Journal of Circuits, Systems and Computers

In this communication, a low-voltage complementary metal-oxide semiconductor (CMOS) current-mode full-wave rectifier (FWR), implemented from a flipped voltage follower-biased MOS translinear loop (FVF-MTL) is presented. The use of flipped voltage follower (FVF) minimizes the overall supply voltage demand by reducing the voltage headroom while maintaining the operation of the MOSFETs in strong inversion mode. The functioning of the proposed FWR circuit has been demonstrated through simulations carried out on Cadence Virtuoso software at $0.18\mu \mathrm{\text{m}}$ CMOS technology at a supply voltage of 0.85V. The proposed FWR works over an input range of $\pm 200\mu \mathrm{\text{A}}$ with a maximum linearity error of 3% in its transfer characteristics. The -3 dB bandwidth of the circuit has been found to be 54.95MHz. The minimum power and maximum power consumed by the circuit are 22.6nW and $415\mu \mathrm{\text{W}}$, respectively. Post-layout simulations, Monte Carlo (MC), corner and temperature analyses have been carried out to estimate the performance and robustness of the proposed circuit.

Neural network innovation has become widely employed in many spheres of society, including education, due to the rapid growth of today’s global technologies and science. Promoting and interpreting wealth creation as a fundamental aspect is necessary. Education must implement efficiency of the management creation and use novel technological and scientific resources to increase the level of management of teaching in this circumstance. The level of management over education systems may also be effectively increased, complementing the framework to manage students’ academic development. An adequate learning idea, a specific instructional orientation, higher academic inspiration, joy of knowledge, and a readiness to study are the qualities intended to be established in the students. Initially, the student data are collected. Further, preprocessing has been achieved by using the normalization technique. Next, a convolutional neural network is proposed as a method for evaluating a student educational management system that takes into account dynamic student behavior during online learning. Moreover, statistical methods such as ANOVA and chi-square test are employed for further analysis. Finally, the performance of the system has been evaluated. Thus, the effectiveness of the student educational management mechanism is analyzed well and effectively.

In computer vision, automatically identifying and locating text in images or videos is an important task. Traditional text detection methods are not effective in detecting complex scenes in irregular rectangular areas such as text bending, spacing, and special shapes, which are mainly reflected in the fragmentation of text detection areas. In this paper, a text detection method based on edge attention mechanism is proposed to better adapt to complex scenes. The proposed method takes Encoder–Decoder as the core idea. First of all, an edge attention module is designed, including global attention and local attention. The global attention module is used to perceive the features of text regions and nontext regions, while the local attention module is used to learn the information of text boundaries. Then a multi-scale feature fusion process is designed, which can strengthen the edge information and key information of text regions. Finally, the model outputs probability maps and threshold maps, and generates high-precision binary maps of text regions. After experimental verification, the proposed method on the public data set significantly reduces the fragmentation of the detection area, improves the detection accuracy of the text area, and has better robustness for text detection scenes with unconventional rectangular areas.

Wireless Sensor Networks (WSN) became a novel technology for ubiquitous livelihood and still remains a hot research topic because of its applicability in diverse domains. Energy efficiency treated as a crucial factor lies in the designing of WSN. Clustering is commonly applied to increase the energy efficiency and reduce the energy utilization. The proper choice of cluster heads (CHs) and cluster sizes is important in a cluster-based WSN. The CHs which are placed closer to base station (BS) are affected by the hot spot issue and it exhausts its energy faster than the usual way. For addressing this issue, a new unequal clustering and routing technique using ant colony optimization (ACO) algorithm is presented. Initially, CHs are chosen and clusters are constructed based on several variables. Next, the ACO algorithm with levy distribution is applied for the selection of optimal paths between two nodes in the network. A comprehensive validation set takes place under diverse situations under the position of BS. The experimental outcome verified the superiority of the presented model under several validation parameters.

This paper investigates the overflow oscillation elimination problem in the fixed-point two-dimensional (2D) digital filter (DF) based on Roesser model subjected to two’s complement overflow (TCO) nonlinearities. By utilizing the system information and behavior of TCO nonlinearities more effectively, a new global asymptotic stability (GAS) criterion for 2D DFs is presented. A comparison of the obtained criterion with existing criteria is made with the help of examples.

