bluuebunny/crossref_metadata_2025 huggingface dataset:
Online reviews on E-commerce platforms in Vietnam: The role of trust in behavioral intention
Authors: Phuong Duy Nguyen Binh , Kien Dang Trung , Thinh Vuong Minh , Phuong Thuy Nguyen Binh
Published: December 2024

This comprehensive study thoroughly explores the factors related to online reviews that influence users’ purchase intention through trust. Drawing on robust theoretical foundations and relevant research, the study establishes connections between trust-promoting factors and user behavioral intentions on e-commerce platforms in Vietnam. The study, which involved a survey of 680 users for data collection and partial least squares structural equation modeling (PLS-SEM) analysis, unveils that the usefulness of online reviews is the key driver of behavioral intention. Moreover, trust plays a pivotal mediating role in linking the quality and rating of online reviews to users’ purchase intentions. These findings not only enrich the theoretical foundation of behavioral research on online platforms but also offer practical managerial implications, empowering e-commerce platforms to develop a comprehensive review system and enhancing users’ online shopping experience.
Predictive model for converting leads into repeat order customer using machine learning
Authors: Maured Deryan Everestha , Kusuma Gede Putra
Published: December 2024

In the competitive business landscape, customer relationship management (CRM) is pivotal for managing customer relationships. Lead generation and customer retention are critical aspects of CRM as they contribute to sustaining business growth and profitability. Also, identifying and converting leads into repeat customers is essential for optimizing revenue and minimizing promotional costs. This study focuses on developing a predictive model using machine learning techniques to convert leads into repeat order customers in conventional businesses. Leveraging data from a motorcycle distribution company in Jakarta and Tangerang, the study compares the performance of various models for predicting repeat orders. This includes individual models like DeepFM, random forest, and gradient boosting decision tree models. Additionally, it explores the effectiveness of stacking these models using logistic regression as a meta-learner. Furthermore, the study implements backward feature elimination for feature selection and hyperband for hyperparameter tuning to enhance model performance. The results indicate that Stacking model using base model default configuration stands out as the most robust, achieving the highest scores in accuracy (0.95), area under the curve receiver-operating characteristic curve (AUC-ROC) (0.67), log loss (0.19), weighted average precision (0.95), weighted average recall (0.95), and weighted average F1- score (0.92), effectively handling the imbalanced dataset.
A system architecture for mixed reality systems in vocational schools in Indonesia
Authors: Suryodiningrat Satrio Pradono , Prabowo Harjanto , Ramadhan Arief , Santoso Harry Budi
Published: December 2024

In Indonesia, vocational schools are less favored compared to K -12 schools. Unfortunately, graduates from vocational schools do not fulfill the minimum requirements set by industries, particularly in the current era of industry revolution 5.0. This revolution aims to establish society 5.0, where humans and robots collaborate closely to achieve improved work outcomes. One technique to enhance the proficiency of graduates and prepare them for the workforce is by implementing a mixed-reality system. that will effectively address a multitude of issues and significantly enhance the caliber of graduates and before the implementation of mixed reality (MR) systems, it is necessary to create system architecture diagrams to ensures that the system can be utilized not only in specific schools but also in any vocational school in Indonesia. This study comprises 5 participants, including experts from both the professional and academic fields, who possess extensive knowledge in the domains of metaverse, MR systems, and information systems. The methodology employed in this study draws inspiration from James Martin’s rapid application development (RAD). The result of this study is a validated system architectural diagram, endorsed by experts, which depicts a metaverse-based MR system designed specifically for vocational schools in Indonesia
Srvycite: a hybrid scientific article recommendation system
Authors: R Sivasankari , Dhilipan J
Published: December 2024

A recommendation system is becoming part of every work done today to reduce the effort of work done by the users in searching for items in need by recommending new items that may be useful. This theme has also been used in research article recommendation systems for recommending articles of interest to researchers from a bulk of digital research documents spread across different databases on the internet. To ease the task of this article recommendation process, we have proposed a novel approach, Srvycite, by utilizing the survey article citation network along with the original research article network. The purpose of utilizing the survey article citation network is to detect the most influential articles that are considered to be important by other researchers in the same field. The Srvycite approach utilizes the text and meta features of articles to recommend papers. To preprocess the text features utilized, we have employed Word2Vec and bidirectional encoder representations from transformers (BERT) for vectorization. Then citation graph and survey citation graphs are generated to find the most influential nodes. The weighted text similarity score is finally computed by combining the cited by values and the text similarity score from the citation and survey citation graph to list articles as recommendations for the user. This system is proven to increase the accuracy of the article recommendation by 3.8 and 2.1 in the case of the precision and recall measures for performance evaluation.
A hybrid approach of pattern recognition to detect marine animals
Authors: Balachandran Vijayalakshmi , Shanmugavel Thanga Ramya , Kadarkarayandi Ramar , Kandhasamy Vijayalakshmi
Published: December 2024

