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  Subjects -> ELECTRONICS (Total: 188 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Acta Electronica Malaysia     Open Access  
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 7)
Advances in Electronics     Open Access   (Followers: 94)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Power Electronics     Open Access   (Followers: 39)
Advancing Microelectronics     Hybrid Journal  
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 344)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 26)
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: 14)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 30)
Bioelectronics in Medicine     Hybrid Journal  
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 21)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 38)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 13)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 1)
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: 304)
ECTI Transactions on Computer and Information Technology (ECTI-CIT)     Open Access  
ECTI Transactions on Electrical Engineering, Electronics, and Communications     Open Access   (Followers: 1)
Edu Elektrika Journal     Open Access   (Followers: 1)
Electrica     Open Access  
Electronic Design     Partially Free   (Followers: 123)
Electronic Markets     Hybrid Journal   (Followers: 7)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
Electronics     Open Access   (Followers: 103)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 10)
Electronics For You     Partially Free   (Followers: 102)
Electronics Letters     Hybrid Journal   (Followers: 26)
Elkha : Jurnal Teknik Elektro     Open Access  
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 55)
Energy Harvesting and Systems     Hybrid Journal   (Followers: 4)
Energy Storage Materials     Full-text available via subscription   (Followers: 3)
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)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 207)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 4)
IACR Transactions on Symmetric Cryptology     Open Access  
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 100)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 81)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 51)
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 Power Electronics Magazine     Full-text available via subscription   (Followers: 75)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 73)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 58)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 26)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 44)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 19)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 26)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 78)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 12)
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  
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 35)
IET Nanodielectrics     Open Access  
IET Power Electronics     Hybrid Journal   (Followers: 57)
IET Smart Grid     Open Access  
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 Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 74)
Industrial Technology Research Journal Phranakhon Rajabhat University     Open Access  
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 38)
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: 13)
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: 11)
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: 11)
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: 35)
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 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: 3)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 176)
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  
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: 29)
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: 26)
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  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 12)
Magnetics Letters, IEEE     Hybrid Journal   (Followers: 7)
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 Magazine, IEEE     Full-text available via subscription   (Followers: 42)
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)
Optical Communications and Networking, IEEE/OSA Journal of     Full-text available via subscription   (Followers: 15)
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)
Pulse     Full-text available via subscription   (Followers: 5)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
Recent Advances in Communications and Networking Technology     Hybrid Journal   (Followers: 3)
Recent Advances in Electrical & Electronic Engineering     Hybrid Journal   (Followers: 9)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 5)
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: 56)
Semiconductors and Semimetals     Full-text available via subscription   (Followers: 1)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Services Computing, IEEE Transactions on     Hybrid Journal   (Followers: 4)
Software Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 78)
Solid State Electronics Letters     Open Access  
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 13)
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 Electrical and Electronic Materials     Hybrid Journal  
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 6)
Ural Radio Engineering Journal     Open Access  
Visión Electrónica : algo más que un estado sólido     Open Access   (Followers: 1)
Wireless and Mobile Technologies     Open Access   (Followers: 6)
Wireless Power Transfer     Full-text available via subscription   (Followers: 4)
Women in Engineering Magazine, IEEE     Full-text available via subscription   (Followers: 11)
Електротехніка і Електромеханіка     Open Access  

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APSIPA Transactions on Signal and Information Processing
Journal Prestige (SJR): 0.404
Citation Impact (citeScore): 2
Number of Followers: 9  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2048-7703 - ISSN (Online) 2048-7703
Published by Cambridge University Press Homepage  [387 journals]
  • Large-scale Landsat image classification based on deep learning methods

    • Authors: Xuemei Zhao; Lianru Gao, Zhengchao Chen, Bing Zhang, Wenzhi Liao
      Abstract: Deep learning has demonstrated its superiority in computer vision. Landsat images have specific characteristics compared with natural images. The spectral and texture features of the same class vary along with the imaging conditions. In this paper, we extend the use of deep learning to remote sensing image classification to large geographical regions, and explore a way to make deep learning classifiers transferable for different regions. We take Jingjinji region and Henan province in China as the study areas, and choose FCN, ResNet, and PSPNet as classifiers. The models are trained by different proportions of training samples from Jingjinji region. Then we use the trained models to predict results of the study areas. Experimental results show that the overall accuracy decreases when trained by small samples, but the recognition ability on mislabeled areas increases. All methods can obtain great performance when used to Jingjinji region while they all need to be fine-tuned with new training samples from Henan province, due to the reason that images of Henan province have different spectral features from the original trained area.
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2019.18
      Issue No: Vol. 8 (2019)
       
