Miracle-dz/UniFAR-FTUs huggingface dataset:
Monitoring of Prosthetic Leg during Rehabilitation using IoT

In the prosthetic field, amputation is one of the important processes that has a great impact on the patient’s life. After the leg is amputated, the patient is fitted with a customized prosthetic leg. The patient gets training to use the newly fitted prosthetic leg in the rehabilitation centre. In the rehabilitation process, exercise is provided to the patient such as walking, running, walking in the staircase etc. The problem that is in rehabilitation is that there is no prior monitoring system. Monitoring the patient's performance in day-to-day exercise is very important. The prosthetist has to visit the rehabilitation center to check the condition of the patient on a daily basis. So, there is a need to design a system that tracks the patient's adaptation to the prosthetic leg. The aim of the paper is that is to monitor the patient's condition in real-time. To implement a system that is used to detect both the normal and the prosthetic leg of the sufferer regularly. To measure the parameters such as the angle and force of the normal and prosthetic leg. To plot a graph based on collected data which is used to analyze the victim when the patient is in the rehabilitation process. And finally, the collected data has been sent to the prosthetist for further treatment.
Virtual Reality-Based Serious Games to Improve Motor Learning in Children with Autism Spectrum Disorder: An Exploratory Study

Children with autism spectrum disorder (ASD) often report impairments in motor learning compared to their peers with typical development (TD). The present study wants to preliminary assess the ability of virtual reality (VR) to foster motor learning in ASD. In particular, 20 children with ASD and 20 children with TD performed two VR-based serious games that required them to perform motor actions with the upper limbs with varying degrees of freedom in the movement. The two serious games were performed twice in a counterbalanced order, and execution time and accuracy were measured. Findings showed that children with ASD were less accurate and took longer to perform the two serious games than their peers with TD. Moreover, no motor learning effect was reported in the serious game that required movements with high-level degrees of freedom, while a motor learning effect was revealed in the game requiring precise goal-directed actions. The children with ASD improved their performance by reducing their execution time across game repetitions. The improvement in time performance did not affect accuracy, which remained stable across repetitions. These findings support the development and implementation of VR-based serious games in ASD intervention, as they could foster motor learning and improve motor skills in this population.
Sep: A New Nonlinear Activation Function for Biomedical Applications and Image Classification

The premise of non-linearity in a neural network is established by an activation function that is crucial for network training and performance evaluation. For many years of theoretical research, lots of activation function have been introduced, but only a certain number of are extensively used in almost all applications including TanH (Tan Hyperbolic), ReLu(Rectified Rectified LinearUnit), Sigmoid, swish, LeakyReLu, Mish. In this research work a nonlinear, nonmonotonic, novel activation Sep is introduced that can be defined as $\mathrm{f}(\theta)=\theta\ ^{*}\sin(\text{sigmoid}(\theta))$. This study demonstrates that the activation function Sep, combined with other common activation functions in neural networks outperforms Relu, Swish, and Mish on various difficult datasets. For example, using a LeNet architecture with Sep to classify the MNIST datasets increased Top-1 test accuracy by 0.44%, 0.22%, and 0.21 % in comparison to the same network with Swish, Mish, and ReLU, respectively.
Two-way Robust Authentication Method to Prevent Shoulder Surfing Attack

Shoulder surfing is a sort of attack in which the attacker observes the victim's login information and attempts to obtain unauthorized access to the system. This may be accomplished by watching the victim's back while they write their password while standing behind them, or by utilizing a camera to capture the victim's screen. In this study, a 2-way robust authentication paradigm, is proposed the user must enter two different pieces of information to log in. A password and security token can be used in conjunction with each other to accomplish this. Because it is more difficult for attackers to acquire unauthorized access to a system, 2-way robust authentication models are more secure than conventional single-factor authentication models. This is because both pieces of information are necessary for an attacker to successfully log in. Two-way robust authentication solutions are not only more secure but also more user-friendly. This is because users only need to remember one password and not the security token. Systems that use 2-way robust authentication are well protected from shoulder surfing attacks. It is more secure and user-friendly than conventional single-factor authentication solutions.
Label Update Based Partial Label Learning for Remote Sensing Scene Classification

A remote sensing scene image frequently contains various objects, hence it is described with multiple labels, all of which are equivalent in status. In fact, however, the importance of these labels to the description of the scene is different from a semantic point of view. In order to obtain the importance distribution of a set of multiple labels, partial label learning (PLL) is introduced into remote sensing scene classification task in this paper. Multiple labels used to describe a scene image are considered as candidate labels in the context of partial labeling learning. During the training process, ‘partial label’ are gradually strengthened, while other labels are gradually weakened, resulting in an indication of the importance of the labels. Specifically, in this paper, a ‘label update based partial label learning’ (LU-PLL) method is proposed to learn the relationship between a set of multiple labels in remote sensing scene images. The model will simultaneously learn the most significant labels and use them as the primary classification basis. Experiments on several classical remote sensing data sets demonstrate the superiority of the proposed method for label importance description.
An Automated Approach for Identification of Oral Squamous Cell Carcinoma based on 2D-ICNN