Acquiring up-to-date knowledge about various animals will have a significant impact on effectively managing species within the ecosystem. Manually identifying animals and their traits continues to be a costly and time-consuming process. The development of a system using the most recent developments in computer vision machine learning was necessary to address the issues of detecting sharks and aquatic species in areas filled with surfers, rocks, and various other potential false positives. In the ocean most of the species are cold-blooded animals hence they cannot be tracked with thermal cameras. Ocean’s dynamic environment affects simple techniques like color separation, intensity histograms, and optical flow. Hence a hybrid approach using convolutional neural network - support vector machine (CNN-SVM) classifier is proposed to perform the pattern recognition. A CNN is employed for feature extraction by using the histogram of gradients value. Subsequently, a SVM classifier is employed to identify and categorise marine species in the vicinity of the seacoast. This serves to notify individuals who engage in swimming activities in the ocean. The suggested model is evaluated against alternative machine learning approaches, and it achieves a superior accuracy of 95% compared to the others.
The 360° beach video: a supporting mindfulness intervention with virtual reality
Authors: Naini Rohmatus , Wibowo Mungin Eddy , Purwanto Edy , Mulawarman Mulawarman , Anwas E. Oos M.
Published: December 2024

This article describes optimizing virtual reality (VR) with a 360° beach video model used for mindfulness interventions. Using VR with 360° beach videos to support the presence of an immersive environment can effectively support mindfulness practices. Students are interested in the integration of technology in school counseling. VR helps in creating immersive environments such as forests, beaches, waterfalls, etc. so that students focus more on practicing mindfulness and attention in the current moment. This article focuses on optimizing 360° beach videos in the breathing mindfulness process so that it helps bring out real experiences. Obstacles to practicing mindfulness include lack of focus, mind wandering and not concentrating. through the use of 360° beach videos with VR can increase focus and be more effective in mindfulness practice.
Ensemble approach to rumor detection with BERT, GPT, and POS features
Authors: Pattanaik Barsha , Mandal Sourav , Tripathy Rudra Mohan , Sekh Arif Ahmed
Published: December 2024

As vast amounts of rumor content are transmitted on social media, it is very challenging to detect them. This study explores an ensemble approach to rumor detection in social media messages, leveraging the strengths of advanced natural language processing (NLP) models. Specifically, we implemented three distinct models: (i) generative pre-trained transformer (GPT) combined with a bidirectional long short-term memory (BiLSTM) network; (ii) a model integrating part-of-speech (POS) tagging with bidirectional encoder representations from transformers (BERT) and BiLSTM, and (iii) a model that merges POS tagging with GPT and BiLSTM. We included additional features from the dataset in all these models. Each model captures different linguistic, syntactical, and contextual features within the text, contributing uniquely to the classification task. To enhance the robustness and accuracy of our predictions, we employed an ensemble method using hard voting. This technique aggregates the predictions from each model, determining the final classification based on the majority vote. Our experimental results demonstrate that the ensemble approach significantly outperforms individual models, achieving superior accuracy in identifying rumors. To determine the performance of our model, we used PHEME and Weibo datasets available publicly. We found our model gave 97.6% and 98.4% accuracy, respectively, on the datasets and has outperformed the state-of-the-art models.
Pilot study on deploying a wireless sensor-based virtual-key access and lock system for home and industrial frontiers
Authors: Eboka Andrew Okonji , Aghware Fidelis Obukohwo , Okpor Margaret Dumebi , Odiakaose Christopher Chukufunaya , Okpako Ejaita Abugor , Ojugo Arnold Adimabua , Ako Rita Erhovwo , Binitie Amaka Patience , Onyemenem Innocent Sunny , Ejeh Patrick Ogholuwarami , Geteloma Victor Ochuko
Published: December 2024

The rise in data processing activities vis-à-vis the consequent rise in adoption and adaptation of information and communication tech related approaches to resolve societal challenges has become both critical and imperative. Virtualization have become the order of the day to bridge various lapses of human mundane tasks and endeavors. Its positive impacts on society cannot be underestimated. This study advances a virtual wireless sensor-based key-card access system with cost-effective solution to manage access to restricted areas within a facility. We seek to integrate virtual key card access, web-access control, solenoid lock integration, and ESP32- controller to create a dependable internet of things (IoT)-enabled access control system. Results show system benefit includes improved security, improved convenience, privacy, efficiency with real-time control capabilities that will allows building administrators to track and manage access to the facility remotely.
Advanced optimization load frequency control for multi - islanded micro grid system with tie-line loading by using PSO
Authors: Pavan Gollapudi , Babu A. Ramesh , Prabhakar Bollu , Venkata Sai T. Datta , Rajeshwari M. , Reddy N. Raj , Kishore P. Venkata
Published: December 2024

