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31,458 Article Results

Robust embedded access control system based on face and encrypted QR with RPi4

10.11591/ijres.v13.i3.pp586-594
Samir Marwan Hammami , Muhammad Alhammami
Facial-based recognition systems are commonly used for building access control, with the accuracy and computing requirements still being improved. On the other hand, QR codes are gaining rising attention as an input interface to many embedded applications. This paper proposes an embedded access control system that customises both previous techniques to be implemented on the CPU of a low-cost Raspberry Pi 4 computer. The achieved system works smoothly with a frame rate of 8.27 FPS, increasing the accessing control's robustness compared to a system based on face recognition only. It also offers the ability to control the access of unknown faces. In tandem with integration, this strengthens security measures, improves user experience, and outperforms conventional access control approaches, creating an attractive offer for many businesses.
Volume: 13
Issue: 3
Page: 586-594
Publish at: 2024-11-01

Energy-efficient clustering and routing using fuzzy k-medoids and adaptive ranking-based wireless sensor network

10.11591/ijres.v13.i3.pp774-785
Haritha K. Sivaraman , Rangaiah Leburu
The 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.
Volume: 13
Issue: 3
Page: 774-785
Publish at: 2024-11-01

Adaptive tunicate swarm optimization with partial transmit sequence for phase optimization in MIMO-OFDM

10.11591/ijres.v13.i3.pp528-541
Abdul Lateef Haroon Phulara Shaik , Sowmya Madhavan , Parameshachari Bidare Divakarachari , Rocío Pérez de Prado , Paramesh Siddappa Parameshwarappa , Kavitha Malali Vishveshwarappa Gowda
Multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) are widely utilized in wireless systems and maximum data rate communications. The MIMO-OFDM technology increases the efficiency of spectrum utilization. The peak-to-average-power ratio (PAPR) minimization in MIMO-OFDM is a complex task in wireless communications systems. In this research, an adaptive tunicate swarm optimization with partial transmit sequence (ATSO-PTS) algorithm is proposed for a reduction of PAPR in MIMO-OFDM. The nonsquare-matrix-based differential space time coding (N-DSTC) scheme is used for the encoding and decoding process of MIMO-OFDM. The N-DSTC encoding and decoding are linear error-correcting codes that are utilized for message transmission over noisy channels. The pre-specified quadrate phase shift keying (QPSK) symbol is deployed for the modulation and demodulation scheme. On the receiver side, the serial to parallel (S/P) conversion, and fast Fourier transform (FFT) are accomplished, alongside the received data bits being demodulated to obtain the output bits. The proposed ATSO-PTS method achieves better results according to performance metrices PAPR, bit error rate (BER) and signal-to-noise-ratio (SNR), with values of about 2.9, 0.01 and 0.025, respectively. This ensures superior results when compared to the existing methods of twin symbol hybrid optimization applied to partial transmit sequence (TSHO-PTS), selective level mapping and PTS (SLM-PTS), and particle swarm and grey wolf (PS-GW) with PTS, respectively.
Volume: 13
Issue: 3
Page: 528-541
Publish at: 2024-11-01

DDoS-attacks prevention using MinE-DT an adaptive security and energy optimization integration of NIPS in wireless sensor networks

10.11591/ijeecs.v36.i2.pp1226-1233
Bharathi Ramachandra , T. P. Surekha
Wireless sensor networks (WSNs) have revolutionized data collection in diverse environments, from industrial settings to natural ecosystems. However, their decentralized nature and energy constraints pose unique security and operational challenges. Previous research provided foundational insights into WSN security but lacked comprehensive strategies for real-time intrusion prevention and efficient energy utilization. Our work employs a multi-layered approach, integrating network intrusion prevention systems (NIPS) with WSNs and leveraging machine learning for threat detection. We developed MinE-DT (minimum energy-direct transmission) hybrid routing an integrated WSN model that not only identifies and mitigates distributed denial-of-service (DDoS) attack but also optimizes energy consumption, ensuring prolonged network longevity without compromising security. The proposed model's distinctiveness lies in its fusion of NIPS with energy-saving algorithms, offering a dual advantage of enhanced security and energy efficiency. Utilizing a combination of simulations and theoretical analysis, our methodology yielded promising results, showcasing significant improvements in threat detection rates and energy conservation.
Volume: 36
Issue: 2
Page: 1226-1233
Publish at: 2024-11-01

Bee-inspired knowledge transfer: synthesizing data for enhanced deep learning explainability