Formal methods have been introduced as the efficient approach for Trojan detection using the model checking approach. However, systematic property generation for detecting a wide range of Trojans is the main challenge in these methods. In other words, the efficient property generation to pinpoint projected Trojan precisely in targeted hardware has been a bottleneck in this approach. This paper evaluated the feasibility of the systematic property generation methodology for Trojan detection in Cryptosystems by analyzing the vulnerable points of an implemented design respecting Trojans’ covered class. The vulnerability analysis reduces the search space of formal methods and considerably improves the scalability of these methods. We have shown in this research that these properties can be generated systematically and prove that the generated properties can be used in conventional model-checking tools to find hard-to-detect Trojans efficiently. Experimental results show that the proposed method reduces Trojan detection time considerably. Also, the memory required to evaluate circuits is possible for several hundreds of clock cycles.

First-order allpass filters are analog filters with a unit magnitude response, but they change the phase shift between input and output signals at various frequencies. This paper presents the transadmittance-mode first-order allpass filters based on the modified current-controlled current differencing transconductance amplifier (M-CCCDTA). The proposed filters utilize a single M-CCCDTA and a grounded capacitor with no external resistors, making them well suited for implementation in an integrated circuit. The gain and phase response of the proposed filters can be adjusted electronically and separately. Also, the high output impedance of the proposed filters at both the input voltage node and the output current node makes cascading easy without the need for buffer devices. A quadrature sinusoidal oscillator based on the proposed first-order allpass filter has been designed as an example of an application. The PSpice simulation and actual experiments are utilized to validate the functionality of the proposed filters. The simulation and experimental results are consistent with the idea of anticipation.

As the basic and core component of electronic systems, CPU security is extremely important to network security. Even an unremarkable faulty instruction on the CPU may lead to serious security problems, such as the operating system crashes or privilege increase since it is often considered as a trusted black box. Therefore, CPU instruction detection is particularly crucial to CPU security. However, most existing methods of CPU instruction detection, based on the inconsistency of microarchitecture and instruction set design, suffer from slow speed and low accuracy. Our work is motivated to propose a practical framework for searching CPU undocumented instruction with fast speed and high accuracy. In this paper, we put forward a general framework InsFinder to detect undocumented instruction on CISC and RISC CPU by an efficient and accurate fuzzing method. It makes use of the instruction format to make advanced predictions, which greatly reduces the search space. Moreover, by introducing classification, de-redundancy, and verification, InsFinder greatly improves the detection accuracy. Experiments show that compared with the existing methods, InsFinder is more effective which costs at least 50% less processing time in detecting undocumented instructions on x86-64, ARM64, and RISC-V, and more accurate which divided the detection results into 4 categories. After filtering, the detection results were reduced from millions to less than 10,000.

As a widely used network security defense technology, network intrusion detection has more deep learning methods used to improve the performance of intrusion detection. However, this method requires a large-scale network traffic data set for training, increasing privacy leakage risk. In this paper, a network intrusion detection algorithm based on Gaussian differential privacy federated learning (NIDS-FLGDP) is proposed. NIDS-FLGDP adopts the client–server architecture of federated learning, introduces the differential privacy of the Gaussian mechanism to ensure the security of the calculation process, uses the improved FedAvg algorithm to reduce communication overhead, and uses the improved 1D CNN to participate in collaborative training for the local model. Optimal parameters for Gaussian differential privacy and the optimal number of participating clients were determined from experiments. Model accuracy rates for binary classification and multi-classification training NIDS-FLGDP are 0.97, 0.975, 0.97 and 0.97, 0.985, 0.96, respectively, for KDD CUP99, NSL_KDD, and UNSW_NB15 network intrusion detection datasets. The results show that NIDS-FLGDP improves intrusion detection performance while protecting network traffic privacy compared with the previous methods. Its applicability and effectiveness have been fully verified, which provides a practical reference for the safe processing and analysis of a large number of diversified network traffic data in the future.

In this paper, a portable low-power low dropout regulator (LDO) with no output capacitance has been designed in 0.18-$\mu$m CMOS technology. In this paper, a PMOS power transistor is used and combined with an overshoot and undershoot suppression circuit to improve the transient response of the circuit. A modified Class-AB operational transconductance amplifier (OTA) is used to reduce power consumption and act as an error amplifier to eliminate low frequency poles. At the same time, a super source follower and a Miller capacitor are used for frequency compensation of the system. Finally, the system can provide a stable voltage for a load step from 0.2 to 25mA in 500ns edge-time for 140pF-load capacitance. The test results show that the circuit achieves a good voltage regulation rate and high transient response.

