ERIRMS Evaluation of the Reliability of IoT-Aided Remote Monitoring Systems of Low-Voltage Overhead Transmission Lines.

Researchers have studied instances of power line technical failures, the significant rise in the energy loss index in the line connecting the distribution transformer and consumer meters, and the inability to control unauthorized line connections. New, innovative, and scientific approaches are required to address these issues while enhancing the reliability and efficiency of electricity supply. This study evaluates the reliability of Internet of Things (IoT)-aided remote monitoring systems specifically designed for a low-voltage overhead transmission line.

Correction: Yuldashev et al. Parking Lot Occupancy Detection with Improved MobileNetV3. 2023, , 7642.

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Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks.

As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem.

Parking Lot Occupancy Detection with Improved MobileNetV3.

In recent years, parking lot management systems have garnered significant research attention, particularly concerning the application of deep learning techniques. Numerous approaches have emerged for tackling parking lot occupancy challenges using deep learning models. This study contributes to the field by addressing a critical aspect of parking lot management systems: accurate vehicle occupancy determination in specific parking spaces.

Development of Language Models for Continuous Uzbek Speech Recognition System.

Automatic speech recognition systems with a large vocabulary and other natural language processing applications cannot operate without a language model. Most studies on pre-trained language models have focused on more popular languages such as English, Chinese, and various European languages, but there is no publicly available Uzbek speech dataset. Therefore, language models of low-resource languages need to be studied and created.

Masked Face Emotion Recognition Based on Facial Landmarks and Deep Learning Approaches for Visually Impaired People.

Current artificial intelligence systems for determining a person's emotions rely heavily on lip and mouth movement and other facial features such as eyebrows, eyes, and the forehead. Furthermore, low-light images are typically classified incorrectly because of the dark region around the eyes and eyebrows. In this work, we propose a facial emotion recognition method for masked facial images using low-light image enhancement and feature analysis of the upper features of the face with a convolutional neural network.

Enhanced Classification of Dog Activities with Quaternion-Based Fusion Approach on High-Dimensional Raw Data from Wearable Sensors.

The employment of machine learning algorithms to the data provided by wearable movement sensors is one of the most common methods to detect pets' behaviors and monitor their well-being. However, defining features that lead to highly accurate behavior classification is quite challenging. To address this problem, in this study we aim to classify six main dog activities (standing, walking, running, sitting, lying down, and resting) using high-dimensional sensor raw data.

A Wildfire Smoke Detection System Using Unmanned Aerial Vehicle Images Based on the Optimized YOLOv5.

Wildfire is one of the most significant dangers and the most serious natural catastrophe, endangering forest resources, animal life, and the human economy. Recent years have witnessed a rise in wildfire incidents. The two main factors are persistent human interference with the natural environment and global warming.

Design and Implementation of Embedded-Based Vein Image Processing System with Enhanced Denoising Capabilities.

In general, it is very difficult to visually locate blood vessels for intravenous injection or surgery. In addition, if vein detection fails, physical and mental pain occurs to the patient and leads to financial loss in the hospital. In order to prevent this problem, NIR-based vein detection technology is developing.

Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning.

Classification of fruit and vegetable freshness plays an essential role in the food industry. Freshness is a fundamental measure of fruit and vegetable quality that directly affects the physical health and purchasing motivation of consumers. In addition, it is a significant determinant of market price; thus, it is imperative to study the freshness of fruits and vegetables.

Automatic Speech Recognition Method Based on Deep Learning Approaches for Uzbek Language.

Communication has been an important aspect of human life, civilization, and globalization for thousands of years. Biometric analysis, education, security, healthcare, and smart cities are only a few examples of speech recognition applications. Most studies have mainly concentrated on English, Spanish, Japanese, or Chinese, disregarding other low-resource languages, such as Uzbek, leaving their analysis open.

Automatic Fire Detection and Notification System Based on Improved YOLOv4 for the Blind and Visually Impaired.

The growing aging population suffers from high levels of vision and cognitive impairment, often resulting in a loss of independence. Such individuals must perform crucial everyday tasks such as cooking and heating with systems and devices designed for visually unimpaired individuals, which do not take into account the needs of persons with visual and cognitive impairment. Thus, the visually impaired persons using them run risks related to smoke and fire.