Oral cancer (OC) is a malignant neoplasm that poses a significant threat to human health, resulting in a considerable fatality rate. India accounts for almost one-third of the global population of OC patients. Among the various types of OC, OSCC is the most common and dominant. Histopathological images are widely considered as the benchmark for diagnosing OSCC. However, this method is known to be time-consuming and requires a high level of expertise due to the heterogeneity of the tumor. To aid accurate diagnosis, artificial Intelligence (AI) techniques have been employed. Deep Learning (DL) algorithms, such as the Two-Dimensional Improved Convolutional Neural Network (2D-ICNN) using GLCM have shown promise in predicting different medical image-based cancers, including breast cancer, OC, and lung cancer. This research work proposes a new approach for classifying OC by utilizing the 2D-ICNN technique. Our method focuses on extracting the best features from biopsy images of OSCC to train the model. By employing GLCM, achieve efficient feature extraction. The findings of our simulations indicate that the suggested model attains an especially elevated level of classification accuracy, amounting to 98.29%. The timely identification of OC, particularly in its early stages, is of paramount importance in facilitating optimal treatment and suitable therapy. The integration of DL algorithms, such as our proposed model, holds promise for improving the diagnosis and management of OC.
Student Surveillance System using Face Recognition

This study aims to enhance campus security and real-time monitoring through the development of an intelligent surveillance system. Computer vision and face recognition technologies, specifically the Haar-cascade algorithm implemented using OpenCV are utilized. The system captures images from multiple cameras at various checkpoints and employs face detection and feature extraction algorithms to identify individuals. By comparing the detected faces with a database of authorized individuals, unauthorized individuals can be promptly identified. The objectives include achieving improved accuracy, real-time tracking, and proactive security measures. While the system demonstrated reliable performance under normal lighting conditions, challenges were encountered in low-light situations, leading to reduced face detection and recognition accuracy. These limitations were primarily attributed to the camera quality and the processing power of local machine. To overcome these limitations, future improvements will involve integrating higher-quality security cameras and more powerful hardware. These enhancements aim to optimize the system's performance and scalability for effective deployment in real-world scenarios. The integration of computer vision, machine learning, and IoT technologies in this study highlights their transformative potential in surveillance systems. In summary, the intelligent surveillance system successfully utilizes computer vision and face recognition techniques for campus security. Despite the limitations encountered, the system demonstrated promising results in real-time monitoring and identification with high accuracy.
Dual Sawtooth-Based Delay Locked Loops for Heterogeneous 3-D Clock Networks

3-D systems and chiplets pose a significant challenge for reliable timing of heterogeneous integrated systems. Delay locked loops are capable of synchronizing clock signals; however, the increasing speed of deeply scaled technologies leads to large and complex delay lines. In this paper, a dual sawtooth-based delay locked loop is proposed to address the increasing difficulty of delay generation in high speed 3-D systems. The proposed architecture lacks a traditional voltage controlled delay line in favor of differential integrators to generate the target delay. The architecture is verified using PTM 7 nm models and achieves locking speeds as low as six cycles for a 1 GHz clock signal.
Modelling Ferroelectric Hysteresis of HZO Capacitor with Jiles-Atherton Model for Non-Volatile Memory Applications

We report a ferroelectric model based on the Jiles-Atherton equations which can successfully reproduce the experimental polarization-voltage hysteresis of thin-film hafnium zirconium oxide (HZO) capacitor devices. Such model is expected to be computationally efficient in circuit design with SPICE and Verilog-A. Our results represent an important advancement towards modelling of ferroelectric HZO capacitors for large-scale memory and neuromorphic circuit integration.
Power Factor Correction by ANN Based Bridgeless Modified Landsman Converter

An increased power factor (PF) frontend battery is used to design a new Electric Vehicle (EV) charger in this paper. In general, Diode Bridge Rectifier (DBR) is used for generating current with low harmonics, but due to its excessive conduction loss, a Modified Landsman converter is established. Power Factor Correction (PFC) is recommended using a Landsman converter with a Bridgeless (BL) architecture for low power applications. The proposed converter solves various Power Quality (PQ) issues like, fluctuations, harmonics, device stress and current ripples. In the proposed work, EV battery which is a non-linear load causes PQ troubles on the input side resulting in input supply deterioration. To make the supply stable, the suggested converter adopts an Artificial Neural Network (ANN) controller. The minimized error from the ANN controller is fed to the PWM generator through which the distortions gets eliminated and a stabilized supply with unity PF operation is obtained. The entire work for enhancing PQ through Bridgeless Modified Landsman Converter (BLML) is carried out in MATLAB and simulation responses are obtained.
Prediction and Analysis of Various Cyber Attack Models in Cyber Physical System in Virtual Environment

