Miracle-dz/UniFAR-FTUs huggingface dataset:
Auto Data Augmentation for Image: A Brief Survey

Auto data augmentation has emerged as a promising alternative to the laborious manual parameter tuning involved in data augmentation policies. However, the existing approaches have limitations in terms of their applicability to a restricted range of models, datasets, and tasks. In this brief survey, we identify and discuss four key issues that auto data augmentation needs to tackle. We categorize auto data augmentation into two main types: closed-loop/open-loop auto data augmentation and online/offline auto data augmentation. Each of these categories is examined in detail, with a comprehensive analysis of their respective performance. Furthermore, we highlight the challenges that the field of auto data augmentation faces and offer insights into potential research directions. Our survey serves as a valuable resource for researchers seeking a deeper understanding of the evolving landscape of auto data augmentation, providing inspiration and guidance for future work in this area.
Local Intensity Gradient Directional Pattern Based Coral Image Classification using Deep Neural Network

Coral reefs are one of the world's most biologically diversified ecosystems on the earth that supports most of the marine life. The identification of coral species is essential in the conservation and monitoring process. Hence, an accurate automatic coral classification system is necessary to identify different types of corals. In this work, a new feature extraction method Local Intensity Gradient Directional Pattern (LIGDP) is proposed to extract features from coral images. The proposed method extracts the two maximum intensity changes for each pixel in the image by mapping four directional orientation approach i.e for each 3ⅹ3 intensity value two more 3x3 intensity values are extracted from four directional values. Finally, the LIGDBP pattern integrates the three 3x3 local grid pixel values extracted. To classify the coral images a new deep convolution neural network (CNN) is proposed with five layers. This custom neural network increases the classification accuracy with less number of parameters. The experiments were conducted with various coral image and video datasets to show the efficiency of the proposed technique. Moreover, the LIGDBP feature descriptor with a custom CNN model is compared with the existing techniques to demonstrate the efficiency of the proposed technique. The proposed model achieves a higher classification rate of 100% on EILAT-2 coral dataset.
HCINet: A Hierarchical Approach of Context Integration in Real-time Semantic Segmentation for Autonomous Driving

A dense pixel-level prediction problem, semantic segmentation of road scenes for autonomous driving requires real-time processing. While sizeable deep learning networks have high accuracy, they are unsuitable for resource-constrained environments such as embedded or edge devices. Lightweight networks suffer from accuracy loss due to limited feature representation capability at a particular scale. To solve this problem, we present a novel convolutional neural network-based lightweight architecture which jointly learns features at multiple scales in a hierarchical fashion. The proposed Hierarchical Context Integration Network (HCINet) follows a multi-pathway design with feature fusion from adjacent branches for effective segmentation. The decoder uses attention and context guidance from a higher-resolution branch to further improve accuracy while being fast and lightweight. Extensive experiments on Cityscapes and CamVid datasets show the effectiveness of the proposed method. Specifically, with only 1.65 Million parameters, our network can achieve 74.5% mean IoU on Cityscapes at 82.4 frames-per-second. At the same time, it achieves 67.9% mIoU at 70 fps on the CamVid dataset.
Estimation of Continuous Arterial Blood Pressure Waveform from Photoplethysmogram in Terms of Loss Function Focus on Blood Pressure

Blood pressure (BP) monitoring is one of the most important factors in preventing cardiovascular disease. Existing blood pressure measurement methods have limitations in that they are inappropriate for real-time blood pressure monitoring. Recently, many studies have been conducted to estimate blood pressure using Photoplethysmogram (PPG), which has several advantages. Arterial blood pressure (ABP) reconstruction studies from PPG have attempted to overcome the limitations of existing studies. The ABP waveform can obtain the most accurate blood pressure and also contribute to the diagnosis and prevention of cardiovascular diseases. In this study, we propose a novel loss function that can be applied to a machine-learning or deep-learning model that reconstructs PPG into ABP. We confirmed the improvement in blood pressure estimation performance by applying the proposed loss function.
Error Resilient Approximate Restoring Divider for Image Processing Application

The number of transistors and the complexity of the circuits both rise as Very Large-Scale Integration (VLSI) technology progresses, which leads to an increase in the amount of power that is required. Due to the wave borrow subtraction that occurs in each row of partial remainder computation, division is a more complicated mathematical operation than addition or multiplication. Computing by approximation is gradually becoming the norm for producing compact, economical, and high-speed solutions, particularly in architectures that are tolerant of mistakes to some level. As a result of the persistence of vision, even minute differences in the results of image and video processing apps go undetected. As a result, applications of this kind provide the optimal setting in which to put approximation computing into practice. The image division application is going to be performed using a 16/8 division module, which is proposed in this study. Approximate Divider Cell ADC1, ADC2, ADC3 and ADC4 are modified versions of the actual cell that makes up the division module. This was done in order to minimize the space and power consumption of the overall design. ADC1, ADC2, ADC3 and ADC4 show area and power improvement of (23.82%, 19.68), (17.41%, 48.56%), (26.82%, 48.88%) and (13.03%, 14.36%) respectively, for approximation factor 8, when compared to exact restoring divider.
P5G: A Patient-Centered Design Method of Virtual Reality Health Game System for Children's Amblyopia Rehabilitation

