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  Subjects -> ELECTRONICS (Total: 207 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Acta Electronica Malaysia     Open Access  
Advanced Materials Technologies     Hybrid Journal   (Followers: 1)
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 8)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 9)
Advances in Electronics     Open Access   (Followers: 100)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Microelectronic Engineering     Open Access   (Followers: 13)
Advances in Power Electronics     Open Access   (Followers: 40)
Advancing Microelectronics     Hybrid Journal  
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 28)
Annals of Telecommunications     Hybrid Journal   (Followers: 9)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 9)
Archives of Electrical Engineering     Open Access   (Followers: 16)
Australian Journal of Electrical and Electronics Engineering     Hybrid Journal  
Batteries     Open Access   (Followers: 9)
Batteries & Supercaps     Hybrid Journal   (Followers: 5)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 31)
Bioelectronics in Medicine     Hybrid Journal  
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 2)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access   (Followers: 1)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 47)
China Communications     Full-text available via subscription   (Followers: 9)
Chinese Journal of Electronics     Hybrid Journal  
Circuits and Systems     Open Access   (Followers: 15)
Consumer Electronics Times     Open Access   (Followers: 5)
Control Systems     Hybrid Journal   (Followers: 307)
ECTI Transactions on Computer and Information Technology (ECTI-CIT)     Open Access  
ECTI Transactions on Electrical Engineering, Electronics, and Communications     Open Access   (Followers: 2)
Edu Elektrika Journal     Open Access   (Followers: 1)
Electrica     Open Access  
Electronic Design     Partially Free   (Followers: 124)
Electronic Markets     Hybrid Journal   (Followers: 7)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
Electronics     Open Access   (Followers: 109)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 10)
Electronics For You     Partially Free   (Followers: 103)
Electronics Letters     Hybrid Journal   (Followers: 26)
Elektronika ir Elektortechnika     Open Access   (Followers: 2)
Elkha : Jurnal Teknik Elektro     Open Access  
Emitor : Jurnal Teknik Elektro     Open Access   (Followers: 2)
Energy Harvesting and Systems     Hybrid Journal   (Followers: 4)
Energy Storage     Hybrid Journal   (Followers: 1)
Energy Storage Materials     Full-text available via subscription   (Followers: 4)
EPE Journal : European Power Electronics and Drives     Hybrid Journal  
EPJ Quantum Technology     Open Access   (Followers: 1)
EURASIP Journal on Embedded Systems     Open Access   (Followers: 11)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 10)
Frequenz     Hybrid Journal   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 1)
IACR Transactions on Symmetric Cryptology     Open Access   (Followers: 1)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 101)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 82)
IEEE Embedded Systems Letters     Hybrid Journal   (Followers: 57)
IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology     Hybrid Journal   (Followers: 3)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 52)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 9)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 1)
IEEE Letters on Electromagnetic Compatibility Practice and Applications     Hybrid Journal   (Followers: 4)
IEEE Magnetics Letters     Hybrid Journal   (Followers: 7)
IEEE Nanotechnology Magazine     Hybrid Journal   (Followers: 42)
IEEE Open Journal of Circuits and Systems     Open Access   (Followers: 3)
IEEE Open Journal of Industry Applications     Open Access   (Followers: 3)
IEEE Open Journal of the Industrial Electronics Society     Open Access   (Followers: 3)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 77)
IEEE Pulse     Hybrid Journal   (Followers: 5)
IEEE Reviews in Biomedical Engineering     Hybrid Journal   (Followers: 23)
IEEE Solid-State Circuits Letters     Hybrid Journal   (Followers: 3)
IEEE Solid-State Circuits Magazine     Hybrid Journal   (Followers: 13)
IEEE Transactions on Aerospace and Electronic Systems     Hybrid Journal   (Followers: 367)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 74)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 62)
IEEE Transactions on Autonomous Mental Development     Hybrid Journal   (Followers: 8)
IEEE Transactions on Biomedical Engineering     Hybrid Journal   (Followers: 39)
IEEE Transactions on Broadcasting     Hybrid Journal   (Followers: 13)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 26)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 46)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 19)
IEEE Transactions on Geoscience and Remote Sensing     Hybrid Journal   (Followers: 225)
IEEE Transactions on Haptics     Hybrid Journal   (Followers: 5)
IEEE Transactions on Industrial Electronics     Hybrid Journal   (Followers: 75)
IEEE Transactions on Industry Applications     Hybrid Journal   (Followers: 40)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 27)
IEEE Transactions on Learning Technologies     Full-text available via subscription   (Followers: 12)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 80)
IEEE Transactions on Services Computing     Hybrid Journal   (Followers: 4)
IEEE Transactions on Signal and Information Processing over Networks     Hybrid Journal   (Followers: 14)
IEEE Transactions on Software Engineering     Hybrid Journal   (Followers: 79)
IEEE Women in Engineering Magazine     Hybrid Journal   (Followers: 11)
IEEE/OSA Journal of Optical Communications and Networking     Hybrid Journal   (Followers: 16)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 5)
IET Cyber-Physical Systems : Theory & Applications     Open Access   (Followers: 1)
