Miracle-dz/UniFAR-FTUs huggingface dataset:
Power Quality Monitoring Algorithm of Substation Based on Decision Level Fusion

Traditional substation power quality monitoring algorithms are easily affected by data truncation, resulting in significant errors in power quality monitoring. For this purpose, a substation power quality monitoring algorithm based on decision layer fusion was designed. We collect and process power quality monitoring data in the station area, measure power quality monitoring indicators in the station area through the decision fusion method, generate power quality monitoring algorithms in the station area, and achieve monitoring of power quality in the station area. The experimental results show that the designed algorithm for power quality monitoring has small errors, good performance, efficiency, and certain application values, which can be used as a reference for subsequent power supply and distribution planning in substations.
Multi-Agent Hierarchical Decision Optimization Method Based on Curriculum Learning

The huge state space and varying action space will make the decision-making be a grand challenge for reinforcement learning. To address this problem, we propose a multi-agent hierarchical decision optimization algorithm, which utilizes macro-action abstraction based on expert experience knowledge and hierarchical abstraction based on hierarchical reinforcement learning theory to decompose the combat task into different hierarchical sub-task modules. The alternating update method is used to learn and train the network. Meanwhile, combined with the curriculum learning method, the training process of the neural network model can be improved. The algorithm is validated in the full-length game of StarCraft II. When playing against the built-in AI of level 7 difficulty, it achieved a win rate of over 93%. The experimental results show that it can effectively improve the collaborative ability of the decision-making model when the state space and the optional action space are huge and the decision-making process is complex.
Multi-UAV Formation Obstacles Avoidance Path Planning Based on PPO Algorithm

The path planning of multi-UAV formations plays a crucial role in various scenarios, including rescue operations, reconnaissance missions, and target strikes. This paper investigates a method for multi-UAV formation obstacle avoidance route planning based on deep reinforcement learning. The proposed method allows UAVs to efficiently plan routes in complex and dynamic three-dimensional obstacle environments. The autonomous decision-making of multiple UAVs is realized by using the proximal policy optimization (PPO) algorithm. In addition, in order to achieve formation obstacle avoidance, we design a reward function to assess the advantages and disadvantages of UAV strategies. Finally, an environment with dynamic obstacles is constructed and trained using the method. Experimental results show that the method is feasible and generalizable.
Design of Low Delay and Low complex Parallel Reconfigurable Filter bank for Hearing aid

Hearing is one the most important sense organs of any human being. It connects the people to the environment around them. Hearing aids provide an opportunity for the hearing disabled people to listen to the environment. However design of a qualitative hearing aid with low cost enables more number of people to utilize the hearing aids for their bright life. Although there are several hearing aid filterbank designs available, it has constraints towards quality, cost, delay and reconfigurability. In this paper a qualitative filterbank design is proposed that has low matching error which leads to good audiogram matching. The proposed design has reduced number of multipliers using half band FIR filter. This reduces the hardware complexity as well as cost of the hearing aid. Also the proposed method is implemented with parallel processing of low pass and high pass bands leading to reduction in the delay to receive the sounds by hearing aid. This avoids interference between speech and visual integration of lip movement. Reconfigurability of the design is one of the important aspects that help in designing the filterbank with different number of bands in different regions (Low pass, High pass and Band pass regions) as well as with different bandwidths. This provides an advantage of using the same filterbank design for different audiograms with different levels of loss in hearing without changing the filter structure.
Simulation and Economic Savings Study of Solar Renewable Systems for a House

The move to sustainable energy is a global challenge that requires the advancement of renewable sources. Photovoltaic and solar thermal systems are promising clean energy technolo-gies. Mathematical models crucially improve our understanding of the performance of these systems. This study focuses on the development of approximate models to estimate the performance of photovoltaic and solar thermal systems in different environments, improving their efficiency and reliability. The models are intended to approximate cost reduction through the promotion of renewable energy and the mitigation of climate change. Using Simulink®, these mathematical models are validated through comparisons with real equipment, showing a maximum error of 6.57%.
Frequency Diversity Array Radar Target Imaging Base on the Joint Design of Intra-Array and Inter-Array Frequency Offset

The range-angle coupling of frequency diversity array (FDA) beampattern will cause ambiguity problem. To solve the problem of coupling, an FDA structure based on the joint design of intra-array frequency offset and inter-array frequency offset is proposed, which the intra-array transmits multiple groups of signals with different frequency offset, and the inter-array frequency offset uses a linear or nonlinear design to focus the transmission energy on the desired range-angle region to achieve the range-angle decoupling. With the designed FDA structure, the degrees-of-freedom (DOFs) in both range and angle domains can be obtained simultaneously, and the range and angle parameters of the target can be determined, to solve the target's ambiguity problem and realize multi-target synthetic aperture radar (SAR) imaging.
Design of Key-dependent S-Box using Chaotic Logistic Map for IoT-Enabled Smart Grid Devices