This manuscript presents the design of a microgrid featuring solar and wind as uncontrollable energy sources, alongside controllable sources like batteries and a diesel generator, aiming to address power supply variations resulting from load fluctuations. Controllers are imperative to mitigate these challenges, and the manuscript emphasizes the need for precise tuning of gain values for optimal electrical energy utilization. In lieu of the trial-and-error approach, particle swarm optimization (PSO) is employed for enhanced steady-state response in the Microgrid. The study also introduces the application of proportional-integral (PI), proportional-integral-derivative (PID), and PID with feed forward (PIDF) controllers to effectively address and resolve identified issues ensuring improved system performance and consistent power supply stability in the microgrid system.
Assessing the user experience of marker-based 3D WebAR applications using user experience questionnaire
Authors: Tuah Nooralisa Mohd , Wan Ahmad Wan Nooraishya , Andrias Ryan MacDonell , Ajor Dg. Senandong , Sura Suaini , Ahmad Rodzuan Ahmad Rizal
Published: December 2024

Marker-based 3D web-based augmented reality (WebAR) applications are an emerging field that merges web technologies with augmented reality. WebAR has gained popularity because of its ability to provide users with a reliable and autonomous platform. Yet, a limited investigation has verified its application and user perspective on its ability to function. This study is designed to evaluate the user experiences of marker-based 3D WebAR applications using the user experience questionnaire (UEQ). This study assesses various elements of the user experience, including attractiveness, clarity, engagement, efficiency, and innovation, utilizing the UEQ. This study aims to analyze user perceptions and interaction patterns thoroughly to get useful insights into the usability and user satisfaction aspects of marker-based 3D WebAR apps. The findings reveal that the WebAR app is both appealing and efficient, instilling confidence in its users. This underscores the pivotal role of user experience in shaping the effectiveness and reception of WebAR applications. This research has the potential to influence the creation of more user-focused and engaging marker-based 3D WebAR experiences, improving user engagement and immersion in web-based augmented reality environments.
Cost-effective IoT-based automated vehicle headlight control system: design and implementation
Authors: Begum Momotaz , Ullah Nayeem , Shuvo Mehedi Hasan , Islam Towhidul , Hossen Thofazzol , Uddin Jia
Published: December 2024

The current world would be difficult without vehicles, which offer vital advantages for social connectivity, mobility, and technical advancement. Though motor vehicles provide benefits to passenger transportation, they also present certain challenges in their use. A major issue is nighttime traffic accidents caused by headlamps from automobiles traveling in reverse directions, that's why there is a high probability of accidents due to the glare on the driver's eyes. The phrase "Troxler effect" refers to an unexpected glare that a motorist recognizes. In this paper, we will provide an optimal solution to this challenge/Troxler effect. The primary objective of this paper is to design an internet of things (IoT)-based smart headlight control model. Our system introduced a cost-effective vehicle’s headlights controlled by light detection. According to this paper, a vehicle’s headlights are automatically rotated down when the sensor detects lights from the opposite direction of the vehicle headlights. We tried to reduce the road accident rate with our proposed system. This type of technology will prove useful in the motor vehicle sector and offer an innovative approach that ensures driver safety as well as increasing economic development.
Connected caregiving: investigating mothers in the era of digital access
Authors: Saidi Anissa , Von Wong Yee , Zachariah@ Omar Tirzah Zubeidah , Pek Lim Seong , Mee Mee Rita Wong , Leng Khoo Kim
Published: December 2024

Mothers have embraced and utilized digital access for nurturing and personal use to enhance their roles while balancing newfound demands. The Internet has provided mothers access to information on various topics, including pregnancy, childbirth, and infant care. Social media tools and platforms have also provided mothers with a space to connect with other mothers, share experiences, and seek support. This scoping review aims to identify the relationship of the focus skills among mothers in utilizing digital access. Four databases, including Scopus, web of science (WOS), education resources information centre (ERIC), and ScienceDirect, were used in this research, which found 36 articles for eligibility. Only 16 articles are eligible for analysis and reference after the exclusion and inclusion process for data collection. Based on the 16 publications examining mothers’ use of internet access, four essential skills have been identified. These included social, digital, cultural, and problem-solving skills and are acknowledged as being related to digital access mothering. The findings show these skills are offered to mothers through digital access, fostering diverse skill sets, contributing to their empowerment, and supporting sustainable development goal 5: gender equality, aiming to enhance women’s roles and ensure equal opportunities through digital inclusion.
Real time hand gesture detection by using convolutional neural network for in-vehicle infortainment systems
Authors: Yaakob Wan Bejuri Wan Mohd , Asmai Siti Azira , Ikram Raja Rina Raja , Rahim Nur Raidah , Khambari Najwan , Azmi Mohd Sanusi , Sholva Yus
Published: December 2024