The evaluation of feedback collected from students at the end of the year is very essential for every educational institution. It is important to improve the teaching–learning process and the annual appraisal process. The existing approach utilizes a Likert scale questionnaire, which allows students to express their level of agreement or disagreement with given statements or provide a neutral response. Additionally, the feedback form includes open-ended questions where students can provide textual feedback. This study introduces a Lexicon-based approach to automatically analyze the textual feedback concerning different aspects of teaching. Aspect-based Sentiment Analysis (ABSA) of student feedback aims to identify sentiments expressed toward various aspects of teachers, such as their ability to address student doubts and their overall knowledge. This study explores linguistic characteristics found in sentences, including negation, modifiers and contact shifters. To assess the sentiment of a sentence, the SentiWordNet lexicon is utilized to assign scores to individual words. Based on these scores, the sentence is categorized as either positive, negative or neutral. According to the experimental findings, the Aspect-Oriented Lexicon-Based (AOLB) approach demonstrates superior performance compared to other baseline methods when it comes to accurately scoring sentiment. The approach achieved a high accuracy rate of 94% for the student feedback dataset-I, 74% for the student feedback dataset-II, 55% for laptop reviews and 59% for restaurant reviews in the SemEval 2014 dataset-III.

To accurately describe the thermal comfort of indoor occupant used to regulate heating, ventilation and air conditioning (HVAC) system is a key goal to reduce energy consumption in intelligent buildings. In this paper, we propose a noncontact measurement of occupant thermal discomfort behavior as an index of thermal comfort. The method takes the existing monitoring image data in the building as the input to infer the occupant thermal discomfort directly, which saves the cost because there is no need to install new sensors in the building and the occupant does not need to wear additional equipment. The framework combined three channels of body posture, motion and performance information to infer occupant thermal discomfort behavior, it consists of a human detection and crop module, a posture analysis module, an optical flow extraction module and a 3D convolutional neural network module. The three channels describe the actions from different perspectives, and the proposed method makes full use of the complementarity of the three modalities to identify the occupant’s thermal discomfort behaviors. Sixteen postures related to thermal discomfort were identified through a questionnaire, and 14,800 video clips containing these postures were collected for experimental evaluation. The results demonstrate the superior performances of our approach to the state-of-the-art techniques. The framework achieves noninvasive, cost-effective thermal comfort evaluation, and has potential value in improving energy efficiency of HVAC systems.

A very simple, compact and low-power second generation voltage conveyor circuit using only eight transistors is proposed in this work. Voltage follower stage is implemented using merged voltage follower (MVF), offering very low output impedance. A simple current mirror forms the input stage of the proposed voltage conveyor. To further enhance the linearity, another circuit is proposed where input transistor in MVF is replaced with DTMOS (Dynamic Threshold Voltage MOSFET), giving $\pm 120$mV linearity range as compared to $\pm 20$mV of the first proposal. Proposed-II VCII provides reduced offset voltage due to decreased effective threshold of DTMOS, and gain bandwidth product of the circuit also improves. Simulation results are computed using 180-nmCMOS technology with supply voltage of $\pm 0.45$V to validate the proposed circuits. Proposed-I and proposed-II VCII consume 85.1$\mu$W and 97.5$\mu$W, respectively. An application of the proposed conveyors as “current to voltage converter”, is also presented.

There is a potential risk of privacy issues due to the disclosure of sensitive information about vehicles in Vehicular ad hoc networks (VANETs), including their real identities and driving trajectories. Anonymous authentication is an effective method to solve the above problems. Ring signature, a cryptographic primitive, offers the ability to create anonymous authentication schemes wherein the verifier can verify the signer’s legitimacy without having knowledge of their actual identity. Researchers have proposed several schemes that aim to protect privacy and maintain anonymity using ring signatures. However, a major drawback of most of these schemes is the linear correlation between the length of the signature and the number of ring members. Our proposed solution involves the construction of a certificateless anonymous authentication scheme that utilizes ring signature with a significantly reduced signature size, which follows a logarithmic relationship with the number of ring members. Through our analysis and comparison, it can be concluded that the privacy information of vehicles is well protected in our scheme, and the communication cost is significantly reduced compared with the existing protocols.

