Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed RIS-aided Joint Spatial Division and Multiplexing

In this paper, we investigate the performance of a distributed Reconfigurable Intelligent Surface (RIS) system as a solution to RIS-aided Joint Spatial Division and Multiplexing (JSDM) in Multi-User Multiple Input Single Output (MU-MISO) scenarios. JSDM is a well-studied massive MIMO (mMIMO) scheme in the literature, where two-stage beamforming at the base station (BS) is used to decrease the channel estimation overhead and mitigate interference. We consider a situation where several users with obstructed direct BS channels are being served through RIS-created links, giving rise to larger channel dimensions than conventional mMIMO systems. JSDM comes in handy in this case to overcome the increased estimation overhead. However, RIS-aided links suffer from rank deficiency which will be prohibitive in serving multiple users solely through these channels. The downlink of a single-cell MU-MISO system employing JSDM is investigated, where a distributed RISs set-up installed between the BS and the users is proposed as a solution to overcome the rank deficiency of the provided alternative link, making it reliable to serve several users through spatial multiplexing. Phase shift configuration of the RISs is optimized through a projected gradient ascent algorithm maximizing the deterministic equivalent of the sum rate expression, providing a low-overhead algorithm dependent on statistical channel state information (CSI) only. Monte Carlo simulations and analytical deterministic equivalent results show that despite the higher double reflection path-loss, distributed RISs systems with increased rank outperform the one RIS rank-deficient system, along with reduced CSI overhead and complexity as a consequence of JSDM.
. (2016). 3D Data Acquisition and Registration using Two Opposing Kinects. In 2016 International Conference on 3D Vision (3DV 2016): Conference Proceeding 25-28 October 2016, Stanford, CA, USA (pp. 128-137). Institute

We present an automatic, open source data acquisition and calibration approach using two opposing RGBD sensors (Kinect V2) and demonstrate its efﬁcacy for dynamic object reconstruction in the context of monitoring for remote lung function assessment. First, the relative pose of the two RGBD sensors is estimated through a calibration stage and rigid transformation parameters are computed. These are then used to align and register point clouds obtained from the sensors at frame level. We validated the proposed system by performing experiments on known-size box objects with the results demonstrating accurate measurements. We also report on dynamic object reconstruction by way of human subjects undergoing respiratory functional assessment.
Land Use and Land Cover Change Detection Based on Urban Spectral Indices: A Case Study of the Coastal Municipalities of Annaba Province, Algeria

Deriving accurate and timely data on land use and land cover change (LULCC) from satellite imagery is essential for sustainable coastal management. This research's objective is to assess, evaluate, and monitor the nature and extent of LULCC in the coastal municipalities of Annaba Province in Algeria using Landsat 7 and Landsat 8 images through the period from 2000 to 2015 and Sentinel-2A images between 2015 and 2019. The image differencing change detection technique was applied based on five urban spectral indices. Urban indices maximize sensitivity to the spectral characteristics of artificial (built-up areas, paved surfaces, mines, etc.) and bare land due to their spectral similarity compared to other LULC types. In the difference (change) images of the derived indices, the positive change pixel values essentially characterize an increase in artificial and bare land, except for one differenced index-the negative change values mainly indicate the same increase. The footprint of this increase was extracted using the random forest classifier, and the classification accuracy was assessed using a stratified random sampling design. The findings showed that the computed accuracy measures varied according to the used image data and spectral index from one municipality to another. For Annaba municipality, the obtained user's accuracy values range from 90.48 to 94.44% for Landsat data and between 70.59 and 80% concerning Sentinel-2A data. To enhance the estimated area proportions of the increase in artificial and bare land, certain changes in surface conditions, such as those in agricultural fields due to pre- and post-harvest conditions, were excluded using Geographic Information System (GIS) techniques. According to the accuracy assessment results, the image differencing approach based on the selected urban indices provided accurate detection of LULCC.
Routability-Driven Power Distribution Network Synthesis with IR-Drop Budgeting

Power distribution network (PDN) interferes with signal routing, in particular when the network is dense to support low supply voltage (Vdd) or tight IR-drop constraint. We address post-placement PDN synthesis with a goal of minimizing PDN strap width sums while IR-drop constraint is respected. The problem can be formulated as mixed-integer quadratically constrained programming (MIQCP) as we present. But PDN synthesis with MIQCP is not scalable if the network is formulated as a whole. Our solution consists of two steps: (1) A conditional generative adversarial network (CGAN) model is developed. It outputs a map of IR-drop budgets (intermediate target voltages) with routing congestion map and current map taken as inputs. (2) MIQCP is applied for each PDN metal layer one by one, with much lower computational complexity. Experiments on a few test circuits indicate that the proposed approach leads to 40% reduction in congestion overflow, compared to state-of-the-art.
GAPPO - A Graph Attention Reinforcement Learning based Robust Routing Algorithm