Nowadays, a variety of technologies on autonomous vehicles have been extensively developed, including in-vehicle infotainment (IVI). It have been noted as one of the key services in the automobile industry. In the near future, people will be able to watch some virtual reality (VR) movies through the streaming service provided in the vehicle. However, a person sometime not tend to be joy while watching espcially when the remote controller or audio sensory controller lack of battery or too far from IVI panel. Thus, the purpose of this research is to design a scheme of real time hand gesture detection for in-vehicle infotainment system, in order to create human computer experience. In this research, the image of human palm hand will be taken by using camera for recognize the hand gesture action. This proposed scheme will recognize human gesture and convert to be computer intruction, that can be understood by IVI device. As a result, it show our proposed scheme can be the most consistent in term of accuracy and loss compared to others method. Overall, this research represents a significant step toward improving better user experience. Furthermore, the proposed scheme is anticipated to contribute significantly to the IVI field, benefiting both academia and societal outcomes.
Data analysis and visualization on titanic and student’s performance datasets-an exploratory study
Authors: Kim Seong-Cheol , Salkuti Surender Reddy , Suresh Alka Manvayalar , Sankaran Madhu Sree
Published: December 2024

Exploratory data analysis (EDA) is all about exploring the data in order to identify any underlying pattern before you try to use it to make a predictive model. It also plays a major role in the data discovery process as it is used to analyze data and to recapitulate their different characteristics, which is displayed efficiently with the help of data visualization methods. This paper aims to identify errors in the dataset, to understand the existing hidden structure and to identify new ones, to detect points in a dataset that deviate to a greater extent from the collected data (outliers), and also to find any relationship or intersection between the variables and constants. Two datasets are used namely ‘Titanic’ and ‘student’s performance’ to perform data analysis and ‘data visualization’ to depict ‘exploratory data analysis’ which acts as an important set of tools for recognizing a qualitative understanding. The datasets were explored and hence it assisted with identifying patterns, outliers, corrupt data, and discovering the relationship between the fields in the dataset.
Factors influencing the integration of web accessibility in Moroccan public e-services
Authors: Ezzahra Chadli Fatima , Moumen Aniss , Gretete Driss , Sabri Zineb
Published: December 2024

Governments worldwide are increasingly digitizing their services to enhance efficiency, transparency, and accessibility for citizens. Morocco has made significant strides in adopting information and communication technology (ICT) and has implemented various initiatives to promote digital transformation across sectors. However, ensuring that digital content and e-services are accessible to everyone, including people with disabilities, is crucial to building an inclusive digital environment. Against this background, this study, based on a qualitative analysis, explores the main factors influencing the integration of web accessibility in the Moroccan public sector from the perspective of web developers and information technology (IT) managers. Through semi-structured interviews and thematic analysis, the findings reveal key barriers such as limited awareness, training deficiencies, and lack of legal framework and available guidelines. Additionally, the study highlights the need for robust managerial backing and greater collaboration with stakeholders, including people with disabilities. By raising awareness and providing actionable insights, this study offers valuable recommendations for policymakers and moves the field forward, providing a foundation for future strategies to enhance web accessibility in the Moroccan public sector.
Implementing a very high-speed secure hash algorithm 3 accelerator based on PCI-express
Authors: Huynh Huu-Thuan , Tran Tuan-Kiet , Dang Tan-Phat
Published: December 2024

In this paper, a high-performance secure hash algorithm 3 (SHA-3) is proposed to handle massive amounts of data for applications such as edge computing, medical image encryption, and blockchain networks. This work not only focuses on the SHA-3 core as in previous works but also addresses the bottleneck phenomenon caused by transfer rates. Our proposed SHA-3 architecture serves as the hardware accelerator for personal computers (PC) connected via a peripheral component interconnect express (PCIe), enhancing data transfer rates between the host PC and dedicated computation components like SHA-3. Additionally, the throughput of the SHA-3 core is enhanced based on two different proposals for the KECCAK-f algorithm: re-scheduled and sub-pipelined architectures. The multiple KECCAK-f is applied to maximize data transfer throughput. Configurable buffer in/out (BIO) is introduced to support all SHA-3 modes, which is suitable for devices that handle various hashing applications. The proposed SHA-3 architectures are implemented and tested on DE10-Pro supporting Stratix 10 - 1SX280HU2F50E1VG and PCIe, achieving a throughput of up to 35.55 Gbps and 43.12 Gbps for multiple-re-scheduled-KECCAK-f-based SHA3 (MRS) and multiple-sub-pipelined-KECCAK-f-based SHA-3 (MSS), respectively.
Performance analysis of parallel prefix adders developed with field programmable gate array technology
Authors: Mahammad Masood Ahmad , Uppala Appala Raju , Prasad Suggala Ram , Marouthu Anusha
Published: December 2024

