Miracle-dz/UniFAR-FTUs huggingface dataset:
A Sparse-Coded Multiplexing Approach for MIMO Over-the-Air Federated Learning

The communication bottleneck of over-the-air federated learning (OA-FL) lies in uploading the gradients of local learning models. In this paper, we study the reduction of the communication overhead in the gradients uploading by using the multiple-input multiple-output (MIMO) technique. We propose a novel sparse-coded multiplexing (SCoM) approach that employs sparse-coding compression and MIMO multiplexing to balance the communication overhead and the learning performance of the FL model. We derive an upper bound on the learning performance loss of the SCoM-based MIMO OA-FL scheme by quantitatively characterizing the gradient aggregation error. We show that the optimal number of multiplexed data streams to minimize the upper bound is given by the minimum of the numbers of transmit and receive antennas. We then formulate an optimization problem of designing precoding and post-processing matrices to minimize the gradient aggregation error, and develop an efficient algorithm to solve the problem. The numerical results indicate the effectiveness of the proposed SCoM approach.
High-Performance Image Splicing Detection utilizing Image Augmentation and Deep Learning

Nowadays, the hassle-free availability of innumerable, easy-to-use image editing software makes image manipulation widespread, and these forged images can be used for various malicious activities. One of the most common methods of digital image deception is image splicing, where multiple portions from different images are combined to make the spliced image. Image splicing can be detected based on machine learning or deep learning techniques. Deep learning-based methods usually provide better performance but with extremely large training data and training time needed, which increases the cost of the model because of its structural complexity. Hence, in this research, we present a Deep Convolution Neural network-based model where a pre-trained network, ResNet50, replaces the initial convolution layers, followed by adding and modifying multiple layers. We have implemented image augmentation to make the dataset diverse for appropriate training of our model. Results from experiments show that our simplified approach, which does not require large training data and time, can accurately differentiate between the spliced images and the authentic images with the best accuracy of 1 and average accuracy of 0.99 for the DSO-1 dataset and best accuracy of 0.96 and average accuracy of 0.95 for the Columbia dataset.
Simulation, detection and prediction of sea floor dynamics

The Dutch part of the North Sea is mapped by two authorities. Both authorities predict the behavior of the sea floor by analyzing time series of bathymetric data. The Hydrographic Service determines parameters for sand wave dynamics from an area, based on deformation analysis. The North Sea Directorate uses a multivariate state space modelling approach to estimate, update and extrapolate trends per grid point. Here, both approaches are described and combined in order to obtain a prediction method that incorporates a sand wave propagation model. The new method is tested on two data sets of different characteristics.
Deep Q-Network Dueling-Based Opportunistic Data Transmission in Blockchain-Enabled M2M Communication

The growth of the opportunistic network (OppNet) in recent years has created a variety of opportunities and concerns. Machine-to-machine (M2M) communications, which are a critical component of OppNet, provide a novel means for connecting and communicating among machine-type communication devices (MTCDs) without the need for human interaction. OppNet data play a significant role in M2M communications and it emphasizes more powerful data storage, computation, processing, as well as the security and stability of data transfer. This paper proposes a joint optimization framework for dueling Deep Q-network (DQN)-based opportunistic data transmission in M2M communication using blockchain technology. The best choice and decision of caching servers, blockchain systems, and computing nodes, can be made in accordance with the dynamic decision-making process by DQN, a decision to increase the system incentives, including high data processing efficiency, lower cost, and improved data interaction security. Simulation results using various parameters demonstrate that the proposed model has more benefits and is more efficient than the existing random, greedy, and Conventional DQN benchmark schemes.
Triboelectric Nanogenerator Based on Different Polymers: Experimental and Simulation Study

In this study, triboelectric nanogenerators are fabricated using a variety of polymers, including PET, PMMA, and OHP sheets. Packaging aluminum foil functions as conducting electrode, while the PET, OHP, and PMMA act as triboelectric layers in the design. A flexible TENG with a 5 x 5 cm2 surface area produced an output voltage between 6 V(AOA), 5 V (APA) and 25 V (APMA). The current work provides a simple and cost- effective method for creating TENG structure, which may be employed for applications involving self-powered devices. By using FEM analysis also for the three TENG structures obtained output response is higher for PMMA used as the dielectric in the TENG i.e. (APPMA), in the same way in practical designed TENG structures also APPMA structure is giving higher response compared to remaining to structures. Furthermore, the built APMA structured TENG was capable of turning on the 19 commercial LEDs.
Information Freshness of V2V Communication in Large-Scale Platooning System