10.11591/ijeecs.v36.i2.pp1052-1069
Kritanat Chungnoy , Tanatorn Tanantong , Pokpong Songmuang
This paper presents the generation method for an explainable model based on the given information of a black box model using a concept of knowledge transfer to synthesize a dataset. The proposed method applies with GAN and Bee algorithm (BA) for data synthesis technique to synthesize a dataset by considering loss value in a knowledge transferring process to inherit the significance of features. The synthesized dataset is used to train for a proxy model as an explainable model. The result of the experiment indicates that knowledge transfer from Bee algo better than generative adversarial network (GAN) in terms of the coefficient of determination R2. In addition, explainable models from the synthesized data of the Bee-based method obtains F1 score superior to those from the GAN-based method in all datasets and settings. The dataset synthesized from the Bee-based method produces the explainable prediction model that has similar top-10 features according to similarity score of 0.6718 using shapley additive explanations (SHAP) feature importance which is higher than those from GAN-based method for 0.4218 in average. Additionally, experimental result to evaluate accuracy shows that F1 score from explainable models from the Bee-based method are closed to F1 score from a model generated from the original dataset.
Volume: 36
Issue: 2
Page: 1052-1069
Publish at: 2024-11-01

Golden jackal optimization-based clustering scheme for energy-aware vehicular ad-hoc networks

10.11591/ijeecs.v36.i2.pp942-951
Mahalakshmi Baladhandapani , Shoaib Kamal , Chevella Anil Kumar , Jegajothi Balakrishnan , Segu Praveena , Ezudheen Puliyanjalil
Clustering in vehicular ad-hoc networks (VANETs) plays a pivotal role in enhancing the reliability and efficiency of transmission among vehicles. VANET is a dynamic and highly mobile network where vehicles form clusters to enable effective data exchange, resource allocation, and cooperative actions. Clustering algorithm, helps vehicles self-organize into clusters based on connectivity and proximity, thus improving scalability and reducing transmission overhead. This cluster enables critical applications such as traffic management, collision avoidance, and data dissemination in VANET, which contribute to more efficient and safer transportation systems. Effective clustering strategy remains an active area of research to address the unique challenges posed by the diverse and rapidly changing environments of VANET. Therefore, this article presents a golden jackal optimization-based energy aware clustering scheme (GJO-EACS) approach for VANET. The presented GJO-EACS technique uses a dynamic clustering approach which adapts to the varying network topologies and traffic conditions, intending to extend the network lifetime and improve energy utilization. The results highlight the potential of the GJO-EACS technique to contribute to the sustainable operation of VANETs, making it a valuable contribution to the field of vehicular networking and smart transportation systems.
Volume: 36
Issue: 2
Page: 942-951
Publish at: 2024-11-01

An improved PSO-based approach for the photovoltaic cell parameters identification in a single diode model

10.11591/ijeecs.v36.i2.pp749-759
Maria Amaidi , Lassaad Zaaraoui , Ali Mansouri
The future power of photovoltaic systems (PVS) is gaining significant attention due to its rising potential. This has resulted in a substantial amount of research emphasizing the importance of optimizing the PVS efficiency. However, the identification of PV cell model parameters remains a challenging task, mainly due to the characteristics of PV cells and their dependence on varying meteorological conditions. In this work, we present a novel methodology based on an improved new multi objective particle swarm optimization (NMOPSO) algorithm for the PV cell parameters identification. The main goal is to minimize the root mean square error (RMSE) and to calculate the series resistance (Rs) by means of its non-linear equation form. The applied algorithm uses an evolving and adaptive search strategy to enhance both speed of convergence for the parameter identification process precision. Through extensive simulations, we demonstrate that proposed approach outperforms current methods in terms of accuracy, precision, and PV parameters extraction.
Volume: 36
Issue: 2
Page: 749-759
Publish at: 2024-11-01

Optimal size and allocation of wind distributed generation in distribution network using particle swarm optimization

10.11591/ijeecs.v36.i2.pp724-732
Swathi Sankepally , Sravana Kumar Bali
The aim of this research is to evaluate the performance of the distribution network by connecting wind distributed generation (DG) and determining the optimal location and size using the particle swarm optimization (PSO) technique, once the wind DG is connected at the optimal location, the output of wind turbines is not constant but varies with changes in wind speed. Wind turbines are designed to generate the energy from the wind. As the output of the wind turbines changes, it influences the power flow and voltage levels in the distribution network. The injection of power from the wind turbines can cause variations in voltages within the distribution network. Additionally, the changing power flow may contribute to power losses in the distribution system. In this paper, the voltages and active power losses are evaluated with the change in wind speed for the IEEE 15 Bus system by conducting load flow analysis in MATLAB. The results reveal optimized solutions that contribute to reduced power losses, increased renewable energy generation, and improved voltage profiles. This research underscores the potential of PSO-based optimization in conforming more efficient and sustainable distribution networks.
Volume: 36
Issue: 2
Page: 724-732
Publish at: 2024-11-01