In many digital systems like high-performance computing and digital signal processing, parallel prefix adders are vital. Field programmable gate array (FPGA) technology is a well-known platform for developing parallel prefix adders. FPGA performance depends on bit size of the adder, the adder structure chosen, and the implementation specifications. An examination of the performance and area of parallel prefix adders developed using FPGA technology is presented in this research work. We look into how different design factors, such as the adder structure and the number of input bits, affect the performance and area of parallel prefix adders. The different adders used are Sklansky, Kogge-Stone, Brent-Kung, Han-Carlson, and Ladner-Fisher adders. These adders are implemented using Verilog hardware description language (Verilog HDL) on FPGA boards. The performance is significantly influenced by choice of adder structure and design factors optimized for area or performance. The suggestions for choosing the best adder structure and design factors for the best performance or optimized area are obtained from the synthesis results. Ladner-Fisher adders is best parallel prefix adder with respect area and performance compared with the Sklansky, Kogge-Stone, Brent-Kung and Han-Carlson. Our synthesis can be used as a guide for designers looking to construct specific hardware on FPGA.
Design of medium grain integrated clock gater for low power clock network
Authors: Nagaraja Shylashree , Sathisha Abhinav , Shivaraj Mamatha Aruvanalli , Nanjundappa Latha Bavikatte , Pandeshwara Prakash Tunga
Published: December 2024

The very large scale integration (VLSI) applications were mainly dependent on area, reliability, and cost rather than power. The power-increasing demand was mainly due to the latest growth of electronic products such as portable mobile phones, laptops, and other devices that needs high speed and low power consumption. The power analysis provides insights on the switching activity of various sequential logic and thus would help early power optimization approaches to be incorporated in the design flow. The medium grain integrated clock gater insertion will help with synthesis flows for other low-power techniques to be applied. The power analysis is performed with a physically driven synthesis network for both leakage and dynamic. The power analysis revealed that medium grain clock gaters help with finer granularity of the clock gating principle thus improving gating efficiency. The medium grain clock gating techniques help the tool understand the activities of various sinks thus helping in the insertion of fine gaters as well. For a single medium grain clock gater, the power savings obtained were 41.37% and 79.35% without and with fine gater insertion respectively while cloning of the medium gaters resulted in 45.1% and 67.4% power savings without and with fine gater insertion respectively. The fine-grain integrated clock gating insertion incurred a maximum of 14.7% increased gate count.
Design of flood warning prototype using ESP32 module-based ultrasonic sensors
Authors: Hasibuan Arnawan , Zahiri Muhtadi , Jannah Misbahul , Roid Fahrian , Almunadiansyah Rizky , Abta Armen , Nrartha I Made Ari
Published: December 2024

Natural disasters such as floods can cause many losses to humans, such as material losses, trauma for the victims, and loss of life. Floods that occur can be caused by various factors such as human activity itself which results in changes in natural spatial planning, so the arrival of floods is also difficult to detect with certainty. Based on this, it is necessary to develop a technological innovation that helps provide a warning of the arrival of a natural disaster. The ESP32 microcontroller is one of the technologies that can be used to create an early warning system for the arrival of floods. The design and manufacture of this technology certainly involves modeling, algorithm planning, assembly of the components of the tools used, including wiring and mechanics as needed. This tool uses an internet of things (IoT) system with the help of an ESP32 microcontroller that supports integration via Wi-Fi and Bluetooth so that it can be connected to a smartphone device as a notification receiver in real time and accurately by notifying the water level which will be an indicator of potential flooding, so that people are more alert in the face of flooding to prevent and minimize the losses that will be experienced.
Comparative analysis of ZigBee, LoRa, and NB-IoT in a smart building: advantages, limitations, and integration possibilities
Authors: Inthasuth Tanakorn , Kaewjumras Yongyut , Somwong Sahapong , Boonsong Wasana
Published: December 2024

This paper compares the performance of various wireless technologies: ZigBee, long range (LoRa), and narrowband internet of things (NB-IoT), which support smart building applications. The highlight of this work is that we focus on wireless communication between the floors of the building by analyzing the performance metrics using the received signal strength indicator (RSSI) and packet loss ratio (PLR). First, the ZigBee tests confirmed reliable packet delivery without any loss over distances up to 40 meters on the same floor, with RSSI results ranging from -65.5 to -87.5 dBm. ZigBee also maintained signal transmission through one cross-floor level, with RSSI values between -60 and -119 dBm. The second set of tests, with LoRa, indicated signal transmission over several floors with slightly improved RSSI values for the 2 dBi antenna compared to those for the -4 dBi antenna, despite increased packet loss with distance. Finally, NB-IoT showed the most consistent long-range connectivity, achieving a stable signal up to 458 meters from the base station with RSSI levels varying from -55.6 to -74.6 dBm, without packet loss in all tests. This study demonstrates how such technologies could be used in smart buildings and provides suggestions on how to determine the most suitable systems and configure them to ensure reliable communication networks within the building.
FPGA implementation of artificial neural network for PUF modeling
Authors: Mispan Mohd Syafiq , Ishak Mohammad Haziq , Jidin Aiman Zakwan , Mohd Nasir Haslinah
Published: December 2024

