International Journal of Reconfigurable and Embedded Systems (IJRES)
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Embedded systems as programmable square wave generator in wireless power transfer
Get PDFThis study focuses on the design and development of programmable frequency generator using embedded devices that are able to produce square wave signals in the wireless power transfer (WPT) transmitter. We validate the accuracy of the output signal by measuring distance error. We validate that our system can change and sweep the frequency and produce high power by measuring the absorbed power in the load. We conduct the frequency sweep analysis to find optimal frequency and the frequency splitting phenomenon. The experiments show that the system can produce and sweep the square wave signals with less than 1% error. We also find that the frequency splitting occurred when distance among two coils in the range 0.5-6.5 cm and the splitting disappeared when the distance is above 7.5 cm. The frequency splitting shows that the measured optimum frequency differs from the calculation. The difference confirms that the programmable frequency generator is needed to adjust the frequency that can transfer maximum power to the load
Timing issues on power side-channel leakage of advanced encryption standard circuits designed by high-level synthesis
Get PDFIn recent years, field programmable gate array (FPGA) have been used in many internet of things (IoT) devices and are equipped with cryptographic circuits to ensure security. However, they are exposed to the risk of cryptographic keys being stolen by side-channel attacks. Countermeasures against side-channel attacks have been developed, but they are becoming more of a threat to IoT devices due to the diversity of attacks. Therefore, it is necessary to understand the basic characteristics of side-channel attacks. Therefore, this study clarifies the relationship between two timing issues, the clock period of the circuit and the power sampling interval, and the amount of side-channel leakage. We design seven advanced encryption standard (AES) circuits with different clock periods and conduct empirical experiments using logic simulations to clarify the correlation between the two timings and the amount of side-channel leakage. T-test is used to evaluate the leakage amount, which is evaluated based on four metrics. From the results, we argue that the clock period and sampling interval do not interfere with each other in the side-channel leakage amount
Channel reconstruction through improvised deep learning architecture for high-speed networks
Get PDFEfficient acquisition of channel state information (CSI) is quite complicated process but immensely essential to exploit probable benefits of massive multiple input multiple output (MIMO) systems. Therefore, a deep learningbased model is utilized to estimate channel feedback in a massive MIMO system. The proposed improvised deep learning-based channel estimation (IDLCE) model enhances channel reconstruction efficiency by using multiple convolutional layers and residual blocks. The proposed IDLCE model utilizes encoder network to compress CSI matrices where decoder network is used to downlink reconstruct CSI matrices. Here, an additional quantization block is incorporated to improve feedback reconstruction accuracy by reducing channel errors. A COST 2,100 model is adopted to analyse performance efficiency for both indoor and outdoor scenarios. Further, deep learning-based model is used to train thousands of parameter and correlation coefficients much faster and to minimize computational complexity. The proposed IDLCE model evaluate performance in terms of normalized mean square error (NMSE), correlation efficiency and reconstruction accuracy and compared against varied state-of-art-channel estimation techniques. Excellent performance results are obtained with large improvement in channel reconstruction accuracy
Radio frequency identification based materials tracking system for construction industry
Get PDFThe construction industry is an industry that is always surrounded by uncertainties and risks. The industry is always associated with a threatindustry which has a complex, tedious layout and techniques characterized by unpredictable circumstances. It comprises a variety of human talents and the coordination of different areas and activities associated with it. In this competitive era of the construction industry, delays and cost overruns of the project are often common in every project and the causes of that are also common. One of the problems which we are trying to cater to is the improper handling of materials at the construction site. In this paper, we propose developing a system that is capable of tracking construction material on site that would benefit the contractor and client for better control over inventory on-site and to minimize loss of material that occurs due to theft and misplacing of materials
Internet based highly secure data transmission system in health care monitoring system
Get PDFThe health care systems in our contemporary countries are advancing rapidly in terms of maturity and professionalism. In an effort to alleviate the current burden on the public health system and boost the popularity of regular health self-checks, this method has been developed for producing prediagnoses that are easier to use, quicker, and more accurate. To ascertain how well the heart is circulating oxygen throughout the body, a pulse test, a painless examination that measures an individual's degree of oxygen saturation, is used. It can be used to evaluate the state of any patient with a disease, particularly those with pulmonary problems. Diseases in these patients could need ongoing observation and care. Our system comes to the rescue in order to resolve this problem. This portable system is simple to use and may be taken anywhere by the subject. The internet of things (IoT) will update the pertinent parameters. This health monitoring system's controller is made up of an adaptor, a saturation of peripheral oxygen (SPO2) sensor (a blood oxygen meter), a temperature sensor, a heart rate sensor, a WiFi module, and a liquid crystal display (LCD)
Deep learning-based channel estimation with application to 5G and beyond networks
