International Journal of Reconfigurable and Embedded Systems (IJRES)
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454 research outputs found
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Agriculture data analysis using parallel k-nearest neighbour classification algorithm
Get PDFA cost-effective and effective agriculture management system is created by utilizing data analytics (DA), internet of things (IoT), and cloud computing (CC). Geographic information system (GIS) technology and remote sensing predictions give users and stakeholders access to a variety of sensory data, including rainfall patterns and weather-related information (such as pressure, humidity, and temperatures). They have unstructured format for sensory data. The current systems do a poor job of analysing such data since they cannot effectively balance speed and memory usage. An effective categorization model (ECM) on agriculture management system is proposed to address this research difficulty. First, a classification technique called priority-based k-nearest neighbour (KNN) is provided to categorize unstructured multi-dimensional data into a structured form. Additionally, the Hadoop MapReduce (HMR) framework is used to do classification utilizing a parallel approach. Data from real-time IoT sensors used in agriculture is the subject of experiments. The suggested approach significantly outperforms previous approaches that are computing time, memory efficiency, model accuracy, and speedup
Design and implementation of an alcohol detection driver system
Get PDFA technology called an alcohol detection driver system is used to stop drunk driving by identifying alcohol in a motorist's breath or blood. This technology correctly measures the amount of alcohol a driver has in their system using sensors and algorithms, and it stops the car from starting if the amount is more than the legal limit. The number of fatal accidents and traffic fatalities caused by drinking could be greatly decreased thanks to this technology. The main focus of this project is to carry out the experiment in lowering the number of alcohol-related incidents on the road. Alcohol detection devices come in a variety of forms right now, including ignition interlocks, passive alcohol sensors, and in-car breathalyzers. Although these systems have reduced the number of drunk driving accidents, there remain questions about their efficiency, dependability, and cost. According to the sensor's specs, the output voltage of the MQ-3 sensor reduces by 69% during the sensor's recovery period of 30 seconds at 69% of baseline resistance. To assess the long-term viability and efficiency of these systems in lowering alcohol-related accidents and enhancing traffic safety, more research is required
Reliability analysis of GAN based transmit modules for active array antenna of phased array radar
Get PDFReliability is one of the most important requirements in our day to day life considering consistency, availability and failure free performance of the product over it’s define mission time. As complexity of the system increases, design for reliable systems is a big challenge. The objective of the reliability prediction analysis is to evaluate the predicted reliability of the active transmit receive modules (TRMs) under specified operating conditions, and to demonstrate that the predicted reliability meets the requirements, also to identify any parts present in the design which leads to higher failure rates. The research shows reliability of generative adversarial network (GAN) based TRMs covering from design to finalization of components as early as practicable in today's short product lifecycles. Using the reliability prediction process, we describe a method for providing design engineers with reliability feedback on their decisions. Using a conventional reliability prediction model, the Telcordia (Bellcore) parts stress prediction model, and some standard rules of thumb, we describe an initial implementation of this technique. It provides systematic identification of likely modes of failure, possible effects of each failure, and the criticality of each failure with regard to reliability, system readiness, mission success, and demand for maintenance/logistic support
A novel compression methodology for medical images using deep learning for high-speed transmission
Get PDFMedical imaging is a rapidly growing field having a high impact on the early detection, diagnosis and surgical planning of diseases. Several imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound (US) imaging generate a higher volume of data, necessitating additional storage and communication requirements. Hence, image compression is utilized in medical field to reduce redundancy and alleviate memory and bandwidth issues. This paper presents a novel deep learning-based compression method to reduce the size of medical images. This method employs a deep convolutional neural network for learning compact representations of medical images, then coded by a Huffman encoder. The compression process is reversed to reconstruct the original image. Several tests are conducted to compare the results with other wellknown compression methods. The proposed model achieved a mean peak signal-to-noise ratio (PSNR) of 42.82 dB with storage space saving (SSS) of 96.15% for CT, 43.88 dB with SSS of 96.25% for MRI, 46.29 dB with SSS of 96.07% for US and 43.51 dB with SSS of 96.95% for X-ray images. The findings showed that the proposed compression technique could greatly compress the image size, saving storage space, facilitating better transmission and preserving critical diagnostic information
Implementation of first order statistical processor on FPGA for feature extraction
Get PDFStatistical calculations on signals commonly used in feature extraction. In software processing, statistical computation is an easy task. However, providing a computer requires high costs for simple statistical processing. Another consideration is the need for implementation with real-time and portable processing. Therefore, an alternative device is needed, one of which is the field programmable gate array (FPGA). FPGA is a logic circuit board that can be reconfigured according to computing needs. FPGA can also be used as a prototyping of electronic chips. However, implementing statistical formulas in FPGA is interesting in developing its architecture. Therefore, this research proposes a logic circuit design that can be used for first-order statistical calculations. Statistical parameters include the mean, variance, standard deviation, skewness, and kurtosis. The validation test was performed on the electrocardiogram (ECG) signal series and compared with manual calculations. Validation shows that the mean and variance has very high accuracy with an average error of less than 0.06%