  • Reliable multicast using remote direct memory access (RDMA) over a passive
           optical cross-connect fabric enhanced with wavelength division
           multiplexing (WDM)

    • Authors: Kin-Wai Leong; Zhilong Li, Yunqu Leon Liu
      Abstract: It has been well studied that reliable multicast enables consistency protocols, including Byzantine Fault Tolerant protocols, for distributed systems. However, no transport-layer reliable multicast is used today due to limitations with existing switch fabrics and transport-layer protocols. In this paper, we introduce a layer-4 (L4) transport based on remote direct memory access (RDMA) datagram to achieve reliable multicast over a shared optical medium. By connecting a cluster of networking nodes using a passive optical cross-connect fabric enhanced with wavelength division multiplexing, all messages are broadcast to all nodes. This mechanism enables consistency in a distributed system to be maintained at a low latency cost. By further utilizing RDMA datagram as the L4 protocol, we have achieved a low-enough message loss-ratio (better than one in 68 billion) to make a simple Negative Acknowledge (NACK)-based L4 multicast practical to deploy. To our knowledge, it is the first multicast architecture able to demonstrate such low message loss-ratio. Furthermore, with this reliable multicast transport, end-to-end latencies of eight microseconds or less (< 8us) have been routinely achieved using an enhanced software RDMA implementation on a variety of commodity 10G Ethernet network adapters.
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2019.17
      Issue No: Vol. 8 (2019)
       
  • Residual sign prediction in transform domain for next-generation video
           coding

    • Authors: Alexey Filippov; Vasily Rufitskiy, Alexander Karabutov, Jianle Chen
      Abstract: In this paper, we present a technique that is known as Residual Sign Prediction in Transform Domain (TDRSP) and is aimed at increasing compression performance by reducing the bits overhead of residue sign. These signs are typically coded by entropy coders in bypass mode that results in the high cost of sign bins which require 1 bit per bin in a bitstream. TDRSP allows us to reduce this cost by predicting residue signs so that the probability of a guess is significantly higher than 50%. Hence, arithmetic coding with contexts becomes applicable to the signs. In contrast to Residual Sign Prediction (RSP) performed in spatial domain, TDRSP avoids switching between domains and carries out calculations completely in transform domain to efficiently decrease the computational complexity of RSP. Simulations performed on top of Versatile Video Coding test model reference software (VTM-1.0) in accordance with the Joint Video Experts Team common test conditions show that more than 2.0% and up to 1.8% of the Bjøntegaard Delta rate can be achieved for All Intra and Random Access configurations, respectively.
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2019.6
      Issue No: Vol. 8 (2019)
       
  • Modern trends on quality of experience assessment and future work

    • Authors: Woojae Kim; Sewoong Ahn, Anh-Duc Nguyen, Jinwoo Kim, Jaekyung Kim, Heeseok Oh, Sanghoon Lee
      Abstract: Over the past 20 years, research on quality of experience (QoE) has been actively expanded even to cover aesthetic, emotional and psychological experiences. QoE has been an important research topic in determining the perceptual factors that are essential to users in keeping with the emergence of new display technologies. In this paper, we provide in-depth reviews of recent assessment studies in this field. Compared to previous reviews, our research examines the human factors observed over various recent displays and their associated assessment methods. In this study, we first provide a comprehensive QoE analysis on 2D display including image/video quality assessment (I/VQA), visual preference, and human visual system-related studies. Second, we analyze stereoscopic 3D (S3D) QoE research on the topics of I/VQA and visual discomfort from the human perception point of view on S3D display. Third, we investigate QoE in a head-mounted display-based virtual reality (VR) environment, and deal with VR sickness and 360 I/VQA with their individual approach. All of our reviews are analyzed through comparison of benchmark models. Furthermore, we layout QoE works on future display and modern deep-learning applications.
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2019.16
      Issue No: Vol. 8 (2019)
       
  • Spectral-spatial feature extraction and supervised classification by
           MF-KELM classifier on hyperspectral imagery

