Sharif Digital Repository

There have been many previous works to determine the determinants of housing prices. All of these works relied on a relatively small set of data, mostly collected with the help of real estate agencies. In this work, we used web mining methods to generate a big, organized dataset from a popular national brokerage website. The dataset contains structural characteristics of more than 139,000 apartments, alongside their location and price. We provided our full dataset for the article, so that other researchers can reproduce our results or conduct further analyses. Using this dataset, we analyzed housing prices of Tehran in order to identify its major determinants. To this aim, we examine the... 

In this letter, we address the theoretical limitations in estimating the mixing matrix in noisy sparse component analysis (SCA) for the two-sensor case. We obtain the Cramer-Rao lower bound (CRLB) error estimation of the mixing matrix. Using the Bernouli-Gaussian (BG) sparse distribution, and some simple assumptions, an approximation of the Fisher information matrix (FIM) is calculated. Moreover, this CRLB is compared to some of the main methods of mixing matrix estimation in the literature. © 2008 IEEE  

In this paper, a sparse component analysis algorithm is presented for the case in which the number of sources is less than or equal to the number of sensors, but the channel (mixing matrix) is time-varying. The method is based on a smoothed l0 norm for the sparsity criteria, and takes advantage of the idea that sparsity of the sources is decreased when they are mixed. The method is able to separate synthetic and speech data, which require very weak sparsity restrictions. It can separate up to 50 mixed signals while being adaptive to channel variation and robust against noise. © 2007 IEEE  

This paper presents a new algorithm for Blind Source Separation (BSS) of Instantaneous speech mixtures in under-determined case. A demixing algorithm which exploits the sparsity of speech signals in the short time Fourier transform (STFT) domain is proposed. This algorithm combines the modified k-means clustering procedure involved in the Line Orientation Separation Technique (LOST) with Smoothed l0-norm minimization (SL0) method. First procedure along with a transformation into a sparse domain tries to estimate the mixing matrix, and the second method tries to extract the sources from the mixtures. Simulation results are presented and compared to the Degenerate Unmixing Estimation Technique... 

Effective marketing is quite vital in many building construction projects that depend on the pre-sale cash in-flow. However, the unsafe condition of the under-construction projects postpones in-person customer visits to the completion date and complicates the marketing process. This safety concern is especially critical for the upper floor units. Although virtual tours of the buildings are used to show the project specifications, they do not convey the impression that customers receive in the real ambiance of in-person visits. This research proposes a novel method called semi-augmented reality to address the safety issue of the under-construction projects during the marketing process. In... 

The present article deals with the employment of combined heat and power (CHP) micro gas turbines using natural gas and cold thermal energy storage system (CTES) in Tehran (with mild climate), Bandarabas (with hot and humid climate) and Kerman (with semi-hot climate). A micro CHP produces electricity to meet the electrical energy needs of the building, and it is also considered to meet part of the heating, cooling and domestic hot water energy needs through a heat pump and refrigeration system. A detailed study considering the effect of CTES system on the selection of micro gas turbines of a residential building located in Tehran is performed. The results show that since the number of micro... 

In this paper, a fast algorithm for overcomplete sparse decomposition, called SL0, is proposed. The algorithm is essentially a method for obtaining sparse solutions of underdetermined systems of linear equations, and its applications include underdetermined sparse component analysis (SCA), atomic decomposition on