Iet Circuits Devices & Systems最新文献

Approximate computing is commonly employed in applications where accuracy is not crucial and aims to enhance circuit performance when inaccurate results are not challenging. The multipliers are power-hungry, and their approximation has been the target of research, especially by using approximate counters. In this study, a low-power and high-speed approximate 4 : 2 counter is proposed to add partial product (PP) bits. Also, a new partial product generation (PPG) is introduced by inserting errors in Karnaugh’s map to reduce the circuit complexity. The counter and PPG make a new radix-4-based 8 × 8 Booth multiplier, which is synthesized targeting a 32-nm carbon nanotube field-effect transistor (CNTFET) technology to determine the hardware characteristics. Looking at the normalized mean error distance (NMED), the multiplier has a 51.33% power–delay product (PDP) saving and acceptable accuracy. Besides, the multiplier which is configured by the counter and PPG accomplishes a 28.31% savings in the PDP × NMED in comparison with other approximate Booth multipliers. The case study of joint photographic experts group (JPEG) compression is performed, and the proposed multiplier outperforms references by higher quality results along with lower power consumption.

The localization of electromagnetic radiation leakage through cabin gaps is a critical and challenging aspect of electromagnetic compatibility (EMC) design for spacecraft with complex electromagnetic environments. This paper proposes a localization method based on synthetic aperture interferometric passive radiometry imaging. Electromagnetic radiation signals are measured at a certain distance from the spacecraft surface to form visibility samples. A Fourier transform pair between the visibility sample and the corrected brightness temperature for electromagnetic radiation leakage is established. The spacecraft surface electromagnetic leakage location image is obtained through the inverse Fourier transform. A sparse sampling method based on ant colony optimization was proposed to improve testing efficiency. The impacts of various factors, including positional parameters, positioning accuracy of the test antenna, scanning parameters, and measurement receiver amplitude/phase errors on the imaging results are analyzed. Experiments were conducted on a 1 m × 1 m × 1 m cabin with 51 holes on one surface, and the algorithm proposed in this paper was validated to effectively image and locate electromagnetic leakage points at different frequencies. The effectiveness of sparse sampling was also verified, with a localization accuracy of 90.2% and a testing time savings of 81.9%.

In the last decade, with the development of the electric vehicle industry and their acceptance in human societies, the participation plan of electric vehicles in supplying the load of the network has been taken into consideration. One of the requirements of this plan is the optimal location of the stations for these vehicles in the network so that they play an effective role in the operation of the network. In this regard, along with the construction of charging and discharging stations for electric vehicles, the construction of renewable sources in the network can play a complementary role for these stations. In this paper, the effect of using renewable resources as a supplement for smart charging stations and the placement of these stations to achieve technical and economic goals have been investigated. In order to manage the demand on the side of consumers and even out the load curve, the time of use mechanism as one of the demand response programs has been considered in this study. In this research, the improved nondominant sorting genetic algorithm is proposed to solve the problem, and the results of the proposed method are also compared with the conventional genetic and particle swarm optimization algorithms. All the simulations have been done in the MATLAB software and on the IEEE 33-bus network. Based on the obtained results, after the implementation of the proposed plan in the distribution network, the objective functions of the loss, voltage drop, and the total cost have been reduced by 13.6%, 58.7%, and 54.4%, respectively, compared to the base conditions of the network.

Wireless communication network in robotic telesurgery can be a huge advantage to smart healthcare systems that allows the surgeon to perform surgery on remote patients utilizing a surgical robot. It enables the surgical robotic manipulator to replicate the natural hand motions of the surgeon, allowing it to carry out operations with greater acuity and dexterity. This paper addresses the development of an intelligent controller to assure the safe functioning of a telesurgical robotic manipulator. The intelligent optimized, adaptive, and learning-based adaptive neuro-fuzzy fractional order sliding mode control (ANFFOSMC) controller is proposed to attain dexterity and acuity of the surgical manipulator for surgical interventions. The proposed controller for telesurgical system exhibits superior accuracy and performance compared to conventional controllers, as evidenced by reduced root mean square error (RMSE), integral squared error (ISE), and integral absolute error (IAE). The performance of the robot is evaluated using performance indices in the occurrence of uncertainties and external disturbances.

The spin transfer tunnel magnetic tunnel junction (STT-MTJ) has been widely used in computers, memory, and other fields because of its nonvolatility, low power consumption, and high capacity for integration, attracting significant attention in recent years. Building an accurate and efficient magnetic tunnel junction (MTJ) behavior model is necessary to accurately describe the physical changes caused by variations in external excitation and guide the design and optimization of magnetic random access memory (MRAM). In this paper, we construct a multiphysical field dynamic behavior model of a perpendicular STT-MTJ, introducing temperature and frequency effects based on the Landau–Lifshitiz–Gilbert–Slinbczewski (LLGS) equation and a macro model. Compared with the LLGS model, our model simplifies the calculation of the tunneling current and shortens the simulation time by ~40%. Compared with the macro model, ours model can more accurately reflect the dynamic physical changes in magnetoresistance under a small signal and transient excitation. Simulation modeling and experimental comparison verify the temperature and frequency dependencies of the model. Our model provides guiding significance for the design, application, and research of MTJ devices’ electromagnetic compatibility characteristics.

