Electronics

In this paper, the implementation of a low-voltage class AB second generation voltage conveyor (VCII) with high current drive capability is presented. Simple realization and good overall performance are the main features of the proposed circuit. Proper solutions and techniques were used to achieve high signal swing and high linearity at Y, X and Z ports of VCII as well as low-voltage operation. The operation of the proposed VCII was verified through SPICE simulations based on TSMC 0.18 µm CMOS technology parameters and a supply voltage of ±0.9 V. The small signal impedance values were 973 Ω, 120 kΩ and 217 Ω at Y, X and Z ports, respectively. The maximum current at the X port was ±10 mA with maximum total harmonic distortion (THD) of 2.4% at a frequency of 1 MHz. Considering a bias current (I_B) of 29 µA and output current at the X port (I_X) of 10 mA, the current drive capability (I_X/I_B) of about 345 was achieved at the X port. The voltage swing at the Z port was (−0.4, 0.4) V. The THD value at the Z port for an input signal with 0.8 V peak-to-peak value and frequency of 1 MHz was 3.9%. The total power consumption was 0.393 µW. Full article

Due to the design principle of parallel processing, belief propagation (BP) decoding is attractive, and it provides good error-correction performance compared with successive cancellation (SC) decoding. However, its error-correction performance is still inferior to that of successive cancellation list (SCL) decoding. Consequently, this paper proposes a novel flip-list- (FL)-enabled belief propagation (BP) method to improve the error-correction performance of BP decoding for polar codes with low computational complexity. The proposed technique identifies the vulnerable channel log-likelihood ratio (LLR) that deteriorates the BP decoding result. The FL is utilized to efficiently identify the erroneous channel LLRs and correct them for the next BP decoding attempt. The preprocessed channel LLR through FL improves the error-correction performance with minimal flipping attempts and reduces the computational complexity. The proposed technique was compared with the state-of-the-art BP, i.e., BP bit-flip (BP-BF), generalized BP-flip (GBPF), cyclic redundancy check (CRC)-aided (CA-SCL) decoding, and ordered statistic decoding (OSD), algorithms. Simulation results showed that the FL-BP had an excellent block error rate (BLER) performance gain up to 0.7dB compared with BP, BP-BF, and GBPF decoder. Besides, the computational complexity was reduced considerably in the high signal-to-noise ratio (SNR) regime compared with the BP-BF and GBPF decoding methods. Full article

This work is dedicated to the assessment of the filtering performances of an optoelectronic sensor for space applications. Particular care is taken concerning the power supply subsystem (here voltage shifter integrated circuit), which is part of the electromagnetic compatibility (EMC) compliance of an imaging equipment embedded on spacecrafts. The proposed methodology aims at two major targets: First, evaluating the Filter Effectiveness (FE) subject concerning varying parameters (including filter topology, parasitic effects and source/load impedance variations); second, quantifying the relative importance of representative equivalent electrical components through sensitivity analyses (nominal and parasitic values). The latter point is of utmost importance considering the expected versatility of such systems, such as manufacturing tolerances, for instance. Nominal values and/or components are often badly defined for confidentiality reasons, lack of knowledge or pure ignorance of inputs. An analytical deterministic formulation (here through the transfer matrix approach) is proposed and completed with an original stochastic strategy (Reduced Order Clustering, ROC). This ensures the reliable assessment of both statistical filter performances and most influential parameters, jointly with computational resources saving relatively to brute force Monte Carlo simulations. Full article

In recent years, gradual improvements in communication and connectivity technologies have enabled new technical possibilities for the adoption of chatbots across diverse sectors such as customer services, trade, and marketing. The chatbot is a platform that uses natural language processing, a subset of artificial intelligence, to find the right answer to all users’ questions and solve their problems. Advanced chatbot architecture that is extensible, scalable, and supports different services for natural language understanding (NLU) and communication channels for interactions of users has been proposed. The paper describes overall chatbot architecture and provides corresponding metamodels as well as rules for mapping between the proposed and two commonly used NLU metamodels. The proposed architecture could be easily extended with new NLU services and communication channels. Finally, two implementations of the proposed chatbot architecture are briefly demonstrated in the case study of “ADA” and “COVID-19 Info Serbia”. Full article

