List of Articles SVD

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  1 - Study of North West Sedimentary Basin of Iran by 3D Modeling of Gravity Data
  Mojtaba  Tavakoli Ali nejati
  Inversion of the gravity data is one of the most interesting numerical tools for obtaining three dimensional geological images. In this paper 3D nonlinear inversion of the gravity data is used to determine the basement topography. The basement was juxtaposed with the re More

  Inversion of the gravity data is one of the most interesting numerical tools for obtaining three dimensional geological images. In this paper 3D nonlinear inversion of the gravity data is used to determine the basement topography. The basement was juxtaposed with the regular array of rectangular prisms in which the thickness of each prism is determined by the inversion procedures. Prepared algorithm is based on singular value decomposition (SVD) method which ca modify the initial model by comparing observed and estimated gravity data. The SVD method is very popular with geophysical data analysts because it is mathematically robust and numerically stable. To illustrate effectiveness of the prepared codes and algorithm related to 3D inversion of gravity data, both synthetic and real data were tested by the mentioned algorithm. The real data were part of the gravity data which were acquired in Moghan area (located in the north-west of Iran). Because of being near to the Baku oil-rich regions and thick sedimentary rocks, the Moghan sedimentary basin is an interesting area from hydrocarbon exploration point of view. Determination of the sedimentary rocks thickness is an important factor in oil and gas exploration issues. The main goal of 3D inversion of the gravity data in the study area is to determine basement the sedimentary rocks thicknesses or the boundary of Ojagh-Gheslagh Formation and its volcanic basement. The modeled boundary of Ojagh-Gheshlagh Formation and its volcanic basement which is obtained by the inversion of gravity data, was previously confirmed by interpretation of seismic data. Manuscript profile
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  2 - Effects of Wave Polarization on Microwave Imaging Using Linear Sampling Method
  Mehdi Salar Kaleji Mohammad  Zoofaghari reza Safian Zaker Hossein  Firouzeh
  10.7508/jist.2015.03.004
  Linear Sampling Method (LSM) is a simple and effective method for the shape reconstruction of unknown objects. It is also a fast and robust method to find the location of an object. This method is based on far field operator which relates the far field radiation to its More

  Linear Sampling Method (LSM) is a simple and effective method for the shape reconstruction of unknown objects. It is also a fast and robust method to find the location of an object. This method is based on far field operator which relates the far field radiation to its associated line source in the object. There has been an extensive research on different aspects of the method. But from the experimental point of view there has been little research especially on the effect of polarization on the imaging quality of the method. In this paper, we study the effect of polarization on the quality of shape reconstruction of two dimensional targets. Some examples are illustrated to compare the effect of transverse electric (TE) and transverse magnetic (TM) polarizations, on the reconstruction quality of penetrable and non-penetrable objects. Manuscript profile
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  3 - Concept Detection in Images Using SVD Features and Multi-Granularity Partitioning and Classification
  Kamran  Farajzadeh Esmail  Zarezadeh Jafar Mansouri
  10.7508/jist.2017.19.004
  New visual and static features, namely, right singular feature vector, left singular feature vector and singular value feature vector are proposed for the semantic concept detection in images. These features are derived by applying singular value decomposition (SVD) " More

  New visual and static features, namely, right singular feature vector, left singular feature vector and singular value feature vector are proposed for the semantic concept detection in images. These features are derived by applying singular value decomposition (SVD) "directly" to the "raw" images. In SVD features edge, color and texture information is integrated simultaneously and is sorted based on their importance for the concept detection. Feature extraction is performed in a multi-granularity partitioning manner. In contrast to the existing systems, classification is carried out for each grid partition of each granularity separately. This separates the effect of classifications on partitions with and without the target concept on each other. Since SVD features have high dimensionality, classification is carried out with K-nearest neighbor (K-NN) algorithm that utilizes a new and "stable" distance function, namely, multiplicative distance. Experimental results on PASCAL VOC and TRECVID datasets show the effectiveness of the proposed SVD features and multi-granularity partitioning and classification method Manuscript profile
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  4 - A New VAD Algorithm using Sparse Representation in Spectro-Temporal Domain
  Mohadese  Eshaghi Farbod Razzazi Alireza Behrad
  10.7508/jist.2019.01.007
  This paper proposes two algorithms for Voice Activity Detection (VAD) based on sparse representation in spectro-temporal domain. The first algorithm was made using two-dimensional STRF (Spectro-Temporal Response Field) space based on sparse representation. Dictionaries More

