Journal of Circuits, Systems and Computers

This paper presents a comprehensive review of the state-of-art high-speed dynamic comparators. The comparator is a critical block of high-speed, low-power analog-to-digital converters, determining the speed and overall power consumption. Therefore, the design of a high-speed comparator with tolerable offset, noise and power consumption is of utmost importance. Recent work reported on high-speed comparator topologies is investigated in detail with the help of simulations in 65nm CMOS technology. Various parameters, such as delay, energy consumption, speed, offset, kickback noise, power delay product, etc., are compared. A detailed comparative study is also presented on several design methodologies.

In this study, the effects of suction and buoyancy are used to analyze the Ternary hybrid nanofluid flow on a stretching sheet through a porous media. Ternary hybrid nanofluid is (Carbon nanotubes) CNT$+$Graphene$+{\mathrm{\text{Al}}}_2{\mathrm{\text{O}}}_3$ with base fluid as Water. Case-1 Buoyancy Assisting flow and Case-2 Buoyancy Opposing flow. Hybrid nanofluids have been used to speed up the heat transfer process. Nonlinear partial differential equations (PDEs) have been converted to ordinary differential equations (ODEs) using Lie group transformations. The ODE45, an algorithmic approach, has been using the aid of this built-in solver, and the resulting Ordinary differential equations were resolved. The general relationship between temperature, velocity, heat transfer rate, and shear stress on a stretchy surface is shown for a range of values of the significant factors. The temperature profiles have been rising with the impact of $\mathrm{\text{Da}},f_w$. Using streamlines to examine the flow pattern of a fluid and a method of machine learning, in terms of modern language, an artificial neural network (ANN) made up of artificial neurons or nodes is known as a neural network. A neural network is a network or circuit of genetic neurons.

In addition to Power Quality (PQ) problems caused by solid state devices in the distribution side of grid connected Photo Voltaic (PV) system, quality of power also suffers in the source side by the usage of converters. Generally, PQ problems mitigation strategy has turned into a critical issue in grid-connected PV systems. This paper proposes a control topology for PQ issues mitigation scheme in grid connected PV systems. The primary goal of this study is to enhance the PQ in power distribution system through eradicating the voltage interruption, voltage swell and sag as well as voltage fluctuation. To accomplish these changes, Unified Power Quality Conditioners (UPQCs) with controllers were utilized. Then the faults are mitigated by the utilization of UPQC with Tree Seeds Algorithm (TSA) — Adaptive Neuro-Fuzzy Interference System (ANFIS). At first, the normal properties of the grid-connected PV systems are dissected. From that point forward, a few interruptions are made in the system and the abnormal characteristics are analyzed. Then the abnormal characteristics of PQ problems are diminished in the utilization of UPQC and TSA–ANFIS controllers. UPQC is one of the best filters topologies which can mitigate and control all PQ problems. The combined algorithms of TSA-ANFIS are used to keep the dc-link voltage in UPQC at consistent level. The proposed hybrid approach is executed in MATLAB/Simulink platform and the performance is studied. Further, the proposed method is compared with existing Elephant Herding Optimization (EHO) technique and also with hybrid EHO-ANFIS techniques. The comparison results showed that the proposed technique is superior to the EHO and EHO-ANFIS techniques.

As a widely used equation in electrostatics, the Poisson equation has significant research value in numerical solution. The basic principle of existing methods is to divide the solution domain into various grids and solve the numerical solutions at each grid node. Therefore, the accuracy of the solution is strongly correlated with the grid density divided. Based on this, this paper proposes a grid-free numerical calculation method that requires far fewer model parameters than traditional methods, and can ignore the order of the equation to solve high-dimensional Poisson equations. Given a Poisson equation, which has a certain type of boundary condition. A certain number of coordinate points are selected on the solution space and its boundary to construct a dataset. Using automatic differentiation technique to fit the differential operator in the equation, a loss function is constructed by incorporating the given boundary conditions or initial conditions, and the final numerical solution is obtained through iterative optimization algorithms. In the numerical experiment section, the algorithm proposed in this paper was used to solve the two-dimensional and three-dimensional Poisson equations with given exact solutions. The relative errors between the numerical solution and the true solution were $8.53e-4$ and $6.40e-2$, which are within the acceptable range. This proves that the proposed algorithm is feasible for solving the two-dimensional and three-dimensional Poisson equations with precise solutions. Secondly, the proposed algorithm is used to solve the four-dimensional Poisson equation with first-type boundary conditions, and the relative error range of the solution was within [0,0.56], which successfully extends the algorithm to solve high-dimensional Poisson equations and verifies its feasibility and efficiency in solving high-dimensional Poisson equations regardless of the dimension restriction.

