Search

Sign language is undoubtedly a common way of communication among deaf and non-verbal people. But it is not common among hearing people to use sign language to express feelings or share information in everyday life. Therefore, a significant communication gap exists between deaf and hearing individuals, despite both groups experiencing similar emotions and sentiments. In this paper, we developed a convolutional neural network–squeeze excitation network to predict the sign language signs and developed a smartphone application to provide access to the ML model to use it. The SE block provides attention to the channel of the image, thus improving the performance of the model. On the other hand, the smartphone application brings the ML model close to people so that everyone can benefit from it. In addition, we used the Shapley additive explanation to interpret the black box nature of the ML model and understand the models working from within. Using our ML model, we achieved an accuracy of 99.86% on the KU-BdSL dataset. The SHAP analysis shows that the model primarily relies on hand-related visual cues to predict sign language signs, aligning with human communication patterns. [ABSTRACT FROM AUTHOR]

Bibliometric analysis is a rigorous method to analyze significant quantities of bibliometric data to assess their impact on a particular field. This study used bibliometric analysis to investigate the academic research on diabetes detection and classification from 2000 to 2023. The PRISMA 2020 framework was followed to identify, filter, and select relevant papers. This study used the Web of Science database to determine relevant publications concerning diabetes detection and classification using the keywords "diabetes detection", "diabetes classification", and "diabetes detection and classification". A total of 863 publications were selected for analysis. The research applied two bibliometric techniques: performance analysis and science mapping. Various bibliometric parameters, including publication analysis, trend analysis, citation analysis, and networking analysis, were used to assess the performance of these articles. The analysis findings showed that India, China, and the United States are the top three countries with the highest number of publications and citations on diabetes detection and classification. The most frequently used keywords are machine learning, diabetic retinopathy, and deep learning. Additionally, the study identified "classification", "diagnosis", and "validation" as the prevailing topics for diabetes identification. This research contributes valuable insights into the academic landscape of diabetes detection and classification. [ABSTRACT FROM AUTHOR]

The traditional base station in C-RAN is divided into three parts: a pool of centralized baseband units (BBUs), a fronthaul network that links the BBUs and remote radio units (RRUs), and RRUs. This paper proposes a novel cooperative algorithm for resource optimization in a time-wavelength division multiplexed (TWDM) passive optical network (PON) incorporating a cloud radio access network (C-RAN). First, a joint collaborative strategy is deployed to optimize cooperative caching and transmission in the wireless and optical domains. Then, the quality of experience (QoE) is improved by bandwidth configuration and caching. Simulation results show that the average throughput of the proposed QoE-aware video cooperative caching and transmission mechanism (QACCTM) algorithm is approximately 30% higher than that of other algorithms. Compared with the relative average residual clutter power (RARCP) and quality-aware wireless edge caching (QAWEC) algorithms, the proposed QACCTM algorithm reduces the access delay by approximately 27.1% and 15.9%, respectively. [ABSTRACT FROM AUTHOR]

Everyone aspires to live a healthy life, but many will inevitably experience some form of disease, illness, or accident that results in disability at some point. Rehabilitation plays a crucial role in helping individuals recover from these disabilities and return to their daily activities. Traditional rehabilitation methods are often expensive, are inefficient, and lead to slow progress for patients. However, in this era of technology, various sensor-based automatic rehabilitation is also possible. A Kinect sensor is a skeletal tracking device that captures human motions and gestures. It can provide feedback to the users, allowing them to better understand their progress and adjust their movements accordingly. In this study, stroke-based rehabilitation is presented along with the Toronto Rehab Stroke Pose Dataset (TRSP). Pre-processing of the raw dataset was performed using various features, and several state-of-the-art classifiers were applied to evaluate the data provided by the Kinect sensor. Among the various classifiers, eXtreme Gradient Boosing (XGB) attained the maximum accuracy of 92% for the TRSP dataset. Furthermore, hyperparameters of the XGB have been optimized using a metaheuristic gray wolf optimizer for better performance. [ABSTRACT FROM AUTHOR]

With the rapid growth of data traffic and mobile devices, it is imperative to provide reliable and stable services during mobility. Heterogeneous Networks (HetNets) and dense networks have been identified as potential solutions to address the upcoming capacity crunch, but they also pose significant challenges related to handover optimization. This paper presents a comprehensive review of recent handover decision algorithms in HetNets, categorizing them based on their decision techniques and summarizing their input parameters, techniques, and performance evaluations. Our study highlights the technical challenges and opportunities related to handovers in HetNets and dense cellular networks and provides key findings from recent studies. The significance of this survey is to provide a comprehensive overview of handover decision algorithms in HetNets and dense cellular networks, which can aid in the development of more advanced handover optimization approaches.