This paper presents the application of the cuckoo search (CS) algorithm in attempts to the minimization of the commutation torque ripple in the brushless DC motor (BLDC). The optimization algorithm was created based on the cuckoo’s reproductive behavior. The lumped-parameters mathematical model of the BLDC motor was developed. The values of self-inductances, mutual inductances, and back-electromotive force waveforms applied in the mathematical model were calculated by the use of the finite element method. The optimization algorithm was developed in Python 3.8. The CS algorithm was coupled with the static penalty function. During the optimization process, the shape of the voltage supplying the stator windings was determined to minimize the commutation torque ripple. Selected results of computer simulation are presented and discussed. Full article

Route optimization has become an increasing problem in the transportation and logistics sector within the development of smart cities. This article aims to demonstrate the implementation of a genetic algorithm adapted to a Vehicle Route Problem (VRP) in a company based in the city of Covilhã (Portugal). Basing the entire approach to this problem on the characteristic assumptions of the Multiple Traveling Salesman Problem (m-TSP) approach, an optimization of the daily routes for the workers assigned to distribution, divided into three zones: North, South and Central, was performed. A critical approach to the returned routes based on the adaptation to the geography of the Zones was performed. From a comparison with the data provided by the company, it is predicted by the application of a genetic algorithm to the m-TSP, that there will be a reduction of 618 km per week of the total distance traveled. This result has a huge impact in several forms: clients are visited in time, promoting provider-client relations; reduction of the fixed costs with fuel; promotion of environmental sustainability by the reduction of logistic routes. All these improvements and optimizations can be thought of as contributions to foster smart cities. Full article

Segmentation of retinal vessels plays a crucial role in detecting many eye diseases, and its reliable computerized implementation is becoming essential for automated retinal disease screening systems. A large number of retinal vessel segmentation algorithms are available, but these methods improve accuracy levels. Their sensitivity remains low due to the lack of proper segmentation of low contrast vessels, and this low contrast requires more attention in this segmentation process. In this paper, we have proposed new preprocessing steps for the precise extraction of retinal blood vessels. These proposed preprocessing steps are also tested on other existing algorithms to observe their impact. There are two steps to our suggested module for segmenting retinal blood vessels. The first step involves implementing and validating the preprocessing module. The second step applies these preprocessing stages to our proposed binarization steps to extract retinal blood vessels. The proposed preprocessing phase uses the traditional image-processing method to provide a much-improved segmented vessel image. Our binarization steps contained the image coherence technique for the retinal blood vessels. The proposed method gives good performance on a database accessible to the public named DRIVE and STARE. The novelty of this proposed method is that it is an unsupervised method and offers an accuracy of around 96% and sensitivity of 81% while outperforming existing approaches. Due to new tactics at each step of the proposed process, this blood vessel segmentation application is suitable for computer analysis of retinal images, such as automated screening for the early diagnosis of eye disease. Full article

The development of convolutional neural networks for deep learning has significantly contributed to image classification and segmentation areas. For high performance in supervised image segmentation, we need many ground-truth data. However, high costs are required to make these data, so unsupervised manners are actively being studied. The Mumford–Shah and Chan–Vese models are well-known unsupervised image segmentation models. However, the Mumford–Shah model and the Chan–Vese model cannot separate the foreground and background of the image because they are based on pixel intensities. In this paper, we propose a weakly supervised model for image segmentation based on the segmentation models (Mumford–Shah model and Chan–Vese model) and classification. The segmentation model (i.e., Mumford–Shah model or Chan–Vese model) is to find a base image mask for classification, and the classification network uses the mask from the segmentation models. With the classifcation network, the output mask of the segmentation model changes in the direction of increasing the performance of the classification network. In addition, the mask can distinguish the foreground and background of images naturally. Our experiment shows that our segmentation model, integrated with a classifier, can segment the input image to the foreground and the background only with the image’s class label, which is the image-level label. Full article