Routing algorithms, which determine how to deliver traffic from the source to the destination, are essential for next-generation networks and the internet. To make optimal routing decisions in complex network environments, researchers have leveraged Deep Reinforcement Learning (DRL) to design next-generation routing mechanisms. However, most existing DRL-based routing algorithms are implemented using traditional Neural Networks (NN), which lack robustness against topology changes. In this paper, we propose a novel algorithm called GAPPO, which integrates Graph Attention Network (GAT) and Proximal Policy Optimization (PPO) to optimize routing policies with the objective of minimizing end-to-end (E2E) latency in networks affected by link failures. To evaluate the performance of the proposed algorithm, we conduct a series of experiments on dynamically changing topologies with link failures under different loads. Experimental results demonstrate that GAPPO outperforms benchmark algorithms in both simulated and real-world networks, confirming its powerful robustness against link failures.
Monitoring of Off-grid Solar Power Plant Prototype for Residential Loads Based on Internet of Things

This research starts from the framework of thinking that off-grid solar power plants are systems that are only supplied by solar panels without any other types of generators, with battery components as storage media. The absence of a power supply when the PLN experiences blackouts in residential homes makes some electronic devices not function. In this study, we make a solar power plant prototype of an off-grid system on a residential load by making an ATS (Automatic Transfer Switch) system as a backup when PLN experiences a blackout. This research also uses Internet of Things technology, which can monitor solar power plants remotely using a web server. This research aims to design and manufacture a prototype of an off-grid solar power plant system at residential loads and to test the analysis of the results of planning an off-grid system prototype using a 15-watt light load and a 4-watt router. In this study, the results were obtained, namely that the off-grid solar power plant system as independent energy was designed and implemented on residential loads, and MK3P 11-pin relay was able to become ATS when the PLN experienced a blackout. The test results of the solar power plant prototype off-grid residential load system are as follows: taking the value of the energy data generated by solar panels for eight days, taking the results of the inverter test without load produces 3.5032 watts, testing the battery with the router load consumes 22.4 watts of energy, and a 15-watt lamp consumes 50.9 watts of energy. The ATS on the load and lamp is 0.97 seconds. The average error ratio on the wattmeter is 11.16%. Off-grid solar power plant systems can be utilized because of the potential off-grid solar power plant performance when PLN experiences blackouts.
Duty Ratio Optimization for a Multi-Level Polyphase Cascaded H-Bridge Grid-Interactive Inverter Using Model Predictive Current Control Based on Explicit Integration Algorithm

The model predictive current control is amongst the potent control strategies which have been used widely in the reformation of industrial varietal applications. It has the capacity to take many inputs and produce many outputs (MIMO) with its known through and through immaculate performance as well as its idiosyncratic stable control techniques. However, its requirements to solve an optimization challenge at each time step can be computationally intensive thereby affecting its real-time control application and impacting negatively on the system performance. In this paper, the idea of duty cycle optimization in accordance with the explicit integration approximation is introduced by application of zero and non-zero rectangular voltage in one control period to minimize current ripples in the grid-interactive system. In furtherance, the error minimization is chosen by the two-rectangular voltage duration. The throw-down total harmonic distortion and reduction in ripple current results are verified by experimentation and simulation.
An improved Photonic Crystal Resonator for Sensing Applications at 100 GHz

In this work, we compare two photonic crystal (PhC) resonators for biosensing applications at around 100 GHz. They are fabricated and characterized to study their sensing performances. Due to an introduced slot within the resonator and an optimized Q-factor of more than 6,000, the sensing capacity is greatly improved.
Impact of Profile Orientation and Position on Soil Resistivity Measurement for Earthing Applications

Providing an effective earthing design is one of the primary challenges in electrical engineering as it encompasses various parameters and factors of different types. Soil resistivity measurements and modelling are among the key aspects considered in earthing analysis, alongside many other important parameters. In light of this, the present paper aims to demonstrate the significance of soil resistivity in relation to earthing performance, while also highlighting the impact of profile selection on the measurements. The study presents and discusses an experimental investigation highlighting the impact of soil heterogeneity on current dissipation in earthing conductors. The experimental setup explores the selection of soil resistivity measurement axes using a Wenner configuration. Multiple scenarios are considered, and the obtained results are thoroughly analysed and discussed. The measurement process involves assessing soil resistivity at various electrode spacing distances, both vertically and horizontally, within an open area. Through these measurements, a comprehensive understanding of the soil structure's heterogeneity is gained. Findings unequivocally demonstrate that soil cannot be considered a uniform medium. This is evident from the observed variations in its structure at different depths and lateral distances. As a result, there is a need to reconsider other earthing design factors, such as the cross-sectional area of the conductors.
Reconstruction of Images with Finite Rate of Innovation from Noisy Tomographic Projections

