Electronics, Volume 10, Issue 22 (November-2 2021) – 158 articles

This study investigates a control strategy for torque vectoring (TV) and active rear wheel steering (RWS) using feedforward and feedback control schemes for different circumstances. A comprehensive vehicle and combined slip tire model are used to determine the secondary effect and to generate desired yaw acceleration and side slip angle rate. A model-based feedforward controller is designed to improve handling but not to track an ideal response. A feedback controller based on close loop observation is used to ensure its cornering stability. The fusion of two controllers is used to stabilize a vehicle’s lateral motion. To increase lateral performance, an optimization-based control allocation distributes the wheel torques according to the remaining tire force potential. The simulation results show that a vehicle with the proposed controller exhibits more responsive lateral dynamic behavior and greater maximum lateral acceleration. The cornering safety is also demonstrated using a standard stability test. The driving performance and stability are improved simultaneously by the proposed control strategy and the optimal control allocation scheme. Full article

The use of printed spiral coils (PSCs) as inductors in the construction of Wireless Power Transmission (WPT) circuits can save space and be integrated with other circuit boards. The challenges and issues of PSCs present for WPT mainly relate to maintaining an inductive characteristic at frequencies in Ultra High Frequency (UHF) band and to maximising the power transfer efficiency (PTE) between primary and secondary circuits. A new technique is proposed to increase the Q-factor relative to that offered by the PSC, which is shown to enhance WPT performance. This paper provides four-turn planar split-ring loops with high Q-factor for wireless power transmission at UHF bands. This design enhances the power transfer efficiency more than 12 times and allows for a greater transfer distance from 5 mm to 20 mm, compared with a conventional planar rectangular spiral coil. Full article

The detection of rotated objects is a meaningful and challenging research work. Although the state-of-the-art deep learning models have feature invariance, especially convolutional neural networks (CNNs), their architectures did not specifically design for rotation invariance. They only slightly compensate for this feature through pooling layers. In this study, we propose a novel network, named LPNet, to solve the problem of object rotation. LPNet improves the detection accuracy by combining retina-like log-polar transformation. Furthermore, LPNet is a plug-and-play architecture for object detection and recognition. It consists of two parts, which we name as encoder and decoder. An encoder extracts images which feature in log-polar coordinates while a decoder eliminates image noise in cartesian coordinates. Moreover, according to the movement of center points, LPNet has stable and sliding modes. LPNet takes the single-shot multibox detector (SSD) network as the baseline network and the visual geometry group (VGG16) as the feature extraction backbone network. The experiment results show that, compared with conventional SSD networks, the mean average precision (mAP) of LPNet increased by 3.4% for regular objects and by 17.6% for rotated objects. Full article

Smart aquaculture is nowadays one of the sustainable development trends for the aquaculture industry in intelligence and automation. Modern intelligent technologies have brought huge benefits to many fields including aquaculture to reduce labor, enhance aquaculture production, and be friendly to the environment. Machine learning is a subdivision of artificial intelligence (AI) by using trained algorithm models to recognize and learn traits from the data it watches. To date, there are several studies about applications of machine learning for smart aquaculture including measuring size, weight, grading, disease detection, and species classification. This review provides and overview of the development of smart aquaculture and intelligent technology. We summarized and collected 100 articles about machine learning in smart aquaculture from nearly 10 years about the methodology, results as well as the recent technology that should be used for development of smart aquaculture. We hope that this review will give readers interested in this field useful information. Full article

The use of innovative and sophisticated malware definitions poses a serious threat to computer-based information systems. Such malware is adaptive to the existing security solutions and often works without detection. Once malware completes its malicious activity, it self-destructs and leaves no obvious signature for detection and forensic purposes. The detection of such sophisticated malware is very challenging and a non-trivial task because of the malware’s new patterns of exploiting vulnerabilities. Any security solutions require an equal level of sophistication to counter such attacks. In this paper, a novel reinforcement model based on Monte-Carlo simulation called eRBCM is explored to develop a security solution that can detect new and sophisticated network malware definitions. The new model is trained on several kinds of malware and can generalize the malware detection functionality. The model is evaluated using a benchmark set of malware. The results prove that eRBCM can identify a variety of malware with immense accuracy. Full article