This paper proposed an innovative mechanical design using the Rocker-bogie mechanism for resilient Trash-Collecting Robots. Mask-RCNN, YOLOV4, and YOLOv4-tiny were experimented on and analyzed for trash detection. The Trash-Collecting Robot was developed to be completely autonomous as it was able to detect trash, move towards it, and pick it up while avoiding any obstacles along the way. Sensors including a camera, ultrasonic sensor, and GPS module played an imperative role in automation. The brain of the Robot, namely, Raspberry Pi and Arduino, processed the data from the sensors and performed path-planning and consequent motion of the robot through actuation of motors. Three models for object detection were tested for potential use in the robot: Mask-RCNN, YOLOv4, and YOLOv4-tiny. Mask-RCNN achieved an average precision (mAP) of over 83% and detection time (DT) of 3973.29 ms, YOLOv4 achieved 97.1% (mAP) and 32.76 DT, and YOLOv4-tiny achieved 95.2% and 5.21 ms DT. The YOLOv4-tiny was selected as it offered a very similar mAP to YOLOv4, but with a much lower DT. The design was simulated on different terrains and behaved as expected. Full article

Traffic lights are an important part of urban roads. They improve traffic conditions but bring about a limitation of driving speed in the space–time domain for vehicles. In this paper, a traffic light model based on a vehicle–road cooperative system is built. The model provides the vehicle with speed constraints when passing the green light in the time–space domain. A global-optimization-based energy management strategy based on dynamic programming (DP) is constructed with the constraints. The simulations are performed for two driving situations of different signal phases with the electric vehicle driven by a single power source. Compared with the traditional fixed speed driving strategy and green light optimal speed advisory (GLOSA) system, the energy management strategy proposed in this paper is able to control operating points of the motor to be distributed in more efficiency areas. A higher economy is achieved from simulation results. Full article

This paper explores a new methodology based on data-driven approaches to identify and track degradation processes in Li-ion batteries. Our goal is to study if it is possible to differentiate the state of degradation of cells that present similar aging in terms of overall parameters (similar remaining capacity, state of health or internal resistance), but that have had different applications or conditions of use (different discharge currents, depth of discharges, temperatures, etc.). For this purpose, this study proposed to analyze voltage waveforms of cells obtained in cycling tests by using an unsupervised neural network, the Self-Organizing Map (SOM). In this work, a laboratory dataset of real Li-ion cells was used, and the SOM algorithm processed battery cell features, thus carrying out smart sensing of the battery. It was shown that our methodology differentiates the previous conditions of use (history) of a cell, complementing conventional metrics such as the state of health, which could be useful for the growing second-life market because it allows for determining more precisely the state of disease of a battery and assesses its suitability for a specific application. Full article

According to statistics, the majority of accidents are attributed to driver negligence, especially when a driver intends to lane change or to overtake another vehicle, which is most likely to cause accidents. In addition, overtaking is one of the most difficult and complex functions for the development of autonomous driving technologies because of the dynamic and complicated task involved in the control strategy and electronic control systems, such as steering, throttle, and brake control. This paper proposes a safe overtaking maneuver procedure for an autonomous vehicle based on time to lane crossing (TLC) estimation and the model predictive control scheme. As overtaking is one of the most complex maneuvers that require both lane keeping and lane changing, a vision-based lane-detection system is used to estimate TLC to make a timely and accurate decision about whether to overtake or remain within the lane. Next, to maintain the minimal safe distance and to choose the best timing to overtake, the successive linearization-based model predictive control is employed to derive an optimal vehicle controller, such as throttle, brake, and steering angle control. Simultaneously, it can make certain that the longitudinal acceleration and steering velocity are maintained under constraints to maintain driving safety. Finally, the proposed system is validated by real-world experiments performed on a prototype electric golf cart and executed in real-time on the automotive embedded hardware with limited computational power. In addition, communication between the sensors and actuators as well as the vehicle control unit (VCU) are based on the controller area network (CAN) bus to realize vehicle control and data collection. The experiments demonstrate the ability of the proposed overtaking decision and control strategy to handle a variety of driving scenarios, including a lane-following function when a relative yaw angle exists and an overtaking function when the approaching vehicle has a different lateral velocity. Full article

