Miracle-dz/UniFAR-FTUs huggingface dataset:
State Observer Under Multi-Rate Sensing Environment and Its Design Using l2-Induced Norm

Since the sensing period of a signal generally depends on the used sensor, it may be different from another, even in a single control system if it involves multiple sensors. This paper investigates a design problem of state observers for linear time-invariant discrete-time plants under a multi-rate sensing environment. The sensing periods of the sensors in the plant are assumed as mutually rational ratios. First, we characterize a state observer for a plant with multi-rate sensing as a periodically time-varying state observer. Then, we discuss the $l_{2}$ performance analysis of a state estimation error with the given periodically time-varying state observer. A linear matrix inequality (LMI) condition is provided for the analysis. By extending the LMI condition for analysis, we also provide that for multi-rate observer synthesis. Finally, we numerically illustrate the effectiveness of the proposed multi-rate state observer. Even if all the sensors have the same period, the sensing timing is not unique. Therefore, we numerically analyze whether the performance changes when the observation timing between multiple sensors is different.
Inverse kinematics associated with the degree of freedoms and constraints of shoulder models during the middle direct punch

The shoulder complex consists of various joints which correspond to specific degrees of freedom (DOF). Musculoskeletal modeling is a method to reconstruct the real motions that involved a big challenge to simulation accuracy. Different shoulder models with various DOF and constraint definitions are available although the accuracy of the simulation is still debated. Thus, the objective of this study was to evaluate DOF and constraint definitions of shoulder models for inverse kinematics (IK) simulation during the middle direct punch (MDP). The experimental marker data in twenty elite martial arts players (65.4±5.8 kg, 172±7.8 cm, 29.5±8.5 years) were collected during the MDP. Four models were chosen as base models (M3, three-DOF between humerus and trunk Glenohumeral joint; M9, three-DOF for Scapulothoracic joint, three-DOF for Acromioclavicular joint, and three-DOF for Glenohumeral joint; Mst, coupled motions for scapula, clavicle, and humerus; Mel, an ellipsoid mobilizer for the Sternoclavicular joint). The subject-specific models were generated for each subject and model by marker data in static posture and scaling tools of OpenSim. The inverse kinematics tool of OpenSim was used to reconstruct MDP motion via models. RMS of marker error was used to compare models as indicators. Results represented significant differences in RMS of marker error for various models during the MDP tasks (P<0.05). Results illustrated the RMS of marker error for model Mel was minimum (12.87±0.09mm), whereas the RMS of marker error for all subject-specific models was lower than generic models. Based on the results the best model is model Mel which indicated this musculoskeletal model of the shoulder complex can reconstruct the MDP motion better than other represented models. In addition, our results indicated using the subject-specific model instead of generic models is vital to access reliable results. The results of this study would be utilized by sport science researchers, sports medicine doctors, coaches, trainers, and players to simulate shoulder motion by musculoskeletal modeling.
Deep Learning Techniques for Visual SLAM: A Survey

Visual Simultaneous Localization and Mapping (VSLAM) has attracted considerable attention in recent years. This task involves using visual sensors to localize a robot while simultaneously constructing an internal representation of its environment. Traditional VSLAM methods involve the laborious hand-crafted design of visual features and complex geometric models. As a result, they are generally limited to simple environments with easily identifiable textures. Recent years, however, have witnessed the development of deep learning techniques for VSLAM. This is primarily due to their capability of modeling complex features of the environment in a completely data-driven manner. In this paper, we present a survey of relevant deep learning-based VSLAM methods and suggest a new taxonomy for the subject. We also discuss some of the current challenges and possible directions for this field of study.
An Optimized Twelve Sector Space Vector Pulse Width Modulation control of PMSM Drive

Optimal signal generation and accurate on-time determination of switches are essential for the improvement of an inverter feed drive’s steady-state and transient responses. This work combines a simple classical optimal current generation process with sector segmentation of six SVPWMs to improve drive performance. An average of two adjacent states is used to get six virtual states to segment six sectors into twelve sectors. Simulation is carried out in Matlab-2021 to see the performance of the proposed system. A simulation has shown that the control method is efficient in controlling the speed dynamics and steady-state error. In addition, the ratio of harmonic power loss to total active power loss of the proposed system is less.
DeepSen: A Deep Learning-based Framework for Sentiment Analysis from Multi-Domain Heterogeneous Data