Field-programmable gate array (FPGA) is a prominent device in developing the internet of things (IoT) application since it offers parallel computation, power efficiency, and scalability. The identification and authentication of these FPGAbased IoT applications are crucial to secure the user-sensitive data transmitted over IoT networks. Physical unclonable function (PUF) technology provides a great capability to be used as device identification and authentication for FPGAbased IoT applications. Nevertheless, conventional PUF-based authentication suffers a huge overhead in storing the challenge-response pairs (CRPs) in the verifier’s database. Therefore, in this paper, the FPGA implementation of the Arbiter-PUF model using an artificial neural network (ANN) is presented. The PUF model can generate the CRPs on-the-fly upon the authentication request (i.e., by a prover) to the verifier and eliminates huge storage of CRPs database in the verifier. The architecture of ANN (i.e., Arbiter-PUF model) is designed in Xilinx system generator and subsequently converted into intellectual property (IP). Further, the IP is programmed in Xilinx Artix-7 FPGA with other peripherals for CRPs generation and validation. The findings show that the Arbiter-PUF model implementation on FPGA using the ANN technique achieves approximately 98% accuracy. The model consumes 12,196 look-up tables (LUTs) and 67 mW power in FPGA.
Low-noise amplifier with pre-distortion architecture for ultra-wide band application in radio frequency
Authors: Siddanna Pradeep Kumar , Divakarachari Parameshachari Bidare
Published: December 2024

Ultra-wide band (UWB) is a wireless technology deployed for transmitting data at high rates over short distances. Similar to Wi-Fi and Bluetooth, UWB is a radio frequency (RF) technology that operates via radio waves. To remove minor noise and glitches, low noise amplifier (LNA) is required because it amplifies weak signals without significantly adding noise. However, UWB has multiple frequencies that require coefficient change due to frequency variations. When low-pass filter (LPF) is employed to solve this, updates are necessary to manage delay and power because the LPF feedback is connected to LNA to increase delay and power consumption. In this research, LNA with a pre-distortion architecture is proposed to remove minor noises and small glitches. It is processed by using pre-distortion as an active component which reduces delay and power consumption in UWB. The pre-distortion process operates in the subthreshold voltage range by providing a transistor to each frequency as input, inturn effectively reducing the noise. The proposed LNA with pre-distortion architecture is developed on 180-nm complementary metal-oxide semiconductor (CMOS) technology using Cadense ASIC tool. The proposed architecture achieves a noise figure (NF) of 2.16 dB and less power consumption of 43.06×10-6 W when compared to the existing techniques namely, cascade amplifiers, W-band LNA, reflectionless receiver (RX), and broadband RF receiver front-end circuits.
Modeling of chimp optimization algorithm node localization scheme in wireless sensor networks
Authors: Arunachalam Sripriya , Vijaya Kumar Ashok Kumar , Reddy Desidi Narsimha , Pathipati Harikrishna , Priyadarsini Nethala Indira , Babu Ramisetti Lova Naga
Published: December 2024

For smart environments in the digital age, wireless sensor networks (WSNs) are needed. Node localization (NL) in WSNs is complicated for recent researchers. WSN localization focuses on finding sensor nodes (SNs) in two dimensions. WSN NL provides decision-making information in packets sent to base stations. This article describes modeling of chimp optimization algorithm node localization system in wireless sensor networks (MCOANL-WSN). The MCOANL-WSN approach uses metaheuristic optimization to locate unknown network nodes. To simulate chimpanzees' cooperative hunting behavior, the MCOANL-WSN approach includes chimp optimization algorithm (COA) into the NL process. The system uses mathematical modeling to represent node collaboration to improve placements. COA-based localization is being proposed for dynamically responding to resource-constrained and dynamic WSNs. Wide-ranging simulations may assess the MCOANL-WSN system's scalability, energy efficiency, and localization accuracy. The findings demonstrate the superiority of the new modeling method over current NL schemes in improving WSN reliability and efficiency in various applications.
Self-attention encoder-decoder with model adaptation for transliteration and translation tasks in regional language
Authors: Nagaraja Shanthala , Chandappa Kiran Y.
Published: December 2024

The recent advancements in natural language processing (NLP) have highlighted the significance of integrating machine transliteration with translation for enhanced language services, particularly in the context of regional languages. This paper introduces a novel neural network architecture that leverages a self-attention mechanism to create an autoencoder without the need for iterative or convolutional processes. The selfattention mechanism operates on projection matrices, feature matrices, and target queries, utilizing the Softmax function for optimization. The introduction of the self-attention encoder-decoder with model adaptation (SAEDM) represents a breakthrough, marking a substantial enhancement in transliteration and translation accuracy over previous methodologies. This innovative approach employs both student and teacher models, with the student model's loss calculated through the probabilities and prediction labels via the negative log entropy function. The proposed architecture is distinctively designed at the character level, incorporating a word-to-word embedding framework, a beam search algorithm for sentence generation, and a binary classifier within the encoder-decoder structure to ensure the uniqueness of the content. The effectiveness of the proposed model is validated through comprehensive evaluations using transliteration and translation datasets in Kannada and Hindi languages, demonstrating its superior performance compared to existing models.
Integration of K-Means and Silhouette score for energy efficiency of wireless sensor networks
Authors: Hilmani Adil , Sabri Yassine , Maizate Abderrahim , Aouad Siham , Koundi Mohammed
Published: December 2024

