Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy-based Kinetic Energy Features for Online Dynamic Security Assessment using Bayes by Backprop Machine Learning Algorithm

In the following work, A machine learning-based Online Dynamic security assessment (DSA) scheme is proposed for which Energy-based Kinetic energy terms are applied as input features. The aim is to develop simple pre-processed input features that are easier to calculate and when applied as inputs, require fewer samples of data to develop a trained model. The model is trained using the current system topological condition. The proposed scheme is implemented using a Bayesian neural network (BNN), which is developed by the Bayes-by-Backprop algorithm. The performance of the proposed input features is demonstrated with IEEE 39 bus test system. When the Energy-based kinetic energy terms are applied as input features it is shown that the model can be trained with fewer samples of data.
Automatic Seed Generation for Weighted Test Pattern Generation

A pseudorandom test pattern is generated by a test pattern generator, where a pseudo-random pattern is probably weighted to increase fault coverage in a built-in self-test. Reduced power consumption and area can enable a scan chain for the efficient weighted pattern. The weighted patterns are used for all scan chains to build a self-test architecture. It improves the fault coverage and removes the fault at a specified output. To achieve this, a built-in self-test requirement should be focused on the highest fault coverage. The initial seed flipping is a technique used to analyze the test coverage and test time. The proposed weighted test pattern generator simulation outputs are obtained by using Xilinx IC design with 14.7 version IC design software.
Novel Lung Cancer Classification and Segmentation Algorithm with Web UI Interface

For humans, it is difficult and time-consuming to use Computed Tomography (CT) pictures to identify lung illnesses. This research work develops a novel algorithm for segmenting the cancer part in the lung and an algorithm is enhanced to classify the lung cancer images. MediNet is a segmentation-based algorithm, used to segregate the cancer-affected region from an unaffected region in the CT image whereas LuNet is the algorithm developed by enhancing an algorithm to achieve the classification of the type of cancer. This MediNet algorithm involves layers such as thirty convolutional layers, three maxpooling layers, and three bi-directional convolutional layers. By obtaining 97% accuracy, MediNet outperformed other models. Besides this, a web application is also developed to assist the doctor with a UI interface. This is developed in such a way that the doctor can upload a cancerous or non-cancerous scan in the application which will be linked to the developed classification and segmentation algorithm Medinet and LuNet. Thebackend will be running in a server that will take the input from the web app and process the output and return back the results to the web application in terms of the presence or absence of cancer as well as segmented cancerous areas in case of presence of cancer.
Coupled Finite-Element-Method-Simulations for Real-Time-Process Monitoring in Metal Forming Digital-Twins

In many industrial processes it is not possible to measure variables that are crucial to the output quality of the manufactured goods due to manufacturing constraints. One possible approach to overcome this lack of information is using a digital-twin of the manufacturing plant. A digital-twin can make use of data generated by numerical simulation techniques to infer the real time state of the machine. Digital-twins are an established technology and will play a crucial role in optimizing manufacturing plants in the near future. The digital real time state of the manufacturing plant can be used to warn machine operators in case a predefined process window is not reached and the output is expected to violate Original Equipment Manufacturer (OEM) requirements. This work shows a developed control system and how using a digital twin improves the predictive power of the system. Machine learning models are used to infer the cooling curves of metal sheets in a press hardening process. They are trained on coupled Finite-Element-Method (FEM) Simulation data. We validate the performance of our models on the simulation data and find accurate estimations of the metal blanks temperature with a root mean squared error of 1.6±0.5 K, with this not being possible to measure beforehand.
Power Allocation in Infrastructure Limited Integration Sensing and Localization Wireless Networks

Multi-functional wireless networks are becoming promising for many modern applications, e.g., the co-design of the waveforms or the shared spectrum between sensing and localization may achieve higher spectrum efficiency. To exploit the full potential of the integrated sensing and localization network (ISLN), we try to provide the general fundamental limits of ISLN. Moreover, the power allocation strategies are formulated and solved accordingly, to show the possible tradeoffs between two different performance metrics. Numerical results are provided to validate our analysis. We can see that, (i) when the network infrastructure is limited, cooperative measurements among agents will become important. (ii) In general cases, the sensing and localization could incorporate via proper power allocation strategies. However, (iii) when the power resources are strictly limited, there will exist an obvious tradeoff between the two tasks.
SNR estimation in EMG signals contaminated with motion artifact