IET Energy Systems Integration     Open Access   (Followers: 1)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 36)
IET Nanodielectrics     Open Access  
IET Power Electronics     Hybrid Journal   (Followers: 60)
IET Smart Grid     Open Access   (Followers: 1)
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 18)
IETE Journal of Education     Open Access   (Followers: 4)
IETE Journal of Research     Open Access   (Followers: 11)
IETE Technical Review     Open Access   (Followers: 13)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
Industrial Technology Research Journal Phranakhon Rajabhat University     Open Access  
Informatik-Spektrum     Hybrid Journal   (Followers: 2)
Instabilities in Silicon Devices     Full-text available via subscription   (Followers: 1)
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 14)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 18)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 12)
International Journal of Antennas and Propagation     Open Access   (Followers: 11)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 4)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 5)
International Journal of Control     Hybrid Journal   (Followers: 11)
International Journal of Electronics     Hybrid Journal   (Followers: 7)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 13)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 3)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Hybrid Intelligence     Hybrid Journal  
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 16)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 10)
International Journal of Nanoscience     Hybrid Journal   (Followers: 1)
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 4)
International Journal of Power Electronics     Hybrid Journal   (Followers: 25)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 4)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 10)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 4)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 6)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 4)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 12)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 4)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 1)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 38)
Journal of Electrical Bioimpedance     Open Access  
Journal of Electrical Bioimpedance     Open Access   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 7)
Journal of Electrical, Electronics and Informatics     Open Access  
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 8)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 9)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 6)
Journal of Electronic Science and Technology     Open Access   (Followers: 1)
Journal of Electronics (China)     Hybrid Journal   (Followers: 5)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
Journal of Engineered Fibers and Fabrics     Open Access   (Followers: 2)
Journal of Field Robotics     Hybrid Journal   (Followers: 4)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 186)
Journal of Information and Telecommunication     Open Access   (Followers: 1)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 3)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 10)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 10)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal   (Followers: 1)
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal   (Followers: 3)
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 11)
Journal of Nuclear Cardiology     Hybrid Journal  
Journal of Optoelectronics Engineering     Open Access   (Followers: 4)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 32)
Journal of Power Electronics     Hybrid Journal   (Followers: 2)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 11)
Journal of Semiconductors     Full-text available via subscription   (Followers: 5)
Journal of Sensors     Open Access   (Followers: 27)
Journal of Signal and Information Processing     Open Access   (Followers: 9)
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer     Open Access  
Jurnal Rekayasa Elektrika     Open Access  
Jurnal Teknik Elektro     Open Access  
Jurnal Teknologi Elektro     Open Access  
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access  
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 2)
Metrology and Measurement Systems     Open Access   (Followers: 6)
Microelectronics and Solid State Electronics     Open Access   (Followers: 28)
Nanotechnology, Science and Applications     Open Access   (Followers: 6)
Nature Electronics     Hybrid Journal   (Followers: 1)
Networks: an International Journal     Hybrid Journal   (Followers: 5)
Open Electrical & Electronic Engineering Journal     Open Access  
Open Journal of Antennas and Propagation     Open Access   (Followers: 9)
Paladyn. Journal of Behavioral Robotics     Open Access   (Followers: 1)
Power Electronics and Drives     Open Access   (Followers: 2)
Problemy Peredachi Informatsii     Full-text available via subscription  
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 7)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
Recent Advances in Communications and Networking Technology     Hybrid Journal   (Followers: 4)
Recent Advances in Electrical & Electronic Engineering     Hybrid Journal   (Followers: 11)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 6)
Revue Méditerranéenne des Télécommunications     Open Access  
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 57)
Semiconductors and Semimetals     Full-text available via subscription   (Followers: 1)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Solid State Electronics Letters     Open Access  
Solid-State Electronics     Hybrid Journal   (Followers: 9)
Superconductor Science and Technology     Hybrid Journal   (Followers: 3)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 3)
Technical Report Electronics and Computer Engineering     Open Access  
TELE     Open Access  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 9)
Transactions on Cryptographic Hardware and Embedded Systems     Open Access   (Followers: 2)