In wireless sensor networks (WSNs), optimizing energy consumption, and ensuring efficient data transmission are crucial for network longevity and performance. This paper introduces an enhanced clustering technique for WSNs that aims to extend network lifetime and ensure reliable data delivery. Instead of regular K-Means clustering, we integrate the Silhouette score method to evaluate cluster quality and decide the optimal number of clusters. This improves how nodes are grouped together in the network. Additionally, we strategically select routing paths from cluster heads to the base station that minimize energy drainage. Comprehensive simulations show our dual optimization approach outperforms standard K-Means in terms of energy efficiency, stable network organization and effective data transmission and overall, the proposed improvements to clustering and routing significantly advance energy-constrained WSNs toward more sustainable and dependable real-world applications
A fast half-subtractor using 8T static random access memory for in-memory computation
Authors: Prabhakar Deepika , Shylashree Nagaraja , Narasimhaiah Sunitha Yariyur , Mariswamappa Yashaswini Biligere , Hemaraj Sheetal Singrihalli
Published: December 2024

The existing system for computation completely incorporates Von-Neumann architecture which has limitations with respect to its memory, parallelism and power constraints. This has affected the efficiency of the computing system. Novel architectural solutions are required to meet the growing demands for improved computational efficiency and power management in very large scale integration (VLSI) systems. To deal with the large-scale data, computation in memory (CIM) has been introduced. The paper presents the half subtractor circuit and the In-memory computation co-design using eight transistors static random access memory (SRAM) cell whose read circuitry is transmission gate based. The proposed half-subtractor with the CIM is implementation is carried out in 180 nm complementary metal– oxide–semiconductor (CMOS) technology. The sensing scheme used is the latch-based sense amplifier along with the 8T SRAM cell. The proposed SRAM with transmission-gate based read circuitry along with latch-based sense amplifier reduces the delay and power consumed during the read operation significantly and a bit reduction during the write operation. The static noise margin (SNM) for read operation has been increased by 9% in the transmission gate-based SRAM as compared to conventional 8T SRAM. The delay of the proposed design has been reduced by 53% during the read operation and 4.43% during the write operation. The power consumed has been reduced by 3% and 8.6% during read and write operations, respectively.
Development and evaluation of robotic exoskeleton arm for enhanced human load carrying efficiency
Authors: Mohd Azam Muhammad Aiman , Mohd Annuar Khalil Azha , Mohamad Sapiee Mohd Razali , Debnath Sanjoy Kumar
Published: December 2024

In recent years, there has been a significant amount of research dedicated to the development of robotic exoskeleton systems. These technologies have been widely explored for their potential in virtual reality, human power enhancement, robotic rehabilitation, human power assist, and haptic interface applications. This research focuses on creating an exoskeleton arm that can assist individuals in carrying heavy objects. The exoskeleton arm is initially designed using Fusion 360, with the identification and calculation of important components such as the exoskeleton structure, motors serving as joints, an electromyography (EMG) sensor, and an Arduino UNO microcontroller. The research involves various aspects of mechanical design, electronic components, and programming. The effectiveness of the developed exoskeleton arm is then tested through experiments involving several individuals lifting a 2.5 kg and 5.0 kg load. The results of the experiments demonstrate that the force generated by the muscles is reduced when using the exoskeleton arm, compared to using a supporting system. Individuals' performance dropped by 36.06% to 50.44% when using an exoskeleton to lift 2.5 kg. This emphasises its effect on muscle activation and efficiency following physical activity. A 10.14% to 23.25% decline in a 5.0 kg lift shows nuanced impacts, emphasising the need for personalised modifications.
Artificial intelligence driven robotic control system for personalized elderly care and foot massage
Authors: Bhatlawande Shripad , Shilaskar Swati , Akotkar Soham , Joshi Anish , Ansari Zayd
Published: December 2024

This research presents an electronic system for providing foot massage to the elderly, along with artificial intelligence (AI) driven voice-controlled conversation bot. The problem under study focuses on the elderly age group suffering from foot related ailments, most commonly foot pain. Also, the risk of depression or anxiety is high for this age group due to social isolation. These problems are addressed by the system under discussion integrated with a voice assistant to converse with the elderly. The AI assisted conversation bot enables the elderly to make customized reminders for their timely medications and provides general updates on essential topics. The system extends to provide the elderly, foot, and calf massage controlled with mobile application. It consists of a low power motor arrangement along with a high computing system. The electronic system was subjected to trials on elderly for verification and validation of the system to assess its ability of providing users with appropriate assistance. The trials were conducted on twenty elderly, aged sixty, and above, living self-sufficiently with foot related ailment. All elderly were subjected to the conversation bot along with the foot and calves’ massage, providing subjective feedback on the system's ability to enhance their quality of life. The subjective feedback after quantification have demonstrated the ability of the system in improving their living standards.
Design and implementation of smart traffic light controller with emergency vehicle detection on FPGA
Authors: Mohamad Hadis Nor Shahanim , Abdullah Samihah , Abdul Sukor Muhammad Ameerul Syafiqie , Hamzah Irni Hamiza , Setumin Samsul , Ibrahim Mohammad Nizam , Azmin Azwati
Published: December 2024