In the era of pervasive computing, a generation of lightweight ciphers was needed to provide near-optimal security; officially, NIST announced the standard lightweight cryptographic primitive. The newly announced standard, ASCON, is based on a 5-bit input and 5-bit output Substitution Box (S-box). This paper proposes a key-dependent S-box methodology for small-size S-box for Internet of Things (IoT) devices widely used in many applications such as Smart Grids, healthcare, etc. We aided this study with the chaotic logistic map to generate a robust S-box. The key-dependent S-box can add an extra layer of security to the cryptosystem specifically for lightweight devices at the minimal cost of generation time of a new S-box. We have investigated the proposed methodology with cryptographic properties of S-box, such as Differential Probability (DP), Linear Probability (LP), Nonlinearity, Strict Avalanche Criteria (SAC), Boomerang Connectivity Table (BCT), Key-space analysis, and implementation on Arduino. We also compared the proposed methodology with other state-of-the-art (SOTA) 5 – bit input and output S-boxes. Experimental results witness the robustness of the proposed study.
Design of a Tri-Band Patch Antenna with Metamaterial Aperture Ground

This article reports on a unique four-arm metamaterial-loaded antenna with a metamaterial aperture ground at sub-7 GHz for 5G applications. The radiator is etched on Rogers-3003, a flexible substrate with a height of 1.52 mm and a r value of 3. To increase the gain of the suggested antenna, a metamaterial aperture is created on the opposite side of the substrate using a stripline structure. CST Microwave Studio 2019 simulates the suggested design. At 5.896 GHz, 7.5703 GHz, and 8.641 GHz, respectively, the findings are multiband.
Diagnosis of Pneumonia Using Distribution of Pertinent Microstructures: An Application of Machine Learning

Detection of patients with pneumonia is an immensely important task that requires expertise and hence may prove costly. The automated methods of Chest X-ray analysis, especially pneumonia have gained attention in the last few years. This reduces cost and time and can be used for better visualization. In recent years the image processing community has worked on this problem with varying rates of success. This paper takes the work forward and applies Chest X-ray imaging to the diagnosis of pneumonia using the proposed Machine Learning pipeline, which involves pre-processing, extracting features, and selecting pertinent features. The feature extraction has been done using the Local Binary Pattern, and feature selection using the Fisher Discriminant Ratio. The proposed model has lesser memory and space complexity as compared to the often-used Convolutional Neural Network models which are Deep Learning models which do not require explicit extraction and selection. Furthermore, our model can handle imbalanced data gracefully. The results are encouraging and the proposed model may prove useful for medical eternity. Our model gives 87.3 % accuracy, 87.4 % sensitivity, and 87.1 % specificity. This model prepares the ground for Machine Learning to be used in the diagnosis of pneumonia. We are also working on another work that combines Conventional Machine Learning with Transfer Learning to accomplish this task.
Approaches to the Development of a Software and Hardware Complex to Collect and Process Data from Open Packets of Wi-Fi Networks

The paper studies a practical implementation of a hardware and software complex for analyzing and collecting data from open packets of Wi-Fi networks (IEEE 802.11 a/b/g/n standard). The main technological limitations of such systems are given and the principal architectures for their creation are proposed, including examples of hardware and software configurations. Examples of operation of such systems and requirements for business applications are given.
Nutrient cycling in a controlled reduced tide (CRT)