Due to the global growth in popularity of Fifth Generation (5G) cellular communications, the demand for shielding against it has risen for a variety of applications, mainly related to cybersecurity but also to isolation, calm areas and so on. This research paper aims to provide a suitable dual-band fractal FSS (Frequency Selective Surface) for the 5G lower band frequencies: 750 MHz and 3.5 GHz. The unit cell is in the shape of a bow tie, where each of the triangular parts are Sierpiński triangles. One major addition to the unit cell is a central metal strip to make the manufacturing of the FSS more feasible and to tune the operation frequencies and bandwidths. As with each different stage of a fractal antenna, the different stages of the fractal FSS design behave differently. For this application, stage 2 is sufficient, as we are able to cover frequency bands among those included in the FR1 5G spectrum. Some equations were derived using linear regression in order to provide specific design tools for building an FSS. These equations have high accuracy and can be used to adapt the proposed design to other frequencies. Some other parameters, which are not represented in the aforementioned equations, can also be adjusted for minor tweaking of the final design. This design performs well except under large incidence angles. This should be taken into account when proposing the installation of a structure based on it. A good agreement between simulation and measurement results is observed. Full article

Transition metal dichalcogenide materials are studied to investigate unexplored research avenues, such as spin transport behavior in 2-dimensional materials due to their strong spin-orbital interaction (SOI) and the proximity effect in van der Waals (vdW) heterostructures. Interfacial interactions between bilayer graphene (BLG) and multilayer tungsten disulfide (ML-WS₂) give rise to fascinating properties for the realization of advanced spintronic devices. In this study, a BLG/ML-WS₂ vdW heterostructure spin field-effect transistor (FET) was fabricated to demonstrate the gate modulation of Rashba-type SOI and spin precession angle. The gate modulation of Rashba-type SOI and spin precession has been confirmed using the Hanle measurement. The change in spin precession angle agrees well with the local and non-local signals of the BLG/ML-WS₂ spin FET. The operation of a spin FET in the absence of a magnetic field at room temperature is successfully demonstrated. Full article

The paper presents an analysis and summary of the current research state concerning the application of machine learning and fuzzy logic for solving problems in electronics. The investigated domain is conceptualized with aim the achievements, trending topics and future research directions to be outlined. The applied research methodology includes a bibliographic approach in combination with a detailed examination of 66 selected papers. The findings reveal the gradually increasing interest over the last 10 years in the machine learning and fuzzy logic techniques for modeling, implementing and improving different hardware-based intelligent systems. Full article

As per recent progress, online social network (OSN) users have grown tremendously worldwide, especially in the wake of the COVID-19 pandemic. Today, OSNs have become a core part of many people’s daily lifestyles. Therefore, increasing dependency on OSNs encourages privacy requirements to protect users from malicious sources. OSNs contain sensitive information about each end user that intruders may try to leak for commercial or non-commercial purposes. Therefore, ensuring different levels of privacy is a vital requirement for OSNs. Various privacy preservation methods have been introduced recently at the user and network levels, but ensuring k-anonymity and higher privacy model requirements such as l-diversity and t-closeness in OSNs is still a research challenge. This study proposes a novel method that effectively anonymizes OSNs using multiple-graph-properties-based clustering. The clustering method introduces the goal of achieving privacy of edge, node, and user attributes in the OSN graph. This clustering approach proposes to ensure k-anonymity, l-diversity, and t-closeness in each cluster of the proposed model. We first design the data normalization algorithm to preprocess and enhance the quality of raw OSN data. Then, we divide the OSN data into different clusters using multiple graph properties to satisfy the k-anonymization. Furthermore, the clusters ensure improved k-anonymization by a novel one-pass anonymization algorithm to address l-diversity and t-closeness privacy requirements. We evaluate the performance of the proposed method with state-of-the-art methods using a “Yelp real-world dataset”. The proposed method ensures high-level privacy preservation compared to state-of-the-art methods using privacy metrics such as anonymization degree, information loss, and execution time. Full article