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Similar Journals
Journal Cover
Sensing and Imaging : An International Journal
Journal Prestige (SJR): 0.255
Citation Impact (citeScore): 1
Number of Followers: 2  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1557-2072 - ISSN (Online) 1557-2064
Published by Springer-Verlag Homepage  [2626 journals]
  • A Modified Whale Optimization Algorithm Based Digital Image Watermarking
           Approach
    • Abstract: Abstract This paper presents a novel digital image watermarking (DIW) scheme. This DIW scheme is based on a hybrid DWT-SVD transform domain and modified whale optimization algorithm (MWOA), making use of multiscale factors (MSFs) to control the trade-off between invisibility and robustness. The values of MSFs have been determined using MWOA, which is a nature-inspired optimization algorithm inspired by the bubble-net hunting strategy of humpback whales. The essential feature of modified WOA is that it provides a global solution and also requires less internal parameters for optimization. Normalized cross-correlation and peak signal to noise ratio are utilized in the objective function design. Results of simulations show that the proposed DIW scheme not only satisfies the need for invisibility but also has better or comparable robustness as compared to other recently published watermarking methods.
      PubDate: 2020-05-25
       
  • Learning an Enhancement Convolutional Neural Network for Multi-degraded
           Images
    • Abstract: Abstract Although image enhancement methods have been widely applied in various outdoor vision systems, the existing methods still face two critical problems. On the one hand, the existing methods only consider a single degradation. However, in practical applications, image quality is usually degraded by multiple factors. The methods designed for the single degradation factor cannot achieve good performance when facing multi-degraded images. On the other hand, the imaging model-based enhancement methods which use prior knowledge or handcrafted features to perform image enhancement may bring some fitting errors. Therefore, considering multiple degradations in images, an image enhancement method is proposed in this paper. Firstly, a new image degradation model based on the multiple scattering model is proposed, which is used to characterize multiple degradations caused by haze, mixed with blur and noise. Then, an image enhancement convolutional neural network (CNN) based on ResNet is proposed to learn the implicit mapping model between low-quality and high-quality images in the pixel domain directly. The CNN network has been trained with an end-to-end learning manner. Experimental results on the synthetic dataset and real-world hazy images verify the superiority of the proposed method, while compared with the state-of-the-art methods.
      PubDate: 2020-05-23
       
  • Surface Acoustic Wave Vapor Sensor with Graphene Interdigital Transducer
           for TNT Detection
    • Abstract: Abstract The performance of the surface acoustic wave (SAW) vapor sensor with graphene interdigital transducer (IDT) is investigated to study the improvement of sensitivity and decrease of secondary effects by the finite element method (FEM). Unlike conventional Al metal electrodes, the observed increase in the SAW phase velocity confirms the existence of elastic loading for the graphene electrode to the contact surface. Accordingly, the mass sensitivity of the SAW device with graphene electrode shows one order of magnitude higher. In the presence of TNT, the resonance frequency shift of the SAW sensor with graphene IDT is approximately 10 times larger than that with Al. Also, graphene electrodes offer the less secondary effects (BAW generations). So the SAW sensor with graphene IDT possesses superior sensing performance.
      PubDate: 2020-04-24
       
