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25,002 Article Results

A fast half-subtractor using 8T static random access memory for in-memory computation

10.11591/ijres.v14.i1.pp273-281
Deepika Prabhakar , Nagaraja Shylashree , Sunitha Yariyur Narasimhaiah , Yashaswini Biligere Mariswamappa , Sheetal Singrihalli Hemaraj
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.
Volume: 14
Issue: 1
Page: 273-281
Publish at: 2025-03-01

Artificial intelligence driven robotic control system for personalized elderly care and foot massage

10.11591/ijres.v14.i1.pp35-47
Shripad Bhatlawande , Swati Shilaskar , Soham Akotkar , Anish Joshi , Zayd Ansari
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.
Volume: 14
Issue: 1
Page: 35-47
Publish at: 2025-03-01

Low-noise amplifier with pre-distortion architecture for ultra-wide band application in radio frequency

10.11591/ijres.v14.i1.pp208-220
Pradeep Kumar Siddanna , Parameshachari Bidare Divakarachari
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.
Volume: 14
Issue: 1
Page: 208-220
Publish at: 2025-03-01

Finite element analysis method as an alternative for furniture prototyping process and product testing

10.11591/ijres.v14.i1.pp231-242
Fesa Putra Kristianto , Zain Amarta , Nicolas Hutasoit , Nuthqy Fariz , Fania Putri Herinda
In the current furniture industry, making furniture goes through many steps. There are ordering materials, designing, building a prototype, and testing samples. This process is considered quite complex, requiring significant costs, and lengthy production time. The application of finite element analysis (FEA) can be a solution to simulate the furniture manufacturing process. Objective of this research was to determine FEA could substitute making and test prototype furniture thereby saving costs and time. This method utilizes ANSYS 18.1 software for more accurate and rapid calculations, incorporating load variables of 400 N, 600 N, 800 N, and 1,000 N, along with gravitational acceleration of 10 \frac{m}{s^2}. The research evaluates the difference (expressed as a percentage) between the results obtained from simulations and those obtained directly from experiments, considering maximum equivalent stress, maximum principal stress, and total deformation values. The final step involves comparing the simulation with direct testing in terms of cost and time. The research results show an average error factor of 5% across all aspect. In terms of cost, the method can save 1,807 USD and reduce production time by up to one month. From these findings, it can be concluded that the process of prototyping and sample testing can be replaced using the finite element method.
Volume: 14
Issue: 1
Page: 231-242
Publish at: 2025-03-01

Comparative analysis of ZigBee, LoRa, and NB-IoT in a smart building: advantages, limitations, and integration possibilities

10.11591/ijres.v14.i1.pp165-175
Tanakorn Inthasuth , Yongyut Kaewjumras , Sahapong Somwong , Wasana Boonsong
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.
Volume: 14
Issue: 1
Page: 165-175
Publish at: 2025-03-01

Development of internet of vehicles and recurrent neural network enabled intelligent transportation system for smart cities

10.11591/ijres.v14.i1.pp291-300
Jyoti Surve , Manoj L. Bangare , Sunil L. Bangare , Urmila R. Pol , Manisha Mali , Meenakshi Meenakshi , Abdullah Alsalmani , Sami A. Morsi
The number of deaths has increased as a direct result of the increased frequency of traffic accidents, congestion, and other risk factors. Developing countries have prioritised the development of intelligent transport systems in order to reduce pollution, traffic congestion, and wasted time. This article describes an intelligent transport system that leverages the internet of vehicles (IoV) and deep learning to forecast traffic congestion. Data is acquired using a car’s global positioning system (GPS), road and vehicle sensors, traffic cameras, and traffic speed, density, and flow. All acquired data is stored in one location on a cloud server. The cloud server also stores historical traffic, road, and vehicle data. Using particle swarm optimisation, features are improved. The optimised dataset is used to train and test recurrent neural networks (RNNs), support vector machines (SVMs), and multi layer perceptrons (MLPs). A deep learning algorithm can predict traffic congestion and make recommendations to drivers on how fast to travel and which route to take. The experimental effort employs the performance measurement system (PeMS) traffic dataset. RNN has achieved accuracy of 95.1%.
Volume: 14
Issue: 1
Page: 291-300
Publish at: 2025-03-01