In this study, we present options for extending the endurance of a lightweight unmanned aerial vehicle (UAV), along with their advantages and disadvantages. We present a developed solution based on the use of gallium–arsenide (GaAs) solar modules installed on a UAV and connected to a custom-made maximum power point tracker (MPPT) with an integrated perturb and observe (P&O) algorithm. The mathematical behavior required to calculate the electrical energy production from solar energy on the UAV from known UAV angles of rotation, the position of the sun in the sky, solar irradiance measurements, the solar module area and the solar modules efficiency is presented. A comparison of the calculated and actual measured electrical energy production results during an aerial mapping mission is presented. We perform a number of aerial mapping mission flights and the experimental results confirm an energy efficiency value of more than 96.27% for the MPPT and extended flight endurance by up to 21.25%. In addition, onboard measurements and other data captured during flights confirm the proposed electrical energy production calculation. Full article

Adding a core to a coupling coil can improve transmission efficiency. However, the added core causes the self-inductance of the coupling coil to increase at a high temperature due to the temperature-sensitive property of the core material’s permeability. The self-inductance increases, causing the resonance frequency to shift down, thereby decreasing the output power. The 3 dB bandwidth of the system can learn of the correspondence between the output power and the resonance frequency. In order to make sure that the output power does not excessively decrease at a high temperature, this study employs a simulation for the LCC-S-based wireless power transfer system. Adding a minor resistance to shift down the lower cutoff frequency ensures that the resonance frequency yielded by the temperature rise can be higher than the lower cutoff frequency, making the output power higher than half of the maximum. Then, an adjustment on the compensation capacitances on the resonant circuit elevates the output power more. The outcomes are consistent with the prediction. Adding the core to the coupling coil improves transmission efficiency; increasing the bandwidth of the system excessively decreases the output power decline at a high temperature for the temperature-sensitive core material permeability. Full article

Due to overlay technologies, service providers have a logical view of the underlay network and can optimize the experience quality without modifying the physical network. However, the cross-layer interaction inevitably causes network fluctuation due to their inconsistent optimization objectives. Aside from that, network failures that occur in both layers not only cause network performance degradation but also significantly increase the frequency of cross-layer interaction. These problems make the network fluctuate for a long time, reduce the network performance, and influence the user experience, especially for time-sensitive applications. In this paper, we design a cross-layer architecture in which the logical layer can satisfy the service function chain demands and maximize the user experience and physical layer so it can optimize the overall network performance. Our cross-layer architecture can make proactive corrections in both layers. Furthermore, we investigate the cross-layer interaction and design two strategies to eliminate fluctuations and make the network converge quickly. Full article

The awarding of public procurement processes is one of the main causes of corruption in governments, due to the fact that in many cases, contracts are awarded to previously agreed suppliers (favouritism); for this selection, the qualification parameters of a process play a fundamental role, seeing as due to their manipulation, bidders with high prices win, causing prejudice to the state. This study identifies processes with anomalies and generates a model for detecting possible corruption in the assignment of process qualification parameters in public procurement. A multi-phase model was used (the identification of anomalies and generation of the detection model), which uses different algorithms, such as clustering (K-Means), Self-Organizing map (SOM), Support Vector Machine (SVM) and Principal Component Analysis (PCA). SOM was used to determine the level of influence of each rating parameter, K-Means to create groups by clustering, semi-supervised learning with SVM and PCA to generate a model to detect anomalies in the processes. By means of a case study, four groups of processes were obtained, highlighting the presence of the group “null economic offer” where the values for the economic offer do not exceed 1%, and a greater weight is given to other qualification parameters, which include direct contracting. The processes in this cluster are considered anomalous. Following this methodology, a semi-supervised learning model is built for the detection of anomalies, which obtains an accuracy of 95%, allowing the detection of procedures where the aim is to benefit a particular supplier by means of the qualification assignment parameters. Full article

Lithium-ion batteries are the most used technology in portable electronic devices. High energy density and high power per mass battery unit make it preferable over other batteries. The existing constant-temperature and constant-voltage charging technique (CT–CV), with a closed loop, lacks a detailed design of control circuits, which can increase charging speed. This article addresses this research gap in a novel way by implementing a simpler feedback proportional integral and differential (PID) control to a closed-loop CT–CV charging circuit. Voltage-mode control (VMC) and average current-mode control (ACM) methods were implemented to maintain the battery voltage, current, and temperature at safe limits. As per simulation results, 23% faster charging is achieved by implementing VMC and almost 50% faster charging is attained by employing the ACM technique in the PID controller. Our proposed control strategy is validated experimentally, which yields up to 25% faster charging of a battery than the reference battery. Full article