Amblyopia is a prevalent ophthalmic disease that occurs during the sensitive period of children's visual development, which can have adverse effects on patients' visual and psychological well-being. In recent years, there has been a significant focus on digital amblyopia therapy, particularly with advancements in virtual reality (VR) technology and functional game design. This study is based on the P5 eHealth approach, a theory of digital healthcare that identifies five essential properties of a user-centered eHealth system, including preventive, predictive, personalized, participatory, and psycho-cognitive. In this paper, we present a P5G design framework, which refers to P5 eHealth for health games, and we have utilized this framework for personalized health game design. Additionally, we have developed a VR game system for children's amblyopia rehabilitation to demonstrate the effectiveness of the P5G design framework. The proposed system incorporates visual function principles and amblyopia rehabilitation strategies, prioritizes binocular vision, and consists of multiple modules for visual training.
Semantic-Aware Remote State Estimation in Digital Twin with Minimizing Age of Incorrect Information

In this paper, we investigate the semantic-aware efficient sampling policy for remote state estimation in a digital twin (DT) empowered smart factory with multiple wireless sensing devices and an edge server. In this setting, wireless sensing devices must continuously sample the factory states and transmit semantic-aware sensing data to the server. Using the received sensing data, the server builds a realtime DT mapping remotely that analyzes and predicts the events in the factory. Since the DT requires continuous data transmission, maintaining the DT inevitably consumes significant amounts of limited wireless resources. To address this issue, we reduce the required amount of data transmission by making wireless devices only send the semantic-aware sensing data that indicates the occurrence of events, otherwise stay idle. In particular, we first invoke the age of incorrect information (AoII) to measure the semantic of the sensing data, which represents the freshness of the concerned events. Next, we formulate an optimization problem that minimizes the long-term AoII of remote state estimation through the devices deciding whether to sample the factory states at each time slot. To solve this problem, we first transform the original problem into a state-wise constrained Markov decision programming (CMDP) and then propose a soft actor-critic (SAC) based algorithm to learn a sampling policy to take sample actions within the sampling rate constraint, while considering packet error. Simulation results show that, the proposed algorithm can reduce the number of samples by up to 44% compared to the error-based sampling scheme, with the same estimation accuracy.
Back Flashover Performance of 500 kV Double Circuit Transmission Lines Struck by Lightning

Effective electricity transmission system is critical to the power system stability at the consumer ends. To ensure an uninterruptible supply, the insulation of the transmission lines must be evaluated and coordinated. Research has shown that lightning contributes the largest percentage of outages in tropical countries. This work presents an Electromagnetic Transient Program (EMTP)-based back flashover performance evaluation for 500 kV double circuit transmission lines during lightning events in Malaysia. The main parameters considered are the resistance of the tower footing and lightning current waveshapes and magnitudes. The results found that those parameters influence the reliability of the transmission system during lightning incidence. Hence, the need to devise an adequate protection system is necessary. The findings from this work are useful to electrical utilities for identifying root causes of the transmission lines back flashover occurrence.
Performance Analysis of Cylindrical Dielectric Resonator Antenna for Ultra-Wideband and Sub-6 GHz Wireless Communication

This study focuses on designing a cylindrical dielectric resonator antenna (CDRA) that can work effectively across a wide frequency range, including the ultra-wideband (UWB) and sub-6 GHz bands. The paper shows the proposed antenna's analysis with the goal of cylindrical shape parameters, while a Rogers RT5880 substrate is used. To better understand the antenna's performance, the study also compares it with previous research on CDRA and similar other shape antennas. The designed CDRA antenna operates at 4.584 GHz in free space and achieves an impressive efficiency of 94.15%. It offers a wide bandwidth of approximately 582 MHz around its resonant frequency of 4.584 GHz. At this frequency, the antenna exhibits a low return loss of -58.28 dB and a substantial gain of 6.916 dB. The primary objective of this research is to enhance the performance of CDRA antenna and explore the influence of cylindrical sizes in dielectric resonator antenna (DRA). The study thoroughly discusses the performance of the proposed CDRA antenna, delves into the reasons behind its effectiveness, and explores the wide implications of using cylindrical sizes in DRA.
Shimmy: Accelerating Inter-Container Communication for the IoT Edge