Get PDFChannel state information (CSI) feedback estimation for a downlink medium in a massive multiple input multiple output (MIMO) system is an essential and critical task to improve channel capacity and performance yield, especially in a frequency division duplex (FDD) multiplexing system. However, spectral efficiency degradation is a massive issue due to high channel feedback overhead. This work proposes a deep learning-based channel estimation (DLCE) model to improve channel reconstruction efficiency and channel overhead reduction accuracy. The proposed deep learning (DL) mechanism consists of encoder and decoder network where encoder network is utilized to compress CSI matrices whereas decoder network is used to decompress obtained CSI matrices. Here, inverse discrete Fourier transform (IDFT) method is utilized to convert CSI matrices of frequency domain into CSI matrices of delay domain. Simulation results are evaluated between uplink and downlink medium in the massive MIMO system considering a co-operation in science and technology (COST) 2,100 model. Here, a significant improvement in correlation and normalized mean square error (NMSE) results is observed. The proposed DLCE model shows superior performance against varied channel estimation techniques in terms of NMSE and correlation efficiency
Moving objects detection based on histogram of oriented gradient algorithm chip for hazy environment
Get PDFThe most important aspects of computer vision are moving object detection (MOD) and tracking. Many signal-processing applications use regional image statistics. Compute-intensive video and image processing with low latency and high throughput is done with field programmable gate array (FPGA) image processing. Local image statistics are used for edge identification and filtering. The histogram of oriented gradients (HoG) algorithm extracts local shape characteristics by equalizing histograms. The objective of the work is to design the hardware chip of the algorithm and perform the simulation in the Xilinx ISE 14.7 simulation environment. The performance of the chip is evaluated in Modelsim 10.0 simulation software to check its feasibility. The performance of the chip design is estimated on Viretx-5 FPGA and compared with the MATLAB-2020 image processing tool-based response time. This form of tracking typically deals with identifying, anchoring, and tracking images and videos. A mask made from a cut-out of the object can then determine the plane's coordinates depending on its position. This type of object tracking is frequently utilized in the field of augmented reality (AR). The algorithm is most suited for object detection using hardware controllers in haze and foggy environments
Optimized Kalman filtering in dynamical environments for thumb robot motion estimation
Get PDFStroke, a prevalent nerve disorder in Indonesia, necessitates post-stroke rehabilitation like physical and occupational therapy. Hand and finger muscle training, crucial for restoring movement, often involves innovative solutions like finger prosthetic robotics arms. In particular, the advancement in thumb robotics emphasizes the estimation of thumb motion, where the ensemble Kalman filter square root (EnKF-SR) and H-infinity methods are deemed dependable for both linear and nonlinear models. Simulation results, using 400 ensembles, demonstrated nearly identical accuracy between the methods, exceeding 99%, with a 6-7% increase in accuracy compared to 200 ensembles. These advancements offer promising prospects for effective post-stroke rehabilitation and improved thumb movement restoration
A novel ensemble deep network framework for scene text recognition
Get PDFIn recent years, scene text recognition (STR) has always been considered a sequence-to-sequence problem. Attention-based techniques have a greater potential for context-semantic modelling, but they tend to overfit inadequate training data. STR is one of the most important and difficult challenges in image-based sequence recognition. A novel framework ensemble deep network (EDN) is proposed, EDN comprises customized convolutional neural network (CNN), and deep autoencoder. Customized CNN is designed by introducing the optimal spatial transformation module for optimizing the input of irregular text to read for same size. Further, deep autoencoder is introduced with effective attention mechanism utilizing the inherent features. The proposed ensemble deep network-proposed system (EDN-PS) approach outperforms the existing state-of-art techniques for both irregular and regular scene-texts and upon further simulations, the proposed model generates better results for IIIT5K, ICDAR-13, ICDAR-15, and CUTE dataset in comparison with the existing system hence our proposed EDN-PS model outperforms the existing state-of-art methods
Machine learning based education data mining through student session streams
Get PDFRecently, significant growth in using online-based learning stream (i.e., elearning systems) have been seen due to pandemic such as COVID-19. Forecasting student performance has become a major task as an institution is focusing on improving the quality of education and students' performance. Data mining (DM) employing machine learning (ML) techniques have been employed in the e-learning platform for analyzing student session streams and predicting academic performance with good effects. A recent, study shows ML-based methodologies exhibit when data is imbalanced. In addressing ensemble learning by combining multiple ML algorithms for choosing the best model according to data. However, the existing ensemblebased model does not incorporate feature importance into the student performance prediction model. Thus, exhibits poor performance, especially for multi-label classification. In addressing this, this paper presents an improved ensemble learning mechanism by modifying the XGBoost algorithm, namely modified XGBoost (MXGB). The MXGB incorporates an effective cross-validation scheme that learns correlation among features more efficiently. The experiment outcome shows the proposed MXGBabased student performance prediction model achieves much better prediction accuracy contrary to the state-of-art ensemble-based student performance prediction model