This research concerns the design and development of an assistive system based on eye tracking, which can be used to improve the quality of life of disabled patients. With the use of their eye movement, whose function is not affected by their illness, patients are capable of communicating with and sending notifications to caretakers, controlling various appliances, including wheelchairs. The designed system is divided into two subsystems: stationary and mobile assistive systems. Both systems provide a graphic user interface (GUI) that is used to link the eye tracker with the appliance control. There are six GUI pages for the stationary assistive system and seven for the mobile assistive system. GUI pages for the stationary assistive system include the home page, smart appliance page, eye-controlled television page, eye-controlled air conditional page, i-speak page and entertainment page. GUI pages for the mobile assistive system are similar to the GUI pages for the stationary assistive system, with the additional eye-controlled wheelchair page. To provide hand-free secure access, an authentication based on facial landmarks is developed. The operational test of the proposed assistive system provides successful and promising results. Full article

An acoustic second-order low-pass filter is proposed for filter banks emulating the operation of a human cochlea. By using a special filter structure and an innovative quality (Q)-factor tuning technique, an independent change of the cutoff frequency (ω₀) and the Q-factor with unchanged gain at low frequencies is achieved in this filter. The techniques applied result in a simple filter design with low Q-factor sensitivity to component mismatch. These filter features greatly simplify the implementation of the electronic cochlea in CMOS technologies. An exemplary filter bank designed and simulated in an X-FAB 180 nm CMOS process is presented, which consumes 1.25–34.75 nW of power per individual filter when supplied with 0.5 V. The 11-channel filter bank covers a 20–20 kHz band, while the Q-factor of each channel can be tuned from 2 to 40. The simulation-predicted sensitivities of Q and ω₀ to process/voltage/temperature (PVT) variations are less than 1%. The input-referred noise is no greater than 22 µV_RMS, and the dynamic range is at least 68 dB for all filters in the bank. Full article

With growing concerns about the depletion of rare-earth elements, managing End-of-Life products has become a key sustainability initiative in the supply chains of global corporations. Recycling, the process of dismantling, separating, and recovery of components and raw materials from wastes, is technologically challenging and should be planned in such a way as to ensure operational efficiency as well as safety. This study explores the Disassembly Line Balancing Problem with Correlated Tasks (DLBP-CT), which is prevalent in the recycling of the Waste of Electrical and Electronic Equipment (WEEE). For this purpose, an original Integer Nonlinear Programming (INLP) model is proposed to find the optimal configuration for the disassembly lines. Given the NP-hard nature of this problem, the Adaptive Genetic Algorithm (AGA) is developed to solve the problem, minimizing the number of workstations and maximizing the relationship between the disassembly tasks. A case example from electronic waste is provided to test the practicality of the developed optimization approach. Sensitivity analysis is conducted to explore the impact of parameter changes in the optimization outcomes. Results are supportive of the applicability of the developed approach and show that it can serve as a strong decision aid tool when selecting the best disassembly process, workstations, and task assignments. Full article

In this review paper, an overview of the application of n-type 4H-SiC Schottky barrier diodes (SBDs) as radiation detectors is given. We have chosen 4H-SiC SBDs among other semiconductor devices such as PiN diodes or metal-oxide-semiconductor (MOS) structures, as significant progress has been achieved in radiation detection applications of SBDs in the last decade. Here, we present the recent advances at all key stages in the application of 4H-SiC SBDs as radiation detectors, namely: SBDs fabrication, electrical characterization of SBDs, and their radiation response. The main achievements are highlighted, and the main challenges are discussed. Full article