The traditional real-time load warning method for distribution transformers has problems such as low recall rate, low warning accuracy, and long warning time, which may lead to potential equipment failures or overload situations not being detected and dealt with in a timely manner, increasing the safety risk of transformer operation and potentially causing safety issues such as equipment damage, fire, or power outage. Therefore, a real-time early warning method of distribution transformer load considering meteorological factor data is designed. The meteorological factor data are collected by the light sensor, humidity sensor, temperature sensor and rainfall sensor, and the load data collection architecture is built by the load monitor, central master station and maintenance station to realize the load data collection of the distribution transformer. The K-nearest neighbor (KNN) method is used to process the missing values of the data, and the LOF algorithm is used to determine the local outliers and eliminate the outliers in the data set to achieve data cleaning. Considering the load loss, hot spot temperature and meteorological factors of the distribution transformer, an early warning model is built, and the cleaned data are input into the model to realize Real-time early warning of the distribution transformer load. The experimental results show that the recall rate of this method varies from 95% to 97%, the accuracy rate of early warning is always above 94%, and the maximum value of early warning time is 0.63s. Having good early warning ability.

In recent years, video surveillance has become an integral part of computer vision research, addressing a variety of challenges in security, memory management and content extraction from video sequences. This paper introduces the Robust Object Detection using Fire Hawks Optimizer with Deep Learning (ROD-FHODL) technique, a novel approach designed specifically for video surveillance applications. Combining object detection and classification the proposed technique employs a two-step procedure. Utilizing the power of the Mask Region-based Convolutional Neural Network (Mask-RCNN) for object detection, we optimize its hyperparameters using the Fire Hawks Optimizer (FHO) algorithm to improve its efficacy. Our experimental results on the UCSD dataset demonstrate the significant impact of the proposed work. It achieves an extraordinary RUNNT of 1.34s on the pedestrian-1 dataset, significantly outperforming existing models. In addition, the proposed system surpasses in accuracy, with a pedestrian-1 accuracy rate of 97.45% and Area Under the Curve (AUC) values of 98.92%. Comparative analysis demonstrates the superiority of the proposed system in True Positive Rate (TPR) versus False Positive Rate (FPR) across thresholds. In conclusion, the proposed system represents a significant advancement in video surveillance, offering advances in speed, precision and robustness that hold promise for enhancing security, traffic management and public space monitoring in smart city infrastructure and other applications.

This paper proposes a novel hybrid technique for enhancing power quality (PQ) in distributed generation (DG) systems by deploying a unified power quality conditioner (UPQC). Here, the proposed hybrid method is the joint execution of white shark optimizer (WSO) and recalling-enhanced recurrent neural network (RERNN), called the WSO-RERNN technique. The primary objective of this novel approach is to effectively mitigate voltage sag and reduce voltage harmonics under varying load conditions. It is important to investigate the voltage sag, swell and harmonic distortion of the system to obtain an enhanced PQ of the energy supply. Therefore, this paper shows the brief impact of PQ in DG utilizing the proposed unified PQ conditioner controller. The WSO-RERNN control technique enhances the performance of the UPQC controller by providing the optimal control signal. By then, the efficiency of the proposed approach is done in MATLAB, and the performance is compared with those of existing optimization techniques, including Ant Lion Optimizer (ALO), Grey wolf optimization (GWO) and Salp swarm algorithm (SSA) methods.