The speed of modern digital systems is severely limited by memory latency (the “Memory Wall” problem). Data exchange between Logic and Memory is also responsible for a large part of the system energy consumption. Logic-in-Memory (LiM) represents an attractive solution to this problem. By performing part of the computations directly inside the memory the system speed can be improved while reducing its energy consumption. LiM solutions that offer the major boost in performance are based on the modification of the memory cell. However, what is the cost of such modifications? How do these impact the memory array performance? In this work, this question is addressed by analysing a LiM memory array implementing an algorithm for the maximum/minimum value computation. The memory array is designed at physical level using the FreePDK $45\mathrm{n}\mathrm{m}$ CMOS process, with three memory cell variants, and its performance is compared to SRAM and CAM memories. Results highlight that read and write operations performance is worsened but in-memory operations result to be very efficient: a 55.26% reduction in the energy-delay product is measured for the AND operation with respect to the SRAM read one. Therefore, the LiM approach represents a very promising solution for low-density and high-performance memories. Full article

CdSe/ZnS quantum dots (QDs) have attracted great consideration from investigators owing to their excellent photo-physical characteristics and application in quantum dot light-emitting diodes (QD-LEDs). The CdSe/ZnS-based inverted QD-LEDs structure uses high-quality semiconductors electron transport layers (ETLs), a multilayered hole transporting layers (HTLs). In QD-LED, designing a device structure with a minimum energy barrier between adjacent layers is very important to achieve high efficiency. A high mobility polymer of poly (9,9-dioctylfluorene-co-N-(4-(3-methylpropyl)) diphenylamine (TFB) was doped with 4,4′-bis-(carbazole-9-yl) biphenyl (CBP) with deep energy level to produce composite TFB:CBP holes to solve energy mismatch (HTL). In addition, we also improved the QD-LED device structure by using zinc tin oxide (ZTO) as ETL to improve device efficiency. The device turn-on voltage Vt (1 cd m⁻²⁾ with ZTO ETL reduced from 2.4 V to 1.9 V significantly. Furthermore, invert structure devices exhibit luminance of 4296 cd m⁻², current-efficiency (CE) of 7.36 cd A^−1, and external-quantum efficiency (EQE) of 3.97%. For the QD-LED based on ZTO, the device efficiency is improved by 1.7 times. Full article

Bone cancer is rare in adults, the most affected persons by this disease are young people and children. The common treatments for bone cancer are surgery, chemotherapy, and targeted therapies; however, all of them have side-effects that decrease the patient’s quality of life. Thermotherapy is one of the most promising treatments for bone cancer; its main goal is to increase the tumor temperature to kill cancerous cells. Although some micro-coaxial antennas have been used to treat bone tumors, most of them are designed to treat soft tissue. Therefore, the purpose of this work is to analyze the thermal behavior of four micro-coaxial antennas specifically designed to generate thermal ablation in bone tissue to treat bone tumors, at 2.45 GHz. The proposed antennas were the metal-tip monopole (MTM), the choked metal-tip monopole (CMTM), the double slot (DS) and the choked double slot (CDS). The design and optimization of the antennas by using the Finite Element Method (FEM) allow to predict the optimal antenna dimensions and their performance when they are in contact with the affected biological tissues (bone, muscle, and fat). In the FEM model, a maximum power transmission was selected as the main parameter to choose the optimum antenna design, i.e., a Standing Wave Ratio (SWR) value around 1.2–1.5. The four optimized antennas were constructed and experimentally evaluated. The evaluation was carried out in multilayer phantoms (fat, muscle, cortical, and cancellous bone) and ex vivo porcine tissue at different insertion depths of the antennas. To fully evaluate the antennas performance, the standing wave ratio (SWR), power loss, temperature profiles, and thermal distributions were analyzed. In the experimentation, the four antennas were able to reach ablation temperatures (>60 °C) and the highest reached SWR was 1.7; the MTM (power loss around 16%) and the CDS (power loss around 6.4%) antennas presented the lowest SWR values depending on the antenna insertion depth, either in multilayer tissue phantom or in ex vivo tissue. These proposed antennas allow to obtain ablation temperatures with an input power of 5 W after 5 min of treatment; these values are lower than the ones reported in the literature. Full article

