Miracle-dz/UniFAR-FTUs huggingface dataset:
An Analytical Study for the Realization of Maximum Efficiency in Wireless Charging System Using a Circular Inductive Coil

The growth of wireless charging technology is increasing due to its efficient power transmission from a few millimeters to several centimeters. Thus, wireless power transfer (WPT) techniques are highly recommended for electric vehicle (EV) battery charging. However, the degradation of transmission efficiency occurs during misalignment among coupling coils. Therefore, the design of coupling coils requires the proper coil geometry such as coil shape, coil turn, coil diameter, and the distance between the turns. In this article, the analytical model of asymmetrical circular coils is taken into consideration to explore the relationship between the coil geometry and mutual inductance and coupling coefficient with identical inner and outer diameters. With this, an inductively coupled resonance power transmission technique is analyzed and simulated by varying the key parameters of coil geometry using MATLAB/Simulink software. The simulated results are examined to calculate the power transmission distance, load resistor, coil geometry, air gap between the coils, and the effect of charging efficiency. It is concluded from the result that the power transmission distance, choice of coil dimensions, and distance between the turns influenced the quality factor, coupling coefficient, mutual inductance, operating frequency, and power transmission efficiency. From the analytical model, the maximum power transmission efficiency is achieved by selecting the optimum values of the design parameters.
Neural Networks & Logistic Regression for FPGA Hardware Trojan Detection

A hardware Trojan is a malware of increasing importance due to the increase in the growing number of digital circuits. A hardware Trojan can enter the circuit at any stage of chip manufacturing. The first step to deal with this malware is to detect the presence of this withering factor inside an integrated circuit. Since the appearance of this malware, various methods including thermal image processing have been proposed to detect hardware Trojans. The first challenge for research in this field is the lack of a database available for thermal images of Trojan chips. Accordingly, different images were taken from a Trojan-affected FPGA using a T4 thermal camera in this study. Our database includes 12 series of thermal images which are captured for each chip 55 seconds after programming. Then, two different methods have been proposed to detect hardware Trojans in the created database. In this paper, we propose a thermal image processing-based Hardware Trojan detection method on FPGA chips using neural networks, assuming the availability of a golden chip. Results demonstrate that if our method is combined with the previous method, the detection rate can be increased significantly.
Enhancing the Healthcare Management System with National Identification: In the Bangladesh Perspective

Enhanced healthcare management system provides essential medical services to the people of Bangladesh. Due to its enormous potential, it has gained particular attention over the past few years. In the face of emergent medical situations, doctors have often found themselves grappling with a pressing challenge: gaining instant access to comprehensive patient data, a task complicated by patients’ potential inability to convey vital information under duress. This underscores the need for a centralized smart healthcare system which emerges as a solution for timely decision-making. This paper presents a comprehensive innovative system for efficient healthcare management which is based on National Identification. This system incorporates the use of a card chip that contains all consumer data and makes it accessible to healthcare providers and relevant authorities. It also ensures a streamlined administrative process and reduces the likelihood of flaws in patient care. The system’s interoperability capability is instrumental in creating a seamless flow of patient data across different healthcare facilities. This not only enhances continuity of care but also eliminates the need for redundant tests and procedures. Ultimately, it has led to more efficient healthcare delivery. The system has also provided a disease detection feature, which has been developed using Decision trees and Naïve Bayes Classifier algorithms to create a predictive model that can detect diseases early based on the symptoms. The overall system has had a significant impact on the healthcare system by improving the quality of healthcare.
A Risk Assessment Method for Online Shopping Based on Index Grading Weighting

While online shopping has various forms and is convenient and fast, the harm to consumers' online shopping rights is endless, and there is a clear need for reasonable and effective online shopping risk assessment. At present, the uneven distribution of commodity related indicator data in e-commerce platforms and the differences in risks contained in different data intervals pose challenges to online shopping risk assessment. This article first divides online shopping products into risk levels through data consistency processing and K-means clustering. Then, an improved Bayesian evaluation based on indicator grading weighting is used to evaluate online shopping risks. The weight is calculated using the Jaccard coefficient according to the impact of each evaluation indicator on the evaluation results at different risk levels, and the average weight for the same risk level is 1. Finally, using data related to a smartphone from an e-commerce platform for evaluation and verification, and comparing the accuracy of evaluation with other algorithms, this article can effectively analyze the correlation between evaluation indicators and online shopping risk assessment results, reduce the impact of introducing weights on classification results, and improve the accuracy of the model in classifying minority samples.
A Dual Control 3-Stage CSR-VCO with 73.69µW Core Power & -112.25 dBc/Hz Phase Noise with Optimum Area Consumption for RF Applications