Platooning system with Vehicle-to-vehicle (V2V) communication can offer the capability to adapt control strategies and uphold vehicle spacing through the exchange of vehicle status information. The stability of the platoon hinges on the promptness of transmitting vehicle status and control data. Yet, the currency of these data is significantly impacted by factors such as transmission interval and interference, particularly in large-scale networks. Diverging from much of the existing literature that predominantly addresses communication delay's impact on platoon stability, this study scrutinizes the freshness of V2V communication information and its ramifications on platoon stability across large-scale regions. Concretely, our work introduces a platooning control model grounded in a constant time gap policy that factors in the age of information (AoI) inherent in V2V communication. Subsequently, we construct a V2V communication model, accounting for the V2V packet transmission scheme and wireless resource allocation strategy. Through a fusion of queuing theory tool and stochastic geometry theory, we delve into the distribution of the average peak AoI (PAoI), unearthing insights into the prevalence of stable platoons in the network. Furthermore, simulation results elucidate the interplay between system parameters and platoon stability within this interconnected communication and control system.
Modeling of Soft Robotic Grippers for Reinforcement Learning-based Grasp Planning in Simulation

Most grasping solutions in the literature and industry rely on learning-based planners developed for grippers with rigid fingers, whose grasp geometries can be abstracted deterministically into simple shapes, typically in terms of a single grip width parameter. Soft grippers, on the other hand, have nonlinear relationships between the actuation input and final geometric shape of the grasping surface. Modeling this relationship is important for training accurate reinforcement learning-based grasp planners. In this paper, we present a prototype cable-driven soft robotic gripper, and describe a computer vision-based technique with LASSO regression to transfer the relationship between the length of the cable and the finger's shape parameters in terms of angular deflections to a PyBullet simulation environment. The average root mean-squared error for this regression model was 0.15 rad. This work forms the first step of a proposed real-to-sim-to-real pipeline for training physically accurate soft robotic grasp planners.
DiaMOS Plant Leaves Disease Classification using Vision Transformer

The consequences of climate change have increased during the past several years. This climate influences each stage of plant production, and farmers are being forced to modify the plantation and advance their crop management techniques utilizing recent technology built on data analytics. Many plant diseases manifest themselves in the leaves. Therefore, successful disease diagnosis requires a thorough understanding of the condition of plants. To provide high-quality agricultural products, timely and precise disease detection and classification are crucial for smart and precision agriculture. In this study, images of the DiaMOS plant leaves dataset that was collected in the field of Sardegna, Italy is utilized to learn and classify plant disease using several fine-tuned z deep learning models like InceptionV3, EfficientNetB0, VGG-16, ResNet50, VGG-19. Here, the Deep learning models are trained to categorize the severity of plant disease and built a vision transformer for classification that achieves higher than the predefined models with an accuracy- 98.5% precision-97.1%, and F1 score – 97.83%.
Predictive Modeling for Osteoporosis Risk Assessment from DXA Scans

A common skeletal condition marked by decreased bone density, osteoporosis is a major global health concern, especially for elderly populations. This study aims to provide a novel method for evaluating osteoporosis risk by combining relevant clinical data with information from Dual-energy X-ray Absorptiometry (DXA) scans. Using a deductive technique and an interpretive attitude, we compiled a large dataset of clinical data and DXA scans from various patient groups. To ensure data integrity, sophisticated data preparation procedures were used, such as alignment, standardization, and management of missing data. To extract pertinent data, characteristic engineering and selection procedures were used, producing a cleaned dataset for modeling. The comprehensive predictive model was created using machine learning techniques such as support vector machines, random forests, and logistic regression. Thorough analysis produced encouraging findings, with R-squared values showing that the model fit the data very well. Furthermore, interpretability methods like Partial Dependence Plots (PDPs) and Shapley Additive explanations (SHAP) values offered insightful information about the significance and connections between features.
A Enhancing Cybersecurity Resilience for Low-Income Farmers in Developing Nations: A Fuzzy Cognitive Mapping Approach

Rural farmers with limited resources in developing nations who integrate technology into their sustainable agricultural practices encounter a range of obstacles, including cybersecurity vulnerabilities, insufficient familiarity with technology, restricted finances, and a lack of financial backing. Ensuring the security of these sustainable farming initiatives becomes imperative to ensure the advancement of sustainability, resilience, and human-centered approaches in agriculture. This, in turn, supports objectives such as food security and poverty alleviation in these developing regions. Unfortunately, the absence of suitable and tailored methods for assessing cybersecurity risks in low-income farming exacerbates this situation. This research article introduces a streamlined approach for evaluating cybersecurity risks that specifically addresses the needs of low-income farmers in developing countries. The proposed methodology leverages Fuzzy Cognitive Mapping and asset-focused assessments to quantify potential risks. To validate its effectiveness, the methodology was applied to a real-world case study: the “Ferme-Ecole” project in Tsevie, Togo. The findings reveal that the “Ferme-Ecole” project faces elevated risks in areas involving human interactions. Overall, the proposed approach proves to be effective, adaptable, and cost-efficient, offering immediate insights and rationale for the efficacy of implemented cybersecurity measures.
Intelligent Transmission Scheduling for Edge Sensing in Industrial IoT Systems