Innovative systems for the detection of air particles in the quarries of the Western Rif, Morocco
Get PDFIn a world where climate change looms large the spotlight often shines on greenhouse gases, but the shadow of man-made aerosols should not be underestimated. These tiny particles play a pivotal role in disrupting Earth's radiative equilibrium, yet many mysteries surround their influence on various physical aspects of our planet. The root of these mysteries lies in the limited data we have on aerosol sources, formation processes, conversion dynamics, and collection methods. Aerosols, composed of particulate matter (PM), sulfates, and nitrates, hold significant sway across the hemisphere. Accurate measurement demands the refinement of in-situ, satellite, and ground-based techniques. As aerosols interact intricately with the environment, their full impact remains an enigma. Enter a groundbreaking study in Morocco that dared to compare an internet of thing (IoT) system with satellite-based atmospheric models, with a focus on fine particles below 10 and 2.5 micrometers in diameter. The initial results, particularly in regions abundant with extraction pits, shed light on the IoT system's potential to decode aerosols' role in the grand narrative of climate change. These findings inspire hope as we confront the formidable global challenge of climate change
Improving the performance of IoT devices that use Wi-Fi
Get PDFProviding quality service to users of the internet of things (IoT) entails addressing two crucial aspects: one related to security and the other concerning the limited resources of IoT devices. We will face a challenge while using timesensitive applications within a network that utilizes a high-performance Wi-Fi technology with exceeding energy consumption. Due to this research challenge, we propose a new algorithm, IoT-quality of service (QoS), designed to achieve a true balance between enhancing the security aspects of IoT devices and improving network-hardware performance. Thus, the algorithm efficiently manages the limited energy resources by monitoring energy levels, communication quality, and queuing delay at access points. This is accomplished by utilizing a streamlined identity management system capable of achieving authentication and access authorization with reduced loading for IoT devices. The research hypothesis underwent validation through a comparative analysis of its performance against the conventional model of a Wi-Fi-based IoT device. This evaluation was conducted utilizing the NS3 simulator and was based on a predetermined set of parameters influencing the examined performance metrics, including power consumption, throughput, delay, and response time. The findings exposed the superiority of the proposed algorithm
Energy-efficient clustering and routing using fuzzy k-medoids and adaptive ranking-based wireless sensor network
Get PDFThe wireless sensor network (WSN) is a vital component of infrastructure that is seeing tremendous demand and quick expansion in a variety of industries, including forestry, airports, healthcare, and the military. Increasing network lifetime and reducing power consumption (PC) are now major goals in WSN research. This research proposes a unique energy-efficient cross-layer WSN design that aims to maximize network lifetime while maintaining quality of service (QoS) criteria to address these challenges. The research initially utilizes the fuzzy k-medoids (FKMeds) clustering technique to group sensor nodes (SN) to improve resilience, scalability, and minimize network traffic. Following that, the hybrid improved grey wolf and ant colony (HIGWAC) optimization approach is applied to choose cluster heads (CH), minimizing distances, reducing latency, and optimizing energy stability. Finally, data is transmitted through the shortest pathways using the adaptive ranking-based energy-efficient opportunistic routing (ARanEOR) protocol, which ensures effective and energy-conserving routing in WSN while dynamically lowering network overhead. Compared to existing approaches, the proposed method in this study outperforms them in terms of energy efficiency, latency, and network longevity
An efficient floating point adder for low-power devices
Get PDFWith an increasing demand for power hungry data intensive computing, design methodologies with low power consumption are increasingly gaining prominence in the industry. Most of the systems operate on critical and noncritical data both. An attempt to generate a precision result results in excessive power consumption and results in a slower system. An attempt to generate a precision result results in excessive power consumption and results in a slower system. For non-critical data, approximate computing circuits significantly reduce the circuit complexity and hence power consumption. For non-critical data, approximate computing circuits significantly reduce the circuit complexity and hence power consumption. In this paper, a novel approximate single precision floating point adder is proposed with an approximate mantissa adder. The mantissa adder is designed with three 8-bit full adder blocks
Internet of thing based health monitoring system using wearable sensors networks
Get PDFMaintaining mental and physical health is becoming increasingly important for maintaining independent living, particularly as the population of people suffering from chronic illnesses like diabetes, heart disease, obesity, and other conditions rises and the average age of many societies keeps rising. Using sensors, monitoring health remotely, and ultimately recognising daily activities have all been proposed as potential strategies. In this work, fatigue threshold and environmental bounds are assessed and provided via an external interface to a microcontroller unit (MCU) in addition to the required restrictions. Rerouting the required boundaries into the long range (LoRa) and Bluetooth module, the MCU is responsible for editing and analysing the raw data to remove the oxygen immersion, pulse, and temperature data. These important restrictions are sent to many terminals, such as PCs and mobile devices, using the remote Bluetooth and LoRa module. For data storage and retrieval, any IoT platform may be used. With caution, the patient is discharged home after the medical experts have carefully evaluated the diseases in light of the new features. To telemonitor patients with heart conditions, the test results show that the framework is efficient and dependable for collecting, sending, and presenting electrocardiogram (ECG) data constantly