This paper presents a meticulously designed Current Starved Ring Voltage-Controlled Oscillator (CSR-VCO) featuring dual control mechanisms. Experimental findings demonstrate that, the primary voltage control empowers the VCO to traverse a broad frequency spectrum from 3 MHz to 30 MHz, with a voltage control range of 0.28 V to 1.2 V. Additionally, the secondary control allows for precise tuning within an ultra-low KHz range. Experimental findings reveal that, with a primary control voltage of 475 mV and a secondary control voltage varying from 0 to 0.6 V, the VCO exhibits a noteworthy output frequency variation spanning from 13.41 MHz to 13.68 MHz, translating to a range of 270 kHz. Remarkably, this VCO excels in power efficiency, with nominal power consumption measuring a mere 73.69 µW. Furthermore, the VCO delivers impressive phase noise performance, registering at an excellent finding of -112.25 dBc/Hz @1MHz offset. Notably, this VCO is engineered to occupy a minimal area footprint, measuring only 0.252 nm2, ensuring optimum area utilization for RF applications. The proposed circuit was designed and implemented in Cadence Virtuoso using 90nm CMOS process technology.
Distributed User Connectivity Maximization in UAV-Based Communication Networks

Multi-agent reinforcement learning has been applied to Unmanned Aerial Vehicle (UAV) based communication networks (UCNs) to effectively solve the problem of time-coupled sequential decision making while achieving scalability. Nevertheless, a transverse comparison on the impact of different levels of inter-agent information exchange on the learning convergence has not been well studied. In this work, we study a distributed user connectivity maximization problem in a UCN, aiming to obtain a trajectory design to optimally guide UAVs' movements in a time horizon to maximize the accumulated number of connected users. Specifically, the problem is first formulated into a time- coupled mixed-integer non-convex optimization problem. A two- stage user association policy is proposed to determine the UAV- user connectivity. A multi-agent deep Q learning algorithm is then designed to solve the optimization, featuring four different levels of information exchange and reward function design. Simulations are conducted to compare the convergence speed and total number of connected users per episode between different levels. The results show that exchanging state information with a deliberated task-specific reward function design yields the best convergence performance in both cases of stationary and dynamic user distributions.
A + AVD for Treatment of Hodgkin Lymphoma Variant of Richter's Transformation

Hodgkin lymphoma variant of Richter's transformation (HvRT) is a rare complication for patients with chronic lymphocytic leukemia (CLL), with an overall poor prognosis. We present the first known case series of patients with HvRT treated with the combination of brentuximab vedotin, doxorubicin, vinblastine, and dacarbazine (A + AVD). In our series of 4 patients, two patients treated with A + AVD for HvRT had durable remissions of 40 and 42 months, while two patients had disease progression and ultimately died. Continued investigation into the optimal management for patients with HvRT is still needed.
A Federated Learning Based Fault Diagnosis in UAV-Reliable Sensor Network

This paper proposes a federated learning-based framework for fault detection in an unmanned aerial vehicle (UAV)-reliable sensor network. The proposed approach is implemented in three stages. In the first stage of implementation, an experimental setup is done for real-time sensor data collection using a UAV module. In the second stage of implementation, a three-tier architecture is designed to perform a federated learning computation. In the third stage, a probabilistic neural network model is implemented in the federated learning framework for fault detection and classification. The real-time data set is used for the validation of the model in terms of fault detection rate, false alarm rate, and false positive rate. The distributed framework ensured data security and optimal resource utilization, which increased the quality of service (QoS) performance of the UAV-reliable sensor network.
Unsupervised pronunciation assessment analysis using utterance level alignment distance with self-supervised representations

The pronunciation quality of second language (L2) learners can be affected by different factors including the following seven factors: Intelligibility, Intonation, Phoneme quality, mispronunciation, Mother tongue influence, Correct placement of pause, and Correct stress placement. An automatic assessment of these seven factors could be helpful for developing computer-assisted language learning systems. In this work, we assess the quality of all seven factors considering an unsupervised approach using DTW based utterance level alignment distance between expert’s and learner’s speech. Unlike the existing works that consider factor specific heuristic based features, we explore Wav2Vec-2.0 based self-supervised representations as a feature for assessing all the seven factors. The distance is computed using the following three distance metrics: Mean absolute error (MAE), Mean squared error (MSE), and Cosine distance (CD). Experiments are conducted on voisTUTOR corpus containing spoken English speech samples from 16 Indian L2 learners annotated with binary ratings (1 and 0) for all the factors. Using each distance metric, for each learner’s speech, four distance values are computed with a set of four expert samples. In the assessment, to circumvent the need for parallel expert data, we consider two (out of four) expert samples synthesized from the state-of-the-art text-to-speech (TTS) systems. We observe that the performance with the considered distance metric based unsupervised assessment approach is significantly more than that with the baseline for six out of seven factors under all three distance metrics and all four experts samples.
Short overview on control algorithms in building occupant-centric control