Edge sensing supported by wireless transmission is one of the core enabling technologies for flexibly implementing the Industrial Internet of Things (IIoT). Balancing network resource consumption and sensing accuracy under dynamic network conditions is a critical challenge. In this work, we bridge the gap between edge sensing performance and transmission design through observability analysis and learning-based methods. Particularly, utilizing observability probability as the key metric, we design the network resource reservation for specific sensing performance demands including stability based on our derived upper and lower probability bounds. Then, to further reduce the overall cost of edge sensing and transmission, an intelligent transmission scheduling method (ITSM) based on deep reinforcement learning is provided, which dynamically schedules the number of transmissions for each sensor. In ITSM, the action space is determined according to the amount of our reserved resources, and both the states of sensing error and fading channel are taken into account. Finally, the superiority of our proposed methods is fully demonstrated through numerical simulations in a typical IIoT system of industrial hot rolling.
Leveraging the Power of Blockchain for Secure Healthcare Data Management System

Despite significant improvements in healthcare innovations and administration, little has been done to address security threats such as data breaches or cybersecurity. To ensure the best services, healthcare authorities must manage healthcare data effectively—not just during or post-pandemic situations such as COVID-19, but also in their regular operations. Blockchain (BC) technology can be utilized to increase the security of healthcare data management. Healthcare providers may create a transparent, tamper-proof system for handling patient records, authentication, and access control by utilizing blockchain. We offer a general blockchain-enabled healthcare architecture for managing healthcare data reflecting various participant interactions. Our system connects all parties, including physicians, patients, governments, supply chains, and other healthcare practitioners, via the Ethereum network. The smart contract code development and testing were done on the Remix IDE platform. We have combined Hyperledger Fabric (HLF) and the InterPlanetary File System (IPFS) to provide decentralized storage. In addition to examining numerous safety concerns, we also compared our solution to other blockchain-based systems and offered a cost analysis of the various transactions it proposes. We have also demonstrated the benefits of using Blockchain for a secure and reliable healthcare system. We also highlight the existing challenges in adopting BC in healthcare data management, as well as potential future research directions, both in terms of deployment and real-world application.
Delay-Phase Precoding to Alleviate Beam Defocus Effect for Circular Arrays

Millimeter-wave (mmWave) and terahertz (THz) communications with hybrid precoding architectures have been regarded as energy-efficient methods to fulfill the vision of high-speed transmissions for 6G communications. Benefiting from the advantages of providing a wide scan range and uniform array pattern, uniform circular array (UCA) has attracted much attention. However, the growing bandwidth of mmWave and THz communications require frequency-independent phase shifts to perform beamforming, which can not be perfectly realized through frequency-independent phase shifters (PSs) in hybrid precoding schemes. This mismatch causes the beam defocus effect in UCA systems, where high-gain beams disappear at non-central frequencies. In this paper, we first investigate the characteristics of the beam defocus effect distinguishing from beam split effect in uniform linear array (ULA) systems. The beam pattern of UCA in both frequency and angular domain is analyzed, characterizing the beamforming loss caused by beam defocus effect. Then, the delay-phase precoding (DPP) architecture leveraging true-time-delay (TTD) is employed to mitigate the beam defocus effect. Finally, performance analysis and simulations are provided to evaluate the performance improvement with DPP architectures.
A Study of Few-Shot Image Classification Model Based on Contrastive Learning and Self-Attention

Few-shot learning aims to solve the problem of model training in scenarios with scarce data. In this paper, a small sample image classification model based on contrastive learning and self-attention is proposed. This method designs a supervised contrastive loss for the pre-training process of the model, which effectively improves the model's generalization performance. In addition, this paper proposes the use of feature maps to measure the similarity between images and utilizes self-attention mechanisms to match image patches, ensuring that the model accurately compares image regions with similar relationships and makes more accurate predictions. Experimental results demonstrate that these two improvements effectively enhance the performance of small sample classification models.
Finding SRLG-Disjoint Primary and Backup Route Pairs using k-SPF algorithm in Optical Networks

There is a strong demand for creating primary/backup optical path pairs between a source and a destination node in order to continue a service when the primary path has some disorder. We also have to consider SRLG (shared risk link group)-disjoint primary/backup path settings for achieving robust path protection against possible network problems on primary paths. To deal with these issues, in this study, we use the k-SPF (shortest path first) algorithm to determine primary/backup path routes between a specified source/destination node pair. We demonstrate that the proposed algorithm can find $k$ SRLG-disjoint primary/backup route pairs with much higher probability and with faster speed compared with other algorithms. In addition, the proposed algorithm can find primary/backup routes with nearly minimum route costs.
A Faster Multi-Cloud Provisioning Framework for Microservice Users