  • FPGA Acceleration of Color Interpolation Algorithm Based on Gradient and
           Color Difference
    • Abstract: Abstract To quickly and efficiently achieve a high-quality color image on the embedded platform, designed and used FPGA to complete Bayer pattern image Color interpolation. In this paper, we proposed a color interpolation algorithm based on image gradient direction and color difference. The algorithm calculates the G component according to the gradient value and color difference at the pixel to be interpolated, and then calculates the missing R component and B component based on the G component interpolation. Finally, the pixels of the image boundary area are interpolated. Tested on the Kodak dataset, the experimental results show that the subjective visual quality and objective image quality evaluation of the algorithm is superior to the conventional algorithm. On the other hand, and reduce problems such as image aliasing and edge blurring. The proposed algorithm has been applied to the actual Xilinx’s FPGA platform, a real-time speed of 120 MHz, enabling real-time image color Interpolation.
      PubDate: 2020-04-20
       
  • Secure Image Block Compressive Sensing Using Chaotic DCT Sparse Basis and
           Partial Chaotic DHT Measurement Matrix
    • Abstract: Abstract This paper proposes a secure image block compressive sensing scheme based on a key-controlled sparse basis matrix and key-controlled measurement matrix. In the proposed scheme, the original image is first divided into blocks, then inter-block scrambling is employed using the Arnold transform. Second, the resulting scrambled blocks are secure CS, which consists of a chaotic discrete cosine transform, sparse basis matrix, and a chaotic discrete Hartley transform measurement matrix, based on row scrambling with a chaotic sequence. The measured data obtained using CS is encrypted using bitwise XOR and pixel scrambling operations based on chaotic maps. The simulation results show that the application of a suitable sub block size for the proposed scheme can achieve good image reconstruction performance in terms of peak-signal to noise ratio merits when compared to the conventional chaotic measurement matrix and random measurement matrix schemes. Our proposed block secure CS can maintain the security of image data, and reduce the CS run time.
      PubDate: 2020-04-11
       
  • Brief Survey of Single Image Super-Resolution Reconstruction Based on Deep
           Learning Approaches
    • Abstract: Abstract With the presentation of super-resolution convolutional neural network, deep learning approach was applied to image super-resolution reconstruction for the first time. By using convolutional neural network, the deep learning approaches can directly learn the mapping relationship between the low-resolution image and high-resolution image, and have achieved better reconstruction effects than the traditional image super-resolution reconstruction methods. Subsequently, a series of improved deep learning approaches have been proposed, and the reconstruction effects have been improved continuously. This paper systematically summa rizes the image super-resolution reconstruction approaches based on deep learning, analyzes the characteristics of different models, and compares the main deep learning models based on the experiments. Furthermore, based on deep learning model, the future research directions of the image super-resolution reconstruction methods based on deep learning models are reasonably predicted.
      PubDate: 2020-04-09
       
  • New Improved Optimized Method for Medical Image Enhancement Based on
           Modified Shark Smell Optimization Algorithm
    • Abstract: Abstract Medical image enhancement is a principal category of the medical image processing which has a great impact on the final diagnosis results. In this paper, a new optimization technique has been presented for enhancing the contrast of the medical images. The main idea here is to propose an optimization problem by considering both global and local enhancement to achieve a strong image enhancement method. The other novelty here is to propose a new improved version of shark smell optimization algorithm to apply to the mentioned optimization problem for enhancing the algorithm convergence. Final results are analyzed based on five different measure indexes and are compared with five popular methods for illustrating the superiority of the presented technique.
      PubDate: 2020-03-21
       
