Miracle-dz/UniFAR-FTUs huggingface dataset:
Precision Agriculture: Crop Image Segmentation and Loss Evaluation through Drone Surveillance

Agriculture is the most important source of livelihood. Crop segmentation has become an important role in precision agriculture which helps farmers to make decisions about crop damage and its production. However, it's a challenging task to achieve precision in the agriculture field. Drone Surveillance helps to achieve that crop yield assessment, crop damage, crop health, and other parameters. This paper focuses on image segmentation of crops, classified into categories like sparse and dense crops with the multitemporal data image taken by Drone. This model proposed and studied shows the loss percentage in crop identification by image segmentation process, it helps farmers to get good compensation for crops to survey through Drone (UAV) techniques. A detailed analysis with outcome of thisis explained further.
A Zero-Touch and NFV-Based Full-Mesh VPNaaS Solution - Demo

NFV has risen to be a solution to abstract Network Functions from the hardware, providing numerous advantages to Network Operators. However, many challenges have appeared with the evolution of NFV. An example are the inter-domain scenarios where services are spanned across multiple independent domains. Using VPNs to interconnect all domains can attenuate the difficulties imposed by such scenarios. In this paper, we present a NFV-based solution for deploying full-mesh VPNs to interconnect different administrative domains, without manual intervention.
Deep Learning-Based Classification of Finger Movements using tEEG and EEG Signals

Mapping electroencephalography (EEG) signals obtained during hand and finger movements to left or right hands has applications in many fields including motor imagery brain-computer interface (MI-BCI) systems. MI-BCI systems are innovative technology to assist patients with severe motor impairments, however, novice users can face the "BCI-illiteracy" issue and fail to control the system. A step towards assisting such patients is to correctly classify their EEG signals collected while the patients imagine or perform hand/finger movements. This work considers using the tripolar electroencephalography (tEEG) instead of the traditional EEG due to higher signal-to-noise (SNR), better spatial resolution, better artifact noise rejection feature, and ability to capture high-frequency features of tEEG, compared to conventional EEG. In this paper, we compare the performance of tEEG and EEG signals in classifying motor imagery (MI) of right and left index fingers by using a deep learning algorithm: convolutional neural network (CNN). The results show that in 5 out of 7 subjects, CNN is able to perform classification with high accuracy, with the highest accuracy being 96.4% for tEEG data.
On Averaging ROC Curves

Receiver operating characteristic (ROC) curves are a popular method of summarising the performance of classifiers. The ROC curve describes the separability of the distributions of predictions from a two-class classifier. There are a variety of situations in which an analyst seeks to aggregate multiple ROC curves into a single representative example. A number of methods of doing so are available; however, there is a degree of subtlety that is often overlooked when selecting the appropriate one. An important component of this relates to the interpretation of the decision process for which the classifier will be used. This paper summarises a number of methods of aggregation and carefully delineates the interpretations of each in order to inform their correct usage. A toy example is provided that highlights how an injudicious choice of aggregation method can lead to erroneous conclusions.
Evaluation of BehaviourCoach – An Exergaming Mobile Application Adapted for Mauritius

There is evidence that the lack of physical activity and unhealthy nutrition are among the major contributing factors to health conditions such as type 2 diabetes, cardiovascular diseases and hypertension. BehaviourCoach is a mobile application developed using an in-house framework which proposes an adaptation of the world-famous Monopoly boardgame incorporating exergaming features. BehaviourCoach includes features to promote sustainable behaviour changes in terms of increased physical exercise and healthier nutrition among participants. Following the development of a first application based on the framework, this paper focuses on its evaluation in terms of usability and perceived impact on individuals. A randomized controlled trial intervention involving sixty (n = 60) participants was undertaken during which data was gathered through questionnaires, observation and interviews. Participants perceived the application as easy to engage with and more importantly, BehaviourCoach had a positive impact on their level of physical exercises, which was more pronounced among those who were less physically engaged prior to the intervention.
Mid-Distance Running Exercise Assistance System via IoT and Exercise-oriented Music

This study explores the effectiveness of a music-assisted IoT exercise system specifically designed for mid-distance running. The primary objectives are to augment the sports experience, facilitate recovery, and surpass the benefits provided by existing solutions. This system ensures a personalized experience through real-time music tempo adaptation according to the heart rate (HR), exercise tracking, customizable music selection, adjustable parameters, and voice recognition technology for effortless completion of questionnaires. In our findings, we observed a significant interaction (p <0.05) in terms of Condition × Exercise Stage for HR, Rating of Perceived Exertion (RPE), Feeling Scale (FS), and Felt Arousal Scale (FAS). It suggests that exercise-oriented music considerably impacts physiological (HR), perceptual (RPE), and affective (FS, FAS) responses. Furthermore, tailored and enjoyable exercise experiences lead to an increase in positive arousal and a decrease in fatigue. The music-assisted IoT exercise system demonstrates potential as an efficacious instrument for promoting physical activity and enhancing health outcomes. Future work will concentrate on further exploring the impact of music on exercise performance and recovery, investigating other musical parameters, integrating sensors for capturing behavioral responses, and enhancing data privacy and security through blockchain technology.
Mechanical Design of a Haptic Hand Exoskeleton for Tele-Exploration of Explosive Devices