Cloud-native technologies consisting of containers, microservices, and service meshes bring the traditional advantages of Cloud Computing like scalability, composability, and rapid deployability to the IoT Edge. An application built on the microservices architecture relies on a collection of individual containerized components offering modular services via REST or gRPC interfaces over the network. Compared to a monolithic application, the magnitude of data and control exchange between the components of a microservices application is several orders higher. Studies have shown that overheads caused by such inter-container communication are a significant hurdle in achieving the sub-50ms latencies required for 5G enabled network Edges comprised of a much smaller compute cluster, unlike the Cloud. In this paper, we present Shimmy - a shared memory-based communication interface for containers that is cleanly integrated into the Kubernetes orchestration architecture while offering significant acceleration for microservices. Results have shown a consistent 3-4x latency improvement over UDP and TCP, as much as 20x latency improvement over RabbitMQ, while significantly reducing memory and CPU usage in large data transfers as well as real-time video streaming.
The Integration of Connectivity and System Integrity Approaches using Machine Learning for Enhancing Network Security

The practise of environment monitoring has substantially advanced because to recent developments in technology for communication, “like the Internet of Things (IoT)”. Technological solutions have the capacity to track, analyse, and understand our surroundings, enabling modernizations that improve the standard of living. Billion intelligent gadgets are connected via the “Internet of Things (IoT)”, so they may easily communicate with each other. It is the computer industry with the quickest rate of growth. On the one hand, Technological solutions are essential for improving a number of practical smart applications that can raise the quality of life. “Machine learning, the Internet of Things”, and other cutting-edge digital technologies enable management to acquire, analyse, interpret, and store huge volumes of data quickly and effectively. Content that may be retrieved later can also be saved using these techniques. This also puts the organization's ability to protect the information and data needed to sufficiently improve network security in grave danger. The purpose of this study is to illustrate how machine learning may be used to integrate connectivity and system integrity techniques for boosting network security. The researchers have critically explained the research topic comprehensively by adopting appropriate research methodology. Furthermore, This study has taken into account secondary and empirical research to gather relevant information related to research topic.
Design of a Robotic Arm for Human Contactless Patient Monitoring Through Measuring Different Bio-signals

The importance of human contactless monitoring was greatly felt during the COVID-19 pandemic. Physical interactions were reduced to maintain safety precautions against spreading the contagious diseases from person to person. Learning lessons from this, various industries, including the healthcare system, are now emphasizing human contactless or touchless alternative solutions. This research work is motivated by the need to address critical healthcare challenges posed by contagious diseases, limited resource availability, and an inadequate number of healthcare workers. The objective of this research work is to design and implement a 3D-printed robotic arm that is equipped with advanced sensors capable of measuring multiple non-invasive vital bio-signals. This robotic arm is equipped with multiple sensors, including a photoplethysmography (PPG) sensor, an electrocardiogram (ECG) sensor, an electrodermal activity (EDA) sensor, and a temperature sensor to measure various parameters such as cuffless blood pressure, heart rate, oxygen saturation (SpO2) from PPG and HRV analysis from ECG, sweat and stress detection, and skin temperature, while presenting the data on a display. One of the key aspects of this research work is its ability to obtain data from R-R intervals to analyze SDNN, SDANN, RMSSD, SDSD, pNN50, and pNN20, offering valuable insights into heart rate variability (HRV) for cardiovascular disease and mental health detection from the sensor implemented in the robotic arm. The hardware results are carefully compared with real-life data to validate the system's effectiveness. This research work is unique due to the implementation of a noble vital sign measurement technique, analysis, and decision-generating system that enhances its effectiveness in the healthcare system.
Real-Time Image Super-Resolution using Drone through GFPGAN and Nvidia Jetson Nano

Blind facial restoration generally uses facial priors, including a reference or a facial geometrical prior, to restore accurate and realistic details. The absence of severely low-quality inputs and the lack of high-quality references, however, significantly restrict its relevance to real-world situations. This work shows a GFPGAN model for blind face restoration that utilizes the rich and varied priors present in a pre-trained face GAN to enhance the quality and perspective of images captured by a drone in real-time. Implementing this model on an edge AI device illustrates the harmonious combination of realism and fidelity in face restoration. This is achieved by evaluating its performance on different images captured from various angles and distances. For processing on edge AI devices, the suggested system uses an Nvidia Jetson Nano, an affordable but powerful single-board computer. We present the results of a feasibility study demonstrating the capacity of GFPGAN to improve the caliber of real-time drone photography.
Proximal Policy Optimization for User Association in Hybrid LiFi/WiFi Indoor Networks