Comparative efficiency analysis of RF power amplifiers with fixed bias and envelope tracking bias

10.11591/ijeecs.v36.i2.pp808-816
Ambily Babu , Bangalore Gangadharaiah Shivaleelavathi , Veeramma Yatnalli
RF power amplifier (RF PA) finds its application in almost all the areas of electronics, mobile communication being identified as a major area. The paper performs a comparative efficiency analysis of RF power amplifiers operating with a fixed bias and an envelope tracking bias. Simulations are performed using Keysight advanced design system (ADS) tool. A class a RF PA operating at a 12 dB gain is fixed for the work. 16 QAM LTE signal operating at 5 MHz input frequency, with a peak to average power ratio (PAPR) of 6.0 dB is used as input signal. An envelope simulation at 2.5 GHz is performed on the RF power amplifier. Simulation result shows an improvement of 12% in power added efficiency (PAE) at 6 dB back-off and 6.422% in mean PAE while using envelope tracking power amplifiers, compared to RF PA with fixed supply. Envelope tracking power amplifiers reduced AM/AM distortions also by a factor of 0.248. The results obtained are much better than that obtained using a conventional RF PA with fixed bias. RF PA being the most power dissipative block in a mobile handset, improving its efficiency contributes directly to a great improvement in the battery lifetime of mobile phones. The major challenges faced by envelope tracking PA (ETPA) designers in achieving this efficiency improvement is also delineated in the paper.
Volume: 36
Issue: 2
Page: 808-816
Publish at: 2024-11-01

Hybrid logistic regression support vector model to enhance prediction of bipolar disorder

10.11591/ijeecs.v36.i2.pp1294-1300
Nisha Agnihotri , Sanjeev Kumar Prasad
Bipolar disorder has become one of the major mental health issues due to stressed life around the world. This is the major reason for suicides these days as these people are unable to convey their feeling and emotions to others. This proposed research shows the logistic regression and support vector machine hybrid model to predict bipolar disorder in patients is to develop an accurate and reliable model that can effectively predict the presence of bipolar disorder in patients based on their clinical and demographic data. The purpose is to make a framework that can help healthcare professionals diagnose bipolar disorder early, thereby enabling timely and appropriate treatment to be provided. The model should take into account various patient-specific features, such as age, gender, family history, medication use, and other medical conditions, in addition to relevant clinical and demographic variables. The aim is to create a model that can accurately classify patients with bipolar disorder and non-bipolar disorder patients while minimizing false-positive and false-negative classifications. The work shows improvement in evaluation detection in performance with hybrid logistic support vector regression (LSVR) to detect disorder and protect them to avoid worse situation.
Volume: 36
Issue: 2
Page: 1294-1300
Publish at: 2024-11-01

An innovative machine learning optimization-based data fusion strategy for distributed wireless sensor networks

10.11591/ijeecs.v36.i2.pp1012-1022
Naganna Shankar Sollapure , Poornima Govindaswamy
Self-sufficient sensors scattered over different regions of the world comprise distributed wireless sensor networks (DWSNs), which track a range of environmental and physical factors such as pressure, temperature, vibration, sound, motion, and pollution. The use of data fusion becomes essential for combining information from various sensors and system performance. In this study, we suggested the multi-class support vector machine (SDF-MCSVM) with synthetic minority over-sampling techniques (SMOTE) data fusion for wireless sensor network (WSN) performance. The dataset includes 1,334 instances of hourly averaged answers for 12 variables from an AIR quality chemical multisensor device. To create a balanced dataset, the unbalanced data was first pre-processed using the SMOTE. The grey wolf optimization (GWO) approach is then used to reduce features in an effort to improve the efficacy and efficiency of feature selection procedures. This method is applied to classify the fused feature vectors into multiple categories at once to improve classification performance in WSNs and address unbalance datasets. The result shows the proposed method reaches high precision, accuracy, F1-score, recall, and specificity. The computational complexity and processing time were decreased in the study by using the proposed method. This is great potential for accurate and timely data fusion in dispersed WSNs with the successful integration of data fusion technologies.
Volume: 36
Issue: 2
Page: 1012-1022
Publish at: 2024-11-01

Comparing feature usage in IMU-based gesture control for omnidirectional robot via wearable glove

