for bridges of three different dimensions were obtained through numerical simulation. Bridges of larger geometric dimensions were found to require higher voltage to reach the melting point, which means higher energy input is required.

In this research, task errors were obtained during performance of a marker location task in which the markers were shown on a computer screen under nine LED lighting conditions; three illuminances (100, 300 and 500 lx) and three color temperatures (3000, 4500 and 6500 K). A total of 47 students participated voluntarily in these tasks. The results showed that task errors in the morning were small and nearly constant across the nine lighting conditions. However in the afternoon, the task errors were significantly larger and varied across lighting conditions. The largest errors for the afternoon session occurred when the color temperature was 4500 K and illuminance 500 lx. There were significant differences between task errors in the morning and afternoon sessions. No significant difference between females and males was found. Task errors for high myopia students were significantly larger than for the low myopia students under the same lighting conditions. In summary, the influence of LED lighting on task accuracy during office hours was not gender dependent, but was time of day and myopia dependent.

Low frequency noises in the p-type polycrystalline silicon thin film transistors are investigated. It shows a pure 1/f${}^{\gamma }$ (with $\gamma$ near one) noise behavior which can be explained by emission and trapping processes of carriers between trapping states. Subsequently, the gate voltage-dependent drain current noise power spectral densities closely follow the mobility fluctuation model, and the average Hooge’s parameter is then extracted. By considering traditional tunneling processes, the flat-band voltage spectral density is extracted and the concentration of traps in the grain boundary is calculated to be $7.17\times 10^{20}{\mathrm{\text{cm}}}^{-3}{\mathrm{\text{eV}}}^{-1}$. By converting the frequency to tunneling depth of carriers in the gate oxide, the spatial distribution of gate oxide trapped charges are obtained. Finally, the distribution of localized states in the energy band is extracted. The experimental results show an exponential deep states and tail states distribution in the band gap while $N_{\mathrm{\text{DD}}}$ is about $1.5\times 10^{20}{\mathrm{\text{cm}}}^{-3}{\mathrm{\text{eV}}}^{-1}$, $T_{\mathrm{\text{DD}}}$ is $\sim$617 K, $N_{\mathrm{\text{TD}}}$ is $\sim 3.6\times 10^{21}{\mathrm{\text{cm}}}^{-3}{\mathrm{\text{eV}}}^{-1}$ and $T_{\mathrm{\text{TD}}}$ is $\sim$265 K.

LED junction temperature is an important parameter for LED performance and life. Experiment investigation of the equivalent impedance method for determining the junction temperature of single LED chip has been reported in the past. In this work, this method was expanded to any LED array. The outcome showed that the average junction temperature of any LED array had a linear relationship with the equivalent impedance, which was in agreement with experiments. Therefore, the equivalent impedance can be used to determine the LED junction temperature.

Chinese Spallation Neutron Source (CSNS) project will use numerous two-dimensional (2D) neutron detectors whose ZnS (Ag) scintillator is doped with ⁶Li. To ensure the consistency of all neutron detectors, a calibration system for the performance of 2D neutron detectors is designed. For radiation protection, the state control of the radiation source gets more and more strict. It is impossible to directly carry out experiments with massive radioactive particles. Thus, the following scheme has been designed. The controlled pulsed laser light source on a 2D mobile platform is used to replace the neutron bombardment to generate the photon. The pulse signal drives the laser diode to generate pulse light. The pulse light source located on the 2D platform is controlled by the core controller, and goes to the wavelength shift fiber through the optical fiber. The host computer (PC) receives the signal from the electronics system, processes data, and automatically calibrates the performance parameters. As shown by the experimental results, the pulse light source can perfectly meet all requirements of 2D neutron detector calibration system.

A novel method to determine the junction temperature of GaN type blue light LED based on the spectral parameters is proposed. In this method, the relationships among LED junction temperature, centroid wavelength and FWHM are obtained in the lab, and then the junction temperature of operating LED of the same type can be derived from this relationship. A comparative analysis of the junction temperatures which are measured with the centroid wavelength-FWHM method and with the forward voltage method is performed. The standard deviation between these two methods is found to be about 2.3${}^{\circ }$C. Therefore, the combination of centroid wavelength and FWHM can be used to determine the junction temperature of GaN-based blue LED.

Paper chip is an efficient and inexpensive device for pesticide residues detection. However, the reasons of detection error are not clear, which is the main problem to hinder the development of pesticide residues detection. This paper focuses on error analysis for pesticide detection performed on paper-based microfluidic chip devices, which test every possible factor to build the mathematical models for detection error. In the result, double-channel structure is selected as the optimal chip structure to reduce detection error effectively. The wavelength of 599.753 nm is chosen since it is the most sensitive detection wavelength to the variation of pesticide concentration. At last, the mathematical models of detection error for detection temperature and prepared time are concluded. This research lays a theory foundation on accurate pesticide residues detection based on paper-based microfluidic chip devices.

This paper presents a study of heterojunction with intrinsic thin layer (HIT) solar cells based on n-type silicon substrates by a simulation software AFORS-HET. We have studied the influence of thickness, band gap of intrinsic layer and defect densities of every interface. Details in mechanisms are elaborated as well. The results show that the optimized efficiency reaches more than 23% which may give proper suggestions to practical preparation for HIT solar cells industry.

Semiconducting thin $\beta$-FeSi₂ film has been recognized as a novel solar cell material due to its high absorption coefficient. In this study, the effects of emitter on the photovoltaic properties of n-$\beta$-FeSi₂/p-Si were analyzed using AFORS-HET program. The simulation results show that the thickness of emitter affects the conversion efficiency of solar cell, and the short-current density decreases sharply with increasing thickness of emitter. Interface state is another key factor influencing the conversion efficiency of solar cell, which degrades solar cell performance. In order to obtain high efficiency battery, interface state density should be less than 10${}^{11}$ cm${}^{-2}$ eV.

Concerning ultrasonic non-destructive testing of ceramic-lined composite steel pipes, a novel bonding flaw locating method based on fractal dimension is proposed. Ultrasonic A-scan method is used on different positions of the composite steel pipe test piece. The fractal dimension of each curve of ultrasonic vibration signal is calculated. The transformation of each fractal dimension is compared and abnormal positions where bonding defects potentially exist are detected. The result indicates that ultrasonic A-scan signal has an excellent fractal conduct characteristic. It is feasible to compare fractal dimension of signal with the normal range and find out abnormal positions, which can provide basis for follow-up inspections.

Reflectance spectrum of nanoporous silicon dioxide (SiO₂) double layer was calculated by using the matrix method. The results were compared with the corresponding spectrum of silicon oxynitride (SiO${}_x$N${}_y$)–porous silicon (PS) double layer which deposited on nanostructured black silicon coatings. The nanoporous silicon dioxide (SiO₂) double layer deposited on nanostructure black silicon antireflection coating presents a lower reflectance in a broad range of solar spectrum. This research outcome may find a wide application in solar cell industry.