The Scheldt Estuary is known for several reasons. Most famous is its function as passage way to the Antwerp harbour. From an ecological point of view the river Scheldt is unique because it is one of the longest tidal rivers in Europe (VLIZ, 2012), still having a valuable ecosystem of fresh water tidal flats and tidal marshes. Tidal areas are known as essential habitats for many birds, fish, benthos and plants (Maris et al., 2007) and they give protection against floods. Tidal flats and marshes have several ecological functions such as providing extra oxygen in the water, the conversion, retention and removal of nitrogen (Gribsholt et al., 2007) and a regulating and recycling function of silica (Struyf et al., 2006). in the last century the tidal range in the Scheldt has risen by 60cm at Antwerp (Sigmaplan, n.d.), increasing the risk of inundations and the loss of marshes due to erosion (ANB, n.d.). Therefore the Flemish ‘Geactualiseerde Sigmaplan’ started with a pilot project to examine whether it is possible to create nature in combination with protection against flooding. The pilot project Lippenbroek uses a former polder to investigate the possibility of combining the creation of flood control areas (FCA) with a controlled reduced tide (CRT). A controlled reduced tide is introduced in a flood control area by placing a sluice system. In this way the natural tidal system is imitated. In march 2006 the sluices opened permanently and the polder evolved to tidal flats and tidal marshes. The Ecosystem group of the Antwerp University coordinates the research programme OMES (Research on the environmental effects of the SIGMA plan). Since the opening (2006) they have gathered extensive data on basic water quality (temperature, conductivity, O 2 , turbidity, pH) and on nutrients such as total dissolved nitrogen (TDIN), organic nitrogen, Dissolved Silica (DSi), Biogenic Silica (BSi), Total Silica, phosphate, total phosphorus,. (Maris & Meire, 2011). With the gathered data from the OMES project it is now possible to draw up the mass balances of nitrogen (N), phosphorus (P) and silica (Si). These balances will be used to explore relations between the removal of N, P or Si and the influencing factors such as the height of the tide, the surface of flooded land, the seasons, the flooding frequency, the type of vegetation,. In the next stage of the research a hypothesis will be formulated and researched by sampling at Lippenbroek. An example of research could be: comparing water from a reed marsh with water from a bare part of the marsh. The processing of the water samples will be done in the same way as in the OMES programme, so it will be possible to compare the data. The aim of this research is to find methods for improving existing CRT’s and new CRT’s in order to increase the nutrient cycling and thus the water purification. The central research question for this research is therefore: ‘Are there methods to adjust existing or future CRT’s to improve the nutrient cycling? ’
Surplus Data Prediction and Classification of Textual-Data Using Machine and Deep Learning Comparative Analysis

In recent years, several classification techniques have become available to categorize texts efficiently. In the field of machine learning, the construction of classifiers involves analyzing and learning from well-labeled datasets with specified categories. Likewise, deep learning's performance improvement in the text categorization sector comes from its ability to achieve high precision with a less complex setup and processing. This work emphasizes machine learning, and deep learning techniques are used to classify textual data. On account of the fact that textual data often carries a significant amount of unnecessary data, pre-processing is a crucial step, e.g.,. fill in the blanks and remove duplicate rows, then clean up the data. Following this, deep learning algorithms such as bidirectional long shortterm memory (BiLSTM), recurrent weighted average (RWA), and conditional random field (CRF) are used for classification, along with traditional machine learning algorithms like naive Bayes, gradient boosting, and support vector machines (SVMs). This work reveals that BiLSTM turned out to be the most accurate, achieving a classification accuracy of 98.5 %, outperforming both other models and baseline investigations.
Fog Computing: Applications in Smart Healthcare

Internet of Things (IoT) applications in healthcare are revolutionizing conventional medical procedures through the integration of networked devices that gather real-time patient healthcare data. By relocating computational resources towards the network edge, fog computing provides an architectural basis that fortifies IoT technologies as there is minimal waiting for responses, less latency, and no bandwidth problems brought on by sending massive volumes of data to far-off cloud servers. The primary application domains where fog computing is transforming smart healthcare are highlighted in this study. The incorporation of fog computing for enabling real-time analysis and processing of information generated by IoT devices is an important milestone in this area of study. This paper investigates the concept of fog computing-based smart healthcare IoT applications, including its architecture, different application domains, and challenges that need to be addressed before it can be implemented. Improved patient outcomes, faster treatments, economic improvements and better decision-making processes are just a few advantages of fog computing. The healthcare industry can develop ground-breaking solutions that improve patient care and modernize healthcare practices by leveraging the capabilities of fog computing at the network edge. This article addresses how fog computing could impact intelligent health-related IoT applications by outlining its architecture, exploring several healthcare-related application domains, and assessing utilization challenges.
Energy Expenditure Estimation for Schoolchildren Using Accelerometers

As societal emphasis on health intensifies, the accurate estimation of energy expenditure (EE) has become crucial in countering obesity, monitoring activity levels, and fostering improved lifestyle habits, particularly amongst adolescents and the elderly. Extant literature presents several limitations: 1) Predominant focus on adults, leaving a research void concerning middle school students. 2) Concentration on everyday activities, largely overlooking typical school-based physical exercises that students regularly engage in. 3) Limited use of sensors, thereby often failing to accurately capture and delineate energy expenditure and movement across varied body parts. This study seeks to bridge these gaps by focusing on the EE assessment during common physical activities performed by middle school students. We employed seven distinct sensors, strategically positioned on various body parts, to record accelerometer, gyroscope, and magnetometer data from 120 middle school students participating in 10 activities, such as dribbling around poles in soccer, basketball shooting, and badminton. This comprehensive approach ensured a more precise capture and estimation of activity dynamics. Utilizing an Enhanced-GRU network structure, we processed the heterogeneous data from multiple sensor locations, achieving a commendable activity classification accuracy of 98.8% and a mean squared error (MSE) of 0.51 METs in EE prediction. Furthermore, an evaluative comparison of single sensor location revealed that the chest, along with left and right hips, yielded the most optimal results.
Metagenomic Binning based on Unsupervised Extreme Learning Machine