Microservices architecture has witnessed a significant surge in the field of software development and deployment in recent times. Microservices architectural approach involves partitioning services based on their characteristics to construct distributed systems, enabling the development of modern software systems. These characteristics can be seamlessly integrated with the distributed structure of multi-cloud environments managed by various Cloud Service Providers (CSPs). Therefore, we propose a framework for managing multi-cloud infrastructure and offer monitoring and management solutions for microservices. Our framework includes functionality for configuring cloud instances provided by CSPs and provisioning cloud nodes more quickly. In particular, our framework enables more efficient and rapid provisioning through CSP Pool, a multi-cloud Application Programming Interface (API) control system. As an example, we deploy a web page application across multiple clouds using Nginx to deliver microservices. Users can trust in the microservices provided in multi-cloud environment as they can receive faster and more reliable services.
Ensemble Feature Reduction Technique for Flower Species Identification

Flowers are the foremost part of our biodiversity and a major source of food and medicines. Flower species identification is popular in the anthology field, due to the numerous health benefits. The identification of flowers by conventional methods is a tedious task. The availability and ubiquity of digital technologies and advancements in pattern recognition techniques, let the idea of automated flower species identification a reality. Current works had many limitations such as high inference time, high dimensionality, low accuracy and increased memory consumption. This paper provides an optimized feature selection technique to accurately identify flower species using machine learning techniques in less time. The objective of work includes balancing of the ecosystem, medicinal purposes, the agricultural industry, environmentalists, educators etc. It is observed that by reducing 50% of features, a significant gain of 44.2% in inference time without loss of accuracy is achieved.
A Quad-Element MIMO Antenna System for UWB and C-band Communication Applications

A quad-element multiple input multiple output (MIMO) antenna arrangement for UWB and cellular (C-band) communication is suggested in this paper. Initially, a single component is constructed and reconfigured into a 4 element MIMO architecture. The introduced MIMO antenna is constructed on an FR4 material (ɛ = 4.3 and tanδ = 0.025) and has an overall dimension of 40×40×1.6 mm3 for a working band of 4.08 to 9.77 GHz. It possesses a peak gain of 4.37 dBi and an average isolation of -26 dB, with a maximum value of -39 dB. With an ECC of 0.002 and a diversity gain of 9.998 dB, the designed MIMO array exhibits exceptional diversity performance over the entire operational spectrum. The antenna's maximum radiation efficiency is greater than 83%, and its radiation patterns are consistent across the operational spectrum. The predicted MIMO's fantastic qualities make it a perfect fit for UWB and C-band wireless communications.
Software Fault Localization using Jaya Algorithm

Localizing the software faults is a complex and time consuming task during software development process. Spectrum based fault localization (SBFL) models are used to solve this problem. However, the existing SBFL models do not consider multi-faults in a software. In this paper, we propose a Jaya algorithm based framework for multi- fault localization in software. We have validated our framework on six multi-fault programs and compared the performance with existing SBFL models. The experimental results demonstrated that our proposed framework is providing better performance than some compared models using single fault as well as multi-fault programs.
Quantum Cascade Lasers in Infra-red Absorption Spectroscopy

The primary emphasis of this research paper centers on advancing spectroscopic methods utilizing midinfrared Quantum Cascade Lasers (QCLs). The aim is to apply these techniques in accurately measuring trace gases for environmental science, breath analysis, and determining absolute spectroscopic parameters. In the field of gas-phase analysis, the use of infrared molecular absorption spectroscopy is critical. This technology excels at detecting and tracking minute molecular entities with pinpoint accuracy and selectivity. The study of how electromagnetic (EM) radiation interacts with molecules or atoms, including processes like as absorption, emission, and scattering, is referred to as molecular absorption spectroscopy. Atomic spectroscopy is primarily concerned with quantum transitions manifested as changes in electron configuration. Molecular spectroscopy, on the other hand, studies transitions that occur within a molecule as a result of internal motions such as nuclei vibrations, rotations, and nuclear spin alignment. Because of their intricacy, molecular spectra are more intricate than their atomic counterparts. Furthermore, molecular spectroscopy provides useful information about the molecular framework, such as bond strength, length, the relative arrangement of its constituent atoms, transition dipole moment, and energy levels. These discoveries are made possible by the use of quantum mechanical models that describe the discrete distribution of energy levels within a molecule. This work aims to improve understanding of infrared spectroscopy (IR) by providing a foundational introduction to the principles of molecular spectroscopy. Furthermore, it provides a thorough explanation of commonly used spectroscopic detection techniques for tracking minute air components and trace chemicals in exhaled human breath.