Azimuth electromagnetic wave is a new type of electromagnetic prospecting technology. It can detect weak electromagnetic wave signal and realize real-time formation conductivity imaging. For effectively optimizing measurement accuracy of azimuth electromagnetic wave imaging tool, the efficient numerical simulation algorithm is required. In this paper, self-adaptive finite element method (FEM) has been used to investigate the azimuth electromagnetic wave logging tool response by adjusting antenna array system in different geological conditions. Numerical simulation examples show the accuracy and efficiency of the method, and provide physical interpretation of amplitude attenuation and phase shift of electromagnetic wave signal. Meanwhile, the high-accuracy numerical simulation results have great value to azimuth electromagnetic wave imaging tool calibration and data interpretation.

This research proposes a three-layer image-spam filtering system. The system filters the image spam by analyzing both the mail header and image. We elaborate the structure of the model and explicate carefully our idea of the design and many technologies related to the model. Experimental results show that this system has satisfactory filtering effect.

In the field of computer vision, camera calibration is a hot issue. For the existing coupled problem of calculating distortion center and the distortion factor in the process of camera calibration, this paper presents an iterative-decreasing calibration method based on regional circle, uses the local area of the circle plate to calculate the distortion center coordinates by iterative declining, and then uses the distortion center to calculate the local area calibration factors. Finally, makes distortion center and the distortion factor for the global optimization. The calibration results show that the proposed method has high calibration accuracy.

Heterogeneous deformation measurement using traditional digital image correlation (DIC) has times error of homogeneous deformation due to localized complexity. In case of small strain window, displacement field error will substantially corrupt the derived strain. On the contrary, large strain window will induce a reasonable information reduction in particular of heterogeneous deformation. In this paper, a novel parameter was put forward to correct displacement field and select strain subset size dynamically. This parameter was determined by localized displacement field that is called the localized displacement non-uniform intensity $(\lambda )$. In addition, there is a simple and effective method to eliminate the rigid body rotation impact on strain measurement. A series of speckle images containing different heterogeneous deformation are simulated finally. Results show that the accuracy on the displacement and strain field can be substantially improved especially in heterogeneous deformation fields.

Although the linear minimum mean square error (LMMSE) filter removes speckle in polarimetric synthetic aperture radar (PolSAR) images, it has the disadvantage of losing edge detail. In this paper, we propose a new filter based on robust independent component analysis (RICA) and LMMSE. This approach describes edge features in a span image by selecting the adaptive direction window and calculating the edge weight value of the spatial domain, and improves the objective function by using a step polynomial to extract the estimate of the source image with minimum noise. This technique preserves not only the edge information in the images, but also the polarimetric information. Experiments were conducted on the NASA/JPL AIRSAR L-band of the San Francisco area, and evaluated by means of the speckle reduction index and the edge preservation index. The experimental results show that the proposed method effectively reduces speckle, retains edges, and preserves the polarimetric scattering mechanisms.

In the field of fruits damage assessment, biospeckle activity is considered relevant to quality properties of plants, such us damage, aging, or diseases. In this paper, biospeckle technique was applied to identify the early bruising of apples. Then a total of 50 undamaged apples were determined to be artificially bruised as samples. Three methods (Fujii, GD, and LSTCA) were used to extract effective information from these speckle images for measuring the intensity of biospeckle activity. The results showed that for all of three methods, the biospeckle activities of the undamaged areas in apple were similar; after the hit, the damaged area showed a lower biospeckle activity. It can be concluded that early bruising can be identified by biospeckle technique.

Traditional manual sorting operation is not suitable for the development of Chinese logistics. For better sorting packages, a QR code recognition technology is proposed to identify the QR code label on the packages in package auto-sorting system. The experimental results compared with other algorithms in literatures demonstrate that the proposed method is valid and its performance is superior to other algorithms.

Hartmann–Shack sensor (HSS) has been used in objective measurement of human eye wave-front aberration, but the research on the effects of sampling point size on the accuracy of the result has not been reported. In this paper, point spread function (PSF) of the whole system mathematical model was obtained via measuring the optical imaging system structure of human eye wave-front aberration measurement. The impact of Airy spot size on the accuracy of system was analyzed. Statistics study show that the geometry of Airy spot size of the ideal light source sent from eye retina formed on the surface of HSS is far smaller than the size of the HSS sample point image used in the experiment. Therefore, the effect of Airy spot on the precision of the system can be ignored. This study theoretically and experimentally justifies the reliability and accuracy of human eye wave-front aberration measurement based on HSS.

To improve the image quality and compensate deficiencies of haze removal, we presented a novel fusion method. By analyzing the darkness channel of each method, the effective darkness channel model that takes the correlation information of each darkness channel into account was constructed. This method was used to estimate the transmission map of the input image, and refined by the modified guided filter in order to further improve the image quality. Finally, the radiance image was restored by combining the monochrome atmospheric scattering model. Experimental results show that the proposed method not only effectively remove the haze of the image, but also outperform the other haze removal methods.

In this paper, a novel method named Haze Removal based on Two Steps (HRTS) for removing the haze has been proposed based on two steps, which obviously improves the image qualities such as color and visibility caused by haze. The proposed method mainly consists of two steps: the preprocessing step by decomposing the input image to reduce the influence of ambient light and the removed haze step for restoring the radiance. We first reduce the effect of the ambient light by decomposing the haze image, estimate the transmission map based on the result of the decomposition, and then use the modified guided filter method to refine it. Finally, the monochrome atmospheric scattering model is combined to restore the radiance image. Experimental results show that the proposed method could effectively remove the haze and obviously improve the color and visibility of the image in the realistic scenes by comparing other existing haze removal methods.

This work proposed a method to acquire regions of fruit, branch and leaf from red apple image in orchard. To acquire fruit image, R-G image was extracted from the RGB image for corrosive working, hole filling, subregion removal, expansive working and opening operation in order. Finally, fruit image was acquired by threshold segmentation. To acquire leaf image, fruit image was subtracted from RGB image before extracting 2G-R-B image. Then, leaf image was acquired by subregion removal and threshold segmentation. To acquire branch image, dynamic threshold segmentation was conducted in the R-G image. Then, the segmented image was added to fruit image to acquire adding fruit image which was subtracted from RGB image with leaf image. Finally, branch image was acquired by opening operation, subregion removal and threshold segmentation after extracting the R-G image from the subtracting image. Compared with previous methods, more complete image of fruit, leaf and branch can be acquired from red apple image with this method.

Robust principal component analysis (RPCA) decomposition is widely applied in moving object detection due to its ability in suppressing environmental noises while separating sparse foreground from low rank background. However, it may suffer from constant punishing parameters (resulting in confusion between foreground and background) and holistic processing of all input frames (leading to bad real-time performance). Improvements to these issues are studied in this paper. A block-RPCA decomposition approach was proposed to handle the confusion while separating foreground from background. Input frame was initially separated into blocks using three-frame difference. Then, punishing parameter of each block was computed by its motion saliency acquired based on selective spatio-temporal interesting points. Aiming to improve the real-time performance of the proposed method, an on-line solution to block-RPCA decomposition was utilized. Both qualitative and quantitative tests were implemented and the results indicate the superiority of our method to some state-of-the-art approaches in detection accuracy or real-time performance, or both of them.

