Journal of Circuits, Systems and Computers

The main objective of this paper is to present novel applications of two recently proposed CMOS transconductors in realizing voltage mode/current mode Tow–Thomas (TT)-equivalent biquads and a linear voltage-controlled oscillator (VCO), all exhibiting a number of advantages over the previously known circuits to realize the same functions. The workability of all the propositions has been demonstrated by SPICE simulation results using TSMC $0.18\mu \mathrm{\text{m}}$ CMOS technology parameters.

The characteristics of automatic dependent surveillance-broadcast (ADS-B) technology, such as unencrypted channels and open data protocols, make it extremely vulnerable to various intentional or unintentional spoofing and jamming, which seriously threatens air traffic safety. Traditional approaches do not update their high-bit data for a long time, which causes the situations of the same encoding results to have different aircraft positions and it is difficult to determine the real position of the aircraft and effectively detect ADS-B jamming. Therefore, it is urgent to propose more effective detection methods for ADS-B spoofing and jamming. First, the methods and principles of frequency shift calculation and automatic monitoring algorithm are introduced. Then, signal consistency detection and error robustness are analyzed, and a track-data-based detection model for abnormal ADS-B data are proposed. The track-data-based detection methods can use multi-point sampling judgment algorithm to realize the reception and processing of data bits. This method makes full use of the information of multiple sampling values of each data bit and the reference power value obtained in the preamble detection, and determines the data value and confidence value through the relationship between the two. The detection methods for ADS-B spoofing and jamming distinguishes the true and false signals and the authenticity of target positions by comparing the arrival time difference of each signal at the ground station. Finally, the research results show that, with the increase of signal to noise ratio, no matter the estimation based on virtual array translation or the estimation based on circular stationary signal, their estimation accuracies are improved with gradually increasing trends.

In this paper, the linear active disturbance rejection control (LADRC) is applied to the position control of a dicing saw chuck table, which is used to improve the steady-state accuracy and anti-interference ability of the control system. Aiming at the problem that LADRC parameters are difficult to adjust, a parameter adjustment method based on an improved sand cat swarm algorithm is proposed. The nonlinear convergence factor strategy and multiple chaotic map optimization strategy are used to improve the algorithm. In order to verify the performance of the proposed method, the improved sand cat swarm optimization algorithm is compared with the standard sand cat swarm optimization algorithm, grey wolf optimization algorithm and salp swarm optimization algorithm. The final experimental results show that using the improved sand cat swarm optimization algorithm to optimize the LADRC controller has higher steady-state accuracy, stronger anti-interference ability, and better optimization results.

Distributions of electroencephalogram (EEG) data vary greatly across different subjects. It is a very important issue how to generalize models across subjects. In this paper, an algorithm is proposed to build high-performance cross-subject motor-imagery brain–computer interfaces (BCIs) for a new subject. First, a novel distance metric is proposed to quantify the joint distribution discrepancy (JDD) between data from different subjects. It gives better evaluations for discrepancies between different distributions than conventional probabilistic metrics. Moreover, it can be extended to design many novel algorithms. Second, a support vector machine combined with JDD (JDMSVM) is proposed for cross-subject classification. For dataset dataIVa, the JDMSVM runs best under 9 out of 15 situations and averagely outperforms counterparts by 10.1%, 9.5%, 3.2% and 1.7%, respectively. For GigaDataset, JDMSVM runs best under 8 of 12 conditions. It averagely outperforms its counterparts by 10.4%, 5.3%, 2.7% and 2.4%, respectively. The experiments demonstrate that the proposed algorithm is effective and competitive for cross-subject BCI.