Due to rapid advances in communication and information technologies, the concept of intelligent buildings is attracting increasing interest. These structures have the ability to anticipate weather patterns, ambient temperature and sunlight, and can adjust heating, ventilation and air-conditioning (HVAC) operations based on historical and real-time data. Despite a considerable amount of recent research focusing on understanding and regulating personalized thermal preferences to satisfy individuals in buildings, a notable gap persists between individual comfort studies and building thermal comfort research. This brief overview aims to review studies on control algorithms that integrate personalized thermal preferences into building control systems, and their effectiveness in balancing occupant comfort and energy efficiency. The studies fall mainly into two categories: normal reactive control (feedback) and advanced predictive control. This categorization provides valuable insights into current trends, identifies research gaps and highlights the need for future work.
Optimizing Asynchronous Extern Execution in Programmable Software Data Planes

P4 has gained a significant attention as a programming language for describing target-independent packet processing. It supports a diverse hardware and software targets through various architecture models that declare external functions called externs representing target-specific functionalities. These externs may require specific or dedicated resources (e.g., cryptography co-processor, FPGA, etc.) for increased processing speed. In order to process tasks in bulk mode, packet processing may need to be temporarily suspended, while waiting for the function to return. Linear execution of the packet processing pipeline implemented by most P4 software targets cannot efficiently handle such situations. Asynchronous packet processing has been proposed to solve this issue by enabling to serve incoming packets while others are processed by an extern. In this paper, we explore existing approaches for extern execution in software data planes and propose a new lightweight asynchronous method for offloading extern execution to dedicated resources, such as cryptography coprocessors, which perform the extern computations. Our analysis show that the propose method can significantly improve the performance of extern execution in various use cases like IPsec, simple encryption and other small tasks and has negligible overhead compared to a prior solution. We also demonstrate that our method has clear benefits on constrained hardware (PcEngines APU single board computer) where the overhead of extern execution has bigger impact on the overall performance and prior approaches are not practical.
Voice-Activated Assistance for the Elderly: Integrating Speech Recognition and IoT

This work aims to develop an elderly assistance system using voice commands by applying speech recognition technology. The system is designed to enhance convenience for elderly individuals and also reduce labor costs associated with hiring caregivers. It leverages the principles of the Internet of Things (IoT) to control various household electrical devices through voice commands. Through Natural Language Processing (NLP), the system processes natural language to interpret voice commands, allowing users to control lighting, open applications, make phone calls, and more. The researchers utilized a Confusion Matrix model to measure the accuracy of the voice command recognition system. It assessed whether the system correctly interpreted the voice commands issued by users. The testing, conducted with a group of individuals aged 55-60, revealed an accuracy rate of 85%. This demonstrates the system’s effectiveness and potential to augment the independence of elderly individuals. In essence, this work presents a voice-command assistance tool that combines speech recognition and IoT, promising enhanced convenience for the elderly and potential savings in caregiving expenses.
Privacy Preserving Federated Learning for Lung Cancer Classification

Lung cancer is characterized by high mortality and incidence rates, making it one of the most prevalent cancers globally. Early detection significantly improves the chances of survival for individuals affected by this disease. The histopathological diagnosis is a crucial factor in determining the specific type of cancer. In recent years, there has been a significant increase in novel computer-aided diagnostic techniques utilizing deep learning algorithms for the early detection of lung cancer. However, sharing sensitive patient data is significantly restricted by regulations such as HIPAA and GDPR, primarily due to privacy concerns. Given the current constraints, institutions face challenges in effectively exchanging information to enhance the accuracy of lung cancer classification. In order to address the issue of privacy in lung cancer classification, we propose a federated learning approach. This methodology involves employing local models with an Inception-v3 backbone to carry out the classification of histopathological images of lung cancer & updating the global model based on the local weights. These images have been obtained from the LC25000 dataset. The lung cancer images from the LC25000 dataset were analyzed, which consisted of three distinct classes. Each class contained a total of 5000 images. The applied model has achieved a classification accuracy of 99.867% in categorizing lung cancer images into three distinct classes. The performance of the proposed framework has demonstrated superiority over other existing methodologies. Furthermore, this solution effectively addresses the privacy concerns associated with the sharing of medical data among different institutions.
Learning to Adapt: Communication Load Balancing via Adaptive Deep Reinforcement Learning