Line-start synchronous motors have attracted researchers’ interest as suitable replacements of asynchronous motors due to their high efficiency, which has been provoked by strict regulations regarding applicable efficiency classes of motors in the EU market. The research becomes even more challenging as it takes into consideration the diverse rotor topologies with different magnet locations for this type of motor. The rotor configuration with an interior asymmetric permanent magnet (PM) array rotor was chosen for analysis and optimization in this paper as this specific configuration is particularly challenging in terms of placing the magnets with adequate dimensions into the existing rotor of the asynchronous motor with a squirrel cage winding, in order simultaneously to obtain good operational characteristics such as high efficiency and power factor, good overloading capability and low material consumption. Therefore, an optometric analysis is performed in order to find the best configuration of the air gap length, magnet thickness, magnet width and number of conductors per slot, along with modifications of the rotor slot. The motor outer dimensions remained unchanged compared with the starting model of the line-start motor derived from the asynchronous motor, which is a product of the company Končar. The optimized model obtained higher efficiency, power factor and overloading capability than the starting model, along with good starting and synchronization capabilities. Full article

Eye tracking is a useful technique for detecting autism spectrum disorder (ASD). One of the most important aspects of good learning is the ability to have atypical visual attention. The eye-tracking technique provides useful information about children’s visual behaviour for early and accurate diagnosis. It works by scanning the paths of the eyes to extract a sequence of eye projection points on the image to analyse the behaviour of children with autism. In this study, three artificial-intelligence techniques were developed, namely, machine learning, deep learning, and a hybrid technique between them, for early diagnosis of autism. The first technique, neural networks [feedforward neural networks (FFNNs) and artificial neural networks (ANNs)], is based on feature classification extracted by a hybrid method between local binary pattern (LBP) and grey level co-occurrence matrix (GLCM) algorithms. This technique achieved a high accuracy of 99.8% for FFNNs and ANNs. The second technique used a pre-trained convolutional neural network (CNN) model, such as GoogleNet and ResNet-18, on the basis of deep feature map extraction. The GoogleNet and ResNet-18 models achieved high performances of 93.6% and 97.6%, respectively. The third technique used the hybrid method between deep learning (GoogleNet and ResNet-18) and machine learning (SVM), called GoogleNet + SVM and ResNet-18 + SVM. This technique depends on two blocks. The first block used CNN to extract deep feature maps, whilst the second block used SVM to classify the features extracted from the first block. This technique proved its high diagnostic ability, achieving accuracies of 95.5% and 94.5% for GoogleNet + SVM and ResNet-18 + SVM, respectively. Full article

The Internet of Things (IoT) encompasses multiple fast-emerging technologies controlling and connecting millions of new devices every day in several application domains. The increased number of interconnected IoT devices, their limited computational power, and the evolving sophistication of cyber security threats, results in increased security challenges for the IoT ecosystem. The diversity of IoT devices, and the variety of QoS requirements among several domains of IoT application, impose considerable challenges in designing and implementing a robust IoT security solution. The aim of this paper is to present an efficient, robust, and easy-to-use system, for IoT cyber security operators. Following a by-design security approach, the proposed system is a platform comprising four distinct yet cooperating components; a distributed AI-enhanced detection of potential threats and anomalies mechanisms, an AI-based generation of effective mitigation strategies according to the severity of detected threats, a system for the verification of SDN routing decisions along with network- and resource-related policies, and a comprehensive and intuitive security status visualization and analysis. The distributed anomaly detection scheme implementing multiple AI-powered agents is deployed across the IoT network nodes aiming to efficiently monitor the entire network infrastructure. Network traffic data are fed to the AI agents, which process consecutive traffic samples from the network in a time series analysis manner, where consecutive time windows framing the traffic of the surrounding nodes are processed by a graph neural network algorithm. Any detected anomalies are handled by a mitigation engine employing a distributed neural network algorithm, which exploits the recorded anomalous events and deploys appropriate responses for optimal threat mitigation. The implemented platform also includes the hypothesis testing module, and a multi-objective optimization tool for the quick verification of routing decisions. The system incorporates visualization and analytics functionality and a customizable user interface. Full article

