Electronics, Volume 13, Issue 4 (February-2 2024) – 138 articles

The widespread adoption of software-defined networking (SDN) technology has brought revolutionary changes to network control and management. Compared to traditional networks, SDN enhances security by separating the control plane from the data plane and replacing the traditional network architecture with a more flexible one. However, due to its inherent architectural flaws, SDN still faces new security threats. This paper expounds on the architecture and security of SDN, analyzes the vulnerabilities of SDN architecture, and introduces common distributed denial of service (DDoS) attacks within the SDN architecture. This article also provides a review of the relevant literature on DDoS attack detection and mitigation in the current SDN environment based on the technologies used, including statistical analysis, machine learning, policy-based, and moving target defense techniques. The advantages and disadvantages of these technologies, in terms of deployment difficulty, accuracy, and other factors, are analyzed. Finally, this study summarizes the SDN experimental environment and DDoS attack traffic generators and datasets of the reviewed literature and the limitations of current defense methods and suggests potential future research directions. Full article

Space–air–ground integrated network (SAGIN) is considered an enabler for sixth-generation (6G) networks. By integrating terrestrial and non-terrestrial (satellite, aerial) networks, SAGIN seems to be a quite promising solution to provide reliable connectivity everywhere and all the time. Its availability can be further enhanced if hybrid free space optical (FSO)/radio frequency (RF) links are adopted. In this paper, the performance of a hybrid FSO/RF communication system operating in SAGIN has been analytically evaluated. In the considered system, a high-altitude platform station (HAPS) is used to forward the satellite signal to the ground station. Moreover, the FSO channel model assumed takes into account the turbulence, pointing errors, and path losses, while for the RF links, a relatively new composite fading model has been considered. In this context, a new link selection scheme has been proposed that is designed to reduced the signaling overhead required for the switching operations between the RF and FSO links. The analytical framework that has been developed is based on the Markov chain theory. Capitalizing on this framework, the performance of the system has been investigated using the criteria of outage probability and the average number of link estimations. The numerical results presented reveal that the new selection scheme offers a good compromise between performance and complexity. Full article

This work introduces and examines a new programmable electronic system, Board ADF339, designed for filtering analog measurement signals of low frequencies. The system operates in a mixed mode in collaboration with a digital controller implemented on the myRIO-1900 FPGA module. It enables the digital selection of the type and frequency settings of the UAF42 integrated circuit. In the technical implementation section, electronic filter and phase shifter circuit diagrams are presented, along with the digital counterpart of the analog filter. Tests of this system were conducted on signals generated using a function generator, which was followed by the filtration of signals occurring in real laboratory setups. A series of real responses from three different laboratory systems and a measurement system utilizing LabVIEW FPGA virtual instruments are demonstrated. After computing SNR indicators for noisy waveforms, the application scope and usability of the board are highlighted. Full article

Image inpainting and Deepfake techniques have the potential to drastically alter the meaning of visual content, posing a serious threat to the integrity of both images and videos. Addressing this challenge requires the development of effective methods to verify the authenticity of investigated visual data. This research introduces UFCC (Unified Forensic Scheme by Content Consistency), a novel forensic approach based on deep learning. UFCC can identify tampered areas in images and detect Deepfake videos by examining content consistency, assuming that manipulations can create dissimilarity between tampered and intact portions of visual data. The term “Unified” signifies that the same methodology is applicable to both still images and videos. Recognizing the challenge of collecting a diverse dataset for supervised learning due to various tampering methods, we overcome this limitation by incorporating information from original or unaltered content in the training process rather than relying solely on tampered data. A neural network for feature extraction is trained to classify imagery patches, and a Siamese network measures the similarity between pairs of patches. For still images, tampered areas are identified as patches that deviate from the majority of the investigated image. In the case of Deepfake video detection, the proposed scheme involves locating facial regions and determining authenticity by comparing facial region similarity across consecutive frames. Extensive testing is conducted on publicly available image forensic datasets and Deepfake datasets with various manipulation operations. The experimental results highlight the superior accuracy and stability of the UFCC scheme compared to existing methods. Full article