In this paper, we propose a robust 2D tomographic reconstruction algorithm for classes of piecewise constant images whose edges are defined by curves with finite rate innovation (FRI). The curve is known to satisfy an annihilation relation associated with the derivative of the image. Given limited numbers of noisy projections and inaccurately known angles, unlike conventional methods that are directly reconstructing images from projections, we first recover the curve exploiting the frequency domain annihilation relation. Once the curve is retrieved, we leverage the spatial domain interpretation of the annihilation relation, and formulate the image reconstruction as a regularized optimization problem. To enhance the reconstruction quality, we further refine the angles given the reconstructed image and iterate the process. Experiments show that by doing so, we can robustly reconstruct the 2D image even from limited numbers of severely corrupted projections and highly inaccurate projection angles.
Response of Three Soil Water Sensors to Variable Solution Electrical Conductivity in Different Soils

Commercial dielectric soil water sensors may improve irrigation management by providing continuous field soil water information. The accuracy of these sensors may be compromised, however, when low quality, high electrical conductivity irrigation water is used. The magnitude of this effect is expected to vary among sensors depending on the sensor measurement frequency (f), decreasing with increasing f, thus providing a potential criterion for sensor selection. We examined the effect of varying solution electrical conductivity (σw) on the sensor‐measured dielectric permittivity (εs) of the following three soil water sensors: the WET (f = 20 MHz), 5TE (f = 70 MHz), and ML2 (f = 100 MHz). We also evaluated the efficacy of using a simple, cost effective, two‐point calibration technique (CAL) to adjust for differences among soils. We found that εs varied dramatically, in some cases more than 15 dielectric units, among sensors in different soils under uniform soil water and σw conditions. The relative εs response of the WET and ML2 sensors was consistent with their respective f, and the 5TE response was not. Thus, the WET εs was almost always greater than the ML2, while the 5TE was generally less than the ML2. In all cases, the ML2 εs could be described with a single, commonly used calibration equation. We also found that varying σw from 1.2 to 6 dS m−1 had little effect on εs, indicating that a single calibration may work for that range of irrigation water quality. Finally, we found that the CAL procedure, as evaluated with the RMSE, was effective for all soils and sensors.
Filtered Intra Template Matching Prediction for Future Video Coding

Intra template matching (intraTMP) is a block copy method for image coding which provides measurable gains for screen content. However, the tradeoff between the coding efficiency and complexity of this method for camera-captured content is less favorable compared to the screen content case. This paper proposes an enhanced intra template matching method which adapts the contents of a copied block to the local characteristics of the current block using an adaptive filter, to improve the performance of the tool for camera-captured content. Parameters of the adaptive filter are derived from the local neighborhood of the current block in both encoder and the decoder sides. Experiments illustrate that the proposed method provides 0.12% to 0.16% average luma coding gain over Enhanced Compression Model (ECM-7.0) exploration software under the common test conditions of the exploration study. Moreover, the proposed method improves the coding efficiency of the intraTMP tool in ECM-7.0 by around 50%.
Microwave Electromagnetic Shielding with Free-Standing Composites Based on Graphene Oxide and Silver Nanowires

With the growing penetration of microwave applications in modern society, exposure to electromagnetic waves (EMWs) may become an important public health problem. In addition, the coexistence of high frequency devices and systems induce electromagnetic Interferences (EMIs) leading to electromagnetic pertubations and lifetime reduction. consequently, shielding materials capable of blocking EMWs are highly needed. In this paper, composite materials based on graphene oxides and silver nanowires were studied as potential EMW shielding materials. Composites were obtained from graphene oxide (GO) with sheet size between 150 and 2000 nm and thicknesses of flakes in range 2–5 nm. Average thickness of free-standing films were around 14.8 $\upmu \mathrm{m}$. We studied the ratio between these nanomaterials to modulate shielding efficiency in a broad microwave frequency range up to 18 GHz. The shielding efficiency of around 6.5 dB was measured for the r-GO free-standing film.
Prediction of Software Security Vulnerabilities from Source Code Using Machine Learning Methods

One of the most significant problems in software engineering is the presence of security vulnerabilities in software. Attackers can exploit these vulnerabilities to gain unauthorized access to systems, leak information, corrupt data, and cause service interruptions. Therefore, in addition to developing secure software, the detection of existing security vulnerabilities in software is also considered as an important research topic. In this study, security vulnerabilities in the source code of software were predicted using machine learning methods. The OWASP Benchmark Test pocket was used as the dataset. This dataset consisted of Java codes and was utilized for training machine learning models Logistic Regression, Decision Tree, Support Vector Machines, K-Nearest Neighbors, and Random Forest. TF-IDF and Doc2Vec methods were employed to extract feature vectors from the source code. In the conducted experimental study, the highest prediction accuracy (0.97) was achieved using the TF-IDF feature extraction method and the Decision Tree, SVM and Logistic Regression algorithms.
Implementation of Smart Lab Using Speech Recognition in Indonesian Language to Control IoT Devices Integrated with Mixed Reality