Network Intrusion Detection Systems (NIDSs) have been proposed in the literature as security tools for detecting anomalous and intrusive network data traffic. However, the existing NIDS frameworks are computation-intensive, thereby making them unsuitable for deployment in resource-constrained networks with limited computational capabilities. This paper aims to address this issue by proposing computationally efficient NIDS framework for detecting anomalous data traffic in resource-constrained networks. The proposed NIDS framework uses an ensemble-based classifier model comprising multiple classifiers, which enables it to achieve high accuracy and detection rate across a wide range of low-footprint and stealth network attacks. The proposed framework also uses feature scaling and dimensionality reduction techniques to minimize the overall computational overhead. The proposed framework consists of two stages. In the first stage, four distinct base-level classifiers are utilized. The classification probabilities of the first stage are used in the modified meta-level classifier. The modified meta-level classifier is trained on the class probabilities of the base-level classifiers combined using a novel proposed probability function. The performance of the proposed NIDS framework is evaluated on a proprietary testbed dataset and two benchmark datasets namely CICIDS-2017 and UNSW-NB15. The results reveal that the proposed NIDS framework provides better performance than the existing NIDS frameworks in terms of false positive rate, despite using a significantly lower number of input features for its analysis.

A novel non-isolated high-gain DC–DC converter for green energy employment is presented and analyzed. The proposed converter comprises a switched capacitor cell, passive clamp circuit, coupled inductors, and voltage multiplier unit. An interleaved boost converter (IBC) is placed on the input side of the proposed design. The voltage multiplier unit (VMU) with the secondary windings of the coupled inductors is located on the load side. It is used to accomplish interleaved power storage. The leakage energy of coupled inductor is recirculated to the load side, and the reverse recovery problem of diodes is effectively suppressed. As a result, a low-on-state resistance power switch with a low voltage rating is employed and minimizing conduction losses. The presented topology is suitable for sustainable energy applications due to its low operating duty cycle, high voltage conversion ratio, and higher efficiency. The output voltage causes substantially low voltage stress on semiconductor switches and passive elements. This proposed paper deals with simulation, parameter selection, experimental design, results and discussion. The simulation is verified using MATLAB/Simulink with a DC voltage of 30–517V. To validate the design analysis, a 1.5-kW experimental prototype is designed with a switching frequency of 10kHz and attained an efficiency of 96.07%.

To identify whether the circuit under test is fault-free or faulty, fault diagnosis is conducted in an analog circuit. However, in the case of fault detection (FD) techniques, the size of FD is the main challenge in testing, especially for complex analog circuits. Hence to reduce the size of FD, specific test nodes are chosen to perform fault diagnosis from the available accessible test nodes. Specific test nodes are selected based on the faults classification efficiency of fault diagnosis. Therefore, this paper proposes an approach for single and multiple soft fault diagnosis using minimum test nodes in analog electronic circuits. The proposed double deep Q-learning-based hybrid Simulated annealing–Tabu search (DDQN-Hybrid SATS) technique determines minimum test nodes with the utilization of distance metric for fault classification. The DDQN-Hybrid SATS technique is used to identify minimum nodes for testing. In this, the double deep Q learning network (DDQN) ensures better reliability and faster convergence in learning but suffers from catastrophic forgetting issues. To prevent such issues, the DDQN approach is optimized using a hybrid SATS algorithm. The hybrid optimization of simulated annealing (SA) and Tabu search (TS) coalesce the advantages of individual optimization procedures to provide the optimum solution in a fast and effective way. The results for a fourth-order low pass filter are presented (i.e., first CUT) and an eight-bit digital to analog converter (i.e., second CUT). Moreover, the simulation experiment reveals that the proposed DDQN-Hybrid SATS technique achieves greater overall fault classification accuracy of about 96.25% than other compared techniques with minimum computation time.

This paper presents a background calibration method assisted with noise-shaping, for a 10-b bridged SAR ADC. It proposes calibrating the mismatches from the MSB capacitors using the LSBs. First-order noise-shaping has been employed to facilitate the calibration as well as the analog-to-digital conversion. Since noise-shaping is able to shape both the comparator input and quantization noise, it is expected to improve the calibration effects as well as the SNR, for the SAR ADC. Measurement results show, upon the proposed calibration, the SAR’s INL and SNR are improved by 3LSB and 9dB, respectively, compared to when the noise shaping is off. Fabricated using a 55nm CMOS MOMCAP technology and tested with a −2.5dB sinewave input, the bridged SAR ADC achieves an SNR of 62.2dB and an INL better than 1LSB, when consuming a power of $72\mu \mathrm{\text{W}}$, at a supply voltage of 1.2V and sampling frequency of 50kHz. The ADC’s active area is $300\times 380\mu {\mathrm{\text{m}}}^2$.