The fabrication complexity and cost associated with nanoscale devices are major concerns. Therefore, to address these challenges, we have introduced a mole fraction-based approach for the sensitivity analysis of a dual material control gate cavity on a source electrically doped polarity-controlled tunnel field effect transistor (DMCG-CS-ED-PC-TFET)-based biosensor for label-free detection of biomolecule species. For this purpose, a polarity bias (electrically doped) of PG-$1=+1.2$ V and PG-$2=-1.2$ V is applied for the formation of n+ drain and p+ source regions, respectively, over the thin silicon body. The proposed device structure overcomes the random dopant fluctuation issues, thereby avoiding thermal budget and fabrication complexity as compared to the conventional TFET. Moreover, the nanogap cavity is created by etching the appropriate portion at the source side oxide layer. Furthermore, we have applied a dual metal work function (M1 and M2) at the gate electrode along with hetero material ${\mathrm{\text{Si}}}_{1-X}{\mathrm{\text{Ge}}}_X$ at the source side region to improve the sensitivity of the device by varying the mole fraction value (X). The performance of the proposed device has been evaluated in terms of variations in carrier concentration profile, electric field variation, energy band diagram, transfer ($I_{\mathrm{\text{DS}}}-V_{\mathrm{\text{GS}}}$) characteristics and the sensitivity in terms of drain current (I${}_{\mathrm{\text{DS}}}$), ON-state current (I${}_{\mathrm{\text{ON}}}$) and switching ratio (I${}_{\mathrm{\text{ON}}}$/I${}_{\mathrm{\text{OFF}}}$). Furthermore, the sensitivity of the proposed device biosensor has been investigated by considering nanocavity dimensions, practical challenges such as various fill factors, and various step profiles generated from steric hindrance. For this purpose, different neutral biomolecules such as Biotin ($k=2.63$), APTES ($k=3.57$), Keratin ($k=8$), Ferrocytochrome C ($k=4.7$) and Gelatin ($k=12$) have been considered in the etched nanocavity region. Additionally, charged biomolecules with positive (negative) charge densities at the oxide semiconductor interface below the nanocavity region have been incorporated to assess the performance of the proposed device using the Silvaco ATLAS device simulator. In this analysis, the proposed biosensor achieves a drain current sensitivity of $5.65\times 10^{10}$ for neutral biomolecules ($k=12$), $6.2\times 10^{10}$ for $\rho$ = $1\times 10^{12}$ cm${}^{-2}$ with $k=12$ and $1.2\times 10^{11}$ for $\rho$ = $-1\times 10^{12}$ cm${}^{-2}$ and $k=12$. Finally, the performance of the proposed biosensor, DMCG-CS-ED-PC-TFET, exhibits higher sensitivity compared to various existing TFET-based biosensors. Hence, the proposed biosensor exhibits the potential candidate for the development of future sensing bio-equipment.

On-chip interconnection networks are primarily designed for efficient, high-performance Tiled Chip Multi-Processors (TCMP) architectures. Bufferless Network-on-Chip (NoC) is a better design option owing to their simpler router structure, area and power efficiency. Deflection routers have similar network performance of buffered designs at low to medium network traffic as deflections are minimal. But when network load increases, deflections also rise rapidly leading to poor network performance because of increased latency, power dissipation and unbalanced traffic. In this work, we propose a subnetwork based adaptive Concentrated Mesh (CMesh) bufferless router where deflections are considerably reduced by redirecting competing flit in one subnetwork to vacant port of the other subnetwork without any additional cycle latency. Simulations conducted over two-dimensional and multidimensional CMesh networks show that our topologically independent, adaptive deflection routing mechanism provides better network load balance and improves performance by minimizing unbounded deflections when compared to designs under consideration.

In this paper, an example of an intelligent security system based on deep learning approach has been analyzed and described. Security is an important element in many aspects of human life. Depending on customer requirements, different levels of security may be required and implemented. Nowadays, artificial intelligence is becoming an indispensable part in many areas of human life and accordingly security systems are also based on the new achievements in this field. In this paper, an intelligent security system based on two methods for identifying persons, gait recognition and face recognition, was described. The two methods mentioned above can be used to give persons access to different buildings, facilities, institutions, etc. or to different indoor or outdoor areas. In light of the above, the aforementioned system uses face and gait recognition to identify persons who should be granted access to a facility or a specific area within the facility. Accordingly, two deep learning models for gait and face recognition have been developed and described. The analysis, the settings defined and the results obtained in relation to the models developed were also described.

This paper presents two new transconductance-boosted architectures of the voltage differencing transconductance amplifier (VDTA). The first architecture (VDTA-I) relies on the technique of transconductance enhancement using partial positive feedback which is realized through negative impedance implemented using a cross-coupled amplifier. The second architecture (VDTA-II) incorporates the concept of gate-to-source voltage enhancement using an inverting amplifier for transconductance boosting. Detailed mathematical formulations for computing transconductance gain and bandwidth are presented using high-frequency small-signal equivalent circuits of both propositions. The functionality of the proposals is validated through SPICE simulations using TSMC 0.18 $\mu$m technology parameters. Both DC and AC analyses are carried out through simulations. An enhancement in transconductance gain of 140% is observed for VDTA-I whereas for VDTA-II it is found to be 53% in comparison to the conventional VDTA. The 3 dB frequencies are recorded to be 160 and 630 MHz, respectively. The PVT analyses are also carried out to test the efficacy of both propositions.

