Journal of Circuits, Systems and Computers

The order of singularity near the vertex of bonded joints is one of the main factors responsible for debonding of electronic device under mechanical or electrical loading. The distribution of singularity field near the vertex of bonded joints is very important to maintain the reliability of smart electronic structure. Piezoelectric material, due to its characteristic direct-converse piezoelectric effect, has naturally received considerable attentions. Piezoelectric materials have been extensively used as transducers and sensors due to their piezoelectric effects that take place between electric fields and mechanical deformation. The order of singularity at a vertex and at a point on singularity line in 3D transversely isotropic piezoelectric joints is analyzed. Eigen analysis based on FEM is used for stress singularity field analysis of piezoelectric bimaterial joints. The eigen equation is used for calculating the order of stress singularity and the angular function of elastic displacement, electric potential, stress and electric displacement. The numerical result shows that the angular functions have large value near the interface edge than the inner portion of the joint. Therefore, there is a possibility to debond and delamination occurs at interface edge of the piezoelectric bimaterial joints, due to the higher stress and electric displacement concentration at the free edge.

The phonon properties of hydrogen passivated graphene with vacancy defects are studied using the forced vibrational method. The phonon density of states (PDOSs), typical mode patterns and localization length are calculated over a broad range of vacancies. We find that phonon properties of graphene strongly depend on the system size. We observe a broadening and softening of the PDOS peaks with the increase of vacancy concentrations. We find an increasing C–H stretching mode with the increase of defect density. Our numerical experiments reveal that the typical mode pattern for the K point in-plane TO (iTO) modes phonon show the spatial localized vibrations persuaded by vacancies, which are in conceptually good agreement with the large D band peak of the Raman spectra comes from the imperfections of crystal. For the K point iTO mode phonons, a typical localization length is on the order ≈ 5 nm for vacancy-type defects at high concentrations of 30% is observed. The localization effects manifest themselves in the projected temperature behavior of the constant-volume specific heat capacity of pristine and disordered graphene samples.

Currently work in robotics is expanding from industrial robots to robots that are employed in the living environment. For robots to be accepted into the real world, they must be capable to behave in such a way that humans do with other humans, and initiate interaction in the same way. This paper focuses on designing a robotic framework that is able to perform an important social function such as, initiating interaction with the target human in multiparty setting in a natural and social ways. However, it is not easy task for the robot to initiate an interaction process with a particular human, especially when the robot and the human are not facing each other or the intended target human is intensely engaged his/her work. In order to initiate an interaction process, we propose a framework that consists of two parts: capturing attention and ensuring attention capture. Evaluation experiments reveal the effectiveness of the proposed system in four viewing situations namely, central field of view (CFOV), near peripheral field of view (NPFOV), far peripheral field of view (FPFOV), and out of field of view (OFOV) respectively.

Speckle noise in medical ultrasound (US) degrades the image quality and reduces its diagnostic value. Reduction of speckle noise is an important pre-processing step for the analysis and processing of medical ultrasound images. Knowledge of the statistics of the log-transformed speckle especially in the multi-resolution transform domain is important for developing effective homomorphic despeckling techniques, the most popular approach of speckle reduction from ultrasound images. In this paper, the bessel K-form (BKF) probability density function (pdf) is proposed as a highly suitable prior for modeling the log-transformed speckle noise in the well-known contourlet transform domain. A maximum likelihood based method is introduced for estimating the parameters of the BKF pdf. The effectiveness of the proposed estimation method is demonstrated using Monte Carlo simulations. The appropriateness of the BKF pdf in modeling the speckle is first studied extensively for simulated noise of different levels in the contourlet transform domain. Next, the suitability of BKF model is investigated for the case of real US images that include neonatal brain and breast tumors. It is shown that, in general the BKF prior can model the statistics of the contourlet transform coefficients corresponding to the log-transformed speckle better than the traditionally used Gaussian, normal inverse Gaussian and generalized Nakagami pdfs.

This paper presents a cognitive state estimation system focused on some effective feature extraction based on temporal and spectral analysis of electroencephalogram (EEG) signal and the proper channel selection of the BIOPAC automated EEG analysis system. In the proposed approach, different frequency components (i) real value; (ii) imaginary value; (iii) magnitude; (iv) phase angle and (v) power spectral density of the EEG data samples during different mental task performed to assess seven types of human cognitive states — relax, mental task, memory related task, motor action, pleasant, fear and enjoying music on the three channels of BIOPAC EEG data acquisition system — EEG, Alpha and Alpha RMS signal. Also the time and time-frequency-based features were extracted to compare the performance of the system. After feature extraction, the channel efficacy is evaluated by support vector machine (SVM) based on the classification rate in different cognitive states. From the experimental results and classification accuracy, it is determined that the overall accuracy for alpha channel shows much improved result for power spectral density than the other frequency based features and other channels. The classification rate is 69.17% for alpha channel whereas for EEG and alpha RMS channel it is found 47.22% and 32.21%, respectively. For statistical analysis standard deviation shows better result for alpha channel and it is found 65.4%. The time-frequency analysis shows much improved result for alpha channel also. For the mean value of DWT coefficients the accuracy is highest and it is 81.3%. Besides the classification accuracy, SVM shows better performance in compare with kNN classifier.