10.11591/ijres.v13.i3.pp542-551
Dahnial Syauqy , Eko Setiawan , Edita Rosana Widasari
To improve the intuitiveness of maneuver control on omniwheeled mobile robot, many hand gesture-based robot controls have been developed. The focus of this research is to develop a wearable system for data acquisition from inertial measurement unit (IMU) sensors and compare its features to be used as gesture recognition using the random forest algorithm. With the need of resource constrained device for wearable system based on microcontrollers, we compared the use of Euler and quaternion-based orientation data as input features. As additional comparison, dimension reduction was also carried out using the principal component analysis (PCA) method. Hand gestures are recognized using data obtained by the IMU sensor embedded in the wearable glove. This study compared the accuracy and size of library files embedded in microcontrollers in several feature usage scenarios. The test evaluation results of all scenarios show that the use of all features provides a balance between high accuracy but small file sizes, respectively 99% and 9.2 KB. However, the use of other fewer features, such as by only using 3 Euler data, 4 quaternion data, or by using PCA algorithm (PC=3) can also be used since the accuracy is still above 90%, with a relatively larger file size.
Volume: 13
Issue: 3
Page: 542-551
Publish at: 2024-11-01

Detection of colorization based image forgeries using convolutional autoencoder method

10.11591/ijeecs.v36.i2.pp1114-1126
Soumyashree Muralidhar Panchal , Asha Kethaganahalli Hanumanthaiah , Bindushree Channabasavaraju Doddasiddavanahalli , Manju More Eshwar Rao , Ambika Belekere Jayaramu
Recently, it has become difficult to recognize and easier to misuse digital images due to the large number of editing tools available. Detecting forgeries in images is crucial for security and forensic purposes. Therefore, this research implements a deep learning (DL) method of convolutional autoencoder (CAE) which improves colorization-based image forgery detection by leveraging spatial and color information, increasing the detection accuracy. At first, the pre-processed input forgery images are used with the wiener filtering-contrast restricted improved histogram equalization (WE-CLAHE) technique. Hybrid dual-tree complex wavelet trigonometric transform (H‑DTCWT) and VGG-16 are used to extract effective features from the clustered data. Improved horse herd optimization (IHH) is employed to reduce the dimensionality of a feature. At last, the CAE model is implemented to significantly recognize the image forgery. The accuracy of CASIA V1 and GRIP datasets of 99.95% and 99.97%, respectively is achieved. Hence, this implemented method obtains a high forgery detection performance than the existing methods.
Volume: 36
Issue: 2
Page: 1114-1126
Publish at: 2024-11-01

Towards automated classification of cognitive states: Riemannian geometry and spectral embedding in EEG data

10.11591/ijeecs.v36.i2.pp1023-1029
Manjunatha Siddappa , Kempahanumaiah M. Ravikumar , Nagendra Kumar Madegowda
Our research explores the application of Riemannian geometry and spectral embedding in the context of electroencephalogram (EEG) signal analysis for cognitive state classification. Leveraging the PyRiemann library and the AlphaWaves dataset, our study employs covariance estimation and the minimum distance to mean (MDM) classifier within a machine learning pipeline. The classification accuracy is assessed through stratified k-fold cross-validation. Furthermore, we introduce a novel visualization approach by calculating the spectral embedding of covariance matrices, providing insights into the underlying structure of the EEG epochs. Our findings showcase the potential of Riemannian geometry and spectral embedding as powerful tools in the domain of EEG-based cognitive state classification, contributing to the broader field of brain signal analysis and paving the way for automated and advanced neurocognitive studies.
Volume: 36
Issue: 2
Page: 1023-1029
Publish at: 2024-11-01

Research on the using of ZnO nanostructures to increase the white light-emitting diodes optics effectiveness

10.11591/ijeecs.v36.i2.pp793-799
Phan Xuan Le , Pham Hong Cong
In conventional white light-emitting diodes (WLEDs), the combination of blue-LED chips with a yellow-phosphor type is the commonly employed method of production. However, this approach often results in low angular correlated color temperature (CCT) homogeneity. To address this issue, this research proposes the incorporation of ZnO nanostructures into WLED packages to enhance color homogeneity. The impacts of varying concentrations of ZnO nanoparticles on the morphologies, scattered energy, and CCT deviations in WLED packages are studied utilizing the Mie-scattering theory and MATLAB measurement techniques to analyze the scattering effects of ZnO nanoparticles. The scattering analysis reveals that the presence of ZnO nanoparticles significantly increases the scattered strength of WLEDs, especially with larger particles’ radii, due to their strong scattering influence. Then, 1 µm is the selected size of the ZnO used in further tests. With different ZnO concentrations (2-50 wt.%) in the phosphor layer, the CCT deviation holds an inverse proportion to the luminous efficiency. Particularly, higher concentrations of ZnO nanoparticles reduce the CCT deviation, leading to improved color homogeneity, but a decline in lumen efficiency. The findings provide the basis of ZnO scattering performance, which can be utilized to explore potential ways for enhancing WLED’s color uniformity and overall performance.
Volume: 36
Issue: 2
Page: 793-799
Publish at: 2024-11-01
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