  • A Total Variation Denoising Method Based on Median Filter and Phase
           Consistency
    • Abstract: Abstract The total variation method is widely used in image noise suppression. However, this method is easy to cause the loss of image details because of over-smoothing, so it is sensitive to the value of parameters. In this work, the total variation method has been modified using a diffusion rate adjuster based on the phase congruency and a fusion filter of median filter and phase consistency boundary, which is called “the MPC-TV method”. Experimental results indicate that MPC-TV method is effective in noise suppression, especially for the removing of speckle noise. It can prevent the image from over-smoothing, especially when the iteration time is larger than the best value. Therefore, it helps to improve the robustness of parameters of TV method to noise variance.
      PubDate: 2020-03-17
       
  • Rolling Guidance Filtering-Orientated Saliency Region Extraction Method
           for Visible and Infrared Images Fusion
    • Abstract: Abstract Different image types can be obtained by different sensors, but all the useful information cannot be extracted from a single image. Infrared images can capture the heat source information of scene targets in low light or severe weather conditions. Visible images provide more detail information about the scene. To obtain the rich image information, we propose a visible and infrared images fusion method based on rolling guidance filtering and saliency region extraction in this paper. A multi-scale image decomposition framework is built by using the edge preserving-smoothing algorithm. The image is decomposed into one base layer with different scales and several detail layers. Meanwhile, the saliency region extraction is implemented on each decomposition layer by combining with the rolling guidance filtering. Weight reconstruction is adopted to obtain the final fusion result. The results show that the proposed algorithm has good subjective and objective evaluation results, better fusion performance and robustness compared to other state-of-the-art fusion methods.
      PubDate: 2020-03-10
       
  • Comparative Analysis of SVM and ANN Classifiers using Multilevel Fusion of
           Multi-Sensor Data in Urban Land Classification
    • Abstract: Abstract Multi-sensor data fusion has recently received remarkably more attraction in urban land classification. The fusion of multi-resolution and multi-sensor remote sensing data can help in comprehending more information about the same land cover features, thereby, enhancing the classification accuracy. In this field of study, a combination of hyperspectral data in a long-wave infrared range and a very high-resolution data in a visible range has been extensively used for exploring the spectral and spatial features for decision level fusion classification. This paper proposes a novel method of integrating the classifier decisions with the additional ancillary information derived from spectral and spatial features for improvement in the classification accuracy of natural and man-made objects in urban land cover. The paper also presents a detailed performance comparative evaluation of two classifiers i.e., support vector machine (SVM) and artificial neural network (ANN) to show the effectiveness of these classifiers. The results obtained from a decision-based multilevel fusion of spectral and spatial information using hyperspectral and visible data have shown improvement in classification accuracy. The results also reveal that the classification accuracy of the SVM classifier is better than ANN in multi-sensor data using decision level fusion of combined feature set analysis.
      PubDate: 2020-03-09
       
  • Hyperspectral Image Classification Based on Segmented Local Binary
           Patterns
    • Abstract: Abstract Recently, local binary patterns (LBP) coupled with principal component analysis has been developed for feature extraction of hyperspectral imagery, which has shown success over traditional methods but is limited in physical meaning representation due to the noise bands existing in hyperspectral data. In order to preserve the intrinsic geometrical structure of original data, we propose a segmented LBP (SLBP) to group correlative bands and then extract spatial-spectral features from each band group. The proposed approach employs the LBP operator on independent subspaces to characterize local texture information and distinct spectral signatures, along with a decision fusion system further improving discriminant power. The proposed approach is compared with several traditional and state-of-the-art methods on two benchmark datasets (i.e., the Indian Pines dataset and the Salinas Valley dataset). Experimental results demonstrate that the proposed SLBP strategy can yield superior classification performance (96.8% for the Indian Pines dataset with an improvement of approximately 6.4% and 4.2% when compared with LBP and MELBP, respectively; 98.1% for the Salinas Valley dataset with an improvement of approximately 3.5% and 1.3% compared with LBP and MELBP, respectively).
      PubDate: 2020-03-03
       
