Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysing Paralinguistic Information from Human Speech and its Applications in Medicine

The recent advances in Speech Emotion Recognition (SER) technology are explored in this work. Two of these include emotion detection using excitation features of speech and emotion recognition using Zero Shot Learning (ZSL). Both methods are speaker specific in nature. The recent research is evaluated on the basis on Geneva Minimalistic Acoustic Parameter Set, which is a proposed standard set of acoustic parameters. SER is heavily based on kernelization for classification. The acoustic parameters are, however, not optimized for computing in a high dimensional form. Dimensionality reduction methods are applied to overcome this. This work also explores how SER can enhance the existing technologies in medicine to detect various vocal pathologies and other disorders that may affect speech production and analyze their progression over time.
A Study on Kinematic Pattern of Fish Undulatory Locomotion Using a Robot Fish

Recent state-of-art researches on robot fish focus on revealing different swimming mechanisms and developing control methods to imitate the kinematics of the real fish formulated by the so-called Lighthill's theory. However, the reason why robot fish must follow this formula has not been fully studied. In this paper, we adopt a biomimetic untethered robot fish to study the kinematics of fish flapping. The robot fish consists of a wire-driven body and a soft compliant tail, which can perform undulatory motion using one motor. A dynamic model integrated with surrounding fluid is developed to predict the cruising speed, static thrust, dynamic thrust, and yaw stability of the robot fish. Three driving patterns of the motor are experimented to achieve three kinematic patterns of the robot fish, e.g., triangular pattern, sinusoidal pattern, and an over-cambered sinusoidal pattern. Based on the experiment results, it is found that the sinusoidal pattern generated the largest average static thrust and steady cruising speed, while the triangular pattern achieved the best yaw stability. The over-cambered sinusoidal pattern was compromised in both metrics. Moreover, the kinematics study has shown that the body curves of the robot fish were similar to the referenced body curves presented by the formula when using the sinusoidal pattern, especially the major thrust generation area. This research provides a guidance on the kinematic optimization and motor control of the undulatory robot fish.
Malware Analysis in Cyber Security based on Deep Learning; Recognition and Classification

Cyber security in wireless communications can be an unwieldy subject, given the amount of malware that has been increasing rapidly in the last few years. This generates serious security problems for public agencies and the government institutions. In order to mitigate the influence of malware deployment and pervasiveness, new recent identification and classification algorithms for malware that adapt deep learning techniques are studied and figured out based on their features and behaviors. This study introduces a deep learning algorithm to identify different malware families. To implement the proposed approach, malware color-based images (RBG) are used directly. Then these malware images are identified and classified by considering the benefits of leveraged Convolutional Neural Networks (CNNs), which have the ability to automatically extract those features. A challenging malware classification experiment using the MaleVis dataset confirms that the adapted model outperforms better functional classification compared to the traditional machine learning models and achieves very good accuracy based on the MaleVis dataset.
Helmet Detection Algorithm Based on Transformable Attention and Feature Fusion

To solve the problem of whether workers wear helmets or not on the actual construction site, this paper proposes a helmet detection algorithm based on the integration of transformable attention and feature fusion. Since the hard hat is a small target, and the feature extraction is susceptible to background information interference and other factors, this paper proposes the TAM-YOLOv5 (Transformable Attention Mechanism-YOLOv5) detection model to detect whether workers wear the hard hat in the actual scene. Then, the detected head-and-shoulder Figure is binary classified in the Double branch-ResNet classification model. To determine whether the worker is wearing a helmet. The transformable attention mechanism is added to TAM-YOLOv5 based on the original YOLOv5 detection model, which will pay more attention to the feature information between channels and spatial positions. To solve the problem of inadequate feature extraction, feature fusion is added to the Double branch-ResNet model to enhance the expression ability of safety hat features. In addition, due to the diversity of safety helmets, this paper introduces mutual channel loss in terms of loss function to enhance the fine-grained classification between safety helmets. In this paper, the proposed algorithm is fully tested, and the results show that the algorithm has improved the recall and accuracy.
GLSUR: POI Recommendations Based on Similar Users and Current Geographic Location

With the rise of location-based social networking, predicting users' future POI(point of interest) has also become a key issue. Now there are some methods that can predict the next point of interest based on the user's own history hidden state. Despite this, these approaches take only into account the user's own behavior trajectory, ignoring the impact of similar users. We design a new recommendation model GLSUR to improve the accuracy of the predictions and provide users with a better experience of the actual situation POI recommendations. The three factors of hidden state, similar users and user embedding are considered simultaneously in GLSUR. We invoke RNN networks as the underlying computational model to calculate the user's own hidden state, the similarity formula determines the similar users, and pass the three tensors into the fully connected layer. Finally, a semi-positive vector formula is used to calculate the closest point of interest and recommend it to the user. We conducted extensive experiments using two real data sets in the real world. The results show that GLSUR provides better accuracy compared to other baseline methods.
Modular Multilevel Converter-Based Microgrid: A Critical Review