Weighted least-squares (WLS) phase unwrapping is widely used in optical engineering. However, this technique still has issues in coping with discontinuity as well as noise. In this paper, a new WLS phase unwrapping algorithm based on the least-squares orientation estimator (LSOE) is proposed to improve phase unwrapping robustness. Specifically, the proposed LSOE employs a quadratic error norm to constrain the distance between gradients and orientation vectors. The estimated orientation is then used to indicate the wrapped phase quality, which is in terms of a weight mask. The weight mask is calculated by post-processing, including a bilateral filter, STDS, and numerical relabeling. Simulation results show that the proposed method can work in a scenario in which the noise variance is 1.5. Comparisons with the four WLS phase unwrapping methods indicate that the proposed method provides the best accuracy in terms of segmentation mean error under the noisy patterns. Full article

A simple and compact antenna with a switchable beam for millimeter-wave communication is proposed in this paper. The antenna has a planar structure, and the design evolution is discussed. The beam switching functionality was achieved by incorporating two PIN diodes in the ground plane of the antenna. By switching ON either of the PIN diodes, the inverted L-shaped stub becomes connected to the ground plane and behaves as a cavity, which causes the dispersion of the radiation pattern. Therefore, a wide-angle (±18${}^{\circ }$) beam-switching property can be achieved using a simple and low-cost technique, without the necessity to implement additional conventional circuits. The proposed antenna is characterized by a good performance in terms of return loss, bandwidth, measured gain up to 7.95 dB, and radiation efficiency up to 84%, making it a proper candidate for IoT technology and millimeter-wave 5G devices. Full article

A large increase in distributed generation integrated within power system networks has resulted in power quality challenges and in the need to resolve complex system faults. The monitoring of the real-time state of the power parameters of the transmission and distribution grid helps to control the stability and reliability of the grid. In such a scenario, having current monitoring equipment that is flexible and easy to install can always be of great help to reduce the price of energy monitoring and to increase the dependability of a smart grid. Advances in magnetic sensor research offer measurement system accuracy that is less complex to install and that can be obtained at a lower less cost. Tunneling magnetoresistive (TMR) sensors can be used to measure the AC current by sensing the magnetic field that is generated by the current-carrying conductor in a contactless manner. This paper illustrates the results of a thorough investigation of factors that can influence the performance of the TMR sensors that are used for the current phasor measurements of a single-phase AC current application, such as the effects of distance, harmonics, and conductor insulation. Full article

The purpose of image dehazing is the reduction of the image degradation caused by suspended particles for supporting high-level visual tasks. Besides the atmospheric scattering model, convolutional neural network (CNN) has been used for image dehazing. However, the existing image dehazing algorithms are limited in face of unevenly distributed haze and dense haze in real-world scenes. In this paper, we propose a novel end-to-end convolutional neural network called attention enhanced serial Unet++ dehazing network (AESUnet) for single image dehazing. We attempt to build a serial Unet++ structure that adopts a serial strategy of two pruned Unet++ blocks based on residual connection. Compared with the simple Encoder–Decoder structure, the serial Unet++ module can better use the features extracted by encoders and promote contextual information fusion in different resolutions. In addition, we take some improvement measures to the Unet++ module, such as pruning, introducing the convolutional module with ResNet structure, and a residual learning strategy. Thus, the serial Unet++ module can generate more realistic images with less color distortion. Furthermore, following the serial Unet++ blocks, an attention mechanism is introduced to pay different attention to haze regions with different concentrations by learning weights in the spatial domain and channel domain. Experiments are conducted on two representative datasets: the large-scale synthetic dataset RESIDE and the small-scale real-world datasets I-HAZY and O-HAZY. The experimental results show that the proposed dehazing network is not only comparable to state-of-the-art methods for the RESIDE synthetic datasets, but also surpasses them by a very large margin for the I-HAZY and O-HAZY real-world dataset. Full article