  • Fixed Pattern Noise Analysis for Feature Descriptors in CMOS APS Images
    • Abstract: Abstract This paper provides a comparative performance evaluation of local features for images from CMOS APS sensors affected by fixed pattern noise for different combinations of common detectors and descriptors. Although numerous studies report comparisons of local features designed for ordinary visual images, their performance on images with fixed pattern noise is far less assessed. The goal of this work is to develop a tool that allows to evaluate the performance of computer vision algorithms and their implementations subject to deviations of the physical parameters of the CMOS sensor. This tool will facilitate the quantification of the high-level effects produced by circuit random noise, enabling the optimization of the sensor during the design flow with specifications much closer to the application scope. Likewise, this tool will provide the electronic designer with a relationship between high-level algorithm accuracy and maximum fixed pattern noise. Thus the contribution is double: (1) to evaluate the performance of both local float type and more recent binary type detectors and descriptors when combined under a variety of image transformations, and (2) to extract relevant information from circuit-level simulation and to develop a basic noise model to be employed in the design of the feature descriptor evaluation. The utility of this approach is illustrated by the evaluation of the effect of column-wise and pixel-wise fixed pattern noise at the sensor on the performance of different local feature descriptors.
      PubDate: 2020-03-03
       
  • Efficient Transmission of an Encrypted Image Through a MIMO–OFDM System
           with Different Encryption Schemes
    • Abstract: Abstract In today’s world, broadband multimedia services are growing at a rapid pace. It is very much essential to secure data in financial, defense, health care, government, and marketing services. To ensure the data from eavesdroppers, the physical layer security (channel coding, modulation schemes, and diversity techniques, etc.) is one of the best solutions. The security attributes at this level reduce maintenance, operational cost, and complexity without affecting the quality of service. Physical layer security with cryptographic techniques makes it well suited for transferring multimedia data through unsecure or potentially risky communication (wired and wireless) path. To provide security attributes to an image/data, one needs to protect the transmitted data from unauthorized access. Several encryption algorithms have been proposed to ensure confidentiality and security for multimedia data over an insecure wireless channel. Cryptography techniques do not involve any system complexity but add a little computation complexity. Use of advanced processors mitigates the computational burden. In this paper, efficient transmission of an encrypted image through a multiple-input multiple-output orthogonal frequency division multiplexing (MIMO–OFDM) system over an additive white Gaussian noise channel with different encryption schemes is presented. The advanced encryption standard (AES), data encryption standard (DES), and Rubik’s Cube encryption algorithms are incorporated to improve security aspects. The comparison aims to select the most appropriate wireless communication systems (2 × 2 MIMO–OFDM and 4 × 4 MIMO–OFDM), and the most suitable image encryption algorithm for efficient transmission of the encrypted image. The quality of the received image is evaluated using the peak signal-to-noise ratio between the original and decrypted image at different signal-to-noise (SNR) ratio. Performance estimates of the cryptosystems are also provided by computing the bit-error-rate at different SNR. The Performance estimates of encryption algorithms (irrespective of the MIMO–OFDM system) are also included, which consists of various performance metrics. The metrics are histogram, entropy, correlation coefficient, number of changing pixel rate, and unified averaged changed intensity. From the MATLAB simulation experiments, it is observed that the performance of 4 × 4 Crypto-MIMO–OFDM system configurations outperformed 2 × 2 Crypto-MIMO–OFDM system configurations. The performance of the Crypto-MIMO–OFDM system is compared with the original MIMO–OFDM system performance without encryption algorithm. For all variants of Crypto-MIMO–OFDM systems, the use of Rubik’s Cube encryption algorithm shows significant improvement over DES and AES.
      PubDate: 2020-02-25
       
