International Journal of Signal and Imaging Systems Engineering
These articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.
Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.
Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.
International Journal of Signal and Imaging Systems Engineering (4 papers in press)
On The Performance of a Fuzzy Variable Structure Satellite Attitude Controller Under Sensor and Actuator Uncertainties by Bilgehan Erkal Abstract: Controlling the attitude of a satellite with high accuracy and stability under both sensor and actuator delay is a great problem. It is possible to correct for errors, but a robust controller is more preferable. In this study, the attitude of a 3-DoF satellite model incorporating uncertainties (delays in handling both sensors and actuators) is controlled using a suitably designed Integral Fuzzy Variable Structure Controller (IFVSC). The attitude control accuracy of the IFVSC is evaluated and compared to other reference controllers (one is a PID Controller and the other is a Loop Shaping Controller). IFVSC is found to perform well with Td=0.2s sensor and actuator data delay. Keywords: Fuzzy Variable Structure Control; Loop Shaping Controller; Satellite Attitude Control; Sensor Data Delay; Actuator Misplacement. DOI: 10.1504/IJSISE.2020.10032027
VAD, feature extraction and modeling techniques for speaker recognition - a review by B.G. Nagaraja Abstract: This article reviews an automatic speaker recognition technology, with an emphasis on state-of-the-art voice activity detection (VAD), feature extraction and speaker modeling techniques that have emerged during the last few years. Researchers in the field of speaker recognition have made a few attempts to recognize the speaker in the language mismatch environment and limited data condition. To address robustness issues, we also elaborate language mismatch and limited data speaker recognition. Further, this paperrnidentified some issues with the existing speaker recognition systems and also investigated areas of possible improvements in speaker recognition field. We conclude the paper with a discussion on the possible future directions. Keywords: VAD; speaker identification; speaker verification; language mismatch; limitedrndata; multilingual; features; modeling techniques.
Robust speaker recognition based on biologically inspired features by Youssef Zouhir, Ines BEN FREDJ, Kaïs Ouni Abstract: This paper propose two speech parameterization techniques for noise-robust speaker recognition: the Normalized Gammachirp Cepstral Coefficients (NGCC) and the Perceptual Linear Predictive normalized Gammachirp (PLPnGc). These techniques are based on a biologically inspired auditory model which simulates the cochlea spectral behaviour. The Gaussian Mixture Model-Universal Background Model (GMM-UBM) based speaker modelling is considered in automatic speaker recognition system. The performances are evaluated in clean and noisy environments using Timit, Aurora and Demand databases. The experimental results in noisy environments showed that the biologically inspired feature extraction techniques give a better recognition rate than state-of-the-art techniques. Keywords: Auditory filter model; Biologically inspired features; Normalized GammachirprnCepstral Coefficients (NGCC); Perceptual Linear Predictive normalized Gammachirp (PLPnGc); Gaussian Mixture Model-Universal Background Model (GMM-UBM); Robust speaker recognition;.
Lossless compression of images with sparse histograms by Souha Jallouli, Sonia Zouari, Nouri Masmoudi, Atef Masmoudi Abstract: Histogram sparseness is a characteristic which is expected by most of the lossless compression algorithms. In fact, they have been designed mainly to process continuous-tone images. Images with sparse histograms are characterized by a small number of pixels\'intensity levels compared to the one implied by the nominal bit depth. Moreover, the active levels may spread throughout the nominal intensity range since they do not occupy all the continuous range. However, the compression efficiency of most of lossless image encoders is severely affected when handling sparse histogram images. This paper presents an analyze of the impact of histogram sparseness on lossless image compression standards in case of predictive coding. In addition, to improve the compression performance for sparse histogram images, a new preprocessing technique is proposed. The method takes advantage of the high likelihood between neighboring image blocks. The initial image is divided into blocks and for each image block, the proposed method associates the most reduced set representing its active symbols and makes the histogram dense. This technique is efficient without implying any modification of the basic code of the state-of the art lossless image compression techniques. We show experimentally that the proposed method outperforms JPEG-LS, CALIC and JPEG 2000 and achieves lower bitrates. Keywords: Lossless image compression; sparse histogram; histogram packing; predictive coding; JPEG-LS; CALIC; JPEG 2000.