  This paper proposes two algorithms for Voice Activity Detection (VAD) based on sparse representation in spectro-temporal domain. The first algorithm was made using two-dimensional STRF (Spectro-Temporal Response Field) space based on sparse representation. Dictionaries with different atomic sizes and two dictionary learning methods were investigated in this approach. This algorithm revealed good results at high SNRs (signal-to-noise ratio). The second algorithm, whose approach is more complicated, suggests a speech detector using the sparse representation in four-dimensional STRF space. Due to the large volume of STRF's four-dimensional space, this space was divided into cubes, with dictionaries made for each cube separately by NMF (non-negative matrix factorization) learning algorithm. Simulation results were presented to illustrate the effectiveness of our new VAD algorithms. The results revealed that the achieved performance was 90.11% and 91.75% under -5 dB SNR in white and car noise respectively, outperforming most of the state-of-the-art VAD algorithms. Manuscript profile
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  5 - CDF (2,2) Wavelet Lossy Image Compression on CPLD
  A. A. Lotfi-Neyestanak Mohammad Mohaghegh Hazrati Mohammad Mohaghegh Hazrati N. Ahmidi
  This paper presents a hardware implementation of CDF(2,2) wavelet image compressor. The design demonstrates that high quality circuit implementation is possible through the use of suitable data organization (partitioned approach) and algorithm-to-architecture mappings ( More

  This paper presents a hardware implementation of CDF(2,2) wavelet image compressor. The design demonstrates that high quality circuit implementation is possible through the use of suitable data organization (partitioned approach) and algorithm-to-architecture mappings (parallel-ism or pipelining). A VHDL code for CDF(2,2) was developed to satisfy our objective. Then it was synthesized in Foundation 5.1 software and downloaded to CPLD XC9572 by a JTAG ByteBlaster cable. The original image was transmitted through serial port. The AVR’s ATmega8535 was used to implement serial protocol to and back from the CPLD. The main goal is to reach a higher performance and throughput with a single CPLD. Details of the encoder design have been discussed and the results are presented. Manuscript profile
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  6 - SVD-Based Adaptive Multiuser Detection for Optimized Chaotic DS-CDMA Systems
  S. Shaerbaf S. A. Seyedin
  In recent years, chaotic signals have created a new area in the designation of wideband communication systems. Most of the activity has focused on DS-CDMA systems, in which the conventional pseudo-noise sequences will be replaced by binary chaotic sequences. Unfortunat More

  In recent years, chaotic signals have created a new area in the designation of wideband communication systems. Most of the activity has focused on DS-CDMA systems, in which the conventional pseudo-noise sequences will be replaced by binary chaotic sequences. Unfortunately, despite the advantages of chaotic systems such as aperiodicity, low cost generation and noise-like spectrum, the performance of most of such designs is not still suitable for multiuser wireless channels. In this paper, we propose a novel method based on singular value decomposition for adaptive multiuser detection in chaos-based DS-CDMA systems. We also propose a new genetic algorithm-based method for the optimal generation of chaotic sequences in such systems. Simulation results show that our proposed nonlinear receiver with optimized chaotic sequences outperforms the conventional DS-CDMA systems with “maximal length” codes as well as non-optimized chaos-based DS-CDMA systems in all channel condition, particularly for under-loaded CDMA condition, which the number of active users is less than processing gain. Manuscript profile