In this contribution, a chaos-based microcontroller electrocardiogram (ECG) signal acquisition-security-transmission system is proposed. It is designed based on a Novel 4Wings-4D Chaotic Oscillator (N4W4DCO) with a hyperbolic sine nonlinearity unbalanced. The classical nonlinear dynamics tools, such as 2D bifurcation and the highest Lyapunov exponent curves, basins of attraction, and power spectral density, help us see that the proposed chaotic oscillator generates periodic oscillations, intermittency + crisis routes to chaos, transient chaos, and the coexistence of 4/2 wings attractors just to name a few dynamics. The data generated using highly chaotic regime are tested using the well-known NIST TEST -800-22 Rev A and the results passed the test successfully. The N4W4DCO oscillator is embedded in an Arduino microcontroller where the discovered interesting dynamics are confirmed. A low-cost ECG acquisition circuit with an AD8232 ECG sensor is also designed and experimented. ECG signals are acquired and directly loaded into MATLAB-Simulink and are successfully encrypted with random data from the N4W4DCO in its chaos regime. The scrambled ECG signals from experiment are sent through an added white gaussian noise (AWGN) channel and thereafter received and decrypted. These results are promising and open the possibility of improving secure telemedicine transmission systems.

This study explores the impact of integrating a gallium arsenide (GaAs) pocket at the source and drain in a dual-material gate-oxide-stack double-gate tunnel field-effect transistor (DMGOSDG-TFET). The performance of this DMGOSDG-TFET, employing work-function engineering and gate-oxide-stack techniques, is compared with a GaAs pocket-doped DMGOSDG-TFET. Using the Silvaco Technology Computer-Aided Design tool, the comparison covers DC characteristics, analog/RF behavior, and circuit-level assessments. The research introduces an optimized heterostructure pocket-doped DMGOSDG-TFET to enhance DC characteristics, analog/RF performance, and DC/transient analysis. This novel architecture effectively suppresses ambipolarity, making it more suitable for current conduction. The incorporation of work-function engineering and a gate-oxide-stack approach enhances the device’s current driving capability, while the use of a highly doped GaAs pocket at the source and drain virtually eliminates ambipolar current conduction. Simulation results indicate that the proposed heterostructure device exhibits a high ON-current and switching ratio. For analog/RF applications, the optimized heterostructure device outperforms conventional DMGOSDG-TFET, offering higher cutoff frequency, transconductance, and other analog/RF parameters. Circuit-level performance is assessed using HSPICE, with a focus on the implementation of a resistive-load inverter for both proposed and conventional device topologies through DC and transient evaluations.

A staggering number of applications rely on the network architecture to carry out their tasks, which has led to a fast growth in wireless sensor networks (WSN). The possibility of harmful activity and data theft is growing as a result of the growth in devices and data. Thus, the network’s regular users have an impact on legitimate data delivery, which lowers customer happiness and worsens network standards. The data have been saved using a variety of security procedures that have been developed in past research studies. However, harmful activity continues to engage in its illegal operations despite their efforts to safeguard data transmission in the network. As a result, a number of recent research projects have concentrated on predicting innovative techniques and processes to offer security in WSN. In comparison to existing methods, this work attempted to offer an effective tighter security for WSN and suggested an ML-Based Secured Routing Protocol (MLSRP) for WSN with improved energy efficiency and overall performance. Energy efficiency is the main requirement of WSNs, hence a clustered network is proposed where the data are routed through the cluster head nodes. In this paper, a multicriteria based decision-making (MCDM) model is used by the MLSRP to perform data routing, clustering, and cluster head election while also analyzing a number of network characteristics that might affect the quality of a node, route, and data. In NS2 software, the suggested framework is put into practice and simulated. The results are then validated to gauge performance. The observed quantitative results reveal that the proposed MLSRP method attains an improved network lifetime by 5% and network throughput of 6%. It reduces energy consumption by 40%, curtails overhead to 37%, and minimizes end-to-end delay by 30% than the other conventional methods. The suggested framework performs better than others when its total performance is compared to that of older methods.

Accurate passenger flow forecasting is crucial in urban areas with growing transit demand. In this paper, we propose a method that combines advanced machine learning with rigorous time series analysis to improve prediction accuracy by integrating different datasets, providing a prescriptive example for passenger flow prediction in urban rail transit systems. The study employs advanced machine learning algorithms and proposes a novel prediction model that combines two-stage decomposition (seasonal and trend decomposition using LOESS–ensemble empirical mode decomposition (STL-EEMD)) and gated recurrent units. First, the STL decomposition algorithm is applied to break down the preprocessed data into trend terms, periodic terms, and irregular fluctuation terms. Then, the EEMD decomposition algorithm is employed to further decompose the irregular fluctuation terms, yielding multiple IMF components and residual residuals. Subsequently, the decomposed data from STL and EEMD are partitioned into training and test sets and normalized. The training set is utilized to train the model for optimal performance in predicting subway short-time passenger flow. The synthesis of these sophisticated methodologies serves to substantially enhance both the predictive precision and the broad applicability of the forecasting models. The efficacy of the proposed approach is rigorously evaluated through its application to empirical metro passenger flow datasets from diverse urban centers, demonstrating marked superiority in predictive performance over traditional forecasting methods. The insights gleaned from this study bear significant ramifications for the strategic planning and administration of public transportation infrastructures, potentially leading to more strategic resource allocation and an enhanced commuter experience.