Agriculture is associated with the production of essential food crops for decades and is the one which is playing an important role in the economy of a country as well as in life of an individual. Due to various uncertain variations in the climatic conditions such as rain and other affecting factors, crop prices vary in an unusual pattern. This variation of prices without the knowledge of the farmer may lead to losses in the economy of the individual who is involved in agriculture. In this paper, we have discussed a well-designed system which accurately predicts the crop prices of future months. We have used a Supervised Machine Learning algorithm that is Decision Tree Regression technique for the design of the prediction model as the data is of continuous form. The parameters, which are considered in the dataset, include crop name, month, year, rainfall and wholesale price index (WPI). We have considered the data of 22 crops in total with 4 parameters. We have developed a user-friendly user interface consisting of 22 crop profiles with the predicted prices. Our results show that the regression model achieved an accuracy of 97.32% which will help the farmer on decision of future crop selection for the growth and also hyperinflation can be avoided.

This paper describes a four-port MIMO antenna array design featuring bow-tie-shaped slot-loaded patches with wideband capabilities that cover the frequency range from 24.2GHz to 30.8GHz. The proposed antenna design is printed on an FR4 substrate and occupies an area of 25×24mm². The MIMO antenna consists of four antenna arrays that are symmetrically placed in an upper-lower configuration. The bow-tie-shaped slots loaded radiators are separated horizontally by 3.48mm and vertically by 5.94mm. Each antenna array contains two elements that are separated by a distance of wavelength/4. The suggested MIMO antenna array delivers a high gain of 19.09dB at 27.8GHz and has a bandwidth of 6.6GHz that covers the frequency band of 24.2–30.8GHz. The research demonstrates the quality of the proposed MIMO antenna through various diversity parameters such as mutual coupling, port correlation, diversity gain, and data rate that can be transmitted over a communication medium. The simulation results are validated and found to be consistent with the experimental results. The presented antenna covers the entire bandwidth allocated to different regions, including Europe (24.25–27.5GHz), Sweden (26.5–27.5GHz), USA (27.5–28.35GHz), China (24.25–27.5GHz), Japan (27.5–28.28GHz), and Korea (26.5–29.5GHz). The proposed MIMO antenna design could be an excellent option for 26/28GHz 5G NR n257, n258, and n260 bands under mm-wave wireless communication systems.

This paper aims to propose an improved image classification model to reduce the cost of model construction. Aiming at the problem that network training usually requires the support of a large number of labeled samples, an image classification model based on semi-supervised deep learning is proposed, which uses labeled samples to guide the network to learn unlabeled samples. A convolutional neural network model for simultaneous processing of labeled and unlabeled data is constructed. The tagged data is used to train the Softmax classifier and provide the initial K-means clustering center for the untagged data. The nonsubsampling contourlet layer is used to replace the first convolutional layer of the full convolutional neural network to extract multi-scale depth features, and the nonsubsampling contourlet full convolutional neural network is constructed. The network can extract multi-scale information of the images to be classified, and extract more discriminative deep image features. In addition, the parameters of the nonsubsampled contourlet layers are pre-set and do not require network training. The proposed method has higher classification accuracy than the contrast method on polarimetric SAR images using the nonsubsampled contourlet full convolutional neural network.

Recent advances in speaker embeddings promote a great development of speaker diarization. However, determining ‘who spoke when’ in the meeting scenarios is still challenging due to similar speaker voices and unknown speaker quantity. In this paper, this research proposes enhanced discriminative features for speaker diarization, including discriminative speaker-specific features based on Siamese networks, and a speaker re-verification method. With Siamese architecture, SiamTDNN, this research first explores latent representations which is capable of modeling intra-class and inter-class differences between speakers, by training with audio pairs. Then, the re-verification method is introduced with a local-global strategy to identify speakers in a multi-person talking scene. Our method provides a novel speaker embedding with enhanced discriminative power for disambiguated speakers and achieves an elevated upper bound on the number of speakers. The proposed speaker embedding achieved an EER of 1.1% and a minDCF of 0.1192 on VoxCeleb1 for the speaker verification task. Extensive experiments on AiShell-4, ICSI, AMI and VoxConverse demonstrate the effectiveness of the proposed method with an average DER reduction of 3% and an RTF of 0.0792.