The privacy and security of online transactions and information exchange has always been a critical issue of e-commerce. However, there is a certain level of tolerance (a share of 36%) when it comes to so-called governments’ rights to monitor electronic mail messages and other information exchange as resulting from the answers of respondents from 51 countries in the latest wave (2017–2020) of the World Values Survey. Consequently, the purpose of this study is to discover the most significant influences associated with this type of tolerance and even causal relationships. The variables have been selected and analyzed in many rounds (Adaptive Boosting, LASSO, mixed-effects modeling, and different regressions) with the aid of a private cloud. The results confirmed most hypotheses regarding the overwhelming role of trust, public surveillance acceptance, and some attitudes indicating conscientiousness, altruistic behavior, and gender discrimination acceptance in models with good-to-excellent classification accuracy. A generated prediction nomogram included 10 ten most resilient influences. Another one contained only 5 of these 10 that acted more as determinants resisting reverse causality checks. In addition, some sociodemographic controls indicated significant variables afferent to the highest education level attained, settlement size, and marital status. The paper’s novelty stands on many robust techniques supporting randomly and nonrandomly cross-validated and fully reproducible results based on a large amount and variety of evidence. The findings also represent a step forward in research related to privacy and security issues in e-commerce. Full article

Neutral-point-clamped multilevel converters are currently a suitable solution for a wide range of applications. It is well known that the capacitor voltage balance is a major issue for this topology. In this paper, a brief summary of the basic topologies, modulations, and features of neutral-point-clamped multilevel converters is presented, prior to a detailed description and analysis of the capacitor voltage balance behavior. Then, the most relevant methods to manage the capacitor voltage balance are presented and discussed, including operation in the overmodulation region, at low frequency-modulation indexes, with different numbers of AC phases, and with different numbers of levels. Both open- and closed-loop methods are discussed. Some methods based on adding external circuitry are also presented and analyzed. Although the focus of the paper is mainly DC–AC conversion, the techniques for capacitor voltage balance in DC–DC conversion are discussed as well. Finally, the paper concludes with some application examples benefiting from the presented techniques. Full article

Today, an electron spin qubit on silicon appears to be a very promising physical platform for the fabrication of future quantum microprocessors. Thousands of these qubits should be packed together into one single silicon die in order to break the quantum supremacy barrier. Microelectronics engineers are currently leveraging on the current CMOS technology to design the manipulation and read-out electronics as cryogenic integrated circuits. Several of these circuits are RFICs, as VCO, LNA, and mixers. Therefore, the availability of a qubit CAD model plays a central role in the proper design of these cryogenic RFICs. The present paper reports on a circuit-based compact model of an electron spin qubit for CAD applications. The proposed model is described and tested, and the limitations faced are highlighted and discussed. Full article

In this present work, different Cross-Coupled Differential Drive (CCDD) CMOS bridge rectifiers are designed using either 32 nm or Tunnel-FET (TFET) technology. Commercial PDK has been used for the 32 nm technology, while lookup tables (LUT) resulting from a physics model have been applied for the TFET. To consider the parasitic effects for the circuit performances, the 32 nm-based circuits have been laid out, while a parasitic model has been included in the TFET LUT for circuit implementation. In this work, the post-layout simulations, including parasitic, demonstrate for conventional CCDD circuits that TFET technology has a larger dynamic range (DR) (>60%) and better 1 V-sensitivity than the 32 nm planar technology has. Note that, in this case, the figure of merit defined by the Voltage Conversion Efficiency (VCE) and Power Conversion Efficiency (PCE) remains somewhat similar. On the other hand, topology proposing better VCE at the cost of low PCE shows lower performance than expected in 32 nm than in reported data for larger technology nodes (e.g., 180 nm). The TFET-based circuit shows a PCE of 70%, VCE of 82% with an 8 dB DR (>60%), and the best 1 V-sensitivity in this work. Because of the low-bias condition and the good reverse current blocking (unidirectional channel), the TFET offers new perspectives for RF-DC rectifier/multiplier topology, which are usually limited with planar technology. Full article