In recent studies, the Contrastive Language–Image Pretraining (CLIP) model has showcased remarkable versatility in downstream tasks, ranging from image captioning and question-answering reasoning to image–text similarity rating, etc. In this paper, we investigate the effectiveness of CLIP visual features in predicting perceptual image quality. CLIP is also compared with competitive large multimodal models (LMMs) for this task. In contrast to previous studies, the results show that CLIP and other LMMs do not always provide the best performance. Interestingly, our evaluation experiment reveals that combining visual features from CLIP or other LMMs with some simple distortion features can significantly enhance their performance. In some cases, the improvements are even more than 10%, while the prediction accuracy surpasses 90%. Full article

The present work aims to develop a digital twin system typical of intelligent manufacturing applications, which has integrated visualization technologies, as well as the process parameter simulation solution. The application under consideration is a typical machining process, with a gantry machine tool controlled by Siemens Programmable Logic Controller(PLC) S7-1200. With the establishment of dual-directional data communication between the physical machine tool and its virtual counterpart based on TCP/IP protocol, real-time visualization, monitoring, and control of the entire working process can be achieved. Furthermore, we integrated with the digital twin system as a solution for real-time process parameter simulation based on finite element modeling (FEM), which enables the real-time monitoring of necessary process parameters, e.g., surface deformation, during the machining process. A preliminary experiment was conducted to validate our proposed digital twin system, and the results demonstrated that our proposed method has satisfactory performance in terms of both control and monitoring of the traditional machining process, and synchronization between the physical and virtual models is also proven to be positive with minimal latency. Full article

The DC solid state transformer (DCSST) is a crucial component for connecting buses of different voltage levels in the DC distribution grid. This paper proposes a Si IGBT/SiC MOSFET hybrid isolated bidirectional DC–DC converter and an optimized modulation strategy (OMS) to reduce the losses and costs of DCSST. Based on the analysis of topology and operating principles, a duty-cycle modulation strategy is proposed and the converter is modeled by the time domain analysis (TDA) method. Through the analysis of switching characteristics, an optimization problem is established, which aims to reduce the conduction losses of switches while ensuring zero-voltage switching (ZVS) for all switches and low-current turn-off for IGBTs simultaneously. The optimization problem is solved by the augmented Lagrangian genetic algorithm (ALGA), and the OMS for the proposed converter is deduced. Finally, a 2 kW experimental prototype with the primary voltage of 405–495 V and the secondary voltage of 150 V is built to verify the effectiveness of the proposed topology and OMS. The switching costs of the proposed converter is reduced by 27.3% and the efficiency is improved by up to 4.04% compared to the existing method. Full article

Coffee crema plays a crucial role in assessing the quality of espresso. In recent years, in response to the rising labor costs, aging population, remote security/authentication needs, civic awareness, and the growing preference for non-face-to-face interactions, robot cafes have emerged. While some people seek sentiment and premium coffee, there are also many who desire quick and affordable options. To align with the trends of this era, there is a need for lightweight artificial intelligence algorithms for easy and quick decision making, as well as monitoring the extraction process in these automated cafes. However, the application of these technologies to actual coffee machines has been limited. In this study, we propose an innovative real-time coffee crema control system that integrates lightweight machine learning algorithms. We employ the GrabCut algorithm to segment the crema region from the rest of the image and use a clustering algorithm to determine the optimal brewing conditions for each cup of espresso based on the characteristics of the crema extracted. Our results demonstrate that our approach can accurately analyze coffee crema in real time. This research proposes a promising direction by leveraging computer vision and machine learning technologies to enhance the efficiency and consistency of coffee brewing. Such an approach enables the prediction of component replacement timing in coffee machines, such as the replacement of water filters, and provides administrators with Before Service. This could lead to the development of fully automated artificial intelligence coffee making systems in the future. Full article