In order to solve the effects of illumination changes and differences of personal features on the face recognition rate, this paper presents a new face recognition algorithm based on Gabor wavelet and Locality Preserving Projections (LPP). The problem of the Gabor filter banks with high dimensions was solved effectively, and also the shortcoming of the LPP on the light illumination changes was overcome. Firstly, the features of global image information were achieved, which used the good spatial locality and orientation selectivity of Gabor wavelet filters. Then the dimensions were reduced by utilizing the LPP, which well-preserved the local information of the image. The experimental results shown that this algorithm can effectively extract the features relating to facial expressions, attitude and other information. Besides, it can reduce influence of the illumination changes and the differences in personal features effectively, which improves the face recognition rate to 99.2%.

In this paper, a fast pre-classified-based super-resolution model has been proposed to overcome the problems of degraded imaging in weak-target real-time detection system, specialized to copper defect detection. To accurately characterize the defected image, textural features based on the statistical function of gray-gradient are presented. Furthermore, to improve the effectiveness and practicality of the online detection, a concept of pre-classified learning is introduced and an edge smoothness rule is designed. Some experiments are carried out on defect images in different environments and the experimental results show the efficiency and effectiveness of the algorithm.

A cross-comparison method was used to assess the SPOT-6 optical satellite imagery against Chinese GF-1 imagery using three types of indicators: spectral and color quality, fusion effect and identification potential. More specifically, spectral response function (SRF) curves were used to compare the two imagery, showing that the SRF curve shape of SPOT-6 is more like a rectangle compared to GF-1 in blue, green, red and near-infrared bands. NNDiffuse image fusion algorithm was used to evaluate the capability of information conservation in comparison with wavelet transform (WT) and principal component (PC) algorithms. The results show that NNDiffuse fused image has extremely similar entropy vales than original image (1.849 versus 1.852) and better color quality. In addition, the object-oriented classification toolset (ENVI EX) was used to identify greenlands for comparing the effect of self-fusion image of SPOT-6 and inter-fusion image between SPOT-6 and GF-1 based on the NNDiffuse algorithm. The overall accuracy is 97.27% and 76.88%, respectively, showing that self-fused image of SPOT-6 has better identification capability.

The infrared traffic image acquired by the intelligent traffic surveillance equipment has low contrast, little hierarchical differences in perceptions of image and the blurred vision effect. Therefore, infrared traffic image enhancement, being an indispensable key step, is applied to nearly all infrared imaging based traffic engineering applications. In this paper, we propose an infrared traffic image enhancement algorithm that is based on dark channel prior and gamma correction. In existing research dark channel prior, known as a famous image dehazing method, here is used to do infrared image enhancement for the first time. Initially, in the proposed algorithm, the original degraded infrared traffic image is transformed with dark channel prior as the initial enhanced result. A further adjustment based on the gamma curve is needed because initial enhanced result has lower brightness. Comprehensive validation experiments reveal that the proposed algorithm outperforms the current state-of-the-art algorithms.

Heart segmentation is an important auxiliary method in the diagnosis of many heart diseases, such as coronary heart disease and atrial fibrillation, and in the planning of tumor radiotherapy. Most of the existing methods for full heart segmentation treat the heart as a whole part and cannot accurately extract the bottom of the heart. In this paper, we propose a new method based on linear gradient model to segment the whole heart from the CT images automatically and accurately. Twelve cases were tested in order to test this method and accurate segmentation results were achieved and identified by clinical experts. The results can provide reliable clinical support.

A pentagram star pattern identification algorithm for three-head star sensors was proposed. Its realization scheme was presented completely. Simulated star maps were produced by letting the three-head star sensor travel around the celestial sphere randomly and image the observed stars. Monte Carlo experiments were carried out. The performances of the pentagram algorithm were evaluated. It proves that its identification success rate reaches up to 98%.

The automatic location of the bar code is a key step in the bar code image recognition system. It is extremely confined that the generalization of the traditional bar code localization algorithms due to the requirements of both direction and quality of bar code, and most of them are only aimed at the single barcode localization. In this paper, we have proposed a novel multi-barcode location algorithm in arbitrary direction based on the accumulation of the linear gray value. First, the line coordinates of the barcode region is determined by the image normalized cross-correlation algorithm. Then the center line of gray value of cumulative distribution is used to analyze the barcode boundary and to determine the number of bar code within the region. Finally, the precise positioning of the barcode region is obtained. The experiments have demonstrated that our proposed method can be used to identify all the bar codes in any area, and automatically locate the bar codes in any direction.

To solve the phase distortion and high error rate in optical signal transmission, an equalized technique is proposed, which aims to improve the constant modulus algorithm (CMA). In order to correct phase rotating and reduce the error rate with 64 quadrature amplitude modulation (QAM), the method takes the mean square error as the judgment and utilizes the time-varying step size. Simulation results demonstrate that the proposed algorithm can improve the convergence speed of constellation points, make the eye opening larger, and the signal noise ratio (SNR) can be increased by 4 dB under the same bit error rate (BER), which is efficient for the recovery of information in high-speed transmission.

Electromagnetic measurement technology is an important method for instrument parameters calibration, detection performance evaluation and complex system theory verification. In this paper, self-adaptive finite element method has been used to compute and analyze the weak electromagnetic wave signal, which is produced by changing electrode structure, transmitting frequency and antenna spacing of the through casing electromagnetic measurement tool. Numerical simulation results show that the detection depth of the tool can be influenced by the electrode structure can have the influence on, the resolution of high-resistivity and low-resistivity formation can be improved by transmitting frequency, the detection accuracy and detection depth of the through casing electromagnetic measurement tool can be influenced by the change of antenna spacing.

Efficient task scheduling is critical to achieve high performance in a heterogeneous multi-core computing environment. The paper focuses on the heterogeneous multi-core directed acyclic graph (DAG) task model and proposes a novel task scheduling method based on an improved chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm. A task priority scheduling list was built. A processor with minimum cumulative earliest finish time (EFT) was acted as the object of the first task assignment. The task precedence relationships were satisfied and the total execution time of all tasks was minimized. The experimental results show that the proposed algorithm has the advantage of optimization abilities, simple and feasible, fast convergence, and can be applied to the task scheduling optimization for other heterogeneous and distributed environment.