The increasing popularity of the Internet of Things (IoT) has significantly impacted our daily lives in the past few years. On one hand, it brings convenience, simplicity, and efficiency for us; on the other hand, the devices are susceptible to various cyber-attacks due to the lack of solid security mechanisms and hardware security support. In this paper, we present IMIDS, an intelligent intrusion detection system (IDS) to protect IoT devices. IMIDS’s core is a lightweight convolutional neural network model to classify multiple cyber threats. To mitigate the training data shortage issue, we also propose an attack data generator powered by a conditional generative adversarial network. In the experiment, we demonstrate that IMIDS could detect nine cyber-attack types (e.g., backdoors, shellcode, worms) with an average F-measure of 97.22% and outperforms its competitors. Furthermore, IMIDS’s detection performance is notably improved after being further trained by the data generated by our attack data generator. These results demonstrate that IMIDS can be a practical IDS for the IoT scenario. Full article

This paper presents the design and characterization of a compact broadband antenna and its MIMO configuration for 28 GHz 5G applications. The antenna was designed using Rogers RT/5880 with a thickness of 1.575 mm and has an overall compact size of 30 mm × 30 mm. The design methodology was initiated by designing a compact conventional microstrip antenna for 28 GHz. Afterward, the rectangular slots were utilized to improve the impedance bandwidth so that antenna covers the globally allocated 28 GHz band spectrum for 5G applications. Furthermore, a compact 2 × 2 MIMO antenna with polarization diversity is designed for high channel capacity systems. The mutual coupling between the closely spaced antenna elements is reduced by using two consecutive iterations of defected ground structure (DGS). The proposed MIMO antenna system offers broad bandwidth, high gain, low mutual coupling, and low envelope correlation coefficient along with high diversity gain, low mean effective gain, and low channel capacity loss. Moreover, the proposed been compared with the state-of-the-art MIMO antenna proposed for 28 GHz application to demonstrate worth of the presented design. Full article

In this paper, a compact multiple-input multiple-output (MIMO) antenna for an off/on-body wireless body area network (WBAN) is presented. The proposed antenna comprises eight elements arranged in a side-by-side, orthogonal, and across configuration on a planar laminate. This MIMO system achieves wideband impedance matching, i.e., fractional bandwidth (FBW) = 111% (7600 MHz) when placed off-body and FBW = 110% (7500 MHz) when placed on-body. The achieved bandwidth covers the ultrawideband (UWB) ranges 3.1–10.6 GHz for UWB-WBANs. To isolate the antenna elements, a Jerusalem cross (JC)-shaped frequency-selective surface (FSS) and meandered structure (MS) was designed and optimized. This proposed isolation mechanism offers at least 20 dB of isolation while maintaining an overall compact profile. Moreover, MIMO performance parameters for off/on-body and the specific absorption rate (SAR) were also evaluated. Stable MIMO performance, acceptable limits of SAR, and optimum radiation characteristics verify its suitability for wideband biotelemetry applications. Full article

Over the last decade, learning theory performed significant progress in the development of sophisticated algorithms and their theoretical foundations. The theory builds on concepts that exploit ideas and methodologies from mathematical areas such as optimization theory. Regularization is probably the key to address the challenging problem of overfitting, which usually occurs in high-dimensional learning. Its primary goal is to make the machine learning algorithm “learn” and not “memorize” by penalizing the algorithm to reduce its generalization error in order to avoid the risk of overfitting. As a result, the variance of the model is significantly reduced, without substantial increase in its bias and without losing any important properties in the data. Full article