With the development of social media, the amount of fake news has risen significantly and had a great impact on both individuals and society. The restrictions imposed by censors make the objective reporting of news difficult. Most studies use supervised methods, relying on a large amount of labeled data for fake news detection, which hinders the effectiveness of the detection. Meanwhile, the focus of these studies is on the detection of fake news in a single modality, either text or images, but actual fake news is more often in the form of text–image pairs. In this paper, we introduce a self-supervised model grounded in contrastive learning. This model facilitates simultaneous feature extraction for both text and images by employing dot product graphic matching. Through contrastive learning, it augments the extraction capability of image features, leading to a robust visual feature extraction ability with reduced training data requirements. The model’s effectiveness was assessed against the baseline using the COSMOS fake news dataset. The experiments reveal that, when detecting fake news with mismatched text–image pairs, only approximately 3% of the data are used for training. The model achieves an accuracy of 80%, equivalent to 95% of the original model’s performance using full-size data for training. Notably, replacing the text encoding layer enhances experimental stability, providing a substantial advantage over the original model, specifically on the COSMOS dataset. Full article

For text big data analysis, we preprocessed text data and constructed a document–keyword matrix. The elements of this matrix represent the frequencies of keywords occurring in a document. The matrix has a zero-inflation problem because many elements are zero values. Also, in the process of preprocessing, the data size of the document–keyword matrix is reduced. However, various machine learning algorithms require a large amount of data, so to solve the problems of data shortage and zero inflation, we propose the use of generative models based on statistics and machine learning. In our experimental tests, we compared the performance of the models using simulation and practical data sets. Thus, we verified the validity and contribution of our research for keyword data analysis. Full article

Recently, hyperdense small cells have been proposed to meet the challenge of the tremendous increment in cellular data service requirements. To reduce the deployment cost, as well as operated cost, these small cells are usually connected to limited backhauls, in which case the backhaul capacity may become a bottleneck in busy hours. In this paper, we propose an optimal scheme for the small cells to utilize the macrocell links as its wireless backhaul. Based on stochastic geometry, the analytical expressions of network capacity with in-band and out-band wireless backhaul are derived and validated using simulation results. The optimized results show that our proposed scheme can significantly improve the network performance in scenarios with a high traffic load. Full article

ZnO nanowire is a promising candidate for large-area gated field emitter arrays. How to improve its temporal response is one of the key problems to be solved for applications. In this work, a device model for a gated ZnO nanowire field emitter with consideration of charging and self-heating effect has been established to investigate its temporal response. It is found that while the charging effect is responsible for the delay at the beginning of the pulse, the self-heating effect which induces delay due to the thermal conduction process can shorten the charging time because of its lowering of nanowire resistance. The response time can be minimized when these two effects are balanced at an optimal field which is below the critical field for thermal runaway. We further investigate the optimal response time of a nanowire with the same resistance but a different length, radius, and electrical properties. The results imply that a lower heat capacity and higher critical temperature for thermal runaway are in favor of a shorter response time, which must be taken into account in the reduction in nanowire resistance for improving response speed. All the above should be useful for the device design of a fast-response gated ZnO nanowire field emitter array. Full article

The issue of sub-module (SM) capacitor voltage unbalance is a hot topic in the current research into the modular multilevel converter (MMC). An excellent strategy comprises mitigating the SM capacitor voltage imbalance by adjusting the SM on time. The traditional capacitor voltage balancing control regulates the speed to maintain accuracy. A unique SM capacitor voltage balancing control strategy is presented in this paper and is based on conventional capacitor voltage balance management and neural network prediction. Firstly, the SM capacitor voltage and arm current are speculated by operating the time series forecasting technique in real time, considering the dynamic changes in the SM capacitor voltage and arm current. Secondly, the SM capacitor voltage distinction between the actual and theoretical value is determined, and a deviation’s mixed Gaussian distribution is established to estimate its compensation voltage. Thirdly, the SM triggering sequence is anticipated by using the neural network along with the pilot values of the SM capacitor voltage, arm current, and the offset compensation value, and the control is executed. Finally, a three-phase, six-leg, eight-module, nine-level MMC model is built to verify the feasibility of the suggested approach. Full article