The performance of electrical connectors can be significantly impacted by periodic variations in contact resistance caused by vibrational stress. Intermittent faults resulting from such stress are characterized by their random and fleeting nature, making it difficult to study and replicate them. This paper proposes a novel method for reproducing intermittent faults in electrical connectors. To implement this method, intermittent fault data are first collected from electrical connectors subjected to different vibration loads. Next, a statistical distribution model is constructed using kernel density estimation (KDE). Based on this model, a fault injector is designed to simulate intermittent faults under varying vibration loads. The simulated faults are then compared to real-world intermittent fault signals in a controlled environment to validate the accuracy of the method. The results demonstrate that the proposed method effectively reproduces intermittent faults in electrical connectors under varying vibration conditions. This approach can be used to better understand the behavior of connectors under vibrational stress and to develop more effective testing and fault diagnosis methods.

In the task of power line inspection, Unmanned Aerial Vehicles (UAVs) are frequently used for capturing images. With the rapid advancement of sensor technology, the spatial, radiometric, and spectral resolutions of UAV images are constantly improving, leading to an increased storage requirement for individual images. Given that UAVs usually operate with limited computational resources, transmission capability and storage space, there are significant challenges in image compression, storage and transmission. This underscores the importance of a high-performance image compression technique. To solve the above problem, we unveil a compression strategy for images that have been acquired through learning utilizing discrete Gaussian mixture-based probability distributions to increase the efficiency of image compression and the fidelity of reconstruction. In addition, to speed up decoding, we employ a parallel context model, which facilitates decoding in a highly parallel manner. Experimental evidence indicates that our approach attains performance that is at the forefront of the field while significantly expediting the decoding process (speeding up the decoding process by more than 49.78%) in our experiments, outpacing traditional coding standards and existing learned compression approaches by 5.75 dB and 1.23 dB in PSNR.

This paper puts forward a new vertical-tunneling-attributed dual-material double-gate heterojunction-TFET (VTDMDG-HTFET). The device structure of VTDMDG-HTFET includes Si${}_{0.7}$Ge${}_{0.3}$ material for the designing of source region. The mechanism of vertical tunneling in VTDMDG-HTFET provides superior electric field at tunneling interface and leads to improved transfer characteristics. VTDMDG-HTFET also includes metal gate work-function engineering approach to facilitate high ON-current and small OFF-current. Application of high-$k$ dielectric material $\mathrm{\text{HfO˙2}}$ in the form of gate oxide enhances the capacitive-coupling at channel-source interface. Moreover, the work includes the comparison of proposed VTDMDG-HTFET with the conventional vertical-tunneling based dual-material double-gate-TFET (VTDMDG-TFET). It has been observed from the simulation results that SiGe source-based VTDMDG-HTFET offers better DC characteristics in terms of superior ON-current, smaller OFF-current, steeper subthreshold-slope, lower threshold voltage, higher transconductance along with smaller drain-induced barrier-lowering (DIBL) effects as compared to its counterpart silicon-source-based VTDMDG-TFET. In this work, reliability of VTDMDG-HTFET has also been examined and compared with VTDMDG-TFET in terms of temperature sensitivity and effect of different interface trap charges. The device simulation and investigations have been carried out using technology computer aided design (TCAD) tool. Moreover, device circuit mixed-mode simulation analysis is carried out for conventional and proposed device based resistive load inverters. A comparison is performed between both the inverters in terms of prorogation delay and switching threshold voltages. Further, the mixed mode simulation analysis is also performed for the proposed device based inverter under the influence of different interface trap charges (ITCs). Study shows that reliability performance of the proposed device is better as compared to its counterpart conventional device. Hence, vertical tunneling and SiGe source-based VTDMDG-HTFET can be a better choice for various applications such as analog/RF, analog and digital electronic circuits, energy harvesting, gas-sensing and memory devices.