A Midimew-connected Mesh Network (MMN) is a minimal distance mesh with wrap-around links network of multiple basic modules (BMs), in which the BMs are 2D-mesh networks that are hierarchically interconnected for higher-level networks. In this paper, we present the architecture of the MMN, addressing of node, routing of message, and evaluate the static network performance of MMN, TESH, mesh and torus networks. In addition, we propose the network-on-chip (NoC) implementation of MMN. With innovative combination of diagonal and hierarchical structure, the MMN possesses several attractive features, including constant degree, small diameter, low cost, small average distance, moderate bisection width and high fault tolerant performance than that of other conventional and hierarchical interconnection networks. The simple architecture of MMN is also highly suitable for NoC implementation. To implement all the links of level-3 MMN, only four layers are needed which is feasible with current and future VLSI technologies.

Aggressive miniaturization has led to severe performance and signal integrity issues in copper-based interconnects in the nanometric regime. As a consequence, development of a proper analytical model for such interconnects is extremely important. In this work, an ABCD parameter matrix-based model is presented for fast and accurate estimation of crosstalk delay and noise for identically coupled copper-based nanointerconnect systems. Using the proposed model, the crosstalk delay and noise are estimated in copper based nanointerconnects for intermediate and global interconnects at the future integrated circuit technology nodes of 21 and 15 nm, respectively. Proposed model has been compared with SPICE and it is found that this model is almost 100% accurate as SPICE with respect to both the crosstalk delay as well as noise. Moreover, this model is as much as ~ 63 and ~ 155 times faster, respectively. From the crosstalk delay and noise analysis of unrepeated interconnects, it is observed that both delay and noise contribution will increase in scaled technology nodes. The same trend is observed also for the repeated interconnects. Also more number of repeaters and higher repeater sizes will be needed for delay minimization as we scale deeper. So as far as crosstalk induced effects are concerned, the copper interconnects will face a huge challenge to overcome in nanometer technology nodes.

Wireless Sensor Network (WSN) Communications technology enables the sensor nodes to observe, identify, understand and respond to changes in surrounding environment. Many of the communication devices for WSN have power constraints, due to their small size and the requirement to operate off small batteries for long periods of time. Thus energy efficiency is an important design criterion. Since energy consumption in the transceiver front ends is dominant, the focus is on reducing the energy consumption on the system level. In this research article the energy model for the alternating quadrature differential binary phase shift keying (AQ-DBPSK) modulation-based transceiver is developed and compared with the energy models for various transceiver architectures, popularly used in WSN nodes. Its suitability for energy efficient operations in WSN applications is investigated in this research article. Through theoretical analysis and simulated results, we discuss how to select the energy efficient transceiver front end for various WSN application scenarios.

Based on the thin-film bulk acoustic resonator (FBAR) technology, a new narrow-band bulk acoustic wave (BAW) filter for Beidou B1 band receiver is designed. The aluminum nitride thin film is used as a piezoelectric layer. The Mason model of air-gap TFBAR (AGR) resonators is established in advanced design system to investigate the relationship between electrical impedance and physical parameters. The simulation results illustrate the effect of the area of parallel unit and the number of resonators on the performance of the BAW filters. By changing the series-parallel unit series and area ratio, the pass band of the BAW filter can reach 1556–1566 MHz, and the out-of-band rejection and the insertion loss can reach 47.5 dB and 3.0 dB, respectively.

This paper proposes two high performance binary-to-binary coded decimal (BCD) conversion algorithms for use in BCD multiplication. These algorithms are based on splitting the 7-bit binary partial product of two BCD digits into two groups, computing the contribution of each group to the equivalent BCD partial product, and adding these contributions to compute the final BCD partial product. Designs for the proposed architectures and their implementations targeting both ASIC and FPGA are compared with others. Implementations of BCD array multipliers using both our conversion circuits and existing conversion circuits have been performed. The synthesis results for both ASIC and FPGA show that the proposed designs are faster and occupying less area than the state-of-the-art conversion circuits. Furthermore, the results obtained from comparing BCD multipliers of various sizes show that the enhancement in the area of the conversion circuit grows into a sizable area improvement in the multiplier circuit.