With the popularity of smart devices and the growth of high-bandwidth applications, the wireless industry is facing an increased surge in data traffic. This challenge highlights the limitations of traditional edge-caching solutions, especially in terms of content-caching effectiveness and network-communication latency. To address this problem, we investigated efficient caching strategies in heterogeneous network environments. The caching decision process becomes more complex due to the heterogeneity of the network environment, as well as due to the diversity of user behaviors and content requests. To address the problem of increased system latency due to the dynamically changing nature of content popularity and limited cache capacity, we propose a novel content placement strategy, the long-short-term-memory–content-population-prediction model, to capture the correlation of request patterns between different contents and the periodicity in the time domain, in order to improve the accuracy of the prediction of content popularity. Then, to address the heterogeneity of heterogeneous network environments, we propose an efficient content delivery strategy: the multi-intelligent critical collaborative caching policy. This strategy models the edge-caching problem in heterogeneous scenarios as a Markov decision process using multi-base-station-environment information. In order to fully utilize the multi-intelligence information, we have improved the actor–critic approach by integrating the attention mechanism into a neural network. Whereas the actor network is responsible for making decisions based on local information, the critic network evaluates and enhances the actor’s performance. We conducted extensive simulations, and the results showed that the Long Short Term Memory content population prediction model was more advantageous, in terms of content-popularity-prediction accuracy, with a 28.61% improvement in prediction error, compared to several other existing methods. The proposed multi-intelligence actor–critic collaborative caching policy algorithm improved the cache-hit-rate metric by up to 32.3% and reduced the system latency by 1.6%, demonstrating the feasibility and effectiveness of the algorithm. Full article

This contribution presents new DC–DC Boost and Buck-Boost converter topologies to track sinusoidal signals in an exact form, that is, with zero error tracking. The proposed topology considers two DC–DC converters connected to the same load, which means that the converters are not in a cascade connection. To track the exact sinusoidal reference, the control algorithm is based on the indirect control algorithm and the harmonic balance method. The main contribution is to consider two control inputs that facilitate and permit canceling out the second harmonic generated by the nonlinearity of the model, and as a result there is a single frequency in the output. The result is such that the converters can track sinusoidal signals with exactly zero error. With this, there is a reduction in the potential negative effects on systems and equipment; some numerical results are presented to corroborate the proposal. Full article

Controlling multi-agent systems (MASs) has attracted increased interest within the control community. Since the control challenge consists of the fact that each agent has limited local capabilities, our adopted solution is tailored so that a group of such entities works together and shares resources and information to fulfill a given task. In this work, we propose a coalitional control solution using the distributed model predictive control (DMPC) framework, suitable for a multi-agent system. The methodology has a switching mechanism that selects the best communication topology for the overall system. The proposed control algorithm was validated in simulation using a homogeneous vehicle platooning application with longitudinal dynamics. The available communication topologies were specifically tailored taking into account the information flow between adjacent vehicles. The obtained results show that when the platoon’s string stability is risked, the algorithm switches between different communication topologies. The resulting coalitions between vehicles ensure an increase in the overall stability of the entire system and prove the efficacy of our proposed methodology. Full article

With the booming rollout of 5G communication, abundant new technologies have been proposed for quality of service requirements. In terms of the betterment in transmission coverage, mobile edge caching (MEC) has shown potential in reducing the transmission outage. The performance of MEC, meanwhile, can be promisingly enhanced by reconfigurable intelligent surfaces (RIS). Under this context, we explore a system comprising a small base-station (SBS) with limited cache capacity, two users, and one RIS. The SBS transmits the contents from the cache or fetches them from the remote backhaul hub to communicate with users through directional and possibly reflective channels. In this point-to-multipoint connection, non-orthogonal multiple access (NOMA) is applied, improving the capacity of the system. To minimize the outage probability, we first propose a caching policy from entropy perspective, based on which we investigate the beamforming and power allocation problem. The issue, however, is non-convex and involves multi-dimensional optimization. To address this, we introduce an efficient block successive upper-bound minimization algorithm, grounded in Gershgorin’s circle theorem. This algorithm aims to find the globally optimal solution for power allocation and RIS beamformer, considering both the channel condition and content popularity. Numerical studies are performed to verify the effectiveness of the proposed algorithm. Full article