Fast charging technology is attracting attention due to the increase in the use of batteries such as EV (Electric Vehicle), LEV (Light Electric Vehicle) and ESSs (Energy Storage Systems). Fast charging of the battery has problems such as fire, heat, and performance degradation of the battery. In the case of fast charging, a large current is applied to the battery to charge it. For this reason, information of battery voltage, battery current and temperature is important when charging a battery. Excessive current, overvoltage, and overheating beyond the standard value can cause deterioration of battery performance and a direct cause of fire. Therefore, the condition of the battery must be operated in the condition that meets the battery standard. To overcome these problems, we are trying to solve problems such as battery overheating and accidents by applying real-time monitoring technology, User Mode and Auto Control Mode. In this paper, we propose a real-time monitoring system based on the PHPOC Wi-Fi Shield. It operates to efficiently manage the charger and battery status based on real-time data, and is verified through real-time monitoring of the proposed system. Full article

The integration of a scan drive circuit is required for flexible and stretchable displays because a rigid scan driver IC is not flexible and stretchable. In this study, decoder-type scan drivers were developed using amorphous IGZO thin-film transistors for both depletion and enhancement mode TFTs. Simulations and measurements show that the proposed decoder-type scan driver operates well for both the enhancement and depletion-mode TFTs without error. The measurement results show that the proposed circuit provides scan pulses well, even with depletion-mode TFTs with a large negative threshold voltage of around −25 V. Full article

In recent years, the electrical and/or electronic architecture of vehicles has been significantly evolving. The new generation of cars demands a considerable amount of computational power due to a large number of safety-critical applications and driver-assisted functionalities. Consequently, a high-performance computing unit is required to provide the demanded power and process these applications while, in this case, vehicle architecture moves toward a centralized architecture. Simultaneously, appropriate software architecture has to be defined to fulfill the needs of the main computing unit and functional safety requirements. However, the process of configuring and integrating critical applications into a vehicle central computer while meeting safety requirements and optimization objectives is a time-consuming, complicated, and error-prone process. In this paper, we firstly present the evolution of the vehicle architecture, past, present, and future, and its current bottlenecks and future key technologies. Then, challenges of software configuration and mapping for automotive systems are discussed. Accordingly, mapping techniques and optimization objectives for mapping tasks to multi-core processors using design space exploration method are studied. Moreover, the current technologies and frameworks regarding the vehicle architecture synthesis, model analysis with regard to software integration and configuration, and solving the mapping problem for automotive embedded systems are expressed. Finally, we propose four research questions as future works for this field of study. Full article

The use of the Hough transforms to identify shapes or images has been extensively studied in the past using software for artificial intelligence applications. In this article, we present a generalization of the goal of shape recognition using the Hough transform, applied to a broader range of real problems. A software simulator was developed to generate input patterns (straight-lines) and test the ability of a generic low-latency system to identify these lines: first in a clean environment with no other inputs and then looking for the same lines as ambient background noise increases. In particular, the paper presents a study to optimize the implementation of the Hough transform algorithm in programmable digital devices, such as FPGAs. We investigated the ability of the Hough transform to discriminate straight-lines within a vast bundle of random lines, emulating a noisy environment. In more detail, the study follows an extensive investigation we recently conducted to recognize tracks of ionizing particles in high-energy physics. In this field, the lines can represent the trajectories of particles that must be immediately recognized as they are created in a particle detector. The main advantage of using FPGAs over any other component is their speed and low latency to investigate pattern recognition problems in a noisy environment. In fact, FPGAs guarantee a latency that increases linearly with the incoming data, while other solutions increase latency times more quickly. Furthermore, HT inherently adapts to incomplete input data sets, especially if resolutions are limited. Hence, an FPGA system that implements HT is inefficient for small sets of input data but becomes more cost-effective as the size of the input data increases. The document first presents an example that uses a large Accumulator consisting of 1100 × 600 Bins and several sets of input data to validate the Hough transform algorithm as random noise increases to 80% of input data. Then, a more specifically dedicated input set was chosen to emulate a real situation where a Xilinx UltraScale+ was to be used as the final target device. Thus, we have reduced the Accumulator to 280 × 280 Bins using a clock signal at 250 MHz and a few tens input points. Under these conditions, the behavior of the firmware matched the software simulations, confirming the feasibility of the HT implementation on FPGA. Full article