Range analysis determines allocation of fixed-point integer bit-widths, which is critical for arithmetic on fixed-point representations. The traditional methods, either simulation-based or static, can be time-consuming and produce coarse bounds, potentially leading to large error bounds and unnecessary bits. In this paper, we propose a new static method to perform fixed-point range analysis towards obtaining the tighter ranges efficiently. The hybrid method, ICAT, combines four techniques, including Interval arithmetic, consistency checking, affine arithmetic and arithmetic transform and is the only method that is aware how far it is from the exact solution. For the benchmarks available with comparable methods, we show that the bit-width allocation can be obtained with better results, and in shorter execution time.

This paper introduces a method for detection and identification of IGBT-based drive open-circuit fault of DTC induction motor drives. The detection mechanism is based on soft set theory and wavelet decomposition, if it is detailed, ⊼-product decision making method and sym2 wavelet decomposition have been used in the detection mechanism. In this method, the stator currents have been used as an input to the system. The stator current has been used for the detection of the fault. The signal analysis has been performed up to the six level details wavelets decomposition. Faulty switch is detected by applying soft set theory to sixth level wavelets transformation. This is the first time applied to inverter in induction motor drives fault detection. The results demonstrate that the proposed fault detection and diagnosis system has very good capabilities.

A high-PSRR low dropout regulator using the first-stage reference-included coarse-filtering technique is presented, for the reduced output ripple of the low noise block (LNB) power supply in the satellite set-top box. Compared with the conventional system, the regulator with the technique is characterized by the two successive negative feedback loops, so as to alleviate the output ripple of the power supply caused by the preceding DC–DC converter's modulation signal. With the technique, the ripple near to the frequency of the LNB supply voltage tuning signal (22 kHz) can be significantly alleviated. To further enhance the PSRR performance without the reference-by-pass capacitor, the bipolar-loop buffer amplifier and the reference-included coarse-filtering regulator are proposed. The circuits are extracted from the LDO layout and simulated with a 0.35 μm BCD process in Cadence. With the typical 16.3 V input supply voltage and loading current 100 mA, the line regulation corresponding to the DC supply voltage is achieved at -72 dB; the PSRR performance of the supply voltage ripple up to the frequency (100 kHz) is better than -70 dB; Moreover, the quiescent current without the loading impedance is less than 153 μA.

This study proposed a pornography classifier using multi-agent learning as a combination of the Bayesian method using color features extracted from skin detection based on the YCbCr color space and the back-propagation neural network method using shape features also extracted from skin detection. The classification of pornographic images was made more robust to the variation of images despite size engineering problems. Previous studies failed to achieve such robustness. Findings showed that the proposed multi-agent learning-based pornography classifier has produced significant TP and TN average rates (i.e., 96% and 97.33%, respectively). In addition, the proposed classifier has achieved a significantly low average rate of FN and FP (i.e., only 4% and 2.67%, respectively). The implementation of this algorithm is crucial and significant not only in identifying pornography but also in blocking Web sites that covertly promote pornography.

This paper deal with soft computing techniques such as artificial neural network (ANN), fuzzy logic controller (FLC) and proportional-integral (PI)-based static series voltage regulator (SSVR) for constant speed prime mover driven self excited induction generator (SEIG) feeding three phases linear and nonlinear loads. The constant speed prime mover such as biogas, biomass and gas-turbine driven standalone generating system has problem of poor voltage regulation. The SSVR is injects series voltage in to system to maintain constant source and load voltage with power quality improvement of source and load current. The SSVR is modeled using insulated gate bipolar junction transistor (IGBT)-based voltage controlled-voltage source converter (VC-VSC) with self supported DC bus. The A 7.5 kW, 415 V, 50 Hz asynchronous machine with voltage regulator and loads are designed, modeled and simulated in MATLAB environment. Simulated results are presented capability of an SEIG system with ANN, fuzzy and PI-based SSVR and their comparison.

This study proposes an energy-efficient and area-efficient dual-path low-density parity-check (LDPC) with Reed–Solomon (RS) decoder for communication systems. Hardware complexity is reduced by applying a dual-path 2-bit modified layered min-sum algorithm (2M-LMSA) to a (2550, 2040) quasi-cyclic LDPC (QC-LDPC) code with the column and row weights of 3 and 15, respectively. The simplified check node units (CNUs) reduce memory and routing complexity as well as the energy needed to decode each bit. A throughput of 11 Gb/s is achieved by using 90-nm CMOS technology at a clock frequency of 208 MHz at 0.9 V with average power of 244 mW on a chip area of 3.05 mm². Decoding performance is further improved by appending the (255, 239) RS decoder after the LDPC decoder. The LDPC plus RS decoder consumes the power of 434 mW on the area of 3.45 mm².