The Cyber Physical System (CPS) is referred as the amalgamation of physical system with control system mechanisms. CPS is largely used in versatile process from automation, controlling and monitoring process. This is the process of converting the physical system into a digital platform through sensing, data storage and monitoring process. Due to the advancement in the Internet of Things (IoT), the cyber security has become a complex parameter. Thus they need to be considered. To overcome the constraints, the machine learning is implemented to provide security concerns for the cyber physical systems. The machine learning provides a platform for the intrusion detection system. The machine learning helps in the classification and detection of the cyber-attacks. This is done through the optimization techniques, which helps to provide the solution for reducing the complexity. The internet of system paved way for the sensing, control, monitoring and communication of information. The attacks in the networking system are identified through the machine learning that enable smart applications in diverse fields.
Cloudization of Smart Metering and Advanced Metering Infrastructure

This paper provides insight into the migration of smart metering and advanced metering infrastructure into the cloud and using cloud computing as a means for companies to provide the services without the need to possess their own infrastructure. Firstly, we pointed out the main issues of the conventional system and explained the general idea of applying cloud computing for smart metering and advanced metering infrastructure. Secondly, we discussed the different software, platform, and infrastructure models that can be used depending on the type of services being provided. Next, we explained the different types of deployments that the cloud could be categorized into. After this, we listed and commented on some benefits and drawbacks of cloud computing for the application of smart metering and advanced metering infrastructure. Finally, we showcased the forerunners of cloud service providers.
A compact and low-complexity power amplifier at sub-6 GHz band for 5G application

This paper proposed a compact and low-complexity power amplifier (PA) at sub-6 GHz band, which is prospective for future 5G applications. Two important specifications are required for the PA in the 5G system are high efficiency and compact size. To obtain high efficiency, one of the most popular solutions is the treatment of high-order harmonics. The harmonic treatment techniques are applied to the input/output matching networks, making the PA complex and bulky. With a trade-off between high efficiency and compact feature, our method only focused on optimizing the PA’s parameters at fundamental frequency. The input and output matching circuits were designed by using step-impedance quasi-Chebyshev lowpass matching method. The cosimulation results showed that the PA achieved the peak power added efficiency (PAE) of 60.7%, the output power of 36.9 dBm, and the power gain of 14.9 dB at 6 GHz. In the bandwidth of 200 MHz between 5.9 GHz and 6.1 GHz, the peak PAE was more than 58%.
Clustering of Risk Factors for Coronary Heart Disease Using The k-prototypes Algorithm

Coronary heart disease is a disease that plays a major role as a cause of death in the world. Data from the World Health Organization (WHO) states that an average of 17.9 million people die each year, which is around 31 percent of the total deaths globally. The Institute for Health Metrics and Evaluation (IHME) also reports that 14.4 percent of the causes of death in Indonesia are coronary heart disease. The magnitude of the influence of coronary heart disease on death results in the need for earlier knowledge about the characteristics of each patient with coronary heart disease. Each patient has different behavior and characteristics towards the cause of coronary heart disease. To find out each characteristic for people with coronary heart disease, need a data to be analyzed. Currently, the amount of data is getting bigger and many are of mixed type, including data on coronary heart disease patients. The aim of this research is to classify coronary heart disease patient data using the k-prototypes algorithm. The best cluster result is k=2, with a silhouette coefficient value of 0.808. Cluster 1 of 493 patients, most of whom were male, had smoking habits with lower averages for the variables age, glucose, body mass index (BMI), diastolic, and heart rate, and had no history of hypertension, blood pressure medication (BPMeds), stroke or. For cluster 2, only 18 female patients had a history of hypertension, diabetes, and all of the numerical variables had a higher average than cluster 1.
Inbuilt Chat GPT Feature in Smartwatches