Recently, the Modular Multilevel Converter (MMC) has drawn significant attention due to its diverse merits and its applicability to a wide range of medium to high-power applications. The growing interest in the MMC can be attributed to its attractive features such as modularity, reliability, and high voltage capability. Significant research has been conducted on the MMC over the last few years to develop its operation and control in various applications. However, the application of MMCs in microgrids remains a largely unexplored topic. Therefore, this paper aims to address this research gap by offering an in-depth review of the latest developments concerning circuit topologies, control schemes, and fault-tolerance strategies of MMC within microgrid applications. This comprehensive review not only provides a synthesized overview of the current state of the art but also paves the way for future investigations in this promising field. The outcomes from this study are expected to stimulate further advancements in MMC applications in microgrid systems, thus contributing to the continuous improvement and evolution of microgrids.
Non-Invasive Glucose Monitoring based on Single Wavelength using Augmented Reality

Surgeons are constantly looking for new technologies to enhance their operations in the environment. Because blood must be extracted via a skin prick, BGM (Blood Glucose Monitoring) approaches are invasive. However, researchers are working to make BGM non-invasive by employing NIR (near-infrared) rays. NIR technology has flaws that prevent it from being used in healthcare settings, such as light ray absorption in human tissue, larger SNR (signal-to-noise ratio), and poor accuracy. Applications to stay augmented reality (AR) are being examined for their potential use by surgeons as it becomes increasingly readily available, cheap, and accessible. The insertion of artificial data into any number of senses to help a user accomplish tasks with greater efficiency is recognized as augmented reality (AR).
Image Classification for Potato Plant Leaf Disease Detection using Deep Learning

Identifying potato leaf diseases at an early stage is a difficult task due to the variability in crop species, crop disease symptoms, and environmental factors. To overcome this challenge, machine learning techniques have been developed. However, current models are limited to specific regions and cannot detect diseases in various crop species. This research proposes a multi-level deep learning model to recognize potato leaf diseases. The model uses a unique convolutional neural network to detect early blight and late blight potato infections from leaf images after extracting potato leaves from plant images using ResNet50 image segmentation. The model is trained and tested using a potato leaf disease dataset, achieving 99.75 percent accuracy. Furthermore, it outperforms state-of-the-art models in terms of accuracy and computational cost.
Facies-Guided Seismic Image Super-Resolution

Seismic field data usually suffer from low-resolution (LR) image quality. In recent years, high-resolution (HR) seismic data acquisitions are either done in high-density seismic surveys or by capturing high- and low-frequency bands in broadband seismic surveys. However, both methods increase the financial, computational, and memory overhead required for oil and gas exploration. Moreover, to improve the resolution of already acquired seismic field data in a certain area, another survey would be carried out in the same area with more advanced equipment. This is a time-consuming high-cost approach. In our previous work, we showcased the application of convolutional neural networks (CNNs) for image super-resolution (SR) to increase the resolution of seismic images. While our application of SR for seismic images is optimized for better human perception, automatic seismic interpretation tasks may not benefit as much, such as facies classification considered in this work. We address this by proposing a novel loss function that incorporates facies information when training the SR model. The proposed loss function is applied to multiple SR models and results show that this facies-guided seismic image SR approach produces improved facies classification results while mitigating major sacrifices on the objective and subjective image quality of the reconstructed SR image.
RRAM-Based Precision-Scaleable Computing-In-Memory Scheme and Its Error Correction Approach

Resistive random access memories (RRAM) based Computing-in-memory (CIM) architecture has attracted enormous attention due to its high energy efficiency. However, the conductance states of RRAM devices are usually limited to 1-4bit which is much lower than the desired 8/16-bit precision, and the additional cost (such as the differential circuit) to map a negative parameter into positive conductance is large, leading to either large accuracy loss or low hardware efficiency. In this work, we propose an efficient precision-scaleable CIM scheme for high-performance computation, by which high-bit (INT8/16) signed multiplication-accumulation (MAC) operation can be constructed using the practical 3-bit RRAM devices with the unbalanced bit-slice method. Next, we analyze both the systemic error caused by the non-zero conductance of devices (when representing the bit of 0) and random errors caused by the conductance variation issue in the device. A linear regression correction approach is proposed to reduce the computation error. Finally, the proposed method is verified in terms of both software accuracy and hardware efficiency, the simulated result shows it is a promising method to be used in practical applications.
Adaptive Tracking and Synchronization Control of Dual-motor Driving Servo System