The pulse carries important physiological and pathological information about the human body. The piezoresistive sensor used to capture vascular pulsation information has transitioned from a single-point to a sensor array. However, the interference signal between channels has become a key bottleneck restricting the development of the sensor array pulse diagnosis equipment. The sensor in contact with vascular pulsation obtains the pulse signal. When some sensors are displaced due to vascular pulsation, other sensors will be driven to move, which will produce interference signals. Signal interference is a common problem for sensor arrays, but few people have analyzed this problem from the perspective of the algorithm. In this paper, an interference signal recognition algorithm of the sensor array based on a convolutional neural network (CNN) is proposed. Firstly, a simple mechanical structure model was established to analyze the generation mechanism of interference signals in one MEMS sensor array acquisition system. Then, a CNN model with fewer parameters was designed for identifying interference signals. Finally, the CNN model was implemented on a field-programmable gate array (FPGA). The results show that the CNN algorithm could identify interference signals well, and the accuracy of the algorithm was 99.3%. The power consumption of the CNN accelerator was 0.673 W at a working frequency of 100 MHz. The interference signal identification algorithm is proposed to ensure the accurate analysis of array signals. FPGA implementation lays the foundation for the miniaturization and portability of the equipment. Full article

In this work, we systematically analyze the impact of three kinds of Mach-Zehnder modulator (MZM) imbalances, including bias deviation, amplitude mismatch, and differential time skew in intensity-modulation direct-detection (IM-DD) links. It is shown that, for power fading limited transmission, the imbalances can be utilized as advantages rather than impairments. Specifically, the bias deviation with single-arm driven mode and amplitude mismatch with differential driven mode can increase the available bandwidth by shifting the frequency of fading notches. Meanwhile, time skew provides another way to avoid fading by shaping the double sideband (DSB) signal into a vestigial sideband (VSB) with an asymmetrical transfer function. In the transmission experiment, 34 Gbaud Nyquist 6/8-ary pulse amplitude modulation (PAM-6/8) signals are used for investigation in a 20 km dispersion-uncompensated standard single-mode fiber (SSMF) link. With the help of a Volterra nonlinear equalizer, all three kinds of imbalances can achieve bit-error rates (BERs) below the 7% and 20% hard-decision forward error correction (HD-FEC) thresholds for PAM-6 and PAM-8 signals, respectively. The received power sensitivity is also compared at the back-to-back (BTB) case and after fiber transmission. Both numerical simulation and experimental demonstration confirm that the dispersion-induced power fading can be effectively suppressed with bias, amplitude, or skew imbalance, providing a feasible solution for transmission distance extension of C-band DD links. Full article

In 2020, a global pandemic led to lockdowns, and subsequent social and business restrictions. These required overnight implementation of emergency measures to permit continued functioning of vital industries. Digital technologies and platforms made this switch feasible, but it also introduced several cyber related vulnerabilities, which students might not have known how to mitigate. For this study, the Global Cyber Security Index and the Cyber Risk literacy and education index were used to provide a cyber security context for each country. This research project—an international, cross-university, comparative, quantitative project—aimed to explore the risk attitudes and concerns, as well as protective behaviours adopted by, students at a South African, a Welsh and a Hungarian University, during the pandemic. This study’s findings align with the relative rankings of the Oliver Wyman Risk Literacy and Education Index for the countries in which the universities reside. This study revealed significant differences between the student behaviours of students within these universities. The most important differences were identified between students’ risk attitudes and concerns. It was also discovered that South African students reported having changed their protective online behaviours to the greatest extent, since the pandemic commenced. Recommendations are made suggesting that cyber security training and education, as well as improving the digital trust and confidence in digital platforms, are critical. Full article

Innovative practices in irrigation systems can bring improvements in terms of economic efficiency and, at the same time, can reduce environmental impacts. Investment in high-tech technologies frequently involves additional costs, but an efficient water management system can increase the lifetime of the equipment. The most utilized electronic device for a smart management, used to pump units from irrigation systems, is the frequency converter. This device can regulate the speed of the motors that control the pumps according to the consumption of water, ensuring that it does not pump more water than is needed. This paper develops a new operating algorithm that ensures the operation of the pumping group at safe operating intervals and identifies the equivalent pump operating points for the entire flow range and pumping height of the pumping group in order to bring smart management to irrigation systems. The parameters monitored and collected for each vertical pump refers to the voltage, current, frequency (speeds) and flow of each hydraulic operating mode. The methodology used is based on the principle of creating an expert system to optimize energy consumption in the pumping groups. The proposed methodology was tested on an irrigation system that includes a pumping group with five pumps, showing its effectiveness in obtaining the optimal solution with a relatively low computational burden and without the violation of any system constraints under any operating conditions. Full article