In a hardware-based neuromorphic computation system, using emerging nonvolatile memory devices as artificial synapses, which have an inelastic memory characteristic, has attracted considerable interest. In contrast, the elastic artificial neurons have received much less attention. An ideal material system that is suitable for mimicking biological neurons is the one with volatile (or mono-stable) resistive change property. Vanadium dioxide (VO₂) is a well-known material that exhibits an abrupt and volatile insulator-to-metal transition property. In this work, we experimentally demonstrate that pulse-driven two-terminal VO₂ devices behave in a leaky integrate-and-fire (LIF) manner, and they elastically relax back to their initial value after firing, thus, mimicking the behavior of biological neurons. The VO₂ device with a channel length of 20 µm can be driven to fire by a single long-duration pulse (>83 µs) or multiple short-duration pulses. We further model the VO₂ devices as resistive networks based on their granular domain structure, with resistivities corresponding to the insulator or metallic states. Simulation results confirm that the volatile resistive transition under voltage pulse driving is caused by the formation of a metallic filament in an avalanche-like process, while this volatile metallic filament will relax back to the insulating state at the end of driving pulses. The simulation offers a microscopic view of the dynamic and abrupt filament formation process to explain the experimentally observed LIF behavior. These results suggest that VO₂ insulator–metal transition could be exploited for artificial neurons. Full article

In the modern era of active network throughput and communication, the study of Intrusion Detection Systems (IDS) is a crucial role to ensure safe network resources and information from outside invasion. Recently, IDS has become a needful tool for improving flexibility and efficiency for unexpected and unpredictable invasions of the network. Deep learning (DL) is an essential and well-known tool to solve complex system problems and can learn rich features of enormous data. In this work, we aimed at a DL method for applying the effective and adaptive IDS by applying the architectures such as Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU). CNN models have already proved an incredible performance in computer vision tasks. Moreover, the CNN can be applied to time-sequence data. We implement the DL models such as CNN, LSTM, RNN, GRU by using sequential data in a prearranged time range as a malicious traffic record for developing the IDS. The benign and attack records of network activities are classified, and a label is given for the supervised-learning method. We applied our approaches to three different benchmark data sets which are UNSW NB15, KDDCup ’99, NSL-KDD to show the efficiency of DL approaches. For contrast in performance, we applied CNN and LSTM combination models with varied parameters and architectures. In each implementation, we trained the models until 100 epochs accompanied by a learning rate of 0.0001 for both balanced and imbalanced train data scenarios. The single CNN and combination of LSTM models have overcome compared to others. This is essentially because the CNN model can learn high-level features that characterize the abstract patterns from network traffic records data. Full article

Achieving multi-scene electronic component detection is the key to automatic electronic component assembly. The study of a deep-learning-based multi-scene electronic component object detection method is an important research focus. There are many anchors in the current object detection methods, which often leads to extremely unbalanced positive and negative samples during training and requires manual adjustment of thresholds to divide positive and negative samples. Besides, the existing methods often bring a complex model with many parameters and large computation complexity. To meet these issues, a new method was proposed for the detection of electronic components in multiple scenes. Firstly, a new dataset was constructed to describe the multi-scene electronic component scene. Secondly, a K-Means-based two-stage adaptive division strategy was used to solve the imbalance of positive and negative samples. Thirdly, the EfficientNetV2 was selected as the backbone feature extraction network to make the method simpler and more efficient. Finally, the proposed algorithm was evaluated on both the public dataset and the constructed multi-scene electronic component dataset. The performance was outstanding compared to the current mainstream object detection algorithms, and the proposed method achieved the highest mAP (83.20% and 98.59%), lower FLOPs (44.26GMAC) and smaller Params (29.3 M). Full article