A 10-bit successive approximation register (SAR) analog-to-digital converter (ADC) with an energy-efficient and area-efficient switching scheme was presented. By using C-2C dummy capacitor and an extra reference $V_{\mathrm{\text{cm}}}$ for the last capacitor, the proposed switching scheme achieves 97.65% switching energy saving, 87.2% capacitor area reduction and 47.06% switches reduction, compare to conventional switching scheme. The ADC was implemented in a 180 nm CMOS technology 1.8 V power supply, at sampling rate of 100 kS/s, the ADC achieves an SNDR of 57.84 dB and consumes 0.975 $\mu \mathrm{\text{W}}$, resulting in a figure-of-merit (FOM) of 15.3 fJ/conversion-step.

Acoustic wave measurement technology is the supporting technology in acoustic wave field. It is important to study acoustic wave with high precision and reliability testing equipment and scientific testing methods. The mathematical model of this acoustic wave measurement system was analyzed on the building of the system. The BP neural network algorithm was used in order to attain the higher accuracy for the acoustic wave measurement system. Frequency domain calibration was carried out by which the amplitude/frequency character curve of this system could be obtained. Then the model of the system was established by BP neural network algorithm. Finally, the validity of the established model was tested. The conclusion was that the math model reflected the original acoustic wave measurement system’s character through the regression result in the frequency domain.

Since weights have an important impact on the prediction results in the combination forecasting, a combination forecasting method based on the fractal dimension weight was proposed to improve the predictive accuracy in this paper. According to the fitting error sequences of the single forecasting models, the reciprocal of box dimension normalization results were taken as the weights of single forecasting models. By using the weighted average method, the results of single forecasting models were combined. Through predicting the concentration of conventional pollutants DO, COD and NH3-N, it is shown that the prediction accuracy of the proposed method in this paper is higher than the other single forecasting models. It is verified that the proposed method in this paper is effective.

The trend of high-speed optical communication is to realize large capacity, fast speed and long haul transmission. However, the dispersion in an optical fiber link can cause signals distortion or even inter symbol interference (ISI). With increasing signal rate and transmission distance in the optical communication system, the development of high-speed optical fiber communication is restricted seriously. This paper is focusing on the dispersion characteristics and compensation of the polarization multiplexing (POLMUX) coherent optical communication system: (i) to study on dispersion theoretically, e.g., chromatic dispersion (CD), polarization mode dispersion (PMD) and dispersion tolerance; (ii) to analyze and compare the performance of dispersion with various 16-ary modulation formats; (iii) to compensate dispersion by the classical dispersion compensation methods.

An extensive, in-depth study of diabetes risk factors (DBRF) is of crucial importance to prevent (or reduce) the chance of suffering from type 2 diabetes (T2D). Accumulation of electronic health records (EHRs) makes it possible to build nonlinear relationships between risk factors and diabetes. However, the current DBRF researches mainly focus on qualitative analyses, and the inconformity of physical examination items makes the risk factors likely to be lost, which drives us to study the novel machine learning approach for risk model development. In this paper, we use Bayesian networks (BNs) to analyze the relationship between physical examination information and T2D, and to quantify the link between risk factors and T2D. Furthermore, with the quantitative analyses of DBRF, we adopt EHR and propose a machine learning approach based on BNs to predict the risk of T2D. The experiments demonstrate that our approach can lead to better predictive performance than the classical risk model.

A novel data hiding method in halftone images with authentication ability was proposed in this paper. A secret image and an authentication image were simultaneously encoded into two halftone images. The secret image was visually reconstructed by stacking the two halftone images together. To verify the validity of the secret, one halftone image was shifted down for several units and stacked with the other one to reveal the authentication image. Experimental results were provided, demonstrating that the proposed method is effective and outperforms existing methods.

This paper considered the parameter identification problem of Hammerstein finite impulse response models and a novel stochastic gradient identification algorithm is derived for the Hammerstein system modeling. By using the gradient search principle and minimizing the quadratic criterion functions, the presented stochastic gradient identification algorithm has a better computational efficiency. The given simulation validates that the proposed algorithm can identify the wind power characteristic curve accurately and contributes to calculate the wind power curtailment prediction.

Energy consumption of numerical control (NC) workshop has lots of characteristics, such as hierarchy, multi-sources and time-varying. These characteristics make the modeling and evaluation of energy consumption in NC workshop very difficult. In this paper, a novel hierarchical model of the energy consumption in NC workshop is presented. Then, the calculation methods of energy efficiency in each layer are given. Furthermore, the acquisition method of the energy consumption data which is easily implemented is put forward and an experiment in NC workshop was made to illustrate the effectiveness of the proposed energy consumption model. The experimental results showed that the model cannot only describe the energy consumption effectively but also provide a way to identify the bottleneck of energy consumption in the workshop.

To improve the performance of gain compensation based on three-segment sound pressure level (SPL) in hearing aids, an improved multichannel loudness compensation method based on eight-segment SPL was proposed. Firstly, the uniform cosine modulated filter bank was designed. Then, the adjacent channels which have low or gradual slopes were adaptively merged to obtain the corresponding non-uniform cosine modulated filter according to the audiogram of hearing impaired persons. Secondly, the input speech was decomposed into sub-band signals and the SPL of every sub-band signal was computed. Meanwhile, the audible SPL range from 0 dB SPL to 120 dB SPL was equally divided into eight segments. Based on these segments, a different prescription formula was designed to compute more detailed gain to compensate according to the audiogram and the computed SPL. Finally, the enhanced signal was synthesized. Objective experiments showed the decomposed signals after cosine modulated filter bank have little distortion. Objective experiments showed that the hearing aids speech perception index (HASPI) and hearing aids speech quality index (HASQI) increased 0.083 and 0.082 on average, respectively. Subjective experiments showed the proposed algorithm can effectively improve the speech recognition of six hearing impaired persons.

The sample size based on the Linex loss function and Blinex loss function is studied in this paper, and the analytical solution of the optimal sample size is deduced on the assumption that the Linex loss function and the normal distribution exist. For the Blinex loss function, an accurate algorithm was presented to obtain the optimal sample size. Furthermore, the optimal sample size is obtained, respectively, by taking Poisson distribution and normal distribution as examples. Due to the wide application of Blinex function in reality, the algorithm presented in this paper has immediate applications.

Currently, the majority of the Network-on-Chip (NoC) researches are based on 2D algorithm or simple 3D structure. However, the congestion and faulty links in the topology can increase the latency and power consumption. In this paper, the authors try to build a novel 3D topology based on hierarchical structure and TSV links which can reduce the latency and power consumption by decreasing the hops during the process of passing the packets. We employ the C++ tool to test our method, and the results show that the performance can be improved about 21%–36% in throughput, also 3%–11% in latency.

The fundamental and the fluorescence anisotropies of New Red and Erythrosine were measured. The intersection angles between the absorption and the emission dipole moments for New Red and Erythrosine are 4.44${}^{\circ }$ and 23.26${}^{\circ }$, respectively. The average angle shift of the emission dipole moment of New Red is 3.91${}^{\circ }$ during the lifetime of the excited state. This indicates that it has a bifurcated linear structure with weak rotational capacity. The average angle shift of the emission dipole moment of Erythrosine is 9.25${}^{\circ }$, indicating that it has a partial planar structure and is easier to rotate. The spatial ground state structures were simulated with Gaussian 09.