This paper proposes an adaptive tracking and synchronization control scheme for dual-motor driving servo system with nonlinear dead-zone. To achieve the tracking performance, the neural network is used to approximate the unknown dynamics, and the approximation is incorporated into the control design to compensate the unknown dynamics. Then, adaptive dynamic surface controller is designed to improve the tracking performance. Moreover, a robust controller is presented based on the mean deviation coupling strategy to guarantee the synchronous operation of dual motors. Simulation results illustrate the performance of the proposed control strategy.
Theoretical Analysis of Power Spectral Density of CP-Based FBMC Signals

Filter Bank Multicarrier (FBMC)-based Offset Quadrature Amplitude Modulation (OQAM) systems contain less sidelobes. However, broadband channels require sophisticated receivers. In order to overcome the limitations of conventional FBMC systems, cyclic prefix (CP) based FBMC systems have been proposed in the literature. CP-based FBMC systems add a cyclic prefix to the transmitted signal, which helps to mitigate the effects of multipath fading and inter symbol interference (ISI). In this article, The Power Spectral Density (PSD) of FBMC and CP-based FBMC for different communication systems are theoretically analyzed, their characteristics are examined and validated through simulations. It shows that the numerical data is in good agreement with both theoretical and simulated PSDs. Our findings suggest that CP-based FBMC systems outperform traditional FBMC systems in terms of PSD. The accuracy of our theoretical derivations is supported by simulation results.
IoT Enabled Innovative Accident Detection and Rescue System

One of the main causes of death worldwide is traffic accidents. This paper provides warning before the situation becomes dangerous and promptly discloses the site of the mishap. Once an accident has happened, their whereabouts is tracked using GPS (Global Positioning System) and an alarm will be sent when the driver is not fit to drive. This technology makes it simple to locate a vehicle, informs the driver when he has taken alcohol, assists in preventing accidents, and provides assistance as quickly as possible after a collision. When we all know, as automobile usage grows dramatically, so do the risks associated with them. High-speed is the primary cause of accidents. As we all know, as automobile usage grows dramatically, so do the risks associated with them driving while intoxicated, distracted driving, mental exhaustion, and usage of electronic devices are the leading causes of accidents. This project specifically addresses accidents that happen as a result of the driver’s negligence. Most of the time, information is not promptly relayed to family members, the ambulance, or the police. As a result, seeking assistance is delayed. Because no one can predict where an accident will occur, we frequently risk being unable to find it. The system introduces a technique for detecting accidents and warning users when they occur. As soon as an accident happens, the vehicle’s technology will identify it and send an email notice with the vehicle’s location and emergency phone numbers. In addition, the patient monitoring system can be used the LM35 temperature and heart rate sensors to check on the victim’s health and report the results to the hospital.
Novel Miniaturized Light-Weight Coaxial Cavity Filters With Electrical Mainline Couplings

This paper provides a comprehensive discussion for a novel class of miniaturized light-weight coaxial filters employed in 700 MHz supported 5G stations. The proposed resonators are characterized with square top plates and zigzag “arms”. All the zigzag arms are inter-coupled in an overlapped fashion, reducing the filter length and specially providing electrical/capacitive (negative) mainline couplings. Owing to the capacitive loadings and large-area square top plates, resonant frequencies can be decreased to very low frequency bands inside a compact size, which further boosts the light-weight application in communication systems. Besides, with the simple construction with only metal structures, this type of filter features low costs. The implementation of both negative and positive cross couplings is also discussed to adapt to the proposed filter configuration, ensuring the flexible placement of transmission zeros (TZs) and the customization of sophisticated filter responses. The good results of a 12-pole Tx filter and a fabricated 10-pole Rx filter have validated the effectivity of the presented structures. Moreover, implemented with the proposed configuration, the weight of a network comprising six quadplexers is expected to be reduced by 32% compared with the device based on conventional technology.
Analysis of the Technical Principles of ChatGPT and Prospects for Pre-trained Large Models

ChatGPT is a pre-trained model in the field of natural language processing. As a generative model, the technical foundation of ChatGPT is a deep learning model called the "Generative Adversarial Network". The pre-trained large model architecture of ChatGPT can be summarized as "corpus system+pre-training+fine-tuning". Under the combined force of massive data, super large models, and enormous computing power, chatGPT has ushered in the era of universal artificial intelligence and formed a new paradigm of generative AI development. Generative AI products represented by ChatGPT will promote the development and implementation of Artificial Intelligence Generated Content (AIGC), and trigger significant changes in areas such as information acquisition methods and economic and social cost structures
Real time Monitoring Method of Welding Defects Based on LSTM Sequence Model