The Internet of Things (IoT) based innovations have been widely applied in smart homes with several specific approaches, particularly in facilitating the operation of an IoT devices. This is the basis for the development of a smart lab system for laboratory rooms. This study proposes the use of speech recognition as one of the approaches that is expected to improve the ease of operation of IoT devices. The input used is in the form of voice commands from the user that recorded by microphone from a Mixed Reality Device, and then Azure Speech Service to convert it into text. The related text is managed with a Natural Language Processing program. The result of this processing is an action or command used to operate the IoT devices. Based on the testing results there are 3 testings that have finished. In the single command testing, the accuracy score obtained was 91,6 %, and the IoT device response time was 2045,677 ms. In the single command with multiple device and multiple room testing, the accuracy score obtained was 90,6 %, and the IoT device response time was 6677,51 ms. In the single command with multiple device and multiple room testing, the accuracy score obtained was 93,75 %, and the IoT device response time was 7734,16 ms. Based on all the testing indicating that the speech recognition system is running optimally to facilitate laboratory users in controlling all equipment in the laboratory to improve effectiveness and efficiency and prevent wastage of electricity.
Mapping of Studies on Ethical Responsibilities of Software Engineers

The need for software practitioners to assume their ethical responsibility is key to resolving moral dilemmas experienced by those affected by the negative outcomes of software development. However, for researchers to investigate the ways and means for software engineers to assume such responsibility, a constant understanding of the current state of affairs about this subject is necessary. This study was undertaken to survey literature authored from 1990 to 2022 to provide an overview of what is considered ethical responsibility owed by software practitioners. Out of the 14 selected articles for analysis, it was found that the majority are journal papers in the form of philosophical studies, whilst validation and experience papers were not found. The study mapped the identified stakeholders to ethical responsibilities owed to them by software practitioners. The results show that the Client/User is the group that software practitioners are mostly responsible to. The small number for papers (14) identified for this study suggests fewer researchers are investigating this area. We recommend empirical studies that suggest, validate, and evaluate solutions that facilitate the assumption of ethical responsibility to improve software development practices.
Prediction of Stock Price Using Machine Learning Techniques

The paper presents a study on stock price prediction using a Long Short-Term Memory (LSTM) model and other machine learning techniques like Random Forest (RF). The LSTM model was trained on historical stock price data and technical indicators to forecast future stock prices accurately. Evaluation metrics demonstrate the LSTM model's superior predictive accuracy compared to benchmark models. The findings highlight the potential of LSTM and machine learning for stock price prediction, offering implications for financial analysts and traders. The results show that both RF and LSTM can predict stock prices reasonably, with LSTM performing slightly better than RF. Through LSTM, R2=0.99, MSE=0.029, RMSE=0.49 has been calculated. The work suggests that deep learning methods such as LSTM can be a promising approach for stock price prediction, especially when dealing with complex and noisy data.
Genetic Algorithm for Microwave Computer-Aided Design: The State of the Art

The design of microwave integrated circuits is complex and has traditionally been done by highly experienced designers. Although several electronic design automation (EDA) tools allow for addressing some complexities of these circuits, they do not always make it possible to optimize the performance objectives of the circuit sought by the designer. The genetic algorithm (GA), a multiobjective optimization evolutionary algorithm, has been used in the design of analog components and circuits for a few decades. The algorithm is robust and efficient and surpasses classic optimization techniques based on numerical methods in many applications. Limitations of the GA include the need to predefine a circuit topology that can achieve the desired objectives and the considerable computing resources required when the algorithm is to perform circuit synthesis. Like digital design, the trend in analog design is towards more automation, which reduces the design complexity, cycle and cost and improves optimization capabilities. This survey showed that the new generation of EDA tools will be based on machine learning and multiple optimization techniques, including evolutionary algorithms such as the GA, a direction taken by several mainstream EDA suppliers.
On the Optimization of UAV-Assisted Wireless Networks for Hierarchical Federated Learning

This paper considers an unmanned aerial vehicle (UAV)-assisted Hierarchical Federated Learning (HFL), where UAVs act as intermediate aggregators. We formulate an optimization problem that aims to find the best UAV placements, user-UAV associations, channel assignments, and user selection to minimize the time needed for HFL to achieve a target learning accuracy. We propose a two-phase approach to solve the problem. The first one deals with the UAV placement using the K-means algorithm. For the second phase, we propose a user association and selection algorithm that prioritizes selecting users who can make a time-efficient and significant contribution to the FL training process. Simulation results show the proposed algorithm’s efficiency and ability to reach the target learning accuracy faster than the considered benchmarks.