The back-off procedure is one of the media access control technologies in 802.11P communication protocol. It plays an important role in avoiding message collisions and allocating channel resources. Formal methods are effective approaches for studying the performances of communication systems. In this paper, we establish a discrete time model for the back-off procedure. We use Markov Decision Processes (MDPs) to model the non-deterministic and probabilistic behaviors of the procedure, and use the probabilistic computation tree logic (PCTL) language to express different properties, which ensure that the discrete time model performs their basic functionality. Based on the model and PCTL specifications, we study the effect of contention window length on the number of senders in the neighborhood of given receivers, and that on the station’s expected cost required by the back-off procedure to successfully send packets. The variation of the window length may increase or decrease the maximum probability of correct transmissions within a time contention unit. We propose to use PRISM model checker to describe our proposed back-off procedure for IEEE802.11P protocol in vehicle network, and define different probability properties formulas to automatically verify the model and derive numerical results. The obtained results are helpful for justifying the values of the time contention unit.

The traditional three-layer Internet of things (IoT) model, which includes physical perception layer, information transferring layer and service application layer, cannot express complexity and diversity in agricultural engineering area completely. It is hard to categorize, organize and manage the agricultural things with these three layers. Based on the above requirements, we propose a new service-oriented grid-based method to set up and build the agricultural IoT. Considering the heterogeneous, limitation, transparency and leveling attributes of agricultural things, we propose an abstract model for all agricultural resources. This model is service-oriented and expressed with Open Grid Services Architecture (OGSA). Information and data of agricultural things were described and encapsulated by using XML in this model. Every agricultural engineering application will provide service by enabling one application node in this service-oriented grid. Description of Web Service Resource Framework (WSRF)-based Agricultural Internet of Things (AIoT) and the encapsulation method were also discussed in this paper for resource management in this model.

The high mounting precision of the fiber underwater acoustic array leads to an array manifold without perturbation. Besides, the targets are either static or slowly moving in azimuth in underwater acoustic array signal processing. Therefore, the covariance matrix can be estimated accurately by prolonging the observation time. However, this processing is limited to poor bearing resolution due to small aperture, low SNR and strong interferences. In this paper, diagonal rejection (DR) technology for Minimum Variance Distortionless Response (MVDR) was developed to enhance the resolution performance. The core idea of DR is rejecting the main diagonal elements of the covariance matrix to improve the output signal to interference and noise ratio (SINR). The definition of SINR here implicitly assumes independence between the spatial filter and the received observations at which the SINR is measured. The power of noise converges on the diagonal line in the covariance matrix and then it is integrated into the output beams. With the diagonal noise rejected by a factor smaller than 1, the array weights of MVDR will concentrate on interference suppression, leading to a better resolution capability. The algorithm was theoretically proved with optimal rejecting coefficient derived under both infinite and finite snapshots scenarios. Numerical simulations were conducted with an example of a linear array with eight elements half-wavelength spaced. Both resolution and Direction-of-Arrival (DOA) performances of MVDR and DR-based MVDR (DR–MVDR) were compared under different SNR and snapshot numbers. A conclusion can be drawn that with the covariance matrix accurately estimated, DR–MVDR can provide a lower sidelobe output level and a better bearing resolution capacity than MVDR without harming the DOA performance.

This paper has a research on resource management and scheduling policy based on grid technology for Agricultural Internet of Things (AIoT). Facing the situation of a variety of complex and heterogeneous agricultural resources in AIoT, it is difficult to represent them in a unified way. But from an abstract perspective, there are some common models which can express their characteristics and features. Based on this, we proposed a high-level model called Agricultural Resource Hierarchy Model (ARHM), which can be used for modeling various resources. It introduces the agricultural resource modeling method based on this model. Compared with traditional application-oriented three-layer model, ARHM can hide the differences of different applications and make all applications have a unified interface layer and be implemented without distinction. Furthermore, it proposes a Web Service Resource Framework (WSRF)-based resource management method and the encapsulation structure for it. Finally, it focuses on the discussion of multi-agent-based AG resource scheduler, which is a collaborative service provider pattern in multiple agricultural production domains.

In modern embedded systems, the increasing number of cores requires efficient cache hierarchies to ensure data throughput, but such cache hierarchies are restricted by their tumid size and interference accesses which leads to both performance degradation and wasted energy. In this paper, we firstly propose a behavior-aware cache hierarchy (BACH) which can optimally allocate the multi-level cache resources to many cores and highly improved the efficiency of cache hierarchy, resulting in low energy consumption. The BACH takes full advantage of the explored application behaviors and runtime cache resource demands as the cache allocation bases, so that we can optimally configure the cache hierarchy to meet the runtime demand. The BACH was implemented on the GEM5 simulator. The experimental results show that energy consumption of a three-level cache hierarchy can be saved from 5.29% up to 27.94% compared with other key approaches while the performance of the multi-core system even has a slight improvement counting in hardware overhead.

To address failures of wind turbine pitch-angle sensors, traditional wind turbine linear parameter varying (LPV) model is transformed into a double-layer convex polyhedron LPV model. On the basis of this model, when the plurality of the sensor undergoes failure and details of the failure are inconvenient to obtain, each sub-controller is designed using distributed thought and gain scheduling method. The final controller is obtained using all of the sub-controllers by a convex combination. The design method corrects the errors of the linear model, improves the linear degree of the system, and solves the problem of multiple pitch angle faults to ensure stable operation of the wind turbine.

A novel low-voltage low-power CMOS voltage reference independent of temperature is presented in this design. After considering the combined effect of (1) a perfect suppression of the temperature dependence of mobility; (2) the compensation of the channel length modulation effect on the temperature coefficient, a temperature coefficient of 10 ppm/${}^{\circ }$C is achieved. Moreover, by adopting the subthreshold MOSFETs, there are no resistors used in the proposed structure. Therefore, the maximum supply current measured at the maximum supply voltage is 70 nA and at 80${}^{\circ }$C. The circuit can be used as a voltage reference for high performance and low power dissipation on a single chip.

A key technique of LTE is how to allocate efficiently the resource of radio spectrum. Traditional Round Robin (RR) scheduling scheme may lead to too many resource residues when allocating resources. When the number of users in the current transmission time interval (TTI) is not the greatest common divisor of resource block groups (RBGs), and such a phenomenon lasts for a long time, the spectrum utilization would be greatly decreased. In this paper, a novel spectrum allocation scheme of cognitive gateway (CG) was proposed, in which the LTE spectrum utilization and CG’s throughput were greatly increased by allocating idle resource blocks in the shared TTI in LTE system to CG. Our simulation results show that the spectrum resource sharing method can improve LTE spectral utilization and increase the CG’s throughput as well as network use time.