An electromyography (EMG) recognition system is essential for enabling a variety of applications. However, motion artifact contaminated with the EMG signal as it passes through or by various tissues may degrade the recognition performance. We present the algorithm for signal-to-noise ratio (SNR) estimation in EMG signals contaminated with motion artifact. Six features derived from the EMG signals are used as the neural network input: skewness (SKEW), kurtosis (KURT), mean absolute value (MAV), wavelength (WL), zero crossing (ZC), and mean frequency (MNF). The estimated SNR values are the neural network output. The best mean and standard deviations of the correlation coefficient (CC) between the actual and estimated SNR values are provided by the MNF $(0.9699 \pm 0.0076)$. Future research may concentrate on determining SNR values using real EMG signals in their natural surroundings.
Computing Thermal Point Clouds by Fusing RGB-D and Infrared Images: From Dense Object Reconstruction to Environment Mapping

Compared with 2D thermal images, visualizing the temperature of objects with their corresponding 3D surfaces provides a more intuitive way to perceive the environment. In this paper, we present an integrated system for large-scale and real-time 3D thermographic reconstruction through fusion of visible, infrared and depth images. The system is composed of an RGB-D and a thermal camera, whose image measurements are aligned with respect to the same coordinate frame. A thermal direct method based on infrared features is proposed and integrated into state-of-art localization algorithms for generating reliable 3D thermal point clouds. The reported experimental results demonstrate that our approach can be used for 3D reconstruction of small and large scale environments based on dual spectrum 3D information.
Smart Customer Care: Scraping Social Media to Predict Customer Satisfaction in Egypt Using Machine Learning Models

This paper proposes the utilization of posts from social media to extract and analyze customer opinions and sentiments towards any specified topic in Egypt. Summarized statistics and sentiment values are then displayed to the consumer (companies such as Vodafone, WE etc.) through both an attractive and functional user interface. Text, location, and time of thousands of posts are scrapped, stored, preprocessed, then managed through topic modelling to infer all the hidden themes and delivered to a Recurrent Neural Network (RNN) to output whether the topic was positive or negative. Topic modelling was implemented using the BERT architecture and AraBert word embedding. Sentiment analysis model training was conducted on approximately 4000 rows of processed data and made use of Arabic glove embedding to speed up sentiment and word pattern recognition. Five models were experimented on: LSTM, GRU, CNN, LSTM + CNN and GRU + CNN. Overall, the GRU was the model with the best results, concluding with an accuracy of (86.19%), loss of (0.3349) and an F1-score of (0.858) when validating through the test data.
A Friendly Interface Medical Service Robot with Fault-tolerant Voice Recognition and Mobile Phone Remote Control

In recent years, due to the impact of COVID-19 around the world, there has been a serious shortage of medical resources. In order to supplement the manpower and fear that medical staff's contact with patients will cause a breach in the epidemic, reduce the workload of nurses, and help nurses perform repetitive tasks so that nurses can concentrate more on the patient's condition. Therefore, this paper proposes M-Robot, which is a friendly interface service robot based on the Android system and can be controlled by voice, touch, and remote control in the medical care field. The system is mainly divided into two parts. One is the web server. The web server is divided into two parts: front-end and back-end. The front-end is responsible for friendly user interface management, and the back-end is for accessing the SQLite database, as well as processing speech recognition and semantic understanding in voice services. In the other part, we use TEMI robot to develop and complete the desired service. Its service content includes environment introduction, delivery service, questionnaire survey, broadcast car, scheduling reminder, follow-up record, and patient instruction video. In the voice control mode, the user can say the wake-up word to the robot and say the required service content, and the robot will execute after receiving the message; in the remote control mode, we provide a friendly web interface for remote control. As well as the information needed to manage various services.
Prediction of Rock and Mineral from Sound Navigation and Ranging Waves using Artificial Intelligence Techniques

Since sound waves penetrate the sea more deeply than radar and light waves, SONAR (Sound Navigation and Ranging) is used to explore and map the ocean. When working in the mining industry, engineers may find SONAR to be an invaluable tool for helping them visualize the location of rocks and minerals by charting frequency signals. Assuming an object is within the sound pulse's range, the sound pulse will reflect off the target and send an echo in the direction of the sonar transmitter if the target is within the range of the sound pulse. The transmitter uses the power source to receive signals and figure out how strong the signals are. It establishes the pause in time between the generation of the pulse and the receiving of its corresponding signal. It analyzes the duration between the emission of the pulse and its matching reception, which estimates the distance and location of the matter. Engineers determine the item using an audio wave. With the assistance of AI, the process of evaluating, organizing, and identifying the item is going to be circumvented in order to accomplish the objectives of immediately identifying the item based on the scheduled bandwidths. In this proposed method, PCA and t-SNE are employed to extract features. Utilizing classification approaches such as Logistic Regression and Random Forest Tree, an accuracy of 72% and 91%, respectively, was attained. Similarly, CNN and LSTM models are also employed and finally they have yielded an accuracy of about 80.77% and 99% respectively.
Energy Management Strategies for Educational Campus Buildings in a Virtual Power Plant Environment with Demand Response

