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.
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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
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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.
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