The smooth operation of the power system is of significant importance for ensuring societal stability. Therefore, regular inspections of the power system are necessary. The most efficient method for power inspection is to utilize Unmanned Aerial Vehicles (UAVs) to assist inspection. However, the UAV inspection still needs human resources to operate and manage the UAVs effectively. This paper proposes a UAV-airport power inspection system, where the airport can operate and manage UAVs instead of humans. The key contribution of the system is the computation offloading technology and the Wireless Power Transfer (WPT) capability. In other words, the airport can help UAVs execute electrical image fault detection and the UAVs can be charged by the airport. We propose a joint optimization strategy for UAV flight speed, local execution frequency for fault detection, and WPT time to maximize the number of images in the electrical dataset captured by the UAV that have undergone power fault detection within the inspection time. Comprehensive experiments are conducted to verify the performance of the UAV-airport power inspection system and our optimization strategy.

The improvement of edge perception layer anomaly detection performance has an immeasurable driving effect on the development of smart cities. However, many existing anomaly detection methods often suffer from problems such as ignoring the correlation between multiple source temporal sequences and losing key features of a single temporal sequence. Therefore, a new anomaly detection method using BiLSTM and attention mechanism is proposed. First, a fusion algorithm TCDCD was formed by combining Data Correlation Detection (DCD) and Temporal Continuity Detection (TCD) to preprocess Edge Perception Data (EPD). Then, BiLSTM is employed to gather deep-level features of EPD, and the attention mechanism is utilized to enhance important features that contribute to anomaly detection. Ultimately, the SoftMax classifier is employed to categorize abnormal data. The experimental findings from the SWaT and WADI datasets demonstrate that the suggested method achieves better performance than other newer anomaly detection methods. Among them, the accuracy, precision, recall and F1 of the proposed method on the SWaT dataset were 96.62%, 94.32%, 96.02% and 94.30%, respectively. In terms of performance, it is superior to traditional EPD anomaly detection models, and has good representational and generalization capabilities.

In this paper, a second-order sinusoidal oscillator (SO) has been presented using current-feedback operational amplifiers (CFOAs) as the active element. The proposed oscillator uses five resistors and two grounded capacitors in its circuit design, making it simple and cost-effective to implement. Its design allows for greater control and flexibility in designing the oscillator circuit, making it an attractive option for a variety of applications. It also provides independent resistive tuning, allowing for greater control and flexibility in designing the oscillator circuit. Additionally, it offers low parameter sensitivity, makes it more stable and reliable. These features make it a valuable tool for engineers and designers in a variety of applications, including signal processing, communication systems, and control systems. The frequency tuning expression achieved has the ability to tune frequencies in a broad range of frequencies. PSPICE tool is used to simulate and analyze the behavior of the proposed circuit. The total harmonic distortion recorded was 5$\%$.

Hyperspectral anomaly detection is a vital aspect of remote sensing as it focuses on identifying pixels with distinct spectral–spatial properties in comparison to their background representations. However, existing methods for anomaly detection in HSIs often overlook the spatial correlation between pixels by converting the three-dimensional tensor data into its folded form of independent signatures, which may lead to insufficient detection performance. To address this limitation, we develop an anomaly detection algorithm from a tensor representation perspective, which begins by separating the observed hyperspectral image into background and anomaly cubes. We leverage the tensor nuclear norm (TNN) to capture the inherent low-rank structure of background cube globally. This allows us to effectively model and represent the background information. To further improve the detection performance, we introduce spatial–spectral total variation (SSTV) for effectively promoting piecewise smoothness of the background tensor, aiding in the identification of anomalies. Additionally, we incorporate RX-derived attention weights-guided ${\ell }_{2,1}$ norm. This encourages group sparsity of anomalous pixels, improving the precision of anomaly detection. To solve our proposed method, we employ the alternating direction method of multipliers (ADMM), ensuring guaranteed convergence and efficient computation. Through experiments on different kinds of hyperspectral real datasets, we have demonstrated that our method surpasses several state-of-the-art detectors.

Resistive switching memory devices based on organic materials are intriguing. These devices are biodegradable and nontoxic to living organisms. In this work, using euphorbia cotinifolia plant extract was investigated for its applicability as an active layer of a resistive switching memory device consisting of silver top electrode and indium-doped tin oxide bottom electrode. This study selected Euphorbia cotinifolia because it is a common, prolific and simple-to-grow plant in many South African homes. When the euphorbia cotinifolia plant is broken, an extract resembling milk is emitted. This extract was collected directly onto the bottom electrode, which was then dried at ambient temperature. For top electrode, silver paste was applied. The entire fabrication process was devoid of heat and electricity. The fabricated device showed impressive properties such as memory hysteresis with $\mathrm{\text{ON/OFF}}=5$, endurance of over $32$ write/erase cycles, and an impressive retention of $\ge 10^3\mathrm{\text{s}}$. Therefore, this system may be a candidate for a nonvolatile and disposable memory device.