There are tasks such as remote exploration and manipulation of explosive objects where high dexterity, accuracy, and practicality are necessary. The proposed haptic hand exoskeleton design uses displacement sensors in both flexion-deflection as well as abduction-adduction to replicate the operator’s main three fingers’ motion and teleoperate a slave robotic hand for disassembling and disposal of explosive objects. The novel design, component selection, and computer-aided design of the haptic virtual prototype were developed and tested.
Design of reduced-order observers for nonlinear sampled-data strict-feedback systems with actuator dynamics and disturbances

ABSTRACT We design reduced-order observers for nonlinear sampled-data strict-feedback systems with actuator dynamics and disturbances. First, we use the property of the Euler model of sampled-data systems and the structure of discrete-time observers to design reduced-order observers of the Euler model. Then, we show that the designed observers are semiglobal and practical in T for the exact model. We also give both numerical and practical examples to illustrate the proposed design of reduced-order observers.
Food Images Classification Based on Improved Convolutional Neural Network

Food image classification plays a crucial role in computer vision research and has immense theoretical significance, along with numerous potential applications in various fields such as smart health, smart retail, and smart home. While several studies have been conducted on this topic, there is a lack of consensus on the optimal approach to achieve high accuracy. In this study, the author propose an improved Convolutional Neural Network (CNN) architecture for food image classification. This study aims to enhance the accuracy of food image classification by examining the influence of different optimizers, loss functions, and activation functions on the performance of four neural networks, including VGG19, MobileNet V2, DenseNet, and a traditional CNN architecture, using the ChineseFoodNet dataset. Besides, the paper will improve the performance of the selected model by utilizing data augmentation techniques and transfer learning. The findings of this study can help researchers and practitioners develop more robust and accurate models, facilitating their application in areas such as smart health and smart homes. It also has practical implications for the development of more accurate food image classification models in computer vision field.
Self-adaptive flow regulation through shape memory alloy valves for microelectronics cooling

To overcome the current limitations of temperature uniformity and high pumping power consumption of current microchannel cold plates, this work proposes to develop an on-chip liquid cooling system that incorporates self-adaptive shape memory alloy valves able to regulate the flow rate to the local and instantaneous needs of the chips, leading to energy savings and improved thermal performance of the microelectronics. The behavior of the valves is achieved due to the use of bimorph SMA wings. Thus, the valve remains closed for low cooling demands, maintaining a low flow rate at the channel and, when the cooling demands increase, the valve opens to allow more flow rate into the channel and improve its heat extraction capacity. It has been evaluated that the SMA valves can double the flow rate in the channel when in open position and the expected energy savings can be quantified between 10 % and 30 % for different heat load scenarios. Finally, the hysteresis of the SMA has been experimentally characterized for a determined working application.
Hand Gesture Based Sign Language Recognition Using Deep Learning

Sign Language Recognition (SLR) serves a very important role in creating a bridge between deaf/mute community and conventional society. SLR uses facial, head, arm, and hand motions to communicate linguistic information. Since mostly used body parts for communication are hands, therefore this paper is based on hand gesture sign language recognition system. The proposed method performs very well not only in detecting hand landmarks but also tracking of hand gesture continuously by using Google's MediaPipe. AlexNet classifier is used for classification of different gestures of Indian Sign Language (ISL) and also, comparison of the proposed method is done with some other existing SLR models. The dataset is a self-generated dataset which contains 15 classes for words and captured by stable webcam. This proposed method presents a system with recognition accuracy of 98.9% and its effectiveness is increased by translating the hand gesture recognition into readable text.
Comparative analysis of feature extraction methods in satellite imagery

Abstract. Feature extraction techniques are extensively being used in satellite imagery and getting impressive attention for remote sensing applications. The state-of-the-art feature extraction methods are appropriate according to the categories and structures of the objects to be detected. Based on distinctive computations of each feature extraction method, different types of images are selected to evaluate the performance of the methods, such as binary robust invariant scalable keypoints (BRISK), scale-invariant feature transform, speeded-up robust features (SURF), features from accelerated segment test (FAST), histogram of oriented gradients, and local binary patterns. Total computational time is calculated to evaluate the speed of each feature extraction method. The extracted features are counted under shadow regions and preprocessed shadow regions to compare the functioning of each method. We have studied the combination of SURF with FAST and BRISK individually and found very promising results with an increased number of features and less computational time. Finally, feature matching is conferred for all methods.
Face Recognition under Varying Illumination Using Green’s Functionbased Bidimensional Empirical Mode Decomposition and Gradientfaces