In this paper, a complementary split ring resonator (CSRR) loaded compact CPW-fed circular shaped monopole UWB antenna with dual band notch characteristics is designed on an FR-4 substrate. The propounded band-notched UWB antenna is integrated with two frequency selective surface layers referred as bandpass frequency selective surface (FSS-1) and reflector (FSS-2) to offer enhanced radiation performance. Bandpass FSS-1 enhances the gain of the antenna by improving directivity and reflector FSS-2 reflects back the backside radiation of the proposed antenna towards the forward direction thereby increasing gain and directivity of the proposed antenna. Furthermore, a two-element orthogonal MIMO antenna is proposed with improved gain using two FSS layers. The complete configuration of the proposed two-element MIMO antenna consists of CSRR-based radiating patches, monopole ground planes and two integrated FSS layers. The proposed MIMO antenna operates in UWB range from 3.46GHz to 16.02GHz with dual notches at 3.9GHz and 7.2GHz. Isolation between MIMO elements is greater than 23 dB. Both antennas are fabricated and investigated at 5.9GHz for vehicular communications. Maximum gain improvements of 8 dB and 6.4 dB are observed in case of the presented single element and MIMO antenna within the entire operating band due to the integration of dual FSS layers. The diversity performance of the suggested MIMO antenna is investigated by analyzing mean effective gain, envelope correlation coefficient, channel capacity loss and diversity gain parameters. The proposed MIMO antenna is a suitable candidate for vehicular and UWB wireless communication applications.

The present prevailing loop closure detection algorithm is mainly applicable for simultaneous localization and mapping (SLAM). Its effectiveness is contingent upon environmental conditions, which can fluctuate due to variations in lighting or the surrounding scenario. Vision-based algorithms, while adept during daylight hours, may falter in nocturnal settings. Conversely, lidar methods hinge on the sparsity of the given scenario. This paper proposes an algorithm that comprehensively utilizes lidar and image features to assign weighted factors for loop closure detection based on multi-modal sensor fusion. First, we use $k$-means clustering to produce a point cloud spatial global bag of words. Second, an improved deep learning method is used to train feature descriptors of images while scan context is also used to detect candidate point cloud features. After that, different feature-weighted factors are assigned for homologous feature descriptors. Finally, the detection result related to the maximum weight factor is designated to the optimal loop closure. The adaptive weighted loop closure (AWLC) algorithm we proposed inherits the advantages of different loop closure detection algorithms and hence it is accurate and robust. The AWLC method is compared with popular loop detection algorithms in different datasets. Experiments show that the AWLC can maintain the effectiveness and robustness of detection even at night or in highly dynamic complex environment.

Service-based systems (SBSs) are a unique category of software systems that dynamically combine various third-party services at runtime to deliver complex and adaptive functionality. This dynamic composition introduces a high level of unpredictability and uncertainty, creating potential anomalies and exerting significant pressure on system maintenance. To tackle this challenge, the conventional approach involves employing prediction-based proactive self-adaptation. Despite the prevalence of existing approaches emphasizing prediction accuracy, the critical aspect of “earliness” in predictions is often overlooked. Striking a balance between early and accurate predictions is paramount in practice. In response, we propose Proactive Self-Adaptation based on Ensemble Prediction (PSA-EP) to effectively balance the trade-off between the prediction earliness and accuracy of in SBSs. At the heart of PSA-EP lies an ensemble prediction model built upon a deep neural network and an enhanced long short-term memory (DNN-ELSTM) architecture. PSA-EP is crafted to empower SBSs to adapt to the inherent unpredictability and instability, facilitating the achievement of their adaptation goals. This adaptation mechanism not only enables effective prediction and analysis of adaptation goal violations but also addresses the reliability and performance of service level agreements (SLAs) governing quality of service (QoS). We evaluated the performance of PSA-EP in a decentralized tele-assistance system using four key metrics, and the experimental results, examined from various perspectives, underscore its exceptional performance.