Recently, the incidence of, and interest in, respiratory diseases has been amplified by severe acute respiratory syndrome coronavirus (SARS-CoV-2) and other respiratory diseases with a high prevalence. Most of these diseases require mechanical ventilation for homecare and clinical therapy. Herein, we propose a portable and non-invasive mechanical fan (NIV) for home and clinical applications. The NIV’s core is a turbine for airflow generation, which can provide and monitor a positive two-level pressure of up to approximately 500 lpm at 50 cmH₂O according to the inspiration/expiration phase. After calibration, the proposed NIV can precisely set the airflow with a pressure between 4 cmH₂O and 20 cmH₂O, providing a versatile device that can be used for continuous positive airway pressure (CPAP) or bilevel positive airway pressure (BiPAP). The airflow is generated by a turbine monitored using a mass flow sensor. The whole NIV is monitored with a 16 MHz clock microcontroller. An analog-to-digital converter is used as the input for analog signals, while a digital-to-analog converter is used to drive the turbine. I2C protocol signals are used to manage the display. Moreover, a Wi-Fi system is interfaced for the transmission/reception of clinical and technical information via a smartphone, achieving a remote-controlled NIV. Full article

The growth of Internet of Things (IoT) systems is driven by their potential to improve efficiency, enhance decision-making, and create new business opportunities across various domains. In this paper, the main selection problems in IoT-type systems, criteria used in multi-criteria evaluation, and multi-criteria methods used for solving IoT selection problems are identified. Then, a Hybrid Group Multi-Criteria Approach for solving selection problems in IoT-type systems is proposed. The approach contains the Best Worst Method (BWM) weighting method, multi-criteria Simple Additive Weighting (SAW), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and Complex Proportional Assessment Method (COPRAS), and a method that combines the solutions obtained using the four considered multi-criteria methods to obtain a single solution. The SAW, TOPSIS, VIKOR, and COPRAS methods were analyzed in relation to their advantages, disadvantages, inputs, outputs, measurement scale, type of normalization, aggregation method, parameters, complexity of implementation, and interactivity. An application of the Hybrid Group Multi-Criteria Approach for IoT platform selection and a comparison between the SAW, TOPSIS, VIKOR, and COPRAS solutions and the solution of the proposed approach is realized. A Spearman correlation analysis is presented. Full article

In the realms of the Internet of Things (IoT) and artificial intelligence (AI) security, ensuring the integrity and quality of visual data becomes paramount, especially under low-light conditions, where low-light image enhancement emerges as a crucial technology. However, the current methods for enhancing images under low-light conditions still face some challenging issues, including the inability to effectively handle uneven illumination distribution, suboptimal denoising performance, and insufficient correlation among a branch network. Addressing these issues, the Multi-Scale Branch Network is proposed. It utilizes multi-scale feature extraction to handle uneven illumination distribution, introduces denoising functions to mitigate noise issues arising from image enhancement, and establishes correlations between network branches to enhance information exchange. Additionally, our approach incorporates a vision transformer to enhance feature extraction and context understanding. The process begins with capturing raw RGB data, which are then optimized through sophisticated image signal processor (ISP) techniques, resulting in a refined visual output. This method significantly improves image brightness and reduces noise, achieving remarkable improvements in low-light image enhancement compared to similar methods. Using the LOL-V2-real dataset, we achieved improvements of 0.255 in PSNR and 0.23 in SSIM, with decreases of 0.003 in MAE and 0.009 in LPIPS, compared to the state-of-the-art methods. Rigorous experimentation confirmed the reliability of this approach in enhancing image quality under low-light conditions. Full article

The trend towards electrification in mobility has led to the increased use of silicon carbide (SiC) semiconductors. These semiconductors are more efficient but also present challenges related to electromagnetic interference (EMI) due to their higher voltage derivatives. This paper introduces a new high-frequency impedance model for electrical machines. The proposed model distinguishes itself from existing approaches by being entirely derived from Finite Element Method (FEM) simulations, which include capacitances in the magnetic simulation. This approach achieves a balance between computational efficiency and high accuracy across the entire frequency spectrum, ranging from 100 Hz to 50 MHz. The model provides valuable insights during the design phase and was rigorously validated using data from 28 samples of an industrial machine. Full article