This paper proposes the design of an efficient ambient solar energy harvesting (SEH) system with enhanced protection for low‐power devices. The proposed SEH system is designed and fabricated on a 1.6 mm Flame Retardant 4 (FR4) substrate using EasyEDA software. The harvester consists of a 3 W monocrystalline solar panel, a solar charger module with maximum power point tracking, a lithium‐ion rechargeable battery and a battery protection integrated circuit, and a DC–DC converter. Each of the SEH system component modules is optimized for better performance. The proposed energy harvester achieves a peak power conversion efficiency of 81.22% with a total output power of 2.35 W by using a CN3791 battery charger module. This approach is cost‐effective, highly sustainable, and virtually maintenance‐free. It can be deployed in various low‐powered devices such as wireless sensor network nodes and Internet of Things microcontrollers to extend their battery lifetimes, thereby achieving environmental‐friendly and sustainable energy harvesting. [ABSTRACT FROM AUTHOR]

This paper proposed the design of a dual-port rectifier with multifrequency operations. The RF rectifier is achieved using a combination of L-section inductive impedance matching network (IMN) at Port-1 with a multiple stubs impedance transformer at Port-2. The fabricated prototype can harvest RF signal from GSM/900, GSM/1800, UMTS/2100, Wi-Fi/2.45 and LTE/2600 frequency bands at (0.94, 1.80, 2.10, 2.46, and 2.63 GHz), respectively. The rectifier occupies a small portion of a PCB board at 0.20 μg × 0.15 λg. The proposed circuit realized a measured peak RF-to-dc (radio frequency direct current) power conversion efficiency (PCE) of (21%, 22.76%, 25.33%, 21.57%, and 22.14%) for an input power of -20 dBm. The RF harvester attains a measured peak RF-to-dc PCE of 72.70% and an output dc voltage of 154 mV for an input power of 3 dBm at 2.46 GHz. Measurement of the proposed rectifier in the ambiance gives a peak dc output voltage of 376.1 mV from the five signal tones. Similarly, a low-powered bq25504-674 evaluation module (EVM) is integrated with the rectifier. The module boost and drive the rectifier output dc voltage to 945 mV. The performance of the proposed rectifier in the ambiance environment makes it a suitablemodule for low-powered RF applications. [ABSTRACT FROM AUTHOR]

Ultra-wideband is a wireless technology arisen for future high speed multimedia applications. It can provide data rate in excess of Gigabits per second by transmitting impulse signal through the free space. However, the ultra-wideband indoor channel models proposed by the IEEE P802.15.3a suffer long multipath propagation. Due to this multipath effect, several studies have been done to improve the bit-error-rate performance of the ultrawideband system in the existence of severe interference. Yet, most of the proposed algorithms were formulated based on the Gaussian distribution, which is not true in ultra-wideband. In this paper, we first analyze the statistical behavior of the CDMA-UWB signal by applying the Kullback-Leibler divergence index. Based on the analysis, a non-Gaussian equalizer is developed by deriving an enhanced bit-error-rate optimization algorithm using the Generalized Gaussian approach. The proposed equalizer has been shown to achieve a performance gain of at least 1.5dB to 2dB over the other equalizers simulated under IEEE P802.15.3a channel models. [ABSTRACT FROM AUTHOR]

ABSTRACT A single layer of equilateral triangular patch antenna is achieved, which operates at 870 MHz and 2.45 GHz suitable for UHF and microwave radio frequency identification application. By shaping the corners of triangular patch, the antenna excites the radiation modes of TM10 and TM21 and suppresses the unwanted TM20 mode to give boresight of directed radiation patterns. This also alters the excited surface distribution of the radiating patch to enhance the antenna gain. A good agreement is obtained between the simulated and experimental results. © 2014 Wiley Periodicals, Inc. Microwave Opt Technol Lett 56:69-72, 2014 [ABSTRACT FROM AUTHOR]

A novel UHF printed loop antenna backed by a conducting loop reflector is proposed for near field RFID tag antenna application. The backed conducting loop reflector is adopted to achieve stronger magnetic field distribution along the loop tag antenna in order to work properly in proximity to the water and metallic object. The proposed antenna is constructed on FR4 substrate with the dimension of 30 mm × 30 mm × 3.2 mm. The simulated results exhibit constant current and uniform magnetic distribution to operate frequency at 915 MHz. The near field performance of the proposed antenna is practically verified by the read range measurement and compared with the commercially-available near field tag antenna to offer a promising solution of above-mentioned problems. [ABSTRACT FROM AUTHOR]