During laser welding, abnormal conditions can result in workpiece defects and product failure. Therefore, a real-time monitoring system is required to detect laser welding processes and ensure product quality. In this study, we developed a real-time laser welding data acquisition system to collect plasma density, laser intensity, and molten pool temperature data during the welding process. Additionally, we established a neural network based on a combination of LSTM and CNN models to rapidly detect laser welding defects. The experimental results demonstrate that this method can effectively identify welding defects with an average accuracy rate of 96%. Furthermore, the LSTM layer can significantly improve the prediction performance of the CNN feature extraction model, particularly for forecasting and non-local features such as sequence offsets and delays.
Investigating the Fractality and Stationarity Behavior of Global Temperature Anomaly Time Series

The global climate has been changing rapidly in recent decades, with significant consequences for the environment and human societies. Understanding the long-term behavior and properties of climate data is crucial for predicting future changes and developing effective mitigation strategies. This study investigates the fractal and stationary properties of global temperature anomaly time series data from 1880 to 2022 using statistical techniques such as the Hurst exponent, rescaled range analysis, detrended fluctuation analysis, augmented Dicky Fuller test, and Kwiatkowski-Phillips-Schmidt-Shin test. The results of the analysis reveal that the global temperature anomaly time series exhibits fractal behavior with a Hurst exponent value of 0.6 during the last 42 years, indicating persistent long-term memory. Additionally, the data show nonstationarity with a significant increasing trend over the entire period of analysis. The authors found evidence of changes in the fractal properties of the data since 1980, possibly due to human-induced climate change. This study provides vital insights into the complexity of global temperature anomaly time series data and highlights the need for continuous tracking and evaluation of climate data to better understand and manage the issues of climate change. The findings have important implications for climate modeling and policy development, highlighting the need for continued efforts to mitigate climate change and its impacts.
Adaptive Optimal Control for Discrete-Time Linear Systems via Hybrid Iteration

In this paper, a novel dynamic programming (DP) and adaptive dynamic programming (ADP) algorithms are proposed, namely hybrid iteration (HI), for discrete-time linear systems. The proposed HI approach fill up the performance gap of two well- known DP algorithms, i.e., policy iteration (PI) and value iteration (VI). In particular, HI drops the need of the prior knowledge of an initial stabilizing control policy required in PI, and at the same time it maintains a fast quadratic convergence rate compared with VI. A data-driven adaptive optimal controller design is also proposed based on the proposed HI algorithm. Simulation results for randomly generated discrete-time linear systems with different system orders demonstrate that the proposed HI approach can significantly save CPU time and reduce the number of learning iterations to converge to the optimal solution comparing with the VI approach. The data-driven HI method is implemented to an application of turbocharged diesel engine with exhaust gas recirculation, and the simulation results illustrate the efficacy of the proposed HI method.
MF-DSNN:An Energy-efficient High-performance Multiplication-free Deep Spiking Neural Network Accelerator

Inspired by the brain structure, Spiking Neural Networks (SNNs) are computing models communicating and calculating through spikes. SNNs that are well-trained demonstrate high sparsity in both weight and activation, distributed spatially and temporally. This sparsity presents both opportunities and challenges for high energy efficiency inference computing of SNNs when compared to conventional artificial neural networks (ANNs). Specifically, the high sparsity can significantly reduce inference delay and energy consumption. However, the temporal dimension greatly complicates the design of spiking accelerators. In this paper, we propose a unique solution for sparse spiking neural network acceleration. First, we adopt a temporal coding scheme called FS coding which differs from the rate coding used in traditional SNNs. Our design eliminates the need for multiplication due to the nature of FS coding. Second, we parallelize the computation required for the neuron at each time point to minimize the access of the weight data. Third, we fuse multiple spikes into one new spike to reduce inference delay and energy consumption. Our proposed architecture exhibits better performance and energy efficiency with less cost. Our experiments show that running MobileNet-V2, MF-DSNN achieves 6× to 22× energy efficiency improvements while having an accuracy degradation of less than 0.9% and using less silicon area on the ImageNet dataset compared to state-of-the-art artificial neural network accelerators.
Memristor based Bit Multipliers

These days in the electronic industry research and work are at its boom for area scaling, interconnection delay, and less power consumption. On CMOS devices work has been already done as they were scaled down to a very small size, but for many applications CMOS alone is insufficient. Here, memristors can be put in use, as they show a wide range of capabilities to overcome problems regarding less interconnection delay, and lesser power consumption. In this paper, memristor based 2×2 and 3×3 multiplier is simulated with different approaches using MRL universal gates and it is compared with CMOS based multiplier and conventional memristor based multiplier. All the simulations for proposed memristor based multipliers are implemented at 45nm channel length of CMOS using the HSPICE-EDA tool.