International Journal of Reconfigurable and Embedded Systems (IJRES)
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Room energy management utilizing internet of things technology for decreasing electricity consumption
Get PDFThis paper proposes a novel internet of things (IoT)-based control system for energy management to reduce electricity consumption from the two most dominant loads in buildings: air conditioners (AC) and lighting. The proposed system provides a comprehensive operational control strategy that integrates scheduling, human detection, ambient temperature, and light intensity for optimal room-level energy management employed. The proposed system employs wireless fidelity (WiFi)-enabled temperature, presence, and light sensors for comprehensive room conditions monitoring. Additionally, a WiFi-connected infrared module serves as an actuator to regulate the AC unit. Testing results demonstrate compelling energy savings, achieving up to 36% for the AC and 72% for the lighting while maintaining a comfortable indoor environment. These results were obtained from an experimental test in a private room within a residence over an 8-hour daytime period with 50% occupancy time. The proposed IoT system offers a highly effective and easily deployable solution for sustainable energy reduction in residential settings
Economical design of WAMS through soft computing: co-optimal PMU placement and communication infrastructure
Get PDFRecently, utilities have developed and deployed wide area measurement systems (WAMS) to improve the electricity grid's ability to monitor, manage, and defend itself. In a typical WAMS setup, multiple measuring devices, communication systems, and energy management systems work together to gather, transmit, and then analyze data. Although there is substantial interdependence among these three capabilities, most research treats them independently. The work presented here minimizes the total cost of the communication infrastructure (CI) by taking into account the price of phasor measurement units (PMUs) and the placement of a phasor data concentrator (PDC) at the same time. The optimum CI and PDC placement has been built with Steiner tree optimization's help. There have also been practical operating scenarios of more realistic working conditions containing pre-installed PMU, pre-installed fiber optic and N-1 contingency. The optimization hurdle has been overcome by utilizing the binary firefly algorithm (BFFA), which has undergone testing on IEEE 14, 30, and 118 bus systems to demonstrate its effectiveness. A comparison has been offered, and it clearly demonstrates the proposed approach's superiority over previously published articles
Clustering with hierarchical routing (GMMCHR): a new gaussian mixture model for wireless sensor networks
Get PDFMilitary surveillance, industrial applications, and real-time environmental monitoring all depend on wireless sensor networks (WSNs). However, due to insufficient power sources for sensor nodes, energy efficiency (EE) and network lifetime (NL) extension are significant challenges. To vanquish these constraints, this investigation suggests a new GMMCHR (Gaussian Mixture Model Clustering with Hierarchical Routing) protocol that combines energy-aware routing with probabilistic clustering. The approach segregates network into NC (Near Clusters) and FC (Far Clusters) based on node distance from the BS. CHs are selected using a fitness function incorporating residual energy and spatial proximity, with FCs formed via Enhanced Gaussian Mixture Models (EGMM) and routing managed through a hierarchical structure. Simulations conducted in MATLAB R2021a under two scenarios—100 nodes in a 100×100 m² region and 200 nodes in a 200×200 m² region—demonstrate significant improvements over the benchmark EEHCHR protocol. In the 100-node scenario, GMMCHR delays the FND (First Node Dead) to 66 rounds, HND (Half Node Dead) to 911 rounds, and LND (Last Node Dead) to 1601 rounds, compared to EEHCHR’s 45, 735, and 1359, respectively. In the 200-node setup, GMMCHR achieves FND at 48 rounds, HND at 904, and LND at 1231, outperforming EEHCHR’s 31, 731, and 1024 rounds. Additionally, GMMCHR maintains over 70% coverage beyond 1200 rounds in Scenario 1 and delivers over 17,000 packets to the base station, significantly higher than EEHCHR. Moreover, the combination of soft clustering in GMM with the hierarchical routing would allow dynamic flexibility, superior load balancing, and improved scalability. Overall, GMMCHR provides an effective and capable method of enhancing the lifetime of the WSN in both small-scale and large-scale systems
A novel approach to transparent and accurate fuel dispensing for consumer protection
Get PDFConsumer rights are exploited around the world and it is necessary for to protect consumer rights by means of safeguarding consumers from various unfair trade practices. Those most vulnerable to such exploitation must be shielded, and this is achieved through consumer protection measures. One such example of unethical behavior is fuel stealing at fuel stations. To overcome this critical issue, a low-cost fuel quantity sensing and monitoring system is proposed in this paper. A fuel detection system will ensure the exact quantity of fuel filled in fuel tank and will detect fuel theft, if any, at fuel pumps. An embedded system is developed for this purpose, consisting of sensors, display devices, communication devices and microcontroller. The quantity of fuel filled in the tank is transmitted to mobile phone of the consumer to avoid fuel theft. Performance of the system is validated by comparing the displayed amount of fuel dispensed and actual filled in the tank and achieve 99.95% accuracy. With this consumer right to get the value for amount paid for the petrol will be protected. This novel feature can be added in the fuel tank of the smart vehicle development and design as a future scope