  • A New Technique for Improving the Estimation of a Reflective Optical Color
           Sensor
    • Abstract: Abstract This paper proposes and experimentally demonstrates a novel technique of a precise color estimation by using a low cost reflective optical color sensor. The digital output signal of this sensor is a pulses series with a frequency proportional to the intensity of the incident light. In this sensor, three independent detector output signals with optimized spectral responsiveness allow discrimination between red, green and blue (RGB) light spectral components. The analytical expressions for RGB absorption efficiencies, which detect the color information, have been derived. An experimental set-up has been designed to determine the color difference, involving design of a portable color analyzer with wired and wireless computer interface. The detailed description and implementation of each design element are presented, while a developed algorithm is also discussed. The proposed algorithm apply a linear transformation to realize standardized colorimetric RGB and CIE31-XYZ color space coordinates responses. To that end, a correlation of the sensor outputs is established, a wide band correction matrix in old estimations is replaced by many narrowband correction matrices and the CIE standard curves of wavelengths are extracted and used to select a suitable narrowband correction matrix. Also, the sequential algorithm has been parallelized to reduce the response time in multiple measurement applications which need the big data processing. The experimental results in the vision spectrum confirm the theoretical analysis. Based on the findings of this experiment, the proposed system constitutes a high resolution and low cost solution for various color-sensing applications and colorimetric fields.
      PubDate: 2020-02-24
       
  • Target detection using supervised machine learning algorithms for GPR data
    • Abstract: Abstract A novel approach of supervised machine learning technique is used in this paper to identify landmines. The work presented here has two contributions. First contribution is three features (major axis, minor axis and principal component analysis) based performance comparison of two machine learning technique: support vector machine classifier and neural network classifier. In the second contribution, a new method of extracting five features (mean, variance, kurtosis, skewness and entropy) is suggested. Support vector machine and neural network classifier are trained on three and five-feature data-set. Collection of ground penetrating radar images with surrogate landmines is done in our lab and a data-base of different feature set is created. In experiments, many surrogate mines and non-mines are considered at various depths for data collection. The performance of classifiers is compared on training and testing data-set. Out of the two classifiers, neural network classifier results with better accuracy of 85–90% for training data samples in both (three feature and five feature) analysis. Two trained classifiers are tested over twenty cases of unseen samples. Neural network classifier gives better results of 5–10% increased accuracy than support vector machine classifier over test set also.
      PubDate: 2020-02-21
       
  • Chaos Theory: An Emerging Tool for Arrhythmia Detection
    • Abstract: Abstract The heart is an important muscular organ of the human body which pumps blood throughout the body. It is essential for human life. Timely and accurate assessment of the functioning of the heart has great relevance for reducing the death rate due to cardiac diseases around the world. If the heart is not able to pump blood smoothly, then heart diseases are likely to appear. These heart diseases are known as arrhythmia. Electrocardiogram (ECG) is a diagnostic tool for assessing the functioning of heart non-invasively. It not only detects cardiovascular diseases, but also examines breathing pattern and mental stress. ECG appears in the form of an electrical signal that comprises of P-QRS-T waves and is captured by pasting electrodes on the surface of the skin in a conductive medium. Features of these wave components, such as clinical frequencies, heart rate (HR) measurement, RR interval measurement, spectral components, non-linearity, trajectory identification, and amplitudes, help doctors to diagnose cardiac arrhythmias accurately. This paper presents a computer aided diagnosis (CAD) system to extract non-linearity and trajectory patterns using the theory of chaos analysis to aid cardiologists diagnose arrhythmia accurately. ECG signal is non-stationary and non-linear in nature due to which it contains multiple time-varying frequencies. So a more reliable and accurate technique like time–frequency transform such as short-time Fourier transform (STFT) etc. is needed. In this paper, STFT is used, which is an efficient technique to observe frequency contents of small non-linear segments in time domain. It is used to determine the sinusoidal frequency and phase content of the local sections of a signal. R-peak is very crucial for classifying cardiac arrhythmia. Therefore, STFT is used for detecting R-peaks and their frequency contents. Due to limited time–frequency resolution, STFT usually misses on some information. Therefore, there is a need of supplementing the existing research on the ECG signal interpretation by using non-linear techniques. These gaps have motivated us to use chaos theory (analysis) for ECG signal analysis. The non-linear techniques are expected to yield supplementary clues about the non-linearities in the considered segment. In chaos analysis, the sketched trajectories represent the flow of the system where each trajectory involves a subregion of the phase space known as an attractor. In phase space, set of points depicts complete status of the cardiac cycles through which the system migrates over time. An attractor showcases the best preview according to the initial conditions and time delay dimension. The shape of an attractor may be oval, egg-shaped, circular and in some cases corn-type. It increases the decision capability of the proposed system by identifying correct arrhythmia type. For validating this research work, physioNet database [Massachusetts Institute of Technology-Beth Israel Hospital Arrhythmia database (M-BArr DB), Ventricular Tachyarrhythmia database (VT DB)] and real time database (R T DB) have been used. The proposed technique has been evaluated on the basis of sensitivity (Se) and positive predictive value (PPV). Se of 99.92% and PPV of 99.93% are obtained for the considered databases (M-BArr DB, VT DB and RT DB). Chaos theory together with STFT has proved itself as a good approach that reduces the occurrence of spurious outcomes and has demonstrated the properties that are typical outlook of deterministic chaotic systems.
      PubDate: 2020-02-12
       
  • Intelligent Integration of Neutron, Density and Gamma Ray Data for
           Subsurface Characterization
    • Abstract: Abstract Earth subsurface recognition through describing the underground layers can be carried out using physical measurements to obtain clearer and more accurate subsurface model. This can be conducted applying well logging method. Among various information that can be provided by different well logging sensors, neutron, density and gamma-ray data are the most considerable in subsurface type determination. However, current analysis of these data stills a subjective task which adds a variable confidence interval to the obtained results. In this study, a real time decision level fusion system uses fuzzy logic approach is introduced to incorporate neutron, density, and gamma-ray information in order to provide more specific interpretation of the subsurface structures. Results of the proposed approach agreed well with the results of an offline subsurface determination program, with an average ratio of about 89.75%. The suggested approach was evaluated against real data from eight wells, and the results were promising to yield more objective interpretation.
      PubDate: 2020-02-06
       
  • Tone Mapping High Dynamic Range Images by Hessian Multiset Canonical
           Correlations
    • Abstract: Abstract Tone mapping algorithms reproduce high dynamic range (HDR) images on low dynamic range images in the standard display devices such as LCD, CRT, projectors, and printers. In this paper, we propose a statistical clustering-based tone mapping technique that would be able to adapt the local content of an image as well as its color. At first, the HDR image is partitioned into many overlapped color patches and we disintegrate each color patch into three segments: patch mean, color variation and color structure. Then based on the color structure component, the extracted color patches are clustered into a number of clusters by k-means clustering technique. For each cluster, the statistical signal processing technique namely Hessian multi set canonical correlations (HesMCC) has been produced to ascertain the transform matrix. Moreover, the HesMCC are fundamentally utilized for performing the dimensionality reduction of patches and to form effective tone mapped images. Contrasting with the current strategies, the procedures in the proposed clustering-based strategy can better adapt image color and its local structures by exploiting the image in the worldwide repetition. Experimental results show that the running time of the proposed method is less about 88.32%, 92%, 68.9%, and 29.4%, while comparing with other existing tone mapping methods.
      PubDate: 2020-01-20
       
  • Novel Neural Network Based CT-NSCT Watermarking Framework Based upon
           Kurtosis Coefficients
    • Abstract: Abstract In the research presented here, the novel neural network based watermarking framework is investigated in the area of transformation, while contourlet transform and nonsubsampled contourlet transform are realized to address the proposed idea via the Kurtosis to choose the band of suitable coefficients. It is to note that there are a number of techniques to deal with the aforementioned watermarking framework through the new integration of contourlet transform and nonsubsampled contourlet transform in connection with the perceptron neural network to extract the logo information, appropriately. There is the optimization technique through the genetic algorithm to provide the optimum results in the procedure of designing, as well. The approaches of the embedding and the de-embedding in case of learning algorithm of the neural network via individual training data set are considered in the present research to carry out a series of experiments with different scenario for the purpose of verifying the proposed techniques, obviously.
      PubDate: 2019-12-18
       
 
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