Smartwatches with built-in chat GPT (Generative Pretrained Transformer) technology have boosted people's capacity to communicate with each other and their devices. Users may talk with their smartwatches using chat GPT technology. It achieves this by being able to read natural language inputs and respond like a human. The in-built chat GPT technology is designed to expedite conversation and increase wearable use. Chat GPT technology allows smartwatches to learn from prior interactions and adapt to users' communication styles, preferences, and behaviors. Machine learning and artificial intelligence make this feasible. Smartwatches' integrated chat GPT technology facilitates communication and offers many other benefits. Voice commands allow users to set reminders and appointments, plan trips, check the weather, and control smart home devices. This feature's capacity to deliver tailored recommendations and suggestions based on user behaviors and preferences enhances the user experience. Built-in chat GPT technology in smartwatches has many advantages, but it also has several drawbacks. Ethical, security, and privacy problems are these obstacles. However, with proper deployment and regulation, incorporating chat GPT technology in smartwatches should outweigh the risks. This will enable natural and simple technology and communication.
Exponential Rate for Bilinear Systems via Passivity-Based Controllers

The theory of finding a controller using Krasovskii passivity has been recently developed and offers local asymptotic stability guarantees, but with the shortcoming of not considering a methodology to design the controller’s parameters such that certain performances are imposed. The current paper proposes an approach to design first and second order Krasovskii passivity-based controllers (K-PBCs) for plants described via the bilinear system framework, and which ensure local exponential stability with optimized convergence rate. The K-BPC’s parameter design procedure is initially expressed in terms of nonlinear matrix inequalities, followed by a method to convexify the problem via a variable change, leading to a set of linear matrix inequalities (LMIs). A numerical example is presented, underlying the advantages of the proposed procedure.
Ensemble Model for Seven-class Categorization of Skin Disease using Dermoscopy Images

The skin is the largest organ and has an essential defensive role in the body. Skin diseases can be either genetic or environmental, and can affect people of all ages, ethnicities, and sexes. A dermatologist is the best medical professional to see for skin problems since they can examine the whole person and take their medical history into account. When abnormalities in the skin are discovered early, especially in the case of skin cancer, the prognosis drastically improves. Because of this, this research proposes an ensemble model consisting of ResN et50 and Convolution Neural Network (CNN) for the multi-class categorization of skin illness using dermoscopy images. The HAM10000 dataset is used, which consists of 10015, dermoscopy images of skin classified into 7 -groups. Accuracy is measured at 0.972, while precision averages 0.911.
Research on Linear EBG Structure for E-Plane Coupling Suppression of Microstrip Array

The strong coupling between antenna units not only affects the performance of antenna units, but also has a very adverse impact on the performance of antenna arrays. Research on coupling suppression between antenna units is of great significance and practical value for antenna array design. A novel electromagnetic bandgap structure which is composed of double L-shaped resonant cell is proposed to effectively suppress the coupling between microstrip antenna units on the E-plane without changing the structure of the antenna array. The current distribution of defected ground is introduced to explain the principle of coupling suppression. Detailed analysis of current and performance parameters proves that the resonant structure has a strong ability to suppress the coupling between antenna units and also has a good robustness.
Exploring the interactions of wildlife and rural communities in Carara National Park, Costa Rica

Park officials at Carara National Park, Costa Rica, face considerable challenges. They believe poaching has become a significant threat to the park's biodiversity and that their enforcement measures, largely due to limited funds, are inadequate. Through family-level interviews in six communities surrounding Carara National Park, this study aims to understand the dynamics of poaching by identifying animals at risk of poaching and identifying communities to target future enforcement measures. Interviews inquired about interactions with wildlife, perceived populations of wildlife, sale of wildlife, opinions towards hunting, and observed hunting activities. Summary statistics, CART modeling and probit regressions are used to analyze the data. Results indicate hunting is not prevalent in terms of the number of families that hunt. However, these families can still have significant impacts on wildlife populations with poaching occurring for both subsistence and market-oriented reasons. Two potential community sets are identified as areas to target enforcement. Based on the number of reported families engaging in hunting, enforcement should be targeted in Bijagual and Tárcoles. However, exploring truthfulness through CART and probit regression analysis indicates enforcement should be targeted in El Sur and La Hacienda. Ultimately, future enforcement measures will be determined by Carara officials. Although potential target communities are identified and management practices such as ecotourism and community-based management are potentially viable avenues for reducing poaching, the park faces considerable challenges for enforcement due to its limited financial capabilities.
Triangular Formation Cluster Space Control for Multi-Agent Entrapment/Escorting Tasks

The task of enabling a multi-agent system for entrapping and escorting static/dynamic targets in the context of cluster space control is addressed. The proposed methodology consists of two main strategies. On one hand, the entrapping and escorting problems are specified in terms of cluster parameter trajectories, resulting in individual robot motions capable of satisfying the required multi-agent formation behavior. On the other hand, a fuzzy logic controller is designed in order to deal with the uncertainty in the model of the platform used in laboratory tests. Experimental results showing a team of three ground mobile robots entrapping a static target, as well as escorting a dynamic target demonstrate the effectiveness of the proposed control scheme.