Metagenomics studies the genetic information of microbial communities in different contexts. As metagenomic DNA is often fragmented and then sequenced into small reads, these reads can be assembled into longer sequences called contigs. An important step in the metagenomic analysis pipeline is Binning, which corresponds to the classification (supervised) or clustering (unsupervised) of reads or contigs. In the case of unsupervised Binning, several Machine Learning algorithms that use DNA sequence descriptors, such as k-mers Frequency and GC Content to perform clustering, have been employed. This paper proposes the use of Unsupervised Extreme Learning Machines (US-ELM) for Metagenomic Binning. The experiments use three datasets with different numbers of species present, and compare the results obtained by US-ELM with respect to the k-means and Maximization Expectation (ME) algorithms. The performance comparison employed metrics widely used in the problem, such as Accuracy, Rand�s index, and Clustering Computation Time. From the experiments, we can see that USELM windenly outperforms the other two clustering methods in accuracy. In terms of computational cost, US-ELM is comparable to k-means, and both algorithms are much faster than EM. Numerical results show the interesting potential of the US-ELM algorithm in the metagenomic binning problem.
Synthetic Information and Digital Twins for Pandemic Science: Challenges and Opportunities

Understanding complex systems requires understanding interactions between different domains and different scales. Pandemic science serves as an exemplar of such complex systems. During the COVID-19 pandemic, a significant amount of health surveillance infrastructure had to be created on the fly. This infrastructure, while useful in many cases, was unable to provide individual-level data across relevant domains due to limitations and privacy barriers. Finding technical solutions to these barriers requires a careful evaluation at scale. Synthetic information (sometimes known as digital twins) coupled with detailed mechanistic models are potent, but underutilized, tools for representing and analyzing complex societal systems. In this paper, we describe how synthetic information can be used to evaluate these technical solutions, and thereby support pandemic preparedness and response. As an illustration, we describe a problem and synthetic data sets that have recently been successfully used as part of a joint US-UK challenge on evaluating privacy-enhancing technologies (PETs). We additionally describe several key decisions we had to make, particularly in navigating the boundary between realism and artificiality. This competition is part of a larger vision for using synthetic data to break the vicious cycle of data stagnation. However, to fully realize this vision for a larger set of domain areas will require advancements in several domains, as we describe in this vision paper.
Towards Peer-to-Peer Split Learning

In this paper, the $N$-split learning scheme is presented. This scheme allows for learning a feed-forward deep neural network on devices with limited memory for model and data. The central idea in this scheme is to divide the model and data over different devices. The model is split layer-wise (in subsets of consecutive layers), while the data is split homogeneously. The device with the first few layers starts the forward pass of the backpropagation algorithm for a training sample from its data subset and passes the output to the device with the next subset of layers. This process continues till the last layers are computed. The last device to compute the forward pass then computes the backward pass, and the process proceeds in the reverse direction, thereby calculating a part of the gradients. The gradients are accumulated for a batch of samples, and the parameters are updated. After completing an epoch, the devices perform a swap of the layers, and the above process starts again. It continues till the model is trained satisfactorily. In an improved variant, called Fast $N$-split learning, the forward and backward passes are considered separately and possibly done on different devices to gain from pipeline parallelism. The layers performing the forward pass store the output in a buffer, which will be used by the devices performing the backward pass. The devices performing backward passes communicate the update for parameters to the devices doing forward passes. As earlier, this continues till the model is trained satisfactorily. Analysis, routines for determining the optimal split for both schemes and simulations are presented for corroboration.
Global Ionospheric Total Electron Content Forecasting Model Using Temporal Convolutional Network

The ionosphere is a crucial component of Earth's atmosphere, with significant impacts on radio transmission, satellite navigation, radar surveillance, and other applications. Total Electron Content (TEC) in the ionosphere is one of the vital parameters used to describe ionospheric activity and characteristics. Due to limited observational methods, we often need the TEC predictions based on historical TEC data. In this study, we employ the Temporal Convolutional Network (TCN) architecture to construct a global ionospheric TEC prediction model. We compare the TEC predictions from the TCN model under different ionospheric activity conditions with those from time-series models and deep learning recurrent structure models. The research findings indicate that the TCN-based model offers higher prediction accuracy and greater stability for global ionospheric TEC forecasts.
Subsistence Farmers' Perceptions of Pluralistic Agriculture Extension in Northern Ghana

Subsistence Farmers’ Perceptions of Pluralistic Agriculture Extension in Northern Ghana by Amos Kwame Egyir Baah MS, University of Maryland University College, 2004 BS, University of Maryland College Park, 1996 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Public Policy and Administration