Mehrdad Valipour

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In this paper, a proper solution for a capacity-limited lossy wireless sensor network subject to packet loss as a problem has been presented by considering multiple sinks and correlated sources. At first, a joint optimization problem with two objectives, rate allocation and transmission structure, is introduced and solved. Also in the lossy network, a(More)
Concept elicitation is centric for machine knowledge extraction and representation in cognitive robot learning. This paper presents a supervised methodology for machine concept elicitation from informal counterparts described in natural languages. The collective opinions of a given concept in ten selected dictionaries are quantitatively analyzed and(More)
Quantizer design for lossy compression with mismatched side information (SI) at the decoder is investigated. The statistical dependency between the source and SI is assumed to be a function of a random variable, dependency variable (DV). According to the available information about the DV, three design methods are proposed; namely, minimax solution, Average(More)
In this paper, a scheme based on 1-D nested lattice quantization followed by multi-level distributed arithmetic coding (MLDAC) as the Slepian-Wolf (SW) code is proposed for the lossy source coding of continuous sources. This system can be employed in distributed video and image coding applications. The output of the quantizer is first converted to binary,(More)
Channel optimized multiple description (MD) vector quantization with symbol-based turbo codes are proposed, where the decoder exploits both nonuniformity of descriptions and their dependencies. The joint source channel coding strategy is devised by setting an entropy constraint for quantizer design, which together with the MD index assignment (IA), control(More)
Multiple descriptions (MD) with symbol-based turbo (SBT) codes are proposed, where the decoder exploits both nonuniformity of descriptions and their dependencies. A distortion-power adaptive system is obtained by setting an entropy constraint for quantizer design, which together with the MD index assignment (IA), control the level of redundancy at the MD(More)
Concept algebra is a denotational mathematics for rigorously manipulating formal concepts and their algebraic operations in knowledge representation, semantic analyses, and machine learning. Properties of concept algebra are formally studied in order to elaborate the nature of formal concepts and their algebraic operations. This leads to a set of algebraic(More)