People usually express their emotions, views, or sentiment in textual form. The textual sentiment analysis (TSA) identifies or classifies opinions or feelings from texts in a predefined class. The TSA is complicated or infeasible manually due to its voluminous nature and unstructured or messy conditions. Therefore, the automatic sentiment analysis method quickly paves the way to identify the hidden sentiment polarity from the textual content. Although a few studies on sentiment analysis were conducted on a single or specific domain, developing the TSA method concerning multi-domains is unexplored in Bengali. This paper presents a deep learning-based framework called DeepSen to detect textual sentiment from Bengali texts into three polarities: positive, negative and neutral. Four benchmark corpora from available domains, Book, Restaurant, Drama and Cricket, have been used to analyze sentiment from multi-domain heterogeneous data. This work investigates six popular machine learning (LR, DT, MNB, SVM, RF, AdaBoost) and five deep learning (CNN, LSTM, GRU, BiGRU, BiLSTM) techniques using four benchmark Bengali corpora to perform TSA tasks. The evaluation result reveals that the BiLSTM method obtained the highest weighted f1-score (0.85) among all models.
Accurate Reconstruction of Electrical Impedance Tomography Images Using Genetic Algorithm

One of the methods to solve the inverse problem of electrical impedance tomography is D-bar method, which is the direct and no repetition solution. In this method, a specific operator called a is used. In the D-bar integral equation, range of integration is considered a circle with the center of origin of coordinates and radius R. This integration is non-zero value only for $k$ ≤ | $R$ |. In this equation, the used radius R, is effective in the resolution of the reconstructed image. In this paper, we used genetic algorithm to obtain the best radius to produce the optimized image with minimum mean square error. Therefore, the considered model was meshed up to three stages, by increasing the discretization nodes and implemented genetic algorithm was evaluated on it. We showed that the optimum radius was increased and mean square error was decreased at each step.
Malicious Node Identification for DNS Data Using Graph Convolutional Networks

Graph Learning has gotten increased attention nowadays. The machine learning fraternity is working towards application of Graph learning coupled with deep neural networks to design and develop state-of-the-art models for problems where data can be represented as knowledge graphs. The simplicity and effective visualization makes this more attractive. DNS is such a huge dynamic network where millions of nodes and connections are available. Thus it is always preferable to use a large dataset for analyzing DNS. In this paper, we introduce the largest ever reported knowledge graph for DNS- node classification problem which has over 6.23 million nodes and 93 million connections. The results and model performance are found to be promising and interesting. We have got an accuracy of 0.95 with a simple GCN model. The dependency of model performance upon feature selection and type of edges (graph density) is also studied in detail. It is evident that choosing a valid set of features improves the model performance phenomenally whereas addition of less important edges, just to make graph dense contribute at a lesser rate towards the model performance improvement.
Screening Pathological Abnormalities in Gastrointestinal Images Using Deep Ensemble Transfer Learning

Globally, gastrointestinal cancers including colorectal and esophageal cause a substantial amount of deaths every year. Early detection of these disorders requires an accurate, rapid, and automated diagnosis approach. Almost all prior deep learning-based gastrointestinal duct analysis research was restricted to polyp identification. However, esophagitis, ulcerative colitis, and numerous pathological findings of gastrointestinal organs must be analyzed together with polyps. This study focused on the detection of pathological findings in gastrointestinal images. A deep ensemble transfer learning approach was introduced to screen pathological findings. Initially, the eight EfficientNet members were used separately to train pathological findings. The ensemble networks were then constructed by incorporating a number of modified softmax averaging mathematical formulae. The ensemble’s efficiency and efficacy across individual networks were then verified quantitatively. The proposed ensemble network correctly predicted samples misclassified in the individual networks. Consequently, it has a 96.40% accuracy, 96.60% precision, and a recall of 96.40% for pathological findings. The proposed work surpassed nearly all recent comparable research in terms of accuracy and efficiency.
Analysis and Visualization of Eye movements in Patients with Glaucoma

Glaucoma is one of the foremost causes that result in irreversible blindness. World Health Organisation (WHO) has estimated that around 4.5 million people are blind due to glaucoma. This vast number is because more than 90% of glaucoma cases are undiagnosed. Early-stage glaucoma detection system helps to identify the disease and prevent it from further blindness. The proposed system analyzed and visualized the eye movement patterns of glaucoma-affected participants using machine learning algorithms. The model collected eye-gaze patterns using an eye-tracking sensor and visualized them based on different eye gaze features. The model classified glaucoma and normal based on eye gaze data. The proposed model has incorporated different visualization techniques to understand how eye gaze patterns of normal and glaucoma differ while performing screen-based tasks such as Free-Viewing Task and Image Searching Task. The proposed system has also incorporated an interactive dashboard using Microsoft PowerBI Software.
Solving the House Numbering Problem in Nigeria: Internet of Things (IoT) As An Emerging Solution