Design and structural modelling of patient-specific 3D-printed knee femur and tibia implants
Get PDFArthritis is a degenerative joint condition that progressively damages the knee, leading to pain, stiffness, and limited mobility. To alleviate these symptoms and restore joint functionality, total knee arthroplasty (TKA) is performed. This procedure becomes necessary due to either sudden trauma to the knee or gradual wear and tear of the meniscus and cartilage. TKA involves meticulous planning, precise bone cutting, and the placement of prosthetic components made from high-density polyethylene and metal alloys. However, traditional methods creating customized knee implants are expensive and time-intensive. This study explores the challenges in manufacturing personalized knee implants for TKA and evaluates the potential of three-dimensional (3D) printing technology in this process. Variations in knee joint anatomy across populations complicate surgery, as optimal outcomes rely on precise alignment and implant dimensions. A preoperative computed tomography (CT) scan identifies the region of interest (ROI), such as the knee joint. The scan data is then processed using computer-aided design (CAD) software to generate a printable file. The patient’s CT scan data is converted into a standard triangulation language (STL) file and CAD models of the knee joint. Errors such as overlapping triangles or open loops in the STL file are corrected, and unwanted geometries near the ROI are removed. Resection techniques are applied to create CAD models tailored to the patient’s bone morphology. Fused deposition modeling (FDM) is then used to produce prototypes of the knee joint and implants. Despite visible layer lines in the printed prototypes, challenges encountered during the process were effectively resolved
SPARTAN–field programmable gate array implementation for analog waveforms generation by direct digital synthesis
Get PDFIn the last thirty years, low power field programmable gate arrays (FPGAs) becoming more commonly used to implement a countless of applications in different electronics industry domains. Due to their flexible design, strong compatibility, parallel computing, and compared to the CPU architecture, FPGA accentuate computing efficiency and con sidered as one of the devices with the lowest application risk and the shortest development cycle among the variety of available programmable circuits families. This article details the design and implementation of a direct digital synthesis (DDS) signal generator using the Spartan-6 FPGA, focusing on high-quality sine wave generation. The system utilizes look-up tables (LUTs) and Block RAM (BRAM) for efficient storage and retrieval of sine wave data, while an 8-bit DAC0808 digital-to-analog converter (DAC) ensures precise waveform output. The FPGA's reconfigurable architecture allows real-time adjustments of frequency and phase, making the design suitable for various signal processing applications and modulation techniques like binary phase shift keying (BPSK)
Self-attention encoder-decoder with model adaptation for transliteration and translation tasks in regional language
Get PDFThe 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
Get PDFIn 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
Algorithm-driven development of a simulation tool for industrial manipulator stability analysis
Get PDFIndustrial manipulators are essential to many manufacturing processes because they increase efficiency and productivity dramatically. However, maintaining operational safety and averting potential risks in industrial environments requires that these manipulators be stable. The development and implementation of an entirely algorithm-driven novel simulation tool intended to assess industrial manipulators’ stability in-depth are presented in this research. The suggested tool combines sophisticated mathematical models with the material properties of the manipulator, such as deflection, stiffness, and damping. To analyses the dynamic behaviour of manipulators under various operating situations, a hypothetical simulation technique to assess the stability of robot manipulators combined with material properties is taken into consideration. The simulation tool offers vital insights into the stability characteristics of manipulators, allowing engineers and designers to enhance their performance and guarantee operational safety. The simulation tool’s usefulness is showcased through case studies and comparative evaluations, emphasizing its capacity to improve the design and implementation of industrial manipulators in practical situations. In summary, this research enhances the field of industrial automation by offering a strong framework for assessing and upgrading the stability of manipulator systems. This, in turn, improves productivity and safety in industrial settings
Low-noise amplifier with pre-distortion architecture for ultra-wide band application in radio frequency
Get PDFUltra-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