cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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 26 Documents
 1   2   3 
Search results for , issue "Vol 13, No 1: March 2024" : 26 Documents clear
Proximate node aware optimal and secure data aggregation in wireless sensor network based IoT environment Priyadarshini, Sushma; Parveen, Asma
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp143-150

Abstract

Internet of things (IoT) has become one of the eminent phenomena in human life along with its collaboration with wireless sensor networks (WSNs), due to enormous growth in the domain; there has been a demand to address the various issues regarding it such as energy consumption, redundancy, and overhead. Data aggregation (DA) is considered as the basic mechanism to minimize the energy efficiency and communication overhead; however, security plays an important role where node security is essential due to the volatile nature of WSN. Thus, we design and develop proximate node aware secure data aggregation (PNA-SDA). In the PNA-SDA mechanism, additional data is used to secure the original data, and further information is shared with the proximate node; moreover, further security is achieved by updating the state each time. Moreover, the node that does not have updated information is considered as the compromised node and discarded. PNA-SDA is evaluated considering the different parameters like average energy consumption, and average deceased node; also, comparative analysis is carried out with the existing model in terms of throughput and correct packet identification.
An efficient high performance reconfigurable canonical sign digit architecture for software defined radio Chalampalem, Vijaya Bhaskar; Pidugu, Munaswamy
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp25-32

Abstract

Software defined radios (SDRs) are highly motivated for wireless device modelling due to their flexibility and scalability over alternative wireless design options. The evolutionary structure of finite impulse response (FIR) filters was designed for a proposed reconfigurable canonical sign digit (CSD) approach. Considering the complex trade-off, this is accomplished with many FIR taps, which is a challenging assignment. On the baseband processing side, design is given with parameterization-controlled FIR filter tap selection. Optimal processing models to overcome the reconfigurable design issues associated with the SDR system for a multi-standard wireless communication system root cosine filter standard are often used to implement multiple FIR channelization topologies, each of which is tied to a particular in-phase and quadrature (IQ) symbol. Additionally, it demonstrates the viability of using a multi-modulation baseband modulator in the SDR system for next-generation wireless communication systems to maximise adaptability with the least amount of computational complexity overhead. The proposed multiplier-less FIR filter-based reconfigurable baseband modulator, according to the experimental results, offers a 6% complexity reduction and a 47% improvement in performance efficiency over the current SDR system.
Cost and performance aware scheduling technique for cloud computing environment Gorva, Santhosh Kumar; Anandachar, Latha C.
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp9-19

Abstract

Recently, lot of interest have been put forth by researchers to improve workload scheduling in cloud platform. However, execution of scientific workflow on cloud platform is time consuming and expensive. As users are charged based on hour of usage, lot of research work have been emphasized in minimizing processing time for reduction of cost. However, the processing cost can be reduced by minimizing energy consumption especially when resources are heterogeneous in nature; very limited work have been done considering optimizing cost with energy and processing time parameters together in meeting task quality of service (QoS) requirement. This paper presents cost and performance aware workload scheduling (CPA-WS) technique under heterogeneous cloud platform. This paper presents a cost optimization model through minimization of processing time and energy dissipation for execution of task. Experiments are conducted using two widely used workflow such as Inspiral and CyberShake. The outcome shows the CPA-WS significantly reduces energy, time, and cost in comparison with standard workload scheduling model.
C4O: chain-based cooperative clustering using coati optimization algorithm in WSN Singh, Preet Kamal; Singh, Harmeet; Kaur, Jaspreet
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp96-104

Abstract

In order to provide sensing services to low-powered IoT devices, wireless sensor networks (WSNs) organize specialized transducers into networks. Energy usage is one of the most important design concerns in WSN because it is very hard to replace or recharge the batteries in sensor nodes. For an energy-constrained network, the clustering technique is crucial in preserving battery life. By strategically selecting a cluster head (CH), a network's load can be balanced, resulting in decreased energy usage and extended system life. Although clustering has been predominantly used in the literature, the concept of chain-based clustering has not yet been explored. As a result, in this paper, we employ a chain-based clustering architecture for data dissemination in the network. Furthermore, for CH selection, we employ the coati optimisation algorithm, which was recently proposed and has demonstrated significant improvement over other optimization algorithms. In this method, the parameters considered for selecting the CH are energy, node density, distance, and the network’s average energy. The simulation results show tremendous improvement over the competitive cluster-based routing algorithms in the context of network lifetime, stability period (first node dead), transmission rate, and the network's power reserves.
Task level energy and performance assurance workload scheduling model in distributed computing environment Bakka, Jagadevi; Lingareddy, Sanjeev C.
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp210-216