The electricity consumption in educational campuses in Malaysia continues to increase as more education buildings are developed to accommodate approximately one million students' placements as highlighted by Malaysia's Ministry of Higher Education (MOHE) in its Education Development Plan 2015–2025. Concurrently, the distributed energy resources mainly solar photovoltaic in Malaysia has increased over the past few years as the nation is moving towards a sustainable energy development. Therefore, an efficient way to pursue these agendas should be developed through integrated approaches to optimise the overall usage of energy in educational campuses. This paper aims to study on the technoeconomic of a microgrid system with grid-connected photovoltaic and battery storage in a Virtual Power Plant (VPP) environment. The VPP operates to manage the energy in an economical way. The energy management strategy is analysed for several buildings in Universiti Teknologi MARA in Shah Alam, Selangor as a case study. In this study, the buildings inside the campus are being connected as a microgrid by performing simulation for three different models (Grid-PV-Diesel Generator, Grid-PV-Battery, Diesel Generator-PV-Battery) using HOMER software. The buildings' load profile, solar irradiation data and other electrical and grid component's parameters are provided to minimise the system's Net Present Cost (NPC). The findings show that the Grid-PV-Diesel generator has the lowest cost. However, considering grid rates hike, the Diesel Generator-PV-Battery show a better result as it is not influenced by any grid rates fluctuations. Therefore, this study would further enhance the public agenda as a public educational institute to support the current changing sustainable energy development.
A Reconfigurable Multilevel Inverters with Minimal Switches for Battery Charging and Renewable Energy Applications

In recent years, classical inverters such as the H-bridged cascaded multilevel inverter, flying capacitor, and flying capacitor multilevel inverter have contributed in electric vehicle and non-conventional energy applications. Due to higher switching and conduction losses, as well as a greater number of power switches and driver circuits, conventional multilevel inverters do not achieve the highest performance. To obtain higher performance while reducing power losses and total harmonic distortion, individual switches are controlled by logic gates. In this proposed work, one of the inverters is considered symmetrical voltage another is asymmetrical voltage for implementing these effective topologies. The proposed single-phase seven-level voltage output and current for both symmetric and asymmetric multilevel inverters are employed to test the intended computation. The MATLAB/Simulink tool is used to implement and investigate the various parameters of proposed topologies.
Summary of Spoken Indian Languages Classification Using ML and DL

Unlike in some other parts of the world, speech recognition technology is legal in the West. It's not to the same degree that this happens in East Asian countries. It's possible that linguistic barriers are a major cause of this chasm. In addition, countries with many languages, such as India, must be taken into account if voice-based language identification is ever going to be practical. The challenge is in finding a technique to clearly and effectively identify the features that may differentiate across languages. The model processes audio data, creating spectrogram images from them before extracting features. Then, the Deep Learning (DL) is employed to streamline the output identification process by emphasizing the most crucial characteristics and attributes. Realizing that a person's vocal signal may be understood or observed was a major inspiration for the concept. This research work employ spectrograms (for visual data) and deep learning techniques to categorize Indic languages inside the IIITH Indic voice database. Finally, a model-based comparative analysis has been conducted by analyzing the accuracy, precision, recall, and f1-score to show that the proposed approach is more robust than existing models.
Autonomous Integrity Monitoring of INS/GPS Integrated Navigation System under Multipath Environment

Inertial navigation solution when fused with GPS solution will result into highly accurate hybrid/integrated navigation solution. Unfortunately, when the aircrafts of landing/take-off in urban environments or on table top runways, the hybrid solution suffer huge disturbance of GPS multipath error. The problems of multipath are often reported on several ground vehicles also. The present paper proposed an integrated navigation solution with detection of multipath error which got introduced in some of measurements of GPS receiver. Further, the effect of this on hybrid navigation solution is studied and detailed analysis is presented. Polynomial kalman filter (PKF) based error detection mechanism is studied and simulation results are presented. The method uses position and velocity information as measurements in identification of GPS multipath error at the early stages, so that the solution of hybrid navigation system will not get degraded with erroneous measurements from GPS receiver. Large errors for reinitialization of hybrid navigation during GPS outages are addressed through gross error check followed by polynomial kalman filter in addition to regular Extended kalman filter (EKF) of INS+GPS integration scheme. The proposed method of detection of multi path error in an aircraft application is demonstrated through simulation test results for a typical aircraft trajectory.
Ramp Jump Control of Single-track Two-wheeled Robot using Reinforcement Learning with Demonstration Data