The difficulty in the energy efficiency analysis of discrete manufacturing system is the lack of evaluation index system. In this paper, a novel evaluation index system with three layers and 10 indexes was presented to analyze the overall energy consumption level of the discrete manufacturing system. Then, with the consideration of the difficulties in directly obtaining machine energy efficiency, a prediction method based on recursive variable forgetting factor identification was put forward to calculate it. Furthermore, a comprehensive quantitative evaluation method of rough set and attribute hierarchical model was designed based on the index structure to evaluate the energy efficiency level. Finally, an experiment was used to illustrate the effectiveness of our evaluation index system and method.

In this paper, multi-objective flexible job shop scheduling problem (MOFJSP) was studied with the objects to minimize makespan, total workload and critical workload. A variable neighborhood evolutionary algorithm (VNEA) was proposed to obtain a set of Pareto optimal solutions. First, two novel crowded operators in terms of the decision space and object space were proposed, and they were respectively used in mating selection and environmental selection. Then, two well-designed neighborhood structures were used in local search, which consider the problem characteristics and can hold fast convergence. Finally, extensive comparison was carried out with the state-of-the-art methods specially presented for solving MOFJSP on well-known benchmark instances. The results show that the proposed VNEA is more effective than other algorithms in solving MOFJSP.

Multi-objective optimization problems (MOPs) need to be solved in real world recently. In this paper, a multi-objective particle swarm optimization based on Pareto set and aggregation approach was proposed to deal with MOPs. Firstly, velocities and positions were updated similar to PSO. Then, global-best set was defined in particle swarm optimizer to preserve Pareto-based set obtained by the population. Specifically, a hybrid updating strategy based on Pareto set and aggregation approach was introduced to update the global-best set and local search was carried on global-best set. Thirdly, personal-best positions were updated in decomposition way, and global-best position was selected from global-best set. Finally, ZDT instances and DTLZ instances were selected to evaluate the performance of MULPSO and the results show validity of the proposed algorithm for MOPs.

China Education Network (CEN) of year 2014 was studied as a complex network object. By searching the domain of “.edu.cn” and filtering some unexpected results, we finally get a network with 14,100,628 pages and 213,513,401 links. The topology of this network was studied to get the features such as out-degree distribution, in-degree distribution and average shortest path length. These features were compared with that of year 2007 and 2004 to observe the evolution mechanisms of CEN. According to the statistical results, it is found that some topology features of CEN such as out-degree distribution, in-degree distribution and average shortest path have changed a lot and the related reasons for these changes are given in this paper.

Rail defects cause numerous railway accidents. Trains are derailed and serious consequences often occur. Compared to traditional bulk wave testing, ultrasonic guided waves (UGWs) can provide larger monitoring ranges and complete coverage of the waveguide cross-section. These advantages are of significant importance for the non-destructive testing (NDT) of the continuously welded rail, and the technique is therefore widely used in high-speed railways. UGWs in continuous welded rail (CWR) and their propagation characteristics have been discussed in this paper. Finite element methods (FEMs) were used to accomplish a vibration modal analysis, which is extended by a subsequent dispersion analysis. Wave structure features were illustrated by displacement profiles. It was concluded that guided waves have the ability to detect defects in the rail via choice of proper mode and frequency. Additionally, thermal conduction that is caused by temperature variation in the rail is added into modeling and simulation. The results indicated that unbalanced thermal distribution may lead to the attenuation of UGWs in the rail.

Relay-assisted (RA) network with relay node selection is a kind of effective method to improve the channel capacity and convergence performance. However, most of the existing researches about the relay selection did not consider the statically channel state information and the selection cost. This shortage limited the performance and application of RA network in practical scenarios. In order to overcome this drawback, a sequence relay selection strategy (SRSS) was proposed. And the performance upper bound of SRSS was also analyzed in this paper. Furthermore, in order to make SRSS more practical, a novel threshold determination algorithm based on the stochastic dynamic program (SDP) was given to work with SRSS. Numerical results are also presented to exhibit the performance of SRSS with SDP.

We present a new approach to repair degenerated and self-intersected mesh of digitized human models, which automatically transforms a raw digitized mesh into a single manifold and closed triangle mesh. The algorithm interactively removes growing neighborhoods of undesired elements and fills resulted surface gaps to yield meshes which subject to user-specified quality criteria. We demonstrate our experimental results by multiple sets of low-quality original models with qualitative measurements compared with a number of existing methods. The results show that the proposed method produces high visual quality meshes with minimal changes of the original mesh appearances.

Frequency hopping (FH) signal is widely adopted by military communications as a kind of low probability interception signal. Therefore, it is very important to research the FH signal detection algorithm. The existing detection algorithm of FH signals based on the time-frequency analysis cannot satisfy the time and frequency resolution requirement at the same time due to the influence of window function. In order to solve this problem, an algorithm based on wavelet decomposition and Hilbert–Huang transform (HHT) was proposed. The proposed algorithm removes the noise of the received signals by wavelet decomposition and detects the FH signals by Hilbert–Huang transform. Simulation results show the proposed algorithm takes into account both the time resolution and the frequency resolution. Correspondingly, the accuracy of FH signals detection can be improved.

To improve the accuracy of the gene encoding (exon) prediction, near period-3 feature exons prediction algorithm is proposed. Near period-3 clustering power spectrum of extrons and introns are extracted as template feature, DNA sequence is divided into frames and moved. Compared with the template feature, the prediction of the Euclidean distance with different weights is realized from each frame. By changing the different feature, number, frame length, gene sequence weight and comparing with period-3 algorithm, the experiment results show that the prediction accuracy of the proposed algorithm is better than that period-3 algorithm.

Material procedure quality forecast plays an important role in quality control. This paper proposes a prediction model based on genetic algorithm (GA) and back propagation (BP) neural network. It can obtain the initial weights and thresholds of optimized BP neural network with the GA global search ability. A material process quality prediction model with the optimized BP neural network is adopted to predict the error of future process to measure the accuracy of process quality. The results show that the proposed method has the advantages of high accuracy and fast convergence rate compared with BP neural network.

This paper presents an attitude solution algorithm based on the Micro-Electro-Mechanical System and quaternion method. We completed the numerical calculation and engineering practice by adopting fourth-order Runge–Kutta algorithm in the digital signal processor. The state space mathematical model of initial alignment in static base was established, and the initial alignment method based on Kalman filter was proposed. Based on the hardware in the loop simulation platform, the short-range flight simulation test and the actual flight test were carried out. The results show that the error of pitch, yaw and roll angle is fast convergent, and the fitting rate between flight simulation and flight test is more than 85%.