Biometric technology has received a lot of attention in recent years. One of the most prevalent biometric traits is the finger-knuckle print (FKP). Because the dorsal region of the finger is not exposed to surfaces, FKP would be a dependable and trustworthy biometric. We provide an FKP framework that uses the VGG-19 deep learning model to extract deep features from FKP images in this paper. The deep features are collected from the VGG-19 model’s fully connected layer 6 (F6) and fully connected layer 7 (F7). After applying multiple preprocessing steps, such as combining features from different layers and performing dimensionality reduction using principal component analysis (PCA), the extracted deep features are put to the test. The proposed system’s performance is assessed using experiments on the Delhi Finger Knuckle Dataset employing a variety of common classifiers. The best identification result was obtained when the Artificial neural network (ANN) classifier was applied to the principal components of the averaged feature vector of F6 and F7 deep features, with 95% of the data variance preserved. The findings also demonstrate the feasibility of employing these deep features in an FKP recognition system. Full article

This paper considers the method for the calculation of magnetic flux density in the vicinity of overhead distribution lines which takes into account the higher current harmonics. This method is based on the Biot–Savart law and the complex image method. The considered method calculates the values of the magnetic flux density for each harmonic component of the current separately at all points of interest (usually lateral profile). In this way, it is possible to determine the contributions of individual harmonic components of the current intensity to the total value of magnetic flux density. Based on the contributions of individual harmonic components, the total (resultant) value of the magnetic flux density at points of interest is determined. Validation of the computational method is carried out by comparison of the results obtained by the considered calculation method with measurement results. Furthermore, the application of the calculation method was demonstrated by calculating magnetic flux density harmonics in the vicinity of two overhead distribution lines of typical phase conductor arrangements. Full article

Employing electromagnetic fields (EMFs) in new wireless communication and sensing technologies has substantially increased the level of human exposure to EMF waves. This paper presents a useful insight into the interaction of electromagnetic fields with biological media that is defined by the heat generation due to induced currents and dielectric loss. The specific absorption rate (SAR) defines the heating amount in a biological medium that is irradiated by an electromagnetic field value. The paper reviews the radio frequency hazards due to the SAR based on various safety standards and organisations, including a detailed investigation of previously published work in terms of modelling and measurements. It also summarises the most common techniques utilised between 1978 and 2021, in terms of the operational frequency spectrum, bandwidth, and SAR values. Full article

An emergency can occur at any time. To overcome that emergency efficiently, we require seamless movement on the road to approach the destination within a limited time by using an Emergency Vehicle (EV). This paper proposes an emergency vehicle management solution (EVMS) to determine an efficient vehicle-passing sequence that allows the EV to cross a junction without any delay. The proposed system passes the EV and minimally affects the travel times of other vehicles on the junction. In the presence of an EV in the communication range, the proposed system prioritizes the EV by creating space for it in the lane adjacent to the shoulder lane. The shoulder lane is a lane that cyclists and motorcyclists will use in normal situations. However, when an EV enters the communication range, traffic from the adjacent lane will move to the shoulder lane. As the number of vehicles on the road increases rapidly, crossing the EV in the shortest possible time is crucial. The EVMS and algorithms are presented in this study to find the optimal vehicle sequence that gives EVs the highest priority. The proposed solution uses cutting-edge technologies (IoT Sensors, GPS, 5G, and Cloud computing) to collect and pass EVs’ information to the Roadside Units (RSU). The proposed solution was evaluated through mathematical modeling. The results show that the EVMS can reduce the travel times of EVs significantly without causing any performance degradation of normal vehicles. Full article

Boosting the capacity of grid-connected wind farms will greatly contribute to increasing the share of sustainable energy in the global generation mix. It is imperative to study the way to quantitatively assess the maximum capacity of grid-connected wind farms in combination with power system stability characteristics. In this work, a method to evaluate the maximum capacity of grid-connected wind farms considering the joint constraints of frequency and voltage stability is proposed based on the global intrinsic property of frequency stability and the local characteristic of voltage stability. Firstly, the maximum capacity of grid-connected wind farms in the power grid with high wind power penetration is assessed globally based on the frequency stability constraints, and then locally considering the voltage stability constraints of each local power grid. Further on, a quantitative method to evaluate the capacity of grid-connected wind farms is proposed based on the correlation between the local static voltage stability margin and the local capacity of grid-connected wind farms, as well as the global constraint of the maximum capacity of grid-connected wind farms. Finally, the effectiveness of the proposed method is verified by the simulation results of an actual regional power grid. Full article