The single-track two-wheeled (STTW) robot ramp jump is a challenging task. This task not only requires the STTW robot to maintain its balance during the various phases of the jump but also requires it to use different speeds for different ramp terrains. This article proposes a deep reinforcement learning method to generate a controller for the STTW robot to complete the ramp jump task. First, the corresponding state space and action space are designed based on the characteristics of ramp jumping. Second, an effective reward function is developed to help train the agent. Then, the demonstration data are introduced into the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to obtain better training results. Finally, the proposed control method is verified in a dynamic simulation environment. Simulation results show that the proposed control method can enable the STTW robot to complete the ramp jump task on the training terrain and on some other test terrains.
Broadcast Collision and Overhead Tradeoff for Enhanced Broadcast Service in IEEE 802.11bc

IEEE 802.11bc introduces enhanced broadcast service (EBCS) to improve data broadcast scheduling and authentication performance in wireless local network (WLAN). The essential design in the EBCS is introducing a central scheduling scheme for data broadcasting, and this makes that the conventional distributed coordination function (DCF) based broadcast performance analysis does not apply. Thus, in this paper, we investigate the frame collision between DCF-based unicast data and scheduling-based broadcast data within the framework of IEEE 802.11bc, which has not been explored yet. Specifically, we study the tradeoff among station arrival intensity, broadcast and unicast frame collision, and broadcast control frame transmission interval, which directly determines the broadcast overhead. We derive an analytical model to quantity the above performance tradeoff. Based on the analytical model and simulation test, we optimize the broadcast control frame transmission interval to balance the broadcast overhead and data collision.
An Overview of Techniques used for Determination of State-of-Charge of Batteries

One of the most significant components of electric vehicles is the battery management system (BMS). It allows a Li-ion battery to run for a long time. A good BMS can detect temperature, measure voltage and current, and control safety alarms to avoid overcharging and over-discharge. On the other hand, estimating battery State-of-Charge (SOC) is one of the most challenging aspects of electric vehicle operation. Many researchers have worked on traditional battery SOC evaluation methods. 28 SOC estimate approaches are examined in this study based on their accuracy and algorithm complexity. This research might help automobile businesses and engineers find the best solution for the application's needs. Several methods like machine learning and computer intelligence are applied to create smarter BMS that meet the needs of an application.
A unique approach towards implementing a data science oriented OBE management system

Outcome based education(OBE) is a common methodology adopted by institutes of higher learning. This aids in bringing out the best from the programs that these institutes provide to students. As a part of this process, various analyses is done to understand the level to which the Teaching-Learning process has been effective, and for accreditors to inspect and accredit the program. This has been commonly implemented in a data oriented manner. The proposed work intends to amalgamate this approach along with a faculty oriented approach, such that it not only brings out the analyses, but it also simplifies the overall process. A part of the proposed work involves a stack based approach to extract properties of questions, such as Course Outcomes(COs), Bloom's Taxonomy levels(BTLs), Difficulty levels(DLs), Total marks, and store it in the Database. The other part includes the attainment calculations for each course, and the analyses for each course based on various attributes. A stack which comprises Python and its libraries like Numpy, Pandas, Flask, has been used to implement and experiment on the same.
Beam Squint Inspired Multiple Access Technique in Massive MIMO Systems

Massive multiple-input multiple-output (mMIMO) systems implementing wideband signaling have been found to suffer from beam squinting problem. In multicarrier systems, beam squint causes spreading of the beam to multiple undesired directions, thereby degrading system capacity. This paper proposes user scheduling and precoding techniques that controls and exploit the beam squinting phenomenon to serve multiple users and improve the overall system capacity. The proposed techniques are based on the lens antenna subarray (LAS) mMIMO system. The proposed system design is explained and numerically evaluated in terms of beam gain and capacity with different number of users and RF chains. The simulation results exhibit significant performance enhancement in terms of beam gain and capacity, corroborating the idea of exploiting the beam squinting phenomenon to serve multiple spatially separated groups of users.