In this study, a novel integrated 4H-SiC reverse-conducting gate turn-off thyristor (GTO) featuring an N-type floating (NF) structure is proposed. The proposed NF-structured 4H-SiC GTO outperforms conventional reverse-conducting GTOs in forward conduction, effectively eliminating the snapback phenomenon. This is achieved by increasing lateral resistance above the P-injector and modifying the electron current path during early turn-on. NF structures with a doping concentration of 2 × 10¹⁴ cm⁻³ and thicknesses exceeding 4 μm have been indicated to successfully eliminate the snapback phenomenon. Moreover, the anode-shorted structure enhances the GTO’s breakdown voltage and concurrently reduces turn-off losses by 85% at low current densities. Full article

This work presents a design for a complementary antenna with circular polarization that has a wide operating bandwidth, stable broadside radiation, and stable gain for X-band applications. The proposed antenna consists of an irregular loop and a parasitic electric dipole, which work together to produce equivalent radiation from the magnetic and electric dipoles. By arranging the dipole and the loop in a specific geometry, this antenna effectively generates circularly polarized wave propagation. A substrate integrated coaxial line (SICL) is applied to feed the antenna through an aperture cutting on the ground. The proposed antenna achieves a wide axial ratio (AR) and impedance bandwidths of 27.4% (from 8.5 to 11.22 GHz, for the AR ≤ 3 dB) and 39.6% (from 7.5 to 11.2 GHz, for the reflection coefficient ≤ −10 dB), respectively. Moreover, the antenna maintains a stable broadside radiation pattern across the operating bandwidth, with an average gain of 10 dBic. This proposed antenna design is competitive for X-band wireless communications. Full article

In industrial electronic equipment or communication equipment, a reference clock should be generated for stable operation of the equipment, which requires precise and stable reference frequency generation. As a method for generating this reference frequency, an analog method called PLL (phase locked-loop) has been devised and widely used. However, in order to make a more precise and stable reference frequency simple and economical, a DDS (direct digital synthesizer) has been developed. In this paper, we propose a stable and accurate method to generate a low frequency of the PWM method via pure logic circuit configuration without a microprocessor for digital reference frequency generation. Depending on the electronic communication equipment, the required reference frequency varies from a low frequency to a very high frequency. The reference frequency synthesis required in these frequency bands has been studied in various ways, but in industries such as railways, the low-frequency band based on the DDS method is used. In particular, it is very important to operate without a single operating error or failure in order to obtain information for stopping the train. Therefore, it is necessary to design a pure logic method that excludes a stored program type processor that minimizes the possibility of temporary interruption due to disturbance such as surge or high voltage. Therefore, through this study, the algorithm is implemented so that the duty ratio is output at 50:50, the circuit is configured so that two target frequencies are generated at the same time, and the performance is verified by generating the low-frequency band used for stopping the railway train. It was confirmed that the accuracy and stability were improved compared to the analog method used for stopping the railway train, and it was verified that the frequency resolution was superior to the similar results obtained in the digital frequency synthesis field so far. Full article

With the rapid development of 6G networks, data transmission speed has significantly increased, making data privacy protection issues even more crucial. The federated learning (FL) is a distributed machine learning framework with privacy protection and secure encryption technology, aimed at enabling dispersed participants to collaborate on model training without disclosing private data to other participants. Nonetheless, recent research indicates that the exchange of shared gradients may lead to information disclosure, and thus FL still needs to address privacy concerns. Additionally, FL relies on a large number of diverse training data to forge efficient models, but in reality, the training data available to clients are limited, and data imbalance issues lead to over fitting in existing federated learning models. To alleviate these issues, we introduce a Novel Federated Learning Framework based on Conditional Generative Adversarial Networks (NFL-CGAN). NFL-CGAN divides the local networks of each client into private and public modules. The private module contains an extractor and a discriminator to protect privacy by retaining them locally. Conversely, the public module is shared with the server to aggregate the shared knowledge of clients, thereby improving the performance of each client local network. Comprehensive experimental analyses demonstrate that NFL-CGAN surpasses traditional FL baseline methods in data classification, showcasing its superior efficacy. Moreover, privacy assessments also verified robust and reliable privacy protection capabilities of NFL-CGAN. Full article