House numbering is the act of assigning a unique number to each building in a street or area in order to make it easier to locate a specific building. Due to poor town and regional planning, street naming and house numbering are major challenges in Nigeria. The disadvantage is being unable to identify a specific house in a location. The purpose of this study is to use the Internet of Things (IoT) as a solution to address the issue of house numbering, specifically in the Ojo Local Government Area of Lagos State, by identifying houses on the street, numbering them, classifying the type of building, and storing the data in a database. The study employs a machine learning technique, the k-nearest neighbor classifier, to train and program the IoT device, with fifty houses serving as a case study. The work was tested using fifty houses to name the street, number the houses, and categorize them into five major groups. The use of Google Maps aided in determining the name and location of a street. The success rate was as high as 0.97 for training and testing data, indicating that the technique used is adequate to address street name and house numbering problems.
UHF PD Imaging Based on Fusion of Positioning Information and Video Information

Ultra-high voltage transmission technology plays an important role in optimal energy allocation and large-scale clean energy consumption because of its advantages of large transmission capacity, long distance and high efficiency. In order to ensure safety and reliability of power system, high-performance and reliable insulation materials are necessary to be used. It is also vital to monitor insulation condition of power system and master operation condition of power system in real time. Since there is rich insulation state information in partial discharge (PD) signal, it is widely used to detect insulation defects in real time and avoid insulation breakdown. Nowadays, research on positioning and imaging of PD is deficient, and relevant research on visualization of PD information is indispensable to be presented. Therefore, this paper proposes an UHF PD imaging method based on fusion of positioning information and video information, which is implemented based on FPGA.
A decision-making system for medical diagnosis based on iterative nearest component analysis and optimized learning

A silent disease is a chronic illness without clear clinical symptoms, and is diagnosed at an advanced stage when there is irreversible damage. Increasingly, automatic machine methods are used for early diagnosis to reduce complications associated with diseases. The performance of previous automated methods, however, was plagued by uncertainty, lack of generalizability, and unreliability. Using the feature aggregation approach and optimized learning, this paper proposes a hybrid model incorporating iterative neighborhood component analysis (iNCA). Using the support vector machine (SVM) algorithm, which has been optimized from the water cycle algorithm (WCA) in the direction of classification, the best results have been reported from the classification of several types of diseases. A key feature of the WCA algorithm is its ability to find the global optimum. When selecting features, the method works rapidly and selects the subset of features that has the lowest error level. In this research, accuracy of diagnostics will be improved and the effects of overfitting will be reduced. We obtained the desired medical data from the UCI database, which contains diseases such as hepatitis, diabetes, kidney failure, and breast cancer datasets. As compared to similar methods that have been published in the last few years for automatic detection of silent diseases, it can be predicted that the results will be better.
Named Entity Recognition Using Transfer Learning with the Fusion of Pre-trained SciBERT Language Model and Bi-directional Long Short Term Memory

The foundation of the study of medical texts and one of the primary information extraction sub-activities is biomedical named entity recognition (BioNER) which brings out important named structures such as diseases, gene, protein, species etc. from biomedical text. Although the BioNER task is fairly comparable to regular NER, it is more difficult to identify biological named entities because of their unique nomenclatural features. In this regard, we present an architecture for neural network using transfer learning with the fusion of pre-trained SciBERT language model and bidirectional long short term memory (BiLSTM). First, the paradigm uses SciBERT, which performs better on biomedical tasks than BERT base, and a Bi-LSTM network in order to gather more comprehensive context data. Ultimately a conditional random field (CRF) layer combined with Bi-LSTM is used to model the dependency between each state and the entire input sequences. At the end, we have evaluated our system on a benchmark dataset named NCBI-Disease corpus. The suggested model has a competitive effectiveness for the NCBI-Disease possessing a 91.00% f1-score, 89.00% precision, 93.00% recall, and 98.00% accuracy. Therefore, with the usage of pre-trained SciBERT model and no feature engineering, our methodology can improve the neural network’s capacity to extract significant information.
Minimum Complementable Coprime Array Design Based on Undirected Adjacency Graph

A coprime array (CA) consisting of two subarrays is of considerable interest in the low redundancy linear array design. As known, discontinuous sensors lead to blurred angle measurements in the difference co-array. This paper proposes an efficient aperture extension method and a matrix completability discriminator based on the undirected adjacency graph of the matrix. First, we construct a zero-filled Hankel matrix with single snapshot signals received from a non-uniform CA. Then, we take the low-rank feature as a heuristic and complete the zero-dilled Hankel matrix. At last, the subarrays of the CA are traversed to design the minimum complementable coprime array (MCCA) via the discriminator. By simulation, we verify that the difference co-array of the MCCA can be completed to the maximum virtual aperture of the CA and reduce the noise.
A Fabric-based Inexpensive Wearable Neckband for Accurate and Reliable Dietary Activity Monitoring