In order to improve the reliability of the spectrum feature extracted by wavelet transform, a method combining wavelet transform (WT) with bacterial colony chemotaxis algorithm and support vector machine (BCC–SVM) algorithm (WT–BCC–SVM) was proposed in this paper. Besides, we aimed to identify different kinds of pesticide residues on lettuce leaves in a novel and rapid non-destructive way by using fluorescence spectra technology. The fluorescence spectral data of 150 lettuce leaf samples of five different kinds of pesticide residues on the surface of lettuce were obtained using Cary Eclipse fluorescence spectrometer. Standard normalized variable detrending (SNV detrending), Savitzky–Golay coupled with Standard normalized variable detrending (SG–SNV detrending) were used to preprocess the raw spectra, respectively. Bacterial colony chemotaxis combined with support vector machine (BCC–SVM) and support vector machine (SVM) classification models were established based on full spectra (FS) and wavelet transform characteristics (WTC), respectively. Moreover, WTC were selected by WT. The results showed that the accuracy of training set, calibration set and the prediction set of the best optimal classification model (SG–SNV detrending-WT–BCC–SVM) were 100%, 98% and 93.33%, respectively. In addition, the results indicated that it was feasible to use WT–BCC–SVM to establish diagnostic model of different kinds of pesticide residues on lettuce leaves.

The degradation degree prediction of lithium-ion battery has been studied through experimental data. Characterization parameters on the degradation degree of lithium-ion battery were deduced under consideration of the internal and external factors. The analysis of discrete degree was proposed to depict the degradation degree for lithium-ion battery. Furthermore, based on fuzzy inference system (FIS), the predicted model of the degradation degree for lithium-ion battery was built and its output was defined as the degenerate coefficient $\beta$, $\beta \in [0,1]$. Finally, by learning, training and simulating, the FIS model has been validated to be reliable and applicable in prediction on the degradation degree of lithium-ion battery. The simulation results show that the degradation degree of lithium-ion battery is more serious when $\beta$ is closer to 1, and the degradation degree is lighter when $\beta$ is closer to 0.

Rapid isolation and detection of special pathogens present in environmental drinking water is critical for water quality monitoring. Numerical analysis and experimental investigations on immunomagnetic capture and isolation of waterborne pathogens with magnetic nanoparticles (MNPs) in microfluidic channel are performed. A finite-element COMSOL-based model is established to demonstrate the novel method of on-chip capturing pathogens using MNPs together with periodic pulse magnetic field. Simulation results determine the optimum magnetic pole current and switching frequency for magnetic separation. With the magnetic isolation experiment platform built up, as a pathogen example of Escherichia coli O157:H7, the performance of the method is experimentally verified. Both numerical and experimental results are found to agree reasonably well. Results of these investigations show that the capture efficiency of the immunomagnetic separation method is more than 92%, which could be encouraging for the design and optimization of MEMS-based biosensor of waterborne pathogen detection.

An algorithm to estimate symmetrical components, orthogonal components and amplitudes of each sinusoidal component in three-phase power system signal under unknown frequencies and unknown biases is presented. The algorithm consists of a signal transformation, a biased adaptive orthogonal decomposition (BAOD) and a symmetrical component estimation. The BAOD can be regarded as a combination of a low pass filter and a number of three-phase frequency estimators in parallel. The symmetrical component estimation employs addition and multiplication rather than operations of trigonometry, division and phase shift. The decomposition property and the convergence property were investigated by Lyapunov theorem, integral manifold of slow adaptation and average method. Two design parameters, bandwidth parameter and frequency adaptive gains, give different effects on the convergence property of frequency adaptation and amplitude estimation independently. Simulation results demonstrate the performance of the method.

The major histocompatibility complex class II (MHC-II) molecule plays a crucial role in immunology. Computational prediction of MHC-II binding peptides can help researchers understand the mechanism of immune systems and design vaccines. Most of the prediction algorithms for MHC-II to date have made large efforts in human leukocyte antigen (HLA, the name of MHC in Human) molecules encoded in the DR locus. However, HLA DQ molecules are equally important and have only been made less progress because it is more difficult to handle them experimentally. In this study, we propose an artificial neural network-based approach called MHC2NNZ to predict peptides binding to HLA DQ molecules. Unlike previous artificial neural network-based methods, MHC2NNZ not only considers sequence similarity features but also captures the chemical and physical properties, and a novel method incorporating these properties is proposed to represent peptide flanking regions (PFR). Furthermore, MHC2NNZ improves the prediction accuracy by combining with amino acid preference at more specific positions of the peptides binding core. By evaluating on 3549 peptides binding to six most frequent HLA DQ molecules, MHC2NNZ is demonstrated to outperform other state-of-the-art MHC-II prediction methods.

To improve the convergence rate and make a balance between the global search and local turning abilities, this paper proposes a decentralized form of artificial bee colony (ABC) algorithm with dynamic multi-populations by means of fuzzy C-means (FCM) clustering. Each subpopulation periodically enlarges with the same size during the search process, and the overlapping individuals among different subareas work for delivering information acting as exploring the search space with diffusion of solutions. Moreover, a Gaussian-based search equation with redefined local attractor is proposed to further accelerate the diffusion of the best solution and guide the search towards potential areas. Experimental results on a set of benchmarks demonstrate the competitive performance of our proposed approach.

A novel phase unwrapping method with five composite fringe patterns and phase-shifting method was proposed for three-dimensional information detection. The composite fringe patterns were composed of phase-shifted sinusoidal and stair phase-coding fringe patterns. The fringe patterns are five step phase-shifted. The relative phase and the stair code-words can be obtained simultaneously based on the five step phase-shifting algorithm. The monochrome composite structure has the improvement on the measurement speed and has the potential of solving the problem of color coupling and color imbalance. Combined with the modified minimum phase map theory, this method can provide more code-words for the full field measurement. Simulation and experimental results were presented in the final part to verify the flexibility and feasibility of the proposed method.

Cognitive radio technology is envisaged to alleviate both spectrum inefficiency and spectrum scarcity problems by exploiting the existing licensed spectrum opportunistically. However, cognitive radio ad hoc networks (CRAHNs) impose unique challenges due to the high dynamic scheduling in the available spectrum, diverse quality of service (QOS) requirements, as well as hidden terminals and shadow fading issues in a harsh radio environment. To solve these problems, this paper proposes a dynamic and variable time-division multiple-access scheduling mechanism (DV-TDMA) incorporated with dual collaborative spectrum sensing scheme for CRAHNs. This study involves the cross-layered cooperation between the Physical (PHY) layer and Medium Access Control (MAC) layer under the consideration of average sensing time, sensing accuracy and the average throughput of cognitive radio users (CRs). Moreover, multiple-objective optimization algorithm is proposed to maximize the average throughput of CRs while still meeting QOS requirements on sensing time and detection error. Finally, performance evaluation is conducted through simulations, and the simulation results reveal that this optimization algorithm can significantly improve throughput and sensing accuracy and reduce average sensing time.