Increased traffic volumes resulting from urbanization, industrialization, and population growth have given rise to complex issues, including congestion, accidents, and traffic violations at intersections. In the absence of a functional smart traffic light system, traffic congestion occurs due to imbalanced traffic flow at intersections. Current traffic management lacks provisions for ensuring the unobstructed movement of emergency vehicles, even a small delay for which can have significant consequences. This paper presents a smart traffic light controller developed using Verilog hardware description language (HDL) in Quartus Prime 21.1 and Questa Intel field programmable gate array (FPGA) Starter Edition 2021.2, and implemented on an Altera DE2-115 FPGA. The controller is designed specifically to detect emergency vehicle at four-way intersections for inputs radio frequency identification (RFID) readers and infrared (IR) sensors. The RFID readers and IR sensors are managed through slide switches on the FPGA board. The smart traffic light controller contains three sub-modules: clock division, counter, and finite state machine (FSM) operation, enabling it to manage traffic in scenarios with emergency vehicles, high traffic density, and low traffic density. This proposed system can alleviate intersection congestion by controlling access and allocating time effectively. In conclusion, the project ensures the smooth passage of emergency vehicles by continuously monitoring their presence and giving them priority in traffic flow.
TENS device for cervical pain during teleworking controlled remotely by mobile application
Authors: Yauri Ricardo , Balvin Juan , Lobo Renzo
Published: December 2024

Monitoring cervical muscle pain during teleworking, exacerbated by the COVID-19 pandemic and increased remote work, highlights electrotherapy as a crucial physical therapy tool to mitigate muscle pain and promote tissue recovery, addressing ergonomic and occupational health problems that affect the well-being of remote workers. The research proposes to design a transcutaneous electrical nerve stimulation (TENS) device to monitor cervical muscle pain during teleworking, addressing the urgent need for technological solutions to mitigate this problem and improve the quality of life of teleworkers through data acquisition and processing, hardware development, implementation device monitoring, and evaluation software. For this, a TENS device was designed with a graphical interface to treat muscle pain in the neck of teachers who do remote work, dividing it into four stages: signal acquisition and generation, Bluetooth communication with an Android device, signal conditioning, and amplification and protection, following a development scheme that includes circuit design in Proteus and the creation of a mobile application in App Inventor. In conclusion, it was obtained that the power supplies have an average error of less than 1%, indicating good general performance and confirming the consistency and optimal performance of the proposed therapies.
Central processing unit load reduction through application code optimization and memory management
Authors: Bhadrayya Sowmya Kandiga , Ravishankar Vishwas Bangalore
Published: December 2024

Central processing unit (CPU) loading refers to the amount of processing power a CPU uses to execute a given set of commands or perform an exact task. Higher CPU load can lead to slower, sluggish performance, reduced lifespan, and reduced system stability. Using the CPU Load trace results, the performance bottlenecks can be identified and suitable methods can be adopted to reduce the load on the CPU. For an ideal embedded system, the CPU should be in idle state for around 70% of CPU usage time. In this paper, three types of optimization techniques are implemented, which include application code optimization, memory management, and implementing interrupt-driven data transfer. Application code can be optimized by getting rid of redundant code, duplicate functions and function inlining, function cloning which reduces the size of the code with increase in reusability. By moving the data, variables to data tightly coupled memory (DTCM) and instructions, functions to instruction tightly coupled memory (ITCM), the speed of the CPU increases which reduces the load on CPU. The conventional polling method which increases the CPU load can be reduced by implementing the same in interrupt-driven data transfer. The load on the CPU has reduced from 89.53% to 29.58%.
Ritual Deposit at Bir Nurayet: Fertility Cult at the Foot of Gebel Magardi, Sudan
Authors: Jórdeczka Maciej , Bobrowski Przemysław , Chłodnicki Marek
Published: December 2024

Bir Nurayet is a place in the Red Sea Hills at the foot of Jebel Magardi. One of the most significant rock art galleries on the African continent exists near this beautiful isolated mountain. In 2010, a stone chest was discovered in one of the valleys, containing a deposit of several dozen clay figurines, mostly phallic-shaped with a few representations of animals, miniature vessels and their fragments, as well as bronze products. Thanks to the radiocarbon date obtained from charcoal, the chronology of the collection was set in the second half of the 6th century AD. The discovery of votive goods is of great value – it allows us to look into the very obscure sphere of rituals of the Blemmyean community that inhabited these areas at that time. Without a doubt, we are dealing here with one of the manifestations of the fertility cult, which was common in the past in many different versions.