Multimodal recognition with deep learning: audio, image, and text

10.11591/ijres.v14.i1.pp254-264
Ravi Gummula , Vinothkumar Arumugam , Abilasha Aranganathan
Emotion detection is essential in many domains including affective computing, psychological assessment, and human computer interaction (HCI). It contrasts the study of emotion detection across text, image, and speech modalities to evaluate state-of-the-art approaches in each area and identify their benefits and shortcomings. We looked at present methods, datasets, and evaluation criteria by conducting a comprehensive literature review. In order to conduct our study, we collect data, clean it up, identify its characteristics and then use deep learning (DL) models. In our experiments we performed text-based emotion identification using long short-term memory (LSTM), term frequency-inverse document frequency (TF-IDF) vectorizer, and image-based emotion recognition using a convolutional neural network (CNN) algorithm. Contributing to the body of knowledge in emotion recognition, our study's results provide light on the inner workings of different modalities. Experimental findings validate the efficacy of the proposed method while also highlighting areas for improvement.
Volume: 14
Issue: 1
Page: 254-264
Publish at: 2025-03-01

Implementing a very high-speed secure hash algorithm 3 accelerator based on PCI-express

10.11591/ijres.v14.i1.pp1-11
Huu-Thuan Huynh , Tuan-Kiet Tran , Tan-Phat Dang
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.
Volume: 14
Issue: 1
Page: 1-11
Publish at: 2025-03-01

Analysing feature selection: impacts towards forecasting electricity power consumption

10.11591/ijres.v14.i1.pp265-272
Azman Ab Malik , Lyu Tao , Noormadinah Allias , Irni Hamiza Hamzah
This study focuses on the development of electrical power forecasting based on electricity usage in Wuzhou, China. To develop a forecasting model, the important features need to be identified. Therefore, this study investigates the performance of the feature selection method, focusing on the mutual information as a filter and random forest as a wrapper-based feature selection. From the experiment, six features have been chosen, whereby both feature selection methods chose almost identical features. Later, the selected features are trained and tested with common machine learning models, namely random forest regressor, support vector regression (SVR), k-nearest neighbor (KNN) regressor, and extreme gradient boosting (XGBoost) regressor. The performances of the feature selections tested on each of the models are measured in terms of mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R²). Findings from the experiment revealed that XGBoost outperform the other machine learning models with RMSE 0.9566 and R² indicated with 0.2561. However, SVR outperformed XGBoost and other model by obtaining MAE 0.6028. It can be concluded that the performance of filter-based outperformed the embedded feature selection.
Volume: 14
Issue: 1
Page: 265-272
Publish at: 2025-03-01

Design and implementation of smart traffic light controller with emergency vehicle detection on FPGA

10.11591/ijres.v14.i1.pp48-59
Nor Shahanim Mohamad Hadis , Samihah Abdullah , Muhammad Ameerul Syafiqie Abdul Sukor , Irni Hamiza Hamzah , Samsul Setumin , Mohammad Nizam Ibrahim , Azwati Azmin
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.
Volume: 14
Issue: 1
Page: 48-59
Publish at: 2025-03-01

Implementation of flexible axis photovoltaic system based on internet of things

10.11591/ijres.v14.i1.pp157-164
Aji Akbar Firdaus , Muhamad Zalani Daud , Parvathy Rajendran , Mahmud Iwan Solihin , Li Wang , Mimi Azmita , Hamzah Arof
Electricity is a crucial aspect in human life. With population growth, ongoing regional development, and continuous construction activities, the demand for electricity and fuel in Indonesia is increasing. The substantial power consumption leads to larger financial expenditures for the community. Additionally, the use of electricity, as it has been traditionally employed, has negative environmental impacts. Solutions are needed to address these issues, and one effort involves the use of renewable energy, such as the development of solar power plants (PLTS). PLTS, also known as solar cells, is preferred as it can be used for various relevant purposes in different locations, particularly in offices, factories, residential areas, and others. However, the use of static, single-axis, and dual-axis solar panels still has drawbacks, such as suboptimal sunlight intensity and high motor power consumption. Therefore, a flexible-axis solar panel tracking system has been developed to follow the direction of sunlight, ensuring optimal power efficiency, and significant electricity generation. The flexible-axis tracker system results in a 34.13% increase in power efficiency.
Volume: 14
Issue: 1
Page: 157-164
Publish at: 2025-03-01

An internet of things-driven smart key system with real-time alerts: innovations in hotel security