The shift to a sustainable energy future is becoming more reliant on large-scale deployment of renewable and distributed energy resources raising concerns about frequency stability. Rate of Change of Frequency (RoCoF) is necessary as a system inertia metric in order for network operators to perform control steps to preserve system operation. This paper presents in a straightforward and illustrative way several relevant aspects of the inertia response and RoCoF calculation that could help to understand and explain the implementation and results of inertial response controllers on power converter-based technologies. Qualitative explanations based on illustrative numerical experiments are used to cover the effects on the system frequency response of reduced rotational inertia in synchronous dominated power systems. One main contribution of this paper is making evident the importance of the governor action to avoid the synchronous machine taking active power from the system during the recovering period of kinetic energy in an under frequency event. Full article

In Network Function Virtualization, the resource demand of the network service evolves with the change of network traffic. VNF dynamic migration has become an effective method to improve network performance. However, for the time-varying resource demand, how to minimize the long-term energy consumption of the network while guaranteeing the Service Level Agreement (SLA) is the key issue that lacks previous research. To tackle this dilemma, this paper proposes an energy-efficient reconfiguration algorithm for VNF based on short-term resource requirement prediction (RP-EDM). Our algorithm uses LSTM to predict VNF resource requirements in advance to eliminate the lag of dynamic migration and determines the timing of migration. RP-EDM eliminates SLA violations by performing VNF separation on potentially overloaded servers and consolidates low-load servers timely to save energy. Meanwhile, we consider the power consumption of servers when booting up, which is existing objectively, to avoid switching on/off the server frequently. The simulation results suggest that RP-EDM has a good performance and stability under machine learning models with different accuracy. Moreover, our algorithm increases the total service traffic by about 15% while ensuring a low SLA interruption rate. The total energy cost is reduced by more than 20% compared with the existing algorithms. Full article

From early design phases to final release, the reliability of modern embedded systems against soft errors should be carefully considered. Several schemes have been proposed to protect embedded systems against soft errors, but they are neither always functional nor robust, even with expensive overhead in terms of hardware area, performance, and power consumption. Thus, system designers need to estimate reliability quantitatively to apply appropriate protection techniques for resource-constrained embedded systems. Vulnerability modeling based on lifetime analysis is one of the most efficient ways to quantify system reliability against soft errors. However, lifetime analysis can be inaccurate, mainly because it fails to comprehensively capture several system-level masking effects. This study analyzes and characterizes microarchitecture-level and software-level masking effects by developing an automated framework with exhaustive fault injections (i.e., soft errors) based on a cycle-accurate gem5 simulator. We injected faults into a register file because errors in the register file can easily be propagated to other components in a processor. We found that only 5% of injected faults can cause system failures on an average over benchmarks, mainly from the MiBench suite. Further analyses showed that 71% of soft errors are overwritten by write operations before being used, and the CPU does not use 20% of soft errors at all after fault injections. The remainder are also masked by several software-level masking effects, such as dynamically dead instructions, compare and logical instructions that do not change the result, and incorrect control flows that do not affect program outputs. Full article

Diesel engine emissions contribute nearly 30% of greenhouse effects and diverse health and environmental problems. Amidst these problems, it is estimated that there will be a 75% increase in energy demand for transportation by 2040, of which diesel fuel constitutes a major source of energy for transportation. Being a major source of air pollution, efforts are currently being made to curb the pollution spread. The use of water-in-diesel (W/D)-emulsified fuels comes as a readily available (and cost-effective) option with other benefits including engine thermal efficiency, reduced costs, and NOx reduction; nonetheless, the inherent effects—power loss, component wear, corrosion, etc. still pose strong concerns. This study investigates the behavior and damage severity of a common rail (CR) diesel fuel injection system using exploratory and statistical methods under different W/D emulsion conditions and engine speeds. Results reveal that the effect of W/D emulsion fuels on engine operating conditions are reflected in the CR, which provides a reliable avenue for condition monitoring. Also, the effect of W/D emulsion on injection system components-piston, nozzle needle, and ball seat–are presented alongside related discussions. Full article