Dietary habits play a significant role in public health and well-being. Monitoring dietary activities is thus essential for maintaining a healthy lifestyle and preventing many widespread diseases, such as diabetes, obesity, and hypertension. In this work, we present a low-cost wearable neckband for automatic diet activity monitoring. The $5 fabric-based device, comprising an electret microphone, a Bluetooth radio module, and a rechargeable Lithium-ion battery, can wirelessly transmit audio to a smart device in real-time. The classification algorithm processes the audio stream in 3s segments and extracts short-time spectral, waveform, and energy-based acoustic features. We compute various statistical functions from the acoustic features to obtain segmental feature vectors, which are subsequently used for machine learning. We perform an experimental evaluation using an in-house dataset collected using the neckband. We compare the performance of different classifiers in distinguishing between drinking, chewing solid foods, and other non-dietary activities. An averaged class-wise F-measure of 81.25% is achieved using the proposed wearable device and a Random Forest (RF) based classifier.
Degrees of Anger Prediction from Speech

In recent times, a huge body of research has been conducted for speech processing applications, particularly speech recognition. However, a few studies have been found to predict users’ anger (i.e., yes/no) from their speech. In this study, we predict users’ degrees of anger (i.e., minimal, mild, moderate, and extreme) based on their speech which has not been studied yet. We apply different machine learning-based approaches for obtaining better accuracies. We extract important features from speech and apply a feature selection technique. We apply machine learning models like Support Vector Machine (SVM), Gaussian Naive BayeS (GNB), and Multinomial Logistic Regression (MLR) along with ensemble models like Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Bagging to predict the degrees of anger from speech. Among the models we have used, XGBoost demonstrates an F1 score of 93.2% which outperforms the state-of-the-art models. To the best of our knowledge, this is the first attempt to predict the distinct degrees of anger of a person by using his speech sample unlike the frequently used binary classification o f a nger states.
Optimized Routing Scheme in Software Defined Networking for Internet of Things & Comparisons Thereof

Internet of things (IoT) is an advanced technology used in many networking application systems. IoT involves huge number of objects or things that generate enormous amounts of data for storage and processing. The association and control of this enormous volume of information requires original thoughts in the plan and the execution of the IoT system in order to process and improve their presentation. In a similar manner, the Software Defined Networks (SDN) is also a popular networking system and extensively used for monitoring and controlling IoT based devices. Furthermore, OpenFlow is a kind networking protocol mainly used for establishing reliable data transmission in SDNs, which transmits the messages with the help of switches and controllers. In SDN, the controller has the responsibility to coordinate the routing operations of entire network by using switches. Along these lines, to improve the system execution, data packets ought to be routed to the best path. In this paper, the Lion Optimization Algorithm (LOA) is proposed to find the best or optimal path in the data plane of SDN for routing a given packet. By selecting the optimal path between the hosts, the packet loss rate will be reduced and also response time will be improved. Execution of LOA in the SDN environment with OpenFlow Switch system is performed utilizing NS-2 Network Simulator. When compared to the conventional models, the results obtained results using the proposed LOA provide an improved performance values in terms of reduced packet loss rate, increased throughput and packet delivery ratio.
An Improved Hybrid Deep Learning Model for PM2.5 Forecasting

The quality of air quality is closely related to people lives and affects our physical and mental health at all times. At the same time, the environmental problems of air pollution are also a crucial factor hindering economic development. Through the prediction of air quality, it is helpful to predict the possible air pollution in the process of economic and social development, to better formulate corresponding countermeasures, and to promote the green development of the industry. In this study, a modified deep learning hybrid model for PM2.5 predictions is proposed based on transfer learning, which integrated long short-term memory (LSTM), multi-layer perceptron (MLP), convolutional neural network (CNN), and residual neural unit into an end-to-end network framework. Firstly, LSTM is applied to capture the temporal characteristics of air quality data, and MLP module is designed for feature conversion. Then, two-dimensional convolutional neural network is adopted to extract the local spatial characteristics between stations, and residual neural units are added to the network to deepen the network depth to prevent the disappearance of gradients. Batch Normalization (BN) is adopted to accelerate the network convergence and improve the initial learning rate. Finally, the prediction results of PM2.5 development trend are obtained. The model is verified by Beijing and Hengshui air quality data. The improved model is not only higher in accuracy than the benchmark model, but also has strong stability.