This paper proposes an integrated control system that cooperates with the four-wheel steering (4WS) and direct yaw moment control (DYC) to improve the vehicle handling and stability. The design works of the four-wheel steering and DYC control are based on sliding mode control. The integration control system produces the suitable 4WS angle and corrective yaw moment so that the vehicle tracks the desired yaw rate and sideslip angle. Considering the change of the vehicle longitudinal velocity that means the comfort of driving conditions, both the driving torque and braking torque are used to generate the corrective yaw moment. Simulation results show the effectiveness of the proposed control algorithm.

Path planning is an essential and inevitable problem in robotics. Trapping in local minima and discontinuities often exist in local path planning. To overcome these drawbacks, this paper presents a smooth path planning algorithm based on modified visibility graph. This algorithm consists of three steps: (1) polygons are generated from detected obstacles; (2) a collision-free path is found by simultaneous visibility graph construction and path search by A${}^{\ast }$ (SVGA); (3) the path is smoothed by B-spline curves and particle swarm optimization (PSO). Simulation experiment results show the effectiveness of this algorithm, and a smooth path can be found fleetly.

Some characteristic parameters were epurated in this paper by analyzing internal and external factors of the degradation degree of lithium-ion battery. These characteristic parameters include open circuit voltage (OCV), state of charge (SOC) and ambient temperature. The degradation degree was evaluated by discrete degree of the array, which is composed of the above parameters. The epurated parameters were verified through adaptive neuro-fuzzy inference system (ANFIS) model building. The expression of degradation coefficient was finally determined. The simulation results show that the expression is reasonable and precise to describe the degradation degree.

Frame aggregation is the most important medium access control (MAC) enhancement of IEEE 802.11n. In frame aggregation, multiple frames are encapsulated into a single frame. In the analysis of 802.11n performance, the existing researches assumed that each station always had a packet for transmission. But actually, sometimes stations may have no packet to transmit. In this paper, we develop an analytical model for IEEE 802.11n in unsaturated conditions. Therefore, the transmission of the station is assumed to be a bulk service queue system. Bulk size is aggregation size. According to the 802.11n standard, when the number of packets in the buffer is smaller than the aggregation size, we can also transmit all the packets in the buffer using A-MPDU. Therefore, bulk size is variable. The throughput and mean access delay are achieved. Numerical results show that the proposed algorithm can effectively increase the throughput and lower the access delay.

In this paper, a speech emotion recognition (SER) algorithm was proposed to improve the emotional perception of hearing-impaired people. The algorithm utilizes multiple kernel technology to overcome the drawback of SVM: slow training speed. Firstly, in order to improve the adaptive performance of Gaussian Radial Basis Function (RBF), the parameter determining the nonlinear mapping was optimized on the basis of Kernel target alignment. Then, the obtained Kernel Function was used as the basis kernel of Multiple Kernel Learning (MKL) with slack variable that could solve the over-fitting problem. However, the slack variable also brings the error into the result. Therefore, a soft-margin MKL was proposed to balance the margin against the error. Moreover, the relatively iterative algorithm was used to solve the combination coefficients and hyper-plane equations. Experimental results show that the proposed algorithm can acquire an accuracy of 90% for five kinds of emotions including happiness, sadness, anger, fear and neutral. Compared with KPCA+CCA and PIM-FSVM, the proposed algorithm has the highest accuracy.

Communication network can be treated as a complex network carrying a variety of services and service can be treated as a network composed of functional entities. There are growing interests in multiplex service entities where individual entity and link can be used for different services simultaneously. Entities and their relationships constitute a service entity network. In this paper, we introduced a service entity network virtualization architecture including service entity network hierarchical model, service entity network model, service implementation and deployment of service entity networks. Service entity network oriented multiplex planning model were also studied and many of these multiplex models were characterized by a significant multiplex of the links or entities in different service entity network. Service entity networks were mapped onto shared physical resources by dynamic resource allocation controller. The efficiency of the proposed architecture was illustrated in a simulation environment that allows for comparative performance evaluation. The results show that, compared to traditional networking architecture, this architecture has a better performance.

In this paper, a speech enhancement method using noise classification and Deep Neural Network (DNN) was proposed. Gaussian mixture model (GMM) was employed to determine the noise type in speech-absent frames. DNN was used to model the relationship between noisy observation and clean speech. Once the noise type was determined, the corresponding DNN model was applied to enhance the noisy speech. GMM was trained with mel-frequency cepstrum coefficients (MFCC) and the parameters were estimated with an iterative expectation-maximization (EM) algorithm. Noise type was updated by spectrum entropy-based voice activity detection (VAD). Experimental results demonstrate that the proposed method could achieve better objective speech quality and smaller distortion under stationary and non-stationary conditions.

Automotive ICs work in wide ambient temperature range up to 150${}^{\circ }$C. It is important to design an over temperature protection mechanism for the reliability of ICs and systems. A thermal protection module for the automotive ICs is reported in this paper. Dual channel detection and decision scheme was designed based on band gap voltage reference. Precision thermal protection point was set by serial resistors and the variations of power supply, temperature and the process were removed by the resistor ratio. The thermal protection module was implemented in CSMC 0.5 $\mu \mathrm{\text{m}}$ 60 V BCD process, incorporated in a CAN transceiver chip. The area of the module was about 0.02 mm² and thus it was very compact and low cost to integrate in chips. The performance of the thermal protection parameters was measured in incubators. The thermal shutdown temperature was about 164.4${}^{\circ }$C and the thermal recovery temperature was about 153${}^{\circ }$C with hysteresis temperature of 10 K. Additionally, the thermal protection module showed good consistency with different chips.

According to the low rank property of the background and the sparse features of the target in infrared image, a novel infrared small target detection method based on the nonnegative matrix factorization (NMF) and compressed sensing technology was presented in this paper. This method trained background model through NMF, and then sampled the infrared image sequences directly using the block compressed sensing technology. Through the alternating direction method of multipliers (ADMM), the infrared small target was extracted and the background was recovered from the image. At the same time, the background was updated by the update algorithm, to adapt to the changes in the background. The simulation results show that the proposed method can detect the infrared target precisely and efficiently.

This paper presents a hybrid particle swarm optimization and gravitational search algorithm based on hybrid mutation strategy (HGSAPSO-M) to optimize economic dispatch (ED) including distributed generations (DGs) considering market-based energy pricing. A daily ED model was formulated and a hybrid mutation strategy was adopted in HGSAPSO-M. The hybrid mutation strategy includes two mutation operators, chaotic mutation, Gaussian mutation. The proposed algorithm was tested on IEEE-33 bus and results show that the approach is effective for this problem.

Discrete manufacturing system generates a large amount of data and information because of the development of information technology. Hence, a management mechanism is urgently required. In order to incorporate knowledge generated from manufacturing data and production experience, a knowledge network model of the energy consumption in the discrete manufacturing system was put forward based on knowledge network theory and multi-granularity modular ontology technology. This model could provide a standard representation for concepts, terms and their relationships, which could be understood by both human and computer. Besides, the formal description of energy consumption knowledge elements (ECKEs) in the knowledge network was also given. Finally, an application example was used to verify the feasibility of the proposed method.