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International Journal of Reconfigurable and Embedded Systems (IJRES)
Published by Institute of Advanced Engineering and Science
ISSN : 20894864     EISSN : 27222608     DOI : -
Core Subject : Economy,
Economics, Econometrics & Finance
The centre of gravity of the computer industry is now moving from personal computing into embedded computing with the advent of VLSI system level integration and reconfigurable core in system-on-chip (SoC). Reconfigurable and Embedded systems are increasingly becoming a key technological component of all kinds of complex technical systems, ranging from audio-video-equipment, telephones, vehicles, toys, aircraft, medical diagnostics, pacemakers, climate control systems, manufacturing systems, intelligent power systems, security systems, to weapons etc. The aim of IJRES is to provide a vehicle for academics, industrial professionals, educators and policy makers working in the field to contribute and disseminate innovative and important new work on reconfigurable and embedded systems. The scope of the IJRES addresses the state of the art of all aspects of reconfigurable and embedded computing systems with emphasis on algorithms, circuits, systems, models, compilers, architectures, tools, design methodologies, test and applications.
Arjuna Subject : -
Articles 16 Documents
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Search results for , issue "Vol 12, No 2: July 2023" : 16 Documents clear
Optimized load balancing mechanism in parallel computing for workflow in cloud computing environment Asma Anjum; Asma Parveen
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 2: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i2.pp276-286

Abstract

Cloud computing gives on-demand access to computing resources in metered and powerfully adapted way; it empowers the client to get access to fast and flexible resources through virtualization and widely adaptable for various applications. Further, to provide assurance of productive computation, scheduling of task is very much important in cloud infrastructure environment. Moreover, the main aim of task execution phenomena is to reduce the execution time and reserve infrastructure; further, considering huge application, workflow scheduling has drawn fine attention in business as well as scientific area. Hence, in this research work, we design and develop an optimized load balancing in parallel computation aka optimal load balancing in parallel computing (OLBP) mechanism to distribute the load; at first different parameter in workload is computed and then loads are distributed. Further OLBP mechanism considers makespan time and energy as constraint and further task offloading is done considering the server speed. This phenomenon provides the balancing of workflow; further OLBP mechanism is evaluated using cyber shake workflow dataset and outperforms the existing workflow mechanism.
Efficient content-based image retrieval using integrated dual deep convolutional neural network Feroza D. Mirajkar; Ruksar Fatima; Shaik A. Qadeer
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 2: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i2.pp297-304

Abstract

Content-based image retrieval (CBIR) uses the content features for retrieving and searching the images in a given large database. Earlier, different hand feature descriptor designs are researched based on cues that are visual such as shape, colour, and texture used to represent these images. Although, deep learning technologies have widely been applied as an alternative to designing engineering that is dominant for over a decade. The features are automatically learnt through the data. This research work proposes integrated dual deep convolutional neural network (IDD-CNN), IDD-CNN comprises two distinctive CNN, first CNN exploits the features and further custom CNN is designed for exploiting the custom features. Moreover, a novel directed graph is designed that comprises the two blocks i.e. learning block and memory block which helps in finding the similarity among images; since this research considers the large dataset, an optimal strategy is introduced for compact features. Moreover, IDD-CNN is evaluated considering the two distinctive benchmark datasets the oxford dataset considering mean average precision (mAP) metrics and comparative analysis shows IDD-CNN outperforms the other existing model.
Development of IoTs-based instrument monitoring application for smart farming using solar panels as energy source Yovanka Davincy Setiawan; Bryan Ghilchrist; Gerry Giovan; Mochammad Haldi Widianto
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 2: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i2.pp248-259