10.11591/ijres.v14.i1.pp145-156
Putra Jaya , Ryan Fikri , Agariadne Dwinggo Samala , Dimas Sanjaya
This paper presents an innovative smart key system designed to enhance the safety and convenience of hotel guests. The system employs an iterative, agile approach encompassing the phases of requirement analysis, design, implementation, and testing. Key components of the input circuitry include limit switches, RFID-RC522 and SW420 vibration sensors, which collectively gather data. This data is processed using an Arduino Uno microcontroller and integrated with internet of things (IoT) technology. On the output side, the system incorporates a solenoid lock and is capable of promptly notifying users via Telegram in response to unauthorized access attempts. Importantly, the system can distinguish between vibrations caused by unauthorized entry and those from legitimate usage. Rigorous testing validates its efficacy, issuing Telegram alerts promptly when detecting security breaches. This technological advancement significantly enhances hotel room security, providing an intelligent real-time solution. The fusion of IoT, Arduino microcontroller, and precise sensor configuration underscores the system's reliability, setting new benchmarks for security in the hospitality sector. The comprehensive approach detailed in this paper offers valuable insights applicable to a wide range of security applications.
Volume: 14
Issue: 1
Page: 145-156
Publish at: 2025-03-01

Optimizing resource allocation in job shop production systems with seasonal demand patterns

10.11591/ijres.v14.i1.pp12-25
Salah Hammedi , Jalloul Elmeliani , Lotfi Nabli
Job shop production systems that encounter seasonal demand patterns in the manufacturing industry are the subject of this article's exploration of the complex challenges of resource allocation. A nuanced understanding of each product's unique production processes, resource requirements, and lead times is necessary for the inherent complexity of job shop production, which characterized by diverse product lines. Resource reallocation becomes more complicated due to seasonal demand patterns, which require manufacturers to seamlessly transition resources between products and adjust strategies dynamically throughout the year. This article explores potential optimization techniques by drawing on insights from related studies on reliability monitoring and Petri nets. Strategically managing resource allocation is highlighted due to its significant impact on a company's competitiveness, adaptability to market changes, and overall financial performance. In the paper, there is a proposed architecture for resource allocation that combines data-driven insights, workforce planning, inventory management, machine allocation, lean principles, and technology integration. Effective strategies for reallocating resources are highlighted through the presentation of case studies and best practices, which include accurate demand forecasting and flexible workforce planning. The final section of the article emphasizes the holistic approach required to navigate the complexities of seasonal demand patterns and achieve sustained competitiveness and customer satisfaction.
Volume: 14
Issue: 1
Page: 12-25
Publish at: 2025-03-01

Performance comparison of indoor navigation and obstacle avoidance methods for low-cost implementation in wheelchairs

10.11591/ijres.v14.i1.pp100-108
Satish Bhogannahalli Ashwathnarayan , Deekshitha Arsa , Sharath Kumar Yerriyuru Narasimhaiah , Shreyas Anchan , Giri Prasath
Wheelchairs are a huge support for the movement of people who have disabilities. The wheelchairs that were traditionally moved using manual effort have given way to powered and smart wheelchairs with various controlling methods. When powered wheelchairs are used indoors, navigation and avoiding obstacles become challenging and tricky for a disabled user. To address these challenges there have been implementations of expensive and high-end systems to make the wheelchair move autonomously but as a result such a wheelchair is not economically viable for many users. Thus, there is a need for an alternative low cost method for users to be able to navigate and move in an indoor environment. The paper reviews low-cost methods for implementing indoor navigation systems, weighing their performances to validate if these methods can be used as a viable alternative to the high-cost systems for autonomous navigation in an indoor environment.
Volume: 14
Issue: 1
Page: 100-108
Publish at: 2025-03-01

Video surveillance system based on artificial vision and fog computing for the detection of lethal weapons

10.11591/ijres.v14.i1.pp191-199
Ricardo Yauri , José Monterrey
Citizen insecurity in underdeveloped third world countries is aggravated by poor management of arms control and illegal trafficking, which requires information technology solutions in intelligent video surveillance systems for the detection of lethal weapons. The literature review highlights the need for an intelligent video surveillance system to combat high crime, using fog computing, which processes data in real time for the detection of weapons and other crimes. Furthermore, at an international level, solutions based on artificial intelligence and deep learning are being implemented for object recognition and weapons detection. Therefore, this paper describes the design of an intelligent video surveillance system based on artificial vision, fog and edge computing to detect lethal weapons in domestic environments, performing weapon classification and data transmission to police centers. The intelligent video surveillance system allows detecting lethal weapons and operates in three stages: an edge node with a Raspberry Pi 4; a detection algorithm based on a convolutional neural network with YOLOv5; and streaming tagged images to a security unit via WhatsApp. The main conclusion is that the system achieved a precision greater than 0.85 and a quick and efficient response in sending alerts, representing a scalable and effective solution against home burglary.
Volume: 14
Issue: 1
Page: 191-199
Publish at: 2025-03-01
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