The imperative for lightweighting technologies, paramount in mission-critical cyber-physical systems (CPSs) including aerospace, automotive and allied sectors, hinges upon optimizing energy efficiency and curbing product weight. Honeycomb structures, celebrated for their exceptional strength-to-weight ratio, have indisputably guided the pursuit of lightweight design. This paper expounds upon the versatility of honeycomb structures by scrutinizing their in-plane mechanical attributes. Leveraging finite element simulations and polynomial fitting, we enhance the prevailing equivalent elastic modulus model for uniform honeycomb structures, expanding its domain to encompass a broader spectrum of relative density values. Deliberations ensue concerning the model’s constraints and its inapplicability to nonuniform honeycomb structures. The investigation introduces nodes as pivotal influencers in the mechanical comportment of nonuniform honeycomb structures, delineating the nexus between the equivalent elastic modulus and node dimensions through a fusion of finite element simulations and mechanical experimentation. Furthermore, this research delves into the tenets and constructs of density-based variable density methodologies within the ambit of topology optimization, with an overarching goal of maximizing stiffness. We furnish a holistic design protocol for optimizing honeycomb structures, underscored by a pragmatic instantiation of the density-based variable density approach. Scrutinizing the geometric interplay between honeycomb structures and design spaces, we posit an innovative paradigm employing concentric circles to approximate cellular envelopes, streamlining numerical cartography and the conversion of optimization outputs into variable density honeycomb configurations. Evaluation of the in-plane mechanical attributes of variable density honeycomb structures reveals that TPU material augments the resilience of both uniform and variable density honeycomb structures, whereas topology optimization amplifies specific stiffness and resilience modulus in variable density honeycomb structures relative to their uniform counterparts. This study sheds light on the complexities of honeycomb structures, providing valuable insights for their optimization in lightweight applications.

In order to ensure the flight safety of small unmanned aerial vehicles (UAVs), a deep neural network-fused mathematical modeling approach is put up for reliable flight control of small UAVs. First, engine torque, thrust eccentricity and initial stop angle are taken into full consideration. A six-degree-of-freedom nonlinear model is formulated for small UAVs, concerning both ground taxiing and air flight status. Then, the model was linearized using the principle of small disturbances. The linearized model expressions for both ground taxiing and air flight were provided. In addition, radial basis function neural networks are used for online approximation to address the nonlinearity and uncertainty caused by changes in aircraft aerodynamic parameters. At the same time, to compensate for the external disturbance and the approximation error of the neural network, the system robustness is improved by selecting reasonable design parameters. This helps the whole flight control system obtain better tracking control performance. At last, some simulation experiments are carried out to evaluate the performance of the proposed mathematical modeling framework. The simulation results show that the proposal has stronger convergence ability, smaller prediction error, and better performance. Thus, proper proactivity can be acknowledged.

This research work focuses on implementation of the FinFET-based complementary metal-oxide-semiconductor (CMOS) Full Adder circuits for different transistor configurations using ASAP7 FinFET model. First, this work examines FinFET-based AND-OR-invert (AOI) gates using different topologies, and second, a FinFET-based CMOS Full Adder circuit at the 7nm technology node is analyzed with respect to its process, voltage and temperature (PVT) variability effect measured in terms of the normalized standard deviation of different performance metrics. The comparison is made between conventional (CFFA1) and proposed (FFA2, FFA3, FFA4, FFA5, FFA6, FFA7 and FFA8) FinFET-based CMOS full adder circuits. The aim is to determine the optimal design configuration of the FinFET full adder circuit with the minimum impact of PVT variability. On examining the power delay product (PDP) variability, it is found that variations in FFA2, FFA3, FFA4, FFA5, FFA6, FFA7 and FFA8 are significantly lower than CFFA1 by 6.30%, 4.68%, 10.30%, 65.48%, 68.05%, 65.61% and 17.20%, respectively. Among all the proposed configurations, normalized standard deviation $\sigma /\mu (\%)$ for the PDP metric is lowest in FFA6, followed by FFA7, FFA5, FFA8, FFA4, FFA2 and FFA3. The normalized standard deviation $\sigma /\mu (\%)$ for power dissipation, however, is lowest in FFA8. In addition, a layout comparison analysis of conventional and proposed full adder circuits reveals that FFA7 has the least area, followed by FFA8, FFA6, FFA5, FFA3, FFA4, FFA2 and CFFA1. The area of FFA2, FFA3, FFA4, FFA5 and FFA6 has decreased by 3.29%, 3.51%, 3.50%, 5.14% and 5.52%, while FFA7 and FFA8 have experienced a decrease in area by 13.87% and 14.36%, respectively, as compared to conventional CFFA1. The proposed layout of FinFET-based CMOS Full Adders can be directly transferred into the foundry’s production line for manufacturing purposes. The overall investigation led us to conclude that FFA8 is the most efficient of all due to the lowest power variation, lower delay variation and lower PDP variation, moreover it has a reduced layout area among all the discussed designs. However, there is a tradeoff in terms of penalty in nominal power, propagation delay and PDP in the proposed topologies.