Abstract

Indonesia is currently carrying out an industrial revolution 4.0. This revolution discusses the application of technology in the industrial sector, one of which is the agricultural sector. In addition to discussing the application of technology, this revolution also supports the use of renewable energy sources and one of them is the application of solar energy. The application of technology in the agricultural sector is expected to help farmers in maintaining crops to reduce the possibility of crop failure. The existence of this statement makes researchers conduct research in the design and construction of systems with internet of things (IoT) technology and utilize solar energy sources as energy sources for the system. The IoT system will utilize the ATmega328P+ESP8266 RobotDyn microcontroller by utilizing the DHT22, MD0127, soil moisture sensor, and BH1750FVI sensors and sending data to Thingspeak by utilizing the internet network with HTTP communication protocols. The system can monitor ecological factors in gardens with a fairly good degree of accuracy and the utilization of solar energy can run the system properly.
Adaptive filters based efficient EEG classification for steady state visually evoked potential based BCI system Manjula Krishnappa; Madaveeranahally Boregowda Anandaraju
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 2: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i2.pp215-221

Abstract

Brain-computer interfaces (BCIs) system is a link to generate a communication between disable people and physical devices. Thus, steady state visually evoked potential (SSVEP) is analysed to improve performance efficiency of BCIs system using multi-class classification process. Thus, an adaptive filtering-based component analysis (AFCA) method is adopted to examine SSVEP from multiple-channel electroencephalography (EEG) signals for BCIs system efficiency enhancement. Further, flickering at varied frequencies is used in a visual stimulation process to examine user intentions and brain responses. A detailed solution for optimization problem and efficient feature extraction is also presented. Here, a large SSVEP dataset is utilized which contains 256 channel EEG data. Experimental results are evaluated in terms of classification accuracy and information transfer rate to measure efficiency of proposed SSVEP extraction method against varied traditional SSVEP-based BCIs. The average information transfer rate (ITR) results are 308.23 bits per minute and classification accuracy is 93.48% using proposed AFCA method. Thus, proposed AFCA method shows decent performance in comparison with state-of-art-SSVEP extraction methods.
Reconfigurable linear feedback shift register for wireless communication and coding Aakanksha Devrari; Adesh Kumar
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 2: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i2.pp195-204

Abstract

Linear feedback shift register (LFSR) is the basic building block of the communication system used in different coding, error detection and correction codes, such as gold, low-density parity check (LDPC), polar, and turbo codes. There are simple shift register-based n-bit counters with a few XOR gates that behave pseudo-randomly. The LFSR is used in chip hardware for high-speed operations, error control, and the generation of pseudo-random numbers. The hardware chip design and performance estimation of the LFSR is the problem for specific communication system. The motivation of the work is to generate the Gold code sequence by the integration of two LFSR. The article proposes the hardware chip design and simulation of two 5-bit LFSR modules used for the gold sequence generator applicable for the communication systems. The novelty of the work is that the design is scalable and can be extended based on the requirements of the systems which is synthesized and experimentally verified on the Zynq-7000 field programmable gate array (FPGA) board. The concept of this design is programmable and can be extended to n-bit based on the applications. The work is supported, and formulated using very high speed integrated circuit hardware description language (VHDL) programming in Xilinx ISE 14.7 software.
Emotion classification for musical data using deep learning techniques Gaurav Agarwal; Sachi Gupta; Shivani Agarwal; Atul Kumar Rai
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 2: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i2.pp240-247

Abstract

This research is done based on the identification and thorough analyzing musical data that is extracted by the various method. This extracted information can be utilized in the deep learning algorithm to identify the emotion, based on the hidden features of the dataset. Deep learning-based convolutional neural network (CNN) and long short-term memory-gated recurrent unit (LSTM-GRU) models were developed to predict the information from the musical information. The musical dataset is extracted using the fast Fourier transform (FFT) models. The three deep learning models were developed in this work the first model was based on the information of extracted information such as zero-crossing rate, and spectral roll-off. Another model was developed on the information of Mel frequencybased cepstral coefficient (MFCC) features, the deep and wide CNN algorithm with LSTM-GRU bidirectional model was developed. The third model was developed on the extracted information from Mel-spectrographs and untied these graphs based on two-dimensional (2D) data information to the 2D CNN model alongside LSTM models. Proposed model performance on the information from Mel-spectrographs is compared on the F1 score, precision, and classification report of the models. Which shows better accuracy with improved F1 and recall values as compared with existing approaches.

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