Abstract

Scientific workload execution on distributed computing platform such as cloud environment is time intense and expensive. The scientific workload has task dependencies with different service level agreement (SLA) prerequisite at different levels. Existing workload scheduling (WS) design are not efficient in assuring SLA at task level. Alongside, induce higher cost as majority of scheduling mechanisms reduce either time or energy. In reducing, cost both energy and makespan must be optimized together for allocating resource. No prior work has considered optimizing energy and processing time together in meeting task level SLA requirement. This paper present task level energy and performance assurance (TLEPA)-WS algorithm for distributed computing environment. The TLEPA-WS guarantees energy minimization with performance requirement of parallel application under distributed computational environment. Experiment results shows significant reduction in using energy and makespan; thereby reduces cost of workload execution in comparison with various standard workload execution models.
Affective analysis in machine learning using AMIGOS with Gaussian expectation-maximization model Kaliappan, Balamurugan; Sudalaiyadumperumal, Bakkialakshmi Vaithialingam; Thalavaipillai, Sudalaimuthu
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp201-209

Abstract

Investigating human subjects is the goal of predicting human emotions in the stock market. A significant number of psychological effects require (feelings) to be produced, directly releasing human emotions. The development of effect theory leads one to believe that one must be aware of one's sentiments and emotions to forecast one's behavior. The proposed line of inquiry focuses on developing a reliable model incorporating neurophysiological data into actual feelings. Any change in emotional affect will directly elicit a response in the body's physiological systems. This approach is named after the notion of Gaussian mixture models (GMM). The statistical reaction following data processing, quantitative findings on emotion labels, and coincidental responses with training samples all directly impact the outcomes that are accomplished. In terms of statistical parameters such as population mean and standard deviation, the suggested method is evaluated compared to a technique considered to be state-of-the-art. The proposed system determines an individual's emotional state after a minimum of 6 iterative learning using the Gaussian expectation-maximization (GEM) statistical model, in which the iterations tend to continue to zero error. Perhaps each of these improves predictions while simultaneously increasing the amount of value extracted.

Page 3 of 3 | Total Record : 26

 1   2   3 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol 14, No 3: November 2025 Vol 14, No 2: July 2025 Vol 14, No 1: March 2025 Vol 13, No 3: November 2024 Vol 13, No 2: July 2024 Vol 13, No 1: March 2024 Vol 12, No 3: November 2023 Vol 12, No 2: July 2023 Vol 12, No 1: March 2023 Vol 11, No 2: July 2022 Vol 11, No 1: March 2022 Vol 10, No 3: November 2021 Vol 10, No 2: July 2021 Vol 10, No 1: March 2021 Vol 9, No 3: November 2020 Vol 9, No 2: July 2020 Vol 9, No 1: March 2020 Vol 8, No 3: November 2019 Vol 8, No 2: July 2019 Vol 8, No 1: March 2019 Vol 7, No 3: November 2018 Vol 7, No 2: July 2018 Vol 7, No 1: March 2018 Vol 6, No 3: November 2017 Vol 6, No 2: July 2017 Vol 6, No 1: March 2017 Vol 5, No 3: November 2016 Vol 5, No 2: July 2016 Vol 5, No 1: March 2016 Vol 4, No 3: November 2015 Vol 4, No 2: July 2015 Vol 4, No 1: March 2015 Vol 3, No 3: November 2014 Vol 3, No 2: July 2014 Vol 3, No 1: March 2014 Vol 2, No 3: November 2013 Vol 2, No 2: July 2013 Vol 2, No 1: March 2013 Vol 1, No 3: November 2012 Vol 1, No 2: July 2012 Vol 1, No 1: March 2012 More Issue