This article proposes a high-frequency, area-efficient high-side bootstrap circuit with threshold-based digital control (TBDC) that is directly charged by BUS voltage (DCBV). In the circuit, the voltage of the bootstrap is directly obtained from the BUS voltage instead of the on-chip low dropout regulator (LDO), which is more suitable for a high operating frequency. An area-efficient threshold-based digital control structure is used to detect the bootstrap voltage, thereby effectively preventing bootstrap under-voltage or over-voltage that may result in insufficient driving capability, increased loss, or breakdown of the power device. The design and implementation of the circuit are based on CSMC 0.25 µm 60 V BCD technology, with an overall chip area of 1.4 × 1.3 mm², of which the bootstrap area is 0.149 mm² and the figure-of-merit (FOM) is 0.074. The experimental results suggest that the bootstrap circuit can normally operate at 5 MHz with a maximum buck converter efficiency of 83.6%. This work plays a vital role in promoting the development of a wide range of new products and new technologies, such as integrated power supplies, new energy vehicles, and data storage centers. Full article

Machine Learning prediction algorithms have made significant contributions in today’s world, leading to increased usage in various domains. However, as ML algorithms surge, the need for transparent and interpretable models becomes essential. Visual representations have shown to be instrumental in addressing such an issue, allowing users to grasp models’ inner workings. Despite their popularity, visualization techniques still present visual scalability limitations, mainly when applied to analyze popular and complex models, such as Random Forests (RF). In this work, we propose Random Forest Similarity Map (RFMap), a scalable interactive visual analytics tool designed to analyze RF ensemble models. RFMap focuses on explaining the inner working mechanism of models through different views describing individual data instance predictions, providing an overview of the entire forest of trees, and highlighting instance input feature values. The interactive nature of RFMap allows users to visually interpret model errors and decisions, establishing the necessary confidence and user trust in RF models and improving performance. Full article

This study is supposed to introduce a valid strategy for increasing the sensitivity of chipless radio frequency identification (RFID) encoders. The idea is to properly select the dielectric substrate in order to enhance the contribution of the sensitive layer and to maximize the frequency shift of the resonance peak. The specific case of a chipless sensor suitable for the detection of humidity in low-humidity regimes will be investigated both with numerical and experimental tests. Full article

Dementia and Alzheimer’s disease are caused by neurodegeneration and poor communication between neurons in the brain. So far, no effective medications have been discovered for dementia and Alzheimer’s disease. Thus, early diagnosis is necessary to avoid the development of these diseases. In this study, efficient machine learning algorithms were assessed to evaluate the Open Access Series of Imaging Studies (OASIS) dataset for dementia diagnosis. Two CNN models (AlexNet and ResNet-50) and hybrid techniques between deep learning and machine learning (AlexNet+SVM and ResNet-50+SVM) were also evaluated for the diagnosis of Alzheimer’s disease. For the OASIS dataset, we balanced the dataset, replaced the missing values, and applied the t-Distributed Stochastic Neighbour Embedding algorithm (t-SNE) to represent the high-dimensional data in the low-dimensional space. All of the machine learning algorithms, namely, Support Vector Machine (SVM), Decision Tree, Random Forest and K Nearest Neighbours (KNN), achieved high performance for diagnosing dementia. The random forest algorithm achieved an overall accuracy of 94% and precision, recall and F1 scores of 93%, 98% and 96%, respectively. The second dataset, the MRI image dataset, was evaluated by AlexNet and ResNet-50 models and AlexNet+SVM and ResNet-50+SVM hybrid techniques. All models achieved high performance, but the performance of the hybrid methods between deep learning and machine learning was better than that of the deep learning models. The AlexNet+SVM hybrid model achieved accuracy, sensitivity, specificity and AUC scores of 94.8%, 93%, 97.75% and 99.70%, respectively. Full article