    • Authors: Wenting Shang; Zebin Wu, Yang Xu, Yan Zhang, Zhihui Wei
      Abstract: The kernel extreme learning machine (KELM) is more robust and has a faster learning speed when compared with the traditional neural networks, and thus it is increasingly gaining attention in hyperspectral image (HSI) classification. Although the Gaussian radial basis function kernel widely used in KELM has achieved promising classification performance in supervised HSI classification, it does not consider the underlying data structure of HSIs. In this paper, we propose a novel spectral-spatial KELM method (termed as MF-KELM) by incorporating the mean filtering kernel into the KELM model, which can properly compute the mean value of the spatial neighboring pixels in the kernel space. Considering that in the situation of limited training samples the classification result is very noisy, the spatial bilateral filtering information on spectral band-subsets is introduced to improve the accuracy. Experiment results show that our method outperforms other kernel functions based on KELM in terms of classification accuracy and visual comparison.
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2019.15
      Issue No: Vol. 8 (2019)
       
  • Special issue on deep learning based detection and recognition for
           perceptual tasks with applications

    • Authors: Li-Wei Kang
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2019.13
      Issue No: Vol. 8 (2019)
       
  • Deep-learning-based macro-pixel synthesis and lossless coding of light
           field images

    • Authors: Ionut Schiopu; Adrian Munteanu
      Abstract: This paper proposes a novel approach for lossless coding of light field (LF) images based on a macro-pixel (MP) synthesis technique which synthesizes the entire LF image in one step. The reference views used in the synthesis process are selected based on four different view configurations and define the reference LF image. This image is stored as an array of reference MPs which collect one pixel from each reference view, being losslessly encoded as a base layer. A first contribution focuses on a novel network design for view synthesis which synthesizes the entire LF image as an array of synthesized MPs. A second contribution proposes a network model for coding which computes the MP prediction used for lossless encoding of the remaining views as an enhancement layer. Synthesis results show an average distortion of 29.82 dB based on four reference views and up to 36.19 dB based on 25 reference views. Compression results show an average improvement of 29.9% over the traditional lossless image codecs and 9.1% over the state-of-the-art.
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2019.14
      Issue No: Vol. 8 (2019)
       
  • Evaluating word embedding models: methods and experimental results

    • Authors: Bin Wang; Angela Wang, Fenxiao Chen, Yuncheng Wang, C.-C. Jay Kuo
      Abstract: Extensive evaluation on a large number of word embedding models for language processing applications is conducted in this work. First, we introduce popular word embedding models and discuss desired properties of word models and evaluation methods (or evaluators). Then, we categorize evaluators into intrinsic and extrinsic two types. Intrinsic evaluators test the quality of a representation independent of specific natural language processing tasks while extrinsic evaluators use word embeddings as input features to a downstream task and measure changes in performance metrics specific to that task. We report experimental results of intrinsic and extrinsic evaluators on six word embedding models. It is shown that different evaluators focus on different aspects of word models, and some are more correlated with natural language processing tasks. Finally, we adopt correlation analysis to study performance consistency of extrinsic and intrinsic evaluators.
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2019.12
      Issue No: Vol. 8 (2019)
       
  • Recent advances in video coding beyond the HEVC standard

    • Authors: Xiaozhong Xu; Shan Liu
      Abstract: The standardization process for Versatile Video Coding (VVC), the next generation video coding standard, was launched in 2018, after several recent advances in video coding technologies had been investigated under the Joint Video Experts Team (JVET) of ITU-T VCEG and ISO/IEC MPEG experts. The recent standard development status (up to VVC working draft 2) shows that the VTM software, the test model for this VVC standard, can achieve over 23% average coding gain under random access configuration when compared to the HM software, the test model of HEVC standard. This paper gives a review of recently developed video coding technologies that have been either adopted into the VVC working draft as part of the standard or under further evaluation for potential inclusions.
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2019.11
      Issue No: Vol. 8 (2019)
       
  • An overview of channel coding for 5G NR cellular communications

    • Authors: Jung Hyun Bae; Ahmed Abotabl, Hsien-Ping Lin, Kee-Bong Song, Jungwon Lee
      Abstract: A 5G new radio cellular system is characterized by three main usage scenarios of enhanced mobile broadband (eMBB), ultra-reliable and low latency communications (URLLC), and massive machine type communications, which require improved throughput, latency, and reliability compared with a 4G system. This overview paper discusses key characteristics of 5G channel coding schemes which are mainly designed for the eMBB scenario as well as for partial support of the URLLC scenario focusing on low latency. Two capacity-achieving channel coding schemes of low-density parity-check (LDPC) codes and polar codes have been adopted for 5G where the former is for user data and the latter is for control information. As a coding scheme for data, 5G LDPC codes are designed to support high throughput, a variable code rate and length and hybrid automatic repeat request in addition to good error correcting capability. 5G polar codes, as a coding scheme for control, are designed to perform well with short block length while addressing a latency issue of successive cancellation decoding.
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2019.10
      Issue No: Vol. 8 (2019)
       