The association of mobile devices with network resources (e.g., base stations, frequency bands/channels), known as load balancing, is critical to reduce communication traffic congestion and network performance. Reinforcement learning (RL) has shown to be effective for communication load balancing and achieves better performance than currently used rule-based methods, especially when the traffic load changes quickly. However, RL-based methods usually need to interact with the environment for a large number of time steps to learn an effective policy and can be difficult to tune. In this work, we aim to improve the data efficiency of RL-based solutions to make them more suitable and applicable for real-world applications. Specifically, we propose a simple, yet efficient and effective deep RL-based wireless network load balancing framework. In this solution, a set of good initialization values for control actions are selected with some cost-efficient approach to center the training of the RL agent. Then, a deep RL-based agent is trained to find offsets from the initialization values that optimize the load balancing problem. Experimental evaluation on a set of dynamic traffic scenarios demonstrates the effectiveness and efficiency of the proposed method.
Phishing Windows Login Credentials Using HTML Full-page Technique

Phishing is a type of cyber-attack that aims to steal sensitive information, such as login credentials and financial data, by masquerading as a trustworthy entity in electronic communication. HTML full-page technique is one of the most recent and sophisticated methods used by cyber criminals in phishing attacks. This paper provides an overview of phishing and HTML full-page technique, explores the underlying technology and mechanisms behind the HTML full-page technique, and discusses potential defenses against this type of attack with a focus on phishing Windows login credentials using HTML full-page technique
Structured OFDM Design for Massive MIMO Systems with Dual-Wideband Effects

Massive multiple-input multiple-output (mMIMO) channels in wideband systems may exhibit the severe delay spread, due to its spatial- and frequency-wideband (dual-wideband) effects. The typical orthogonal frequency division multiplexing (OFDM) technology have to insert a larger number of cyclic prefix (CP) to overcome the inter-symbol interference (ISI) induced by delay spread. The additional CP overhead will counteract the improvement of spectral efficiency by the large antenna array. To address the issue, this paper proposes a structure OFDM modulation approach to reduce the CP overhead for wideband mMIMO systems with dual-wideband effects. Specifically, we propose a joint spatial precoding and frequency domain equalization scheme to maximize the system spectrum efficiency, where the closed-form solutions of precoding vectors and equalization matrices are derived by exploiting the unique characteristic of composite channel matrix. Numerical simulations indicate that the proposed scheme can effectively deal with the dual-wideband effects and significantly improve the spectral efficiency with low CP overhead.
Extended Theory of Planned Behavior

First described by Ajzen in 1985, the Theory of Planned Behavior (TPB) is one of the most widely used theories to predict and explain behavioral intentions and behavior. According to the TPB, attitude, subjective norm and perceived behavioral control towards a certain behavior determine the intention to perform this behavior. In relation to our study, attitude describes the extent to which student teachers evaluate the implementation of ESD in schools as positive or negative. The subjective norm refers to the perceived social pressure that important reference people from professional or private life expect ESD implementation in their work as teachers. Perceived behavioral control describes the perceived control of actually being able to implement ESD in schools.
A Review on Battery Energy Storage State of Charge Estimation for Grid Integration with Embedded Renewable Energy Generation

The anticipated growth of inverter-based variable renewable energy systems (VRESs) such as solar and wind to the power grid results in an equivalent reduction of conventional synchronous generators, thus reducing the rotational inertia of the power system stability. Battery energy storage systems (BESS) are vital for the future growth of renewable energy systems because they provide varied grid support services including grid stability. Hence, managing the battery state of charge (SoC), is a very important aspect of maintaining its life and managing the variability of renewable energy sources (RESs). Therefore, this paper provides a review of SoC estimation methods including model-based and data-driven approaches. This area of research remains a research gap that requires continuous reporting to reflect the battery SoC true estimation. In addition, this review will help researchers, academics, engineers, and industry choose the most suitable SoC estimation approach for future applications with VRESs.
Small Object Detection in Infrared Images Using Attention Mechanism and Sigmoid Function

Infrared image-based object detection is challenging due to inherent low resolution and scarce details, especially when locating small objects that cover minimal region in the image. We present a methodology for detecting small objects in infrared imagery captured by UAVs, emphasizing the use of attention mechanisms to increase detection precision. Using YOLOX as our baseline network, we integrated a modified Recursive Feature Pyramid (RFP) to enhance vehicle detection in the VisDrone-Vehicle dataset. To amplify the spatial details of these smaller objects, we incorporated an attention module and sigmoid function, bridging the connection between the primary and secondary Feature Pyramid Networks (FPNs). The sigmoid function further refines the RFP outputs by filtering out background noise. Comparative experiments demonstrate that the proposed network outperforms the baseline in detecting vehicles in infrared images.