Industry 4.0 is a new paradigm that is transforming the industrial scenario. It has generated a large amount of scientific studies, commercial equipment and, above all, high expectations. Nevertheless, there is no single definition or general agreement on its implications, specifically in the field of automation and supervision systems. In this paper, a review of the Industry 4.0 concept, with equivalent terms, enabling technologies and reference architectures for its implementation, is presented. It will be shown that this paradigm results from the confluence and integration of both existing and disruptive technologies. Furthermore, the most relevant trends in industrial automation and supervision systems are covered, highlighting the convergence of traditional equipment and those characterized by the Internet of Things (IoT). This paper is intended to serve as a reference document as well as a guide for the design and deployment of automation and supervision systems framed in Industry 4.0. Full article

The advantage of FPGAs lies in their ability to implement a fully hardware solution for interfacing with various input/output (I/O) devices. Each block can work in parallel with all the others, simplifying the satisfaction of timing constraints. However, this hardware utilization consumes FPGA resources that could otherwise be allocated to the primary project. An alternative involves employing a small “soft-core” processor to implement I/O in software. With the goal of designing and evaluating a new tiny soft-core processor optimized for FPGA resources in I/O, a novel processor named Baby8 is developed. It is an 8-bit CISC soft-core processor optimized for reduced FPGA resources, including program size for 8-bit applications. The number of instructions is not large, but any instruction can access arbitrary memory locations. The performance and resource utilization of the newly designed processor are evaluated and compared with a variety of other soft-core processors. The results demonstrate its competitive performance, achieving an average maximum clock frequency of approximately 57 MHz and a power consumption of around 2 mW. Furthermore, it conserves nearly half of the FPGA resources in implementation. Full article

The essence of this work, which is an extension of the author’s previous research, is an analysis of computational intelligence algorithms that the support safe control of an autonomous object moving in a large group of other autonomous objects. Linear and dynamic programming methods with neural constraints on the process state, as well as positional and matrix game methods, were used to synthesize computational algorithms for the safe trajectory of one’s own object. The aim of the comparative analysis of intelligent computational methods for the safe trajectory of an object was to show, through their use, the possibility of taking into account the risk of collision resulting from both the degree of cooperation of objects while observing traffic laws and the impact of the environment in the form of visibility and the complexity of the situation. Simulation tests of the algorithms were carried out on the example of a real navigation situation of several dozen objects passing each other at sea. Full article

Unmanned aerial vehicles (UAVs) have recently been widely employed as effective wireless platforms for aiding users in various situations, particularly in hard-to-reach scenarios like post-disaster relief efforts. This study employs multiple UAVs to cover users in overlapping locations, necessitating the optimization of UAV-user association to maximize the spectral and energy efficiency of the UAV network. Hence, a connected bipartite graph is formed between UAVs and users using graph theory to accomplish this goal. Then, a maximum weighted matching-based maximum flow (MwMaxFlow) optimization approach is proposed to achieve the maximum data rate given users’ demands and the UAVs’ maximum capacities. Additionally, power control is applied using the $\mathcal{M}$-matrix theory to optimize users’ transmit powers and improve their energy efficiency. The proposed strategy is evaluated and compared with other benchmark schemes through numerical simulations. The simulation outcomes indicate that the proposed approach balances spectral efficiency and energy consumption, rendering it suitable for various UAV wireless applications, including emergency response, surveillance, and post-disaster management. Full article

Evaluating hand function presents a significant challenge in the realm of remote rehabilitation, particularly when highlighting the need for comfort and practicality in wearable devices. This research introduces an innovative wearable device-based Internet of Things (IoT) system, specifically designed for the assessment of hand function, with a focus on a wearable wristband. The system, enhanced by cloud technology, offers comprehensive solutions for remote health management and therapeutic services. Firstly, it uses electromyography (EMG) signals from the arm to assess hand function. By employing sophisticated classification and regression models, this system can automatically identify user gestures and accurately measure grip strength. Additionally, the integration of additional sensor data ensures that the system fulfills essential criteria for hand function assessment. Leaving conventional grip strength classification methods, this study explored four distinct regression models to accurately represent the grip strength curve. The findings reveal that the Random Forest Regression (RFR) model is the most effective, achieving an R² score of 0.9563 on the test data. This significant outcome not only confirms the practicality of the wearable wristband, which relies on EMG signals, but also underscores the potential of the IoT system in assessing hand function. Full article