Hybrid Light-Fidelity (LiFi) and Wireless-Fidelity (WiFi) indoor networking has been envisioned as a promising technology to alleviate radio frequency (RF) spectrum crunch to accommodate the ever-increasing data rate demand in indoor scenarios. The hybrid LiFi/WiFi indoor networks can utilize the advantages of fast data transfer from LiFi and wider coverage of WiFi, thus complementing well with each of these technologies and increasing the network performance compared to the standalone networks. This work investigates the user-access point (AP) association problem to achieve better network-wide spectral efficiency. We first derive a mathematical formulation of the sum data rate maximization problem by determining the AP selection for each user in indoor networks with either static or mobile users. It is shown that the formulated network control problem is a mixed integer nonlinear nonconvex problem. A proximal policy optimization (PPO) based reinforcement learning method is then proposed to determine the AP assignment. The network performance is evaluated by comparing it with exhaustive search, signal strength strategy, and trust region policy optimization methods. The results indicate that the designed method achieves a network-wide data rate with up to 100% and 97.7% optimality in static and mobile user scenarios, respectively.
Cache-Assisted Content Delivery for NOMA-Based Satellite-Edge-Terminal Cooperation Networks

To satisfy differentiated service requirements of delay-sensitive and computing-intensive tasks in satellite communication networks, we propose a cache-assisted low-latency content distribution scheme in multi-cell multi-carrier nonorthogonal multiple access (MCMC-NOMA)-based satellite-edge-terminal cooperation environments. In the paper, a delay minimization problem is formulated to achieve optimal content distribution by jointly optimizing the allocation of subchannels and transmit power, which is decomposed and solved in each slot by using many-to-one matching and successive convex approximation, respectively. Simulation results show that the proposed model significantly improve network latency and content distribution in comparison to its existing counterparts in cloud-edge-terminal cooperation networks.
An Exploration of the Use Cases for Artificial Intelligence in Data Access Optimization

This paper provides an exploration of the use cases for artificial intelligence (AI) in information access optimization. AI-based tactics to statistics optimization have the ability to enhance performance and reduce cost drastically. The paper discusses the characteristics of AI-pushed optimization techniques, together with fusion of heterogeneous facts assets, predictive modeling, forecasting, anomaly detection, and clustering. It also explores the numerous statistics assets that can be optimized using AI, which includes conventional database systems, disbursed information lakes, and streaming event statistics. Moreover, the paper considers the demanding situations related to AI-enabled facts get admission to optimization, such as loss of availability of labeled data and issue in imposing commercial enterprise good judgment. Finally, excellent practices and considerations for implementation are mentioned. Through exploring the use instances for AI in records get entry to optimization, this paper provides important insights for builders and businesses considering the usage of AI for data get entry to optimization.
Analysis of Computer Image Processing Technology based on Intelligent Optimization Algorithm

With the continuous development of computer technology, computer image processing technology has become an indispensable part of modern society. Computer image processing technology mainly includes image acquisition, image enhancement, image compression, image recognition and so on. In this process, intelligent optimization algorithm plays a very important role. This paper will introduce the application of intelligent optimization algorithm in computer image processing technology in detail.
An improved Spectral Analysis based Subspace Detection for Hyperspectral Image Classification

Hyperspectral imagery, with its rich spectral information, is indispensable in various remote sensing applications. However, working with hyperspectral data introduces two formidable challenges: the curse of dimensionality and the predicament of class imbalance. The inherent high dimensionality necessitates substantial computational resources, while class imbalance undermines overall classification accuracy due to the scarcity of samples in training for certain classes. To mitigate the curse of dimensionality, this research analyses a few diverse feature extraction techniques, including Principal Component Analysis (PCA), Sparse PCA, segmented PCA, and Linear Discriminant Analysis (LDA). Although these methods can reduce the features from the original dataset, however sometimes depend on certain global statistics such as variance. Also, these methods could not handle the small classes as they could not represent the overall characteristics of the samples. Therefore, the proposed method incorporates the solution to the class imbalance problem as well as reduces the features based on the given classes. The ensuing classification phase employs a kernel Support Vector Machine (KSVM), achieving an accuracy of 88.81% only when the class imbalance exists. Later the class imbalances are resolved through the incorporation of Synthetic Minority Over-sampling Technique (SMOTE). The proposed approach reduces the input features and generates synthetic data for each class based on the proportions original simultaneously rectifying class imbalance while preserving dataset characteristics. Remarkably, this strategy results in an improvement of accuracy to 98.69%. This study underscores the significance of hyperspectral imagery, delineates its inherent challenges, and presents novel solutions that substantially enhance classification performance, making hyperspectral data more accessible and valuable in remote sensing endeavors.