This paper proposes a new dielectrically modulated (DM) twin cavity nanotube biosensor and its sensitivity evaluation suitability by the transit time $(\tau )$ and device efficiency $(g_m/I_{\mathrm{\text{ds}}})$. In addition, the vertical structure of the device also helps the uniform spreading of biomolecules within the nanogap cavity region. The inner cavity of the proposed biosensor offers more space for the stabilization of biomolecules and uses the benefits of material solubility. In this paper, the sensing ability of the biosensor is examined in terms of $\tau$ and $g_m/I_{\mathrm{\text{ds}}}$ for five neutral biomolecules. Furthermore, the charged biomolecule and deoxyribonucleic acid (DNA) are separately investigated for positive as well as negative charge densities. Also, various optimizations on the device are analyzed for different biomolecules in this paper.

This research paper deals with a directional high gain PCB $1\times 8$ antenna array for 2.4ISM band utilizations. To achieve this antenna array, a well-matched equal-split 9dB-power splitter is designed and integrated with the suggested antenna array. It exhibits a wide operating range of 506MHz (2.022–2.528GHz) and splits the feed power to 8 equal-in-phase output quantities. Then eight identical patch elements with good reflection coefficient, high gain and excellent radiation efficiency are connected at the eight-output ports of the 1 × 8-power divider in order to obtain an array antenna consisting of eight radiating elements. A quarter-wave impedance adapter is utilized to obtain a perfect matching of impedance between patches and the power divider. The suggested directional antenna resonates at 2.4GHz with good impedance matching via offering reflection coefficient $S_{11}=-24.34$dB and voltage standing wave ratio (VSWR) of 1.16. Moreover, it offers good radiation traits like the enhanced gain of 14.76dB, an excellent radiating efficiency equals about 98.86% and a directional radiation pattern. Initially, the proposed planar directional antenna array has been designed and simulated utilizing high-frequency structure simulator (HFSS) EM simulation tool, the results are validated with another simulation tool via computer simulation technology (CST) software. This printed antenna array is a potential candidate to operate at around 2.4GHz for RFID reader utilizations due to its outstanding impedance and radiation characteristics.

This paper takes the wind turbine of the new energy pumping unit as the study subject, which mainly analyzes realistic wind speed estimation on the windward side of the wind turbine and then proposes a wind turbine speed control method based on wind speed estimation. The support vector regression (SVR) model is built first in this study, and the parameters of the SVR model are optimized by the particle swarm algorithm. The SVR II wind speed prediction model is constructed and the working characteristic curve of the wind turbine is generated by gathering wind speed and output power data from the wind turbine at the assigned test location. The prediction model can compensate for the disturbance of system inertia and make the output wind speed more accurate. The effective wind speed acting on the turbine surface can be obtained by substituting the predicted wind speed into the optimal blade tip velocity ratio equation. Suppose the wind turbine is in maximum power tracking control. In that case, the optimal speed can be given directly by the model SCR III, and the speed can be tracked in real-time by the controller to accomplish the largest wind power capture. The optimal speed tracking control uses the calculated effective wind speed if the wind turbine is under constant power output control. In this paper, 14,000 sets of characteristic samples were extracted within the range of wind speed variation of 4–20m/s, and model verification was carried out for models SVR-I, SVR-II and SVR-III. Finally, the predicted effective wind speed value and the actual value are within ±0.3m/s of each other. In practical engineering applications, the forecasted effective wind speed value can entirely substitute the actual value. This work put forward a maximum power tracking control approach that can satisfy new energy pumping units’ energy conservation needs.

In this study, we introduce a distributed algorithm that is specifically designed to address optimization problems featuring a decomposable objective function and equality constraints. To minimize the amount of communication required, we incorporate an event-triggered mechanism that enables information exchange only when variable values exceed predefined thresholds. Importantly, our proposed algorithm possesses a distinctive characteristic where the determination of step size is solely based on the properties of the objective function, regardless of the structure of the communication network. Even in situations where changes occur in the network structure, our algorithm remains valid without necessitating any updates to its step size. Assuming strong convexity and smoothness in local objective functions, along with appropriate event-triggered thresholds, our algorithm achieves a convergence rate that is linear. Several numerical experiments provide evidence supporting the effectiveness and superiority of our proposed approach.