A novel face recognition approach under varying illumination condition based on Green’s function in tension-based bid imensional empirical mode decomposition (GiT-BEMD) and gradient faces (GBEMDGF) is present. Firstly, face image was illumination normalization by discrete cosine transform that an appropriate number of DCT coefficients are truncated in logarithm domain. And then, two intrinsic mode functions (IMFs) that relevant of intrinsic physical significances of face images are produced by Gi T-BEMD. At the same time, gradient faces is used to improve the high frequency component of face images and to extract illumination insensitive facial feature. The facial feature of discriminately are fused using IMFs and illumination insensitive feature. Secondly, the principal component analysis is adopted to reduce the dimension of face image. The nearest neighbourhood classifier based on cosine distance is implemented for face classification. Experimental results on Yale B database and CUM PIE face database demonstrate that the present technique is robust to varying lighting resource.
Time- and spectrally-resolved mesoscopic Raman and fluorescence imaging of carious enamel by a CMOS SPAD-based spectrometer

Dental caries is the most widespread non-communicable disease affecting 2.5 billion people worldwide. Recently various optical spectroscopy methods have gained a lot of interest in dental research and caries detection due to their noninvasive and nonionizing nature. We demonstrate here, to the best of our knowledge, for the first time simultaneous Raman, fluorescence intensity and fluorescence lifetime imaging of carious human enamel with a single device based on a time-resolved single-photon avalanche diode (SPAD) sensor fabricated in 110-nm CMOS technology. The sensor has identical 256 spectral channels each of them containing an integrated tunable 7-bit time-to-digital converter (TDC) enabling time- and spectrally-resolved photon counting. We were able to show with the spectrometer built around the CMOS SPAD sensor and by utilizing a simple unsupervised machine learning algorithm (K-means) that a more comprehensive and reliable images of the enamel and caries-affected regions can be obtained by combining the information from the three different optical spectroscopy methods. We believe that the technology presented here could pave the way for novel multimodal optical devices not only in dental research and clinical dentistry but also in other biomedical applications in which molecular and chemical changes are studied.
Convolution Neural Network for Facial Kinship Verification

The process of detecting whether two people in a given pair of face pictures are biologically related or not is known as facial kinship verification. Deep learning-based techniques, in particular Convolutional Neural Networks (CNNs), have excelled at this challenge in recent years. In this article, we will compare a number of pre-trained CNN models and hybrid models to one another. Also, try to check performance by ensembling these models. The models are trained on the large-scale KinFaceW-I dataset and evaluated on the KinFaceW- II dataset, achieving state-of-the-art performance. In order to evaluate the performance, we have made our new dataset similar to the KinFaceW dataset. Additionally, our technique exhibits resilience to a variety of facial variables, including alterations in age, posture, and expression. Overall, can get a potential answer to the problem of facial kinship verification, which is crucial in numerous disciplines such as forensic investigation, family history research, and social media analysis. At last paper identified a ensembled or single models which work well on KinFace dataset and new dataset introduced by this paper.
WFDB Software for Python: A toolkit for physiological signals

The Waveform Data Base (WFDB) software for Python is an open-source, less-featured toolkit developed under the shadow of the WFDB package. This library supports various PhysioNet databanks and hence provides excellent pre-processing and feature extraction of biosignals (ECG, EEG, PPG, ABP, ICP, etc.). The structure of this library is simple and can be easily merged with other python features to enhance the experience of the datasets. The detailed discussion of the arrhythmia dataset and complete usage of the WFDB for the python toolkit is shown in this paper. This work is intended solely for informing the user of the functionality, existence, and applicability of the WFDB software for Python.
Week-long Multimodal Data Acquisition of Occupational Risk Factors in Public Administration Workers

Work-related disorders are a growing issue for office workers and represent a significant burden to public health. Work aspects such as sitting for prolonged periods and occupational stress are modifiable risk factors highly associated with occupational disorders in office workers. The PrevOccu-pAI Project (Prevention of Occupational Disorders in Public Administrations based on Artificial Intelligence) objectively investigates relationships between a variety of occupational risk factors and physiological outcomes. For this purpose, a data acquisition protocol was carried out at the Portuguese Tax and Customs Authority. Physiological, movement, and environmental signals from office workers were acquired during five consecutive workdays using a smartphone, a smartwatch, and two electromyography sensors. Additionally, demographic, occupational, and pain information were collected through questionnaires. The present manuscript provides a detailed description of the PrevOccupAI acquisition protocol. The collected data is used to gather knowledge regarding modifiable factors at the individual and organisational levels.
Validating SMT Solvers via Skeleton Enumeration Empowered by Historical Bug-Triggering Inputs