Automatic Object Detection Algorithm-Based Braille Image Generation System for the Recognition of Real-Life Obstacles for Visually Impaired People.

The global prevalence of visual impairment due to diseases and accidents continues to increase. Visually impaired individuals rely on their auditory and tactile senses to recognize surrounding objects. However, accessible public facilities such as tactile pavements and tactile signs are installed only in limited areas globally, and visually impaired individuals use assistive devices such as canes or guide dogs, which have limitations.

Design and Implementation of a Video/Voice Process System for Recognizing Vehicle Parts Based on Artificial Intelligence.

With the recent development of artificial intelligence along with information and communications infrastructure, a new paradigm of online services is being developed. Whereas in the past a service system could only exchange information of the service provider at the request of the user, information can now be provided by automatically analyzing a particular need, even without a direct user request. This also holds for online platforms of used-vehicle sales.

Towards Tangible Vision for the Visually Impaired through 2D Multiarray Braille Display.

This paper presents two methodologies for delivering multimedia content to visually impaired people with the use of a haptic device and braille display. Based on our previous research, the research using Kinect v2 and haptic device with 2D+ (RGB frame with depth) data has the limitations of slower operational speed while reconstructing object details. Thus, this study focuses on the development of 2D multiarray braille display using an electronic book translator application because of its accuracy and high speed.

Carrier Transport Properties of MoS Asymmetric Gas Sensor Under Charge Transfer-Based Barrier Modulation.

Over the past few years, two-dimensional materials have gained immense attention for next-generation electric sensing devices because of their unique properties. Here, we report the carrier transport properties of MoS Schottky diodes under ambient as well as gas exposure conditions. MoS field-effect transistors (FETs) were fabricated using Pt and Al electrodes.

A Reduced Graphene Oxide Based Radio Frequency Glucose Sensing Device Using Multi-Dimensional Parameters.

A reduced graphene oxide (RGO) based glucose sensor using a radio frequency (RF) signal is demonstrated. An RGO with outstanding electrical property was employed as the interconnector material between signal electrodes in an RF electric circuit, and it was functionalized with phenylbutyric acid (PBA) as a linker molecule to bind glucoses. By adding glucose solution, the fabricated sensor with RGO and PBA showed detecting characteristics in RF signal transmission and reflection.

Optimization of lamp arrangement in a closed-conduit UV reactor based on a genetic algorithm.

The choice for the arrangement of the UV lamps in a closed-conduit ultraviolet (CCUV) reactor significantly affects the performance. However, a systematic methodology for the optimal lamp arrangement within the chamber of the CCUV reactor is not well established in the literature. In this research work, we propose a viable systematic methodology for the lamp arrangement based on a genetic algorithm (GA).

Numerical study of the effects of surface roughness on water disinfection UV reactor.

UV reactors are an emerging choice as a big barrier against the pathogens present in drinking water. However, the precise role of reactor's wall roughness for cross flow ultraviolet (CF-UV) and axial flow ultraviolet (AF-UV) water disinfection reactors are unknown. In this paper, the influences of reactor's wall roughness were investigated with a view to identify their role on the performance factors namely dose distribution and reduction equivalent dose (RED).

Elastically deformable model-based motion-tracking of left ventricle.

The motion of the myocardium is a sensitive indicator of many types of heart disease. Quantitative characterization of this motion is essential for the accurate diagnosis and treatment of heart disease. Although several magnetic resonance imaging (MRI) techniques, such as tagged MRI and phase contrast MRI, provide noninvasive tools to obtain correlation of the position of points within the myocardium between images taken at subsequent time phases, the accurate tracking of the movement of these points remains a challenge due to the relatively low out-of-plane resolution of these imaging techniques.

Cardiac segmentation by a velocity-aided active contour model.

Heart disease is one of the more life-threatening diseases. Accurate diagnosis and treatment are central to the survival of patients. Numerous diagnostic methods that can assess abnormalities of the heart have been developed.