Although JSON documents are being used in several emerging applications (e.g., Big Data applications, IoT, mobile computing, smart cities, and online social networks), there is no consensual or standard language for updating JSON documents (i.e., creating, deleting or changing such documents, where changing means inserting, deleting, replacing, copying, moving, etc., portions of data in such documents). To fill this gap, we propose in this paper an SQL-like language, named JUpdate, for updating JSON documents. JUpdate is based on a set of six primitive update operations, which is proven complete and minimal, and it provides a set of fourteen user-friendly high-level operations with a well-founded semantics defined on the basis of the primitive update operations. Full article

Information security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issues of malicious activities taken place by intruders, hackers and attackers in the form of authenticity obstruction, gridlocking data traffic, vandalizing data and crashing the established network. The issue of emerging suspicious activities is managed by the domain of Intrusion Detection Systems (IDS). The IDS consistently monitors the network for the identification of suspicious activities, and generates alarm and indication in the presence of malicious threats and worms. The performance of IDS is improved by using different machine learning algorithms. In this paper, the Nesterov-Accelerated Adaptive Moment Estimation–Stochastic Gradient Descent (HNADAM-SDG) algorithm is proposed to determine the performance of Intrusion Detection Systems IDS. The algorithm is used to optimize IDS systems by hybridization and tuning of hyperparameters. The performance of algorithm is compared with other classification algorithms such as logistic regression, ridge classifier and ensemble algorithms where the experimental analysis and computations show the improved accuracy with 99.8%, sensitivity with 99.7%, and specificity with 99.5%. Full article

A wide-band divide-by-2 injection-locked frequency divider (ILFD) based on a distributed dual-resonance high-order tank is presented. The ILFD employs a distributed LC network as the dual resonance tank and achieves an ultra-wide locking range. Fabricated in a 65 nm 1P7M LP-CMOS process, the divide-by-2 ILFD consumes 7 mW from a 0.7 V power supply and realizes a locking range of 87.0%, from 13 GHz to 33 GHz. The core circuit occupies an area of 0.22 mm × 0.5 mm. Full article

Deep neural networks (DNNs) are widely used in many artificial intelligence applications; many specialized DNN-inference accelerators have been proposed. However, existing DNN accelerators rely heavily on certain types of DNN operations (such as Conv, FC, and ReLU, etc.), which are either less used or likely to become out of date in future, posing challenges of flexibility and compatibility to existing work. This paper designs a flexible DNN accelerator from a more generic perspective rather than speeding up certain types of DNN operations. Our proposed Nebula exploits the width property of DNNs and gains a significant improvement in system throughput and energy efficiency over multi-branch architectures. Nebula is a first-of-its-kind framework for multi-branch DNNs. Full article

When we compress a large amount of data, we face the problem of the time it takes to compress it. Moreover, we cannot predict how effective the compression performance will be. Therefore, we are not able to choose the best algorithm to compress the data to its minimum size. According to the Kolmogorov complexity, the compression performances of the algorithms implemented in the available compression programs in the system differ. Thus, it is impossible to deliberately select the best compression program before we try the compression operation. From this background, this paper proposes a method with a principal component analysis (PCA) and a deep neural network (DNN) to predict the entropy of data to be compressed. The method infers an appropriate compression program in the system for each data block of the input data and achieves a good compression ratio without trying to compress the entire amount of data at once. This paper especially focuses on lossless compression for image data, focusing on the image blocks. Through experimental evaluation, this paper shows the reasonable compression performance when the proposed method is applied rather than when a compression program randomly selected is applied to the entire dataset. Full article