This work suggests an all-digital temperature sensor with a high sampling rate that is based on a time-to-digital converter (TDC). Two on-chip voltage-controlled oscillators (VCOs) are used in the design of the sensor core, which senses temperatures between $-40^{\circ }$C and 200${}^{\circ }$C. For digital code conversion, the outputs of the VCO are fed into two asynchronous counters. In both low- and high- resolution modes, the error following two-point calibration is observed between $-1.08^{\circ }$C and $+1.06^{\circ }$C. The sensor’s ability to function in both high- and low-resolution modes based on conversion time is an important feature. At a sampling frequency of 0.19MHz, the maximum resolution achieved is 0.18${}^{\circ }$C. Additionally, the sensor has control logic built in to turn off the sensing as soon as the conversion is complete. At 90-nm process, 1.1V supply voltage and 27${}^{\circ }$C, the proposed sensor occupies $0.044{\mathrm{\text{mm}}}^2$ and consumes $817.5\mu \mathrm{\text{W}}$.

Recently, wireless body area network (WBAN) becomes a hot research topic in the advanced healthcare system. The WBAN plays a vital role in monitoring the physiological parameters of the human body with sensors. The sensors are small in size, and it has a small-sized battery with limited life. Hence, the energy is limited in the multi-hop routing process. The patient data is collected by the sensor, and the data are transmitted with high energy consumption. It causes failure in the data transmission path. To avoid this, the data transmission process should be optimized. This paper presents an advanced authentication and energy-efficient routing protocol (AAERP) for optimal routing paths in WBAN. Patients’ data are aggregated from the WBAN through the IoMT devices in the initial stage. To secure the patient’s private data, a hybrid mechanism of the elliptic curve cryptosystem (ECC) and Paillier cryptosystem is proposed for the data encryption process. Data security is improved by authenticating the data before transmission using an encryption algorithm. Before the routing process, the data encryption approach converts the original plain text data into ciphertext data. This encryption approach assists in avoiding intrusions in the network system. The encrypted data are optimally routed with the help of the teamwork optimization algorithm (TOA) approach. The optimal path selection using this optimization technique improves the effectiveness and robustness of the system. The experimental setup is performed by using Python software. The efficacy of the proposed model is evaluated by solving parameters like network lifetime, network throughput, residual energy, success rate, number of packets received, number of packets sent, and number of packets dropped. The performance of the proposed model is measured by comparing the obtained results with several existing models.

In the domain of process virtual machine (PVM) binary translation, the difference in address space layout between the guest program and the translated program requires the recalculation of jump instruction targets, resulting in suboptimal execution efficiency. This paper presents a novel method called SPC-Indexed Indirect Branch Hardware Cache Redirecting (SPCIC) technique. SPCIC utilizes specialized branch instruction to represent indirect branches from guest programs while frequently-used target addresses are cached in a customized hardware mapping table. When translating an indirect branch, SPCIC queries the jump target cache first to achieve a fast redirection unless the destination address is not cached. Besides, SPCIC merely falls back to the software-based remapping approach when the query fails, improving the translation efficiency to the greatest extent. SPCIC is implemented on the QEMU platform to accelerate the translation of ARM payloads into RISC-V. Experiments are carried on SPEC2006 to demonstrate the effectiveness of SPCIC for reducing the runtime overhead of indirect branch translation. The experimental results indicate up to 11% average improvement and 35% maximum improvement are obtained on the selected benchmark.