SMT solvers check the satisfiability of logic formulas over first-order theories, which have been utilized in a rich number of critical applications, such as software verification, test case generation, and program synthesis. Bugs hidden in SMT solvers would severely mislead those applications and further cause severe consequences. Therefore, ensuring the reliability and robustness of SMT solvers is of critical importance. Although many approaches have been proposed to test SMT solvers, it is still a challenge to discover bugs effectively. To tackle such a challenge, we conduct an empirical study on the historical bug-triggering formulas in SMT solvers' bug tracking systems. We observe that the historical bug-triggering formulas contain valuable skeletons (i.e., core structures of formulas) as well as associated atomic formulas which can cast significant impacts on formulas' ability in triggering bugs. Therefore, we propose a novel approach that utilizes the skeletons extracted from the historical bug-triggering formulas and enumerates atomic formulas under the guidance of association rules derived from historical formulas. In this study, we realized our approach as a practical fuzzing tool HistFuzz and conducted extensive testing on the well-known SMT solvers Z3 and cvc5. To date, HistFuzz has found 111 confirmed new bugs for Z3 and cvc5, of which 108 have been fixed by the developers. More notably, out of the confirmed bugs, 23 are soundness bugs and invalid model bugs found in the solvers' default mode, which are essential for SMT solvers. In addition, our experiments also demonstrate that HistFuzz outperforms the state-of-the-art SMT solver fuzzers in terms of achieved code coverage and effectiveness.
Towards Multiple Pipelines Network Emulation with P7

Network emulation traditionally relies on software-based solutions. While extremely useful in many scenarios, it suffers from performance fidelity and inherent scalability constraints. With the advent of P4 and programmable switches like Tofino, new opportunities for hardware-based network emulation are emerging. P7 (P4 Programmable Patch Panel) offers a solution for high-fidelity 100G traffic network emulation, including different link characteristics such as latency, jitter, packet loss, and bandwidth, as well as the ability to define custom topologies. However, it currently lacks support for custom P4 code in emulated devices. This is where multiple pipelines network emulation comes in. In this demonstration, we show how to emulate a topology using P7 and incorporate custom P4 code into each emulated node. We allocate a dedicated pipe for user-defined P4 code and allow users to conFigure tables for each node separately.
Guidelines and Experimental Hydraulic performance Evaluation for Single-Phase CDUs Under Steady and Transient Events

About 40% of the energy utilized in data centers is used for cooling systems, and this percentage has increased significantly in recent years. Data center server racks receive indirect cooling from computer room air conditioning units (CRACs) or computer room air handler units (CRAHs), and chilled air is sent to the racks through a raised floor plenum to cool the server room. This approach is inefficient because the server room has excessive cooling while the IT equipment has inadequate cooling. The data center industry has begun to use thermally and energy-efficient single-phase liquid cooling solutions as a result of the tremendous increase in IT power density and energy usage. One of the most popular liquid cooling systems will be examined in the current study, which is based on numerous circumstances. Under various secondary coolant conditions, the hydro-thermal performance of different liquid-to-air and liquid-to-liquid coolant distribution units (CDUs) will be assessed experimentally using multi-racks loaded with different numbers of thermal testing vehicles (TTVs), and the system response to any change in the flow will be examined by disconnecting and reconnecting the TTVs cooling loops in the multi-racks through different sequences of transient events. Moreover, a set of rules and guidelines will be established to commission these units.
Temperature Dependent Thermal Properties of Thin Film Hafnium Oxide

HfO2 is a crucial material for the next generation's transistors, memristors, and neuromorphic devices. Typical electronic components employ HfO2 films with a nanometer-scale thickness, whose thermal properties may depend on film thickness and temperature and could significantly impact device temperature, performance, and reliability. Using frequency-domain thermoreflectance, the thermal conductivity and volumetric heat capacity of HfO2 films of varying thicknesses at temperatures between 300 K and 480 K are determined. It was discovered that the thermal conductivity of thin-film HfO2 increases with temperature. Due to its increased crystalline content, the thicker sample has a higher thermal conductivity and a smaller volumetric heat capacity.
A Finite element method for Shear Stress study on cancer cell proliferation

Metastatic progression of tumors requires the coordinated dissemination of cancerous cells through interstitial tissues and their replication in distant body locations. Despite their importance in cancer treatment decisions, key factors, such as cell shape adaptation and the role it plays in dense tissue invasion by cancerous cells, are not well understood. With this work, we will continue further work on the study of the effect of shear stress on cancer cell proliferation. We present a coupled Keller-Segel and Darcy-Brinkman model implemented in finite elements to obtain numerical results.