Convolutional neural networks (CNNs) are widely used in modern applications for their versatility and high classification accuracy. Field-programmable gate arrays (FPGAs) are considered to be suitable platforms for CNNs based on their high performance, rapid development, and reconfigurability. Although many studies have proposed methods for implementing high-performance CNN accelerators on FPGAs using optimized data types and algorithm transformations, accelerators can be optimized further by investigating more efficient uses of FPGA resources. In this paper, we propose an FPGA-based CNN accelerator using multiple approximate accumulation units based on a fixed-point data type. We implemented the LeNet-5 CNN architecture, which performs classification of handwritten digits using the MNIST handwritten digit dataset. The proposed accelerator was implemented, using a high-level synthesis tool on a Xilinx FPGA. The proposed accelerator applies an optimized fixed-point data type and loop parallelization to improve performance. Approximate operation units are implemented using FPGA logic resources instead of high-precision digital signal processing (DSP) blocks, which are inefficient for low-precision data. Our accelerator model achieves 66% less memory usage and approximately 50% reduced network latency, compared to a floating point design and its resource utilization is optimized to use 78% fewer DSP blocks, compared to general fixed-point designs. Full article

Wireless power transfer (WPT) has been extensively studied from various aspects such as far field and near field, operating frequency, coil design, matched capacitance values, misaligned locations of transmitting and receiving coils, distance variance between them, target loads in the specific locations, environment, and operating conditions. This is due to the usefulness of WPT technology in many applications, including the revolutionary method of auto-recharging of unmanned aerial vehicles (UAVs). This paper presents analytical modeling of a WPT-link with two orthogonal transmitting coils arranged to produce an omnidirectional magnetic field suitable for charging a moving rotating load, maximizing energy transfer without any feedback from the receiving end. To achieve a suitable 2D WPT simulation system, as well as an accurate control design, the mutual coupling values in terms of receiver angular rotation are simulated using Ansys software. Power transfer is maximized by using extremum seeking control (ESC), making use of the input power as an objective function with specific parameter values that represent the WPT model to obtain the results. The results shown are those of the input power transmitted by the transmitting-end coils to a load of an orbiting mobile UAV. Based on the simulation results, the controller can achieve maximum power transfer in 100 µs of duration when the speed of the UAV is close to 314 rad/s. Full article

With advancements in engineering and science, the application of smart systems is increasing, generating a faster growth of the IoT network traffic. The limitations due to IoT restricted power and computing devices also raise concerns about security vulnerabilities. Machine learning-based techniques have recently gained credibility in a successful application for the detection of network anomalies, including IoT networks. However, machine learning techniques cannot work without representative data. Given the scarcity of IoT datasets, the DAD emerged as an instrument for knowing the behavior of dedicated IoT-MQTT networks. This paper aims to validate the DAD dataset by applying Logistic Regression, Naive Bayes, Random Forest, AdaBoost, and Support Vector Machine to detect traffic anomalies in IoT. To obtain the best results, techniques for handling unbalanced data, feature selection, and grid search for hyperparameter optimization have been used. The experimental results show that the proposed dataset can achieve a high detection rate in all the experiments, providing the best mean accuracy of 0.99 for the tree-based models, with a low false-positive rate, ensuring effective anomaly detection. Full article

This paper presents a 10 bit 100 MS/s asynchronous successive approximation register (SAR) analog-to-digital converter (ADC) without calibration for industrial control system (ICS) applications. Several techniques are adopted in the proposed switching procedure to achieve better linearity, power and area efficiency. A single-side-fixed technique is utilized to reduce the number of capacitors; a parallel split capacitor array in combination with a partially thermometer coded technique can minimize the switching energy, improve speed, and decrease differential non-linearity (DNL). In addition, a compact timing-protection scheme is proposed to ensure the stability of the asynchronous SAR ADC. The proposed ADC is fabricated in a 28 nm CMOS process with an active area of 0.026 mm${}^2$. At 100 MS/s, the ADC achieves a signal-to-noise-and-distortion ratio (SNDR) of 51.54 dB and a spurious free dynamic range (SFDR) of 55.12 dB with the Nyquist input. The measured DNL and integral non-linearity (INL) without calibration are +0.37/−0.44 and +0.48/−0.63 LSB, respectively. The power consumption is 1.1 mW with a supply voltage of 0.9 V, leading to a figure of merit (FoM) of 35.6 fJ/conversion-step. Full article