  • A deep learning-based method for vehicle licenseplate recognition in
           natural scene

    • Authors: Jianzong Wang; Xinhui Liu, Aozhi Liu, Jing Xiao
      Abstract: Vehicle license platerecognition in natural scene is an important research topic in computer vision. The license plate recognition approach in the specific scene has become a relatively mature technology. However, license plate recognition in the natural scene is still a challenge since the image parameters are highly affected by the complicated environment. For the purpose of improving the performance of license plate recognition in natural scene, we proposed a solution to recognize real-world Chinese license plate photographs using the DCNN-RNN model. With the implementation of DCNN, the license plate is located and the features of the license plate are extracted after the correction process. Finally, an RNN model is performed to decode the deep features to characters without character segmentation. Our state-of-the-art system results in the accuracy and recall of 92.32 and 91.89% on the car accident scene dataset collected in the natural scene, and 92.88 and 92.09% on Caltech Cars 1999 dataset.
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2019.8
      Issue No: Vol. 8 (2019)
       
  • Robust adaptive beamforming with enhancing the interference suppression
           capability

    • Authors: Linxian Liu; Yang Li
      Abstract: The steering vector mismatch causes signal self-nulling for adaptive beamforming when the training data contain the desired signal component. To prevent signal self-nulling, many beamformers use robust technology, which is usually equivalent to the diagonal loading approach. Unfortunately, the diagonal loading approach achieves better signal enhancement at the cost of losing its interference suppression capability, especially at high input signal-to-noise ratio. In this paper, a novel robust adaptive beamforming method is developed to improve the interference suppression capability. The proposed beamformer is based on the worst-case performance optimization technology with a new estimated steering vector and a special set parameter. Firstly, a subspace which is orthogonal to the interference's steering vector is obtained by using the interference-plus-noise covariance matrix; then a new steering vector which is orthogonal to each interference's steering vector is estimated; finally, the beamformer's weight is solved with the worst-case performance optimization technology with a special set parameter. Theoretical analysis of the interference suppression principle is analyzed in detail, and some simulation results are presented to evaluate the performance of the proposed beamformer.
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2019.9
      Issue No: Vol. 8 (2019)
       
  • Combining acoustic signals and medical records to improve pathological
           voice classification

    • Authors: Shih-Hau Fang; Chi-Te Wang, Ji-Ying Chen, Yu Tsao, Feng-Chuan Lin
      Abstract: This study proposes two multimodal frameworks to classify pathological voice samples by combining acoustic signals and medical records. In the first framework, acoustic signals are transformed into static supervectors via Gaussian mixture models; then, a deep neural network (DNN) combines the supervectors with the medical record and classifies the voice signals. In the second framework, both acoustic features and medical data are processed through first-stage DNNs individually; then, a second-stage DNN combines the outputs of the first-stage DNNs and performs classification. Voice samples were recorded in a specific voice clinic of a tertiary teaching hospital, including three common categories of vocal diseases, i.e. glottic neoplasm, phonotraumatic lesions, and vocal paralysis. Experimental results demonstrated that the proposed framework yields significant accuracy and unweighted average recall (UAR) improvements of 2.02–10.32% and 2.48–17.31%, respectively, compared with systems that use only acoustic signals or medical records. The proposed algorithm also provides higher accuracy and UAR than traditional feature-based and model-based combination methods.
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2019.7
      Issue No: Vol. 8 (2019)
       
  • A review of blind source separation methods: two converging routes to
           ILRMA originating from ICA and NMF

    • Authors: Hiroshi Sawada; Nobutaka Ono, Hirokazu Kameoka, Daichi Kitamura, Hiroshi Saruwatari
      Abstract: This paper describes several important methods for the blind source separation of audio signals in an integrated manner. Two historically developed routes are featured. One started from independent component analysis and evolved to independent vector analysis (IVA) by extending the notion of independence from a scalar to a vector. In the other route, nonnegative matrix factorization (NMF) has been extended to multichannel NMF (MNMF). As a convergence point of these two routes, independent low-rank matrix analysis has been proposed, which integrates IVA and MNMF in a clever way. All the objective functions in these methods are efficiently optimized by majorization-minimization algorithms with appropriately designed auxiliary functions. Experimental results for a simple two-source two-microphone case are given to illustrate the characteristics of these five methods.
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2019.5
      Issue No: Vol. 8 (2019)
       
  • Semi-fragile speech watermarking based on singular-spectrum analysis with
           CNN-based parameter estimation for tampering detection

    • Authors: Kasorn Galajit; Jessada Karnjana, Masashi Unoki, Pakinee Aimmanee
      Abstract: A semi-fragile watermarking scheme is proposed in this paper for detecting tampering in speech signals. The scheme can effectively identify whether or not original signals have been tampered with by embedding hidden information into them. It is based on singular-spectrum analysis, where watermark bits are embedded into speech signals by modifying a part of the singular spectrum of a host signal. Convolutional neural network (CNN)-based parameter estimation is deployed to quickly and properly select the part of the singular spectrum to be modified so that it meets inaudibility and robustness requirements. Evaluation results show that CNN-based parameter estimation reduces the computational time of the scheme and also makes the scheme blind, i.e. we require only a watermarked signal in order to extract a hidden watermark. In addition, a semi-fragility property, which allows us to detect tampering in speech signals, is achieved. Moreover, due to the time efficiency of the CNN-based parameter estimation, the proposed scheme can be practically used in real-time applications.
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2019.4
      Issue No: Vol. 8 (2019)
       
  • Neutral-to-emotional voice conversion with cross-wavelet transform F0
           using generative adversarial networks

    • Authors: Zhaojie Luo; Jinhui Chen, Tetsuya Takiguchi, Yasuo Ariki
      Abstract: In this paper, we propose a novel neutral-to-emotional voice conversion (VC) model that can effectively learn a mapping from neutral to emotional speech with limited emotional voice data. Although conventional VC techniques have achieved tremendous success in spectral conversion, the lack of representations in fundamental frequency (F0), which explicitly represents prosody information, is still a major limiting factor for emotional VC. To overcome this limitation, in our proposed model, we outline the practical elements of the cross-wavelet transform (XWT) method, highlighting how such a method is applied in synthesizing diverse representations of F0 features in emotional VC. The idea is (1) to decompose F0 into different temporal level representations using continuous wavelet transform (CWT); (2) to use XWT to combine different CWT-F0 features to synthesize interaction XWT-F0 features; (3) and then use both the CWT-F0 and corresponding XWT-F0 features to train the emotional VC model. Moreover, to better measure similarities between the converted and real F0 features, we applied a VA-GAN training model, which combines a variational autoencoder (VAE) with a generative adversarial network (GAN). In the VA-GAN model, VAE learns the latent representations of high-dimensional features (CWT-F0, XWT-F0), while the discriminator of the GAN can use the learned feature representations as a basis for a VAE reconstruction objective.
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2019.3
      Issue No: Vol. 8 (2019)
       
  • Checkerboard artifacts free convolutional neural networks

    • Authors: Yusuke Sugawara; Sayaka Shiota, Hitoshi Kiya
      Abstract: It is well-known that a number of convolutional neural networks (CNNs) generate checkerboard artifacts in both of two processes: forward-propagation of upsampling layers and backpropagation of convolutional layers. A condition for avoiding the artifacts is proposed in this paper. So far, these artifacts have been studied mainly for linear multirate systems, but the conventional condition for avoiding them cannot be applied to CNNs due to the non-linearity of CNNs. We extend the avoidance condition for CNNs and apply the proposed structure to typical CNNs to confirm whether the novel structure is effective. Experimental results demonstrate that the proposed structure can perfectly avoid generating checkerboard artifacts while keeping the excellent properties that CNNs have.
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2019.2
      Issue No: Vol. 8 (2019)
       
  • Grayscale-based block scrambling image encryption using YCbCr color space
           for encryption-then-compression systems

    • Authors: Warit Sirichotedumrong; Hitoshi Kiya
      Abstract: A novel grayscale-based block scrambling image encryption scheme is presented not only to enhance security, but also to improve the compression performance for Encryption-then-Compression (EtC) systems with JPEG compression, which are used to securely transmit images through an untrusted channel provider. The proposed scheme enables the use of a smaller block size and a larger number of blocks than the color-based image encryption scheme. Images encrypted using the proposed scheme include less color information due to the use of grayscale images even when the original image has three color channels. These features enhance security against various attacks, such as jigsaw puzzle solver and brute-force attacks. Moreover, generating the grayscale-based images from a full-color image in YCbCr color space allows the use of color sub-sampling operation, which can provide the higher compression performance than the conventional grayscale-based encryption scheme, although the encrypted images have no color information. In an experiment, encrypted images were uploaded to and then downloaded from Twitter and Facebook, and the results demonstrated that the proposed scheme is effective for EtC systems and enhances the compression performance, while maintaining the security against brute-force and jigsaw puzzle solver attacks.
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2018.33
      Issue No: Vol. 8 (2019)
       
  • Learning from past mistakes: improving automatic speech recognition output
           via noisy-clean phrase context modeling

    • Authors: Prashanth Gurunath Shivakumar; Haoqi Li, Kevin Knight, Panayiotis Georgiou
      Abstract: Automatic speech recognition (ASR) systems often make unrecoverable errors due to subsystem pruning (acoustic, language and pronunciation models); for example, pruning words due to acoustics using short-term context, prior to rescoring with long-term context based on linguistics. In this work, we model ASR as a phrase-based noisy transformation channel and propose an error correction system that can learn from the aggregate errors of all the independent modules constituting the ASR and attempt to invert those. The proposed system can exploit long-term context using a neural network language model and can better choose between existing ASR output possibilities as well as re-introduce previously pruned or unseen (Out-Of-Vocabulary) phrases. It provides corrections under poorly performing ASR conditions without degrading any accurate transcriptions; such corrections are greater on top of out-of-domain and mismatched data ASR. Our system consistently provides improvements over the baseline ASR, even when baseline is further optimized through Recurrent Neural Network (RNN) language model rescoring. This demonstrates that any ASR improvements can be exploited independently and that our proposed system can potentially still provide benefits on highly optimized ASR. Finally, we present an extensive analysis of the type of errors corrected by our system.
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2018.31
      Issue No: Vol. 8 (2019)
       
  • Analysis and generation of laughter motions, and evaluation in an android
           robot

    • Authors: Carlos Toshinori Ishi; Takashi Minato, Hiroshi Ishiguro
      Abstract: Laughter commonly occurs in daily interactions, and is not only simply related to funny situations, but also to expressing some type of attitudes, having important social functions in communication. The background of the present work is to generate natural motions in a humanoid robot, so that miscommunication might be caused if there is mismatching between audio and visual modalities, especially in laughter events. In the present work, we used a multimodal dialogue database, and analyzed facial, head, and body motion during laughing speech. Based on the analysis results of human behaviors during laughing speech, we proposed a motion generation method given the speech signal and the laughing speech intervals. Subjective experiments were conducted using our android robot by generating five different motion types, considering several modalities. Evaluation results showed the effectiveness of controlling different parts of the face, head, and upper body (eyelid narrowing, lip corner/cheek raising, eye blinking, head motion, and upper body motion control).
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2018.32
      Issue No: Vol. 8 (2019)
       
  • Towards Visible and Thermal Drone Monitoring with Convolutional Neural
           Networks

    • Authors: Ye Wang; Yueru Chen, Jongmoo Choi, C.-C. Jay Kuo
      Abstract: This paper reports a visible and thermal drone monitoring system that integrates deep-learning-based detection and tracking modules. The biggest challenge in adopting deep learning methods for drone detection is the paucity of training drone images especially thermal drone images. To address this issue, we develop two data augmentation techniques. One is a model-based drone augmentation technique that automatically generates visible drone images with a bounding box label on the drone's location. The other is exploiting an adversarial data augmentation methodology to create thermal drone images. To track a small flying drone, we utilize the residual information between consecutive image frames. Finally, we present an integrated detection and tracking system that outperforms the performance of each individual module containing detection or tracking only. The experiments show that, even being trained on synthetic data, the proposed system performs well on real-world drone images with complex background. The USC drone detection and tracking dataset with user labeled bounding boxes is available to the public.
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2018.30
      Issue No: Vol. 8 (2019)
       
  • Decentralized tracing protocol for fingerprinting system

    • Authors: Minoru Kuribayashi; Nobuo Funabiki
      Abstract: In conventional studies, cryptographic techniques are used to ensure the security of transaction between a seller and buyer in a fingerprinting system. However, the tracing protocol from a pirated copy has not been studied from the security point of view though the collusion resistance is considered by employing a collusion secure fingerprinting code. In this paper, we consider the secrecy of parameters for a fingerprinting code and burdens at a trusted center, and propose a secure tracing protocol jointly executed by a seller and a delegated server. Our main idea is to delegate authority to a server so that the center is required to operate only at the initialization phase in the system. When a pirated copy is found, a seller calculates a correlation score for each user's codeword in an encrypted domain, and identifies illegal users by sending the ciphertexts of scores as queries to the server. The information leakage from the server can be managed at the restriction of response from the server to check the maliciousness of the queries.
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2018.28
      Issue No: Vol. 8 (2019)
       
  • Optimal I-frame assignment based on Nash bargaining solution in HEVC

    • Authors: Chia-Hung Yeh; Ren-Fu Tseng, Mei-Juan Chen, Chuan-Yu Chang
      Abstract: In most of video coding standards such as high efficiency video coding (HEVC), I-frame assignment is periodic even when the content change is minor, which degrades the coding efficiency. This paper proposes an I-frame assignment method based on Nash bargaining solution (NBS) in game theory to solve this problem. The encoded sequence is divided into several subsequences. Each subsequence is regarded as a game. All group of picture (GOP) in a subsequence is further divided into several sets of GOP. Each set of GOP is regarded as a player and compete for the number of I-frames. The optimal I-frame assignment is determined based on the generalized NBS. Experimental results show the proposed method outperforms HEVC by 5.21% bitrate saving.
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2018.27
      Issue No: Vol. 8 (2019)
       
  • Noise bias compensation for tone mapped noisy image using prior knowledge

    • Authors: Sayaka Minewaki; Taichi Yoshida, Yoshinori Takei, Masahiro Iwahashi, Hitoshi Kiya
      Abstract: A large number of studies have been made on denoising of a digital noisy image. In regression filters, a convolution kernel was determined based on the spatial distance or the photometric distance. In non-local mean (NLM) filters, pixel-wise calculation of the distance was replaced with patch-wise one. Later on, NLM filters have been developed to be adaptive to the local statistics of an image with introduction of the prior knowledge in a Bayesian framework. Unlike those existing approaches, we introduce the prior knowledge, not on the local patch in NLM filters but, on the noise bias (NB) which has not been utilized so far. Although the mean of noise is assumed to be zero before tone mapping (TM), it becomes non-zero value after TM due to the non-linearity of TM. Utilizing this fact, we propose a new denoising method for a tone mapped noisy image. In this method, pixels in the noisy image are classified into several subsets according to the observed pixel value, and the pixel values in each subset are compensated based on the prior knowledge so that NB of the subset becomes close to zero. As a result of experiments, effectiveness of the proposed method is confirmed.
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2018.29
      Issue No: Vol. 8 (2019)
       
  • Development of a computationally efficient voice conversion system on
           mobile phones

    • Authors: Shuhua Gao; Xiaoling Wu, Cheng Xiang, Dongyan Huang
      Abstract: Voice conversion aims to change a source speaker's voice to make it sound like the one of a target speaker while preserving linguistic information. Despite the rapid advance of voice conversion algorithms in the last decade, most of them are still too complicated to be accessible to the public. With the popularity of mobile devices especially smart phones, mobile voice conversion applications are highly desirable such that everyone can enjoy the pleasure of high-quality voice mimicry and people with speech disorders can also potentially benefit from it. Due to the limited computing resources on mobile phones, the major concern is the time efficiency of such a mobile application to guarantee positive user experience. In this paper, we detail the development of a mobile voice conversion system based on the Gaussian mixture model (GMM) and the weighted frequency warping methods. We attempt to boost the computational efficiency by making the best of hardware characteristics of today's mobile phones, such as parallel computing on multiple cores and the advanced vectorization support. Experimental evaluation results indicate that our system can achieve acceptable voice conversion performance while the conversion time for a five-second sentence only takes slightly more than one second on iPhone 7.
      PubDate: 2019-01-01T00:00:00.000Z
      DOI: 10.1017/ATSIP.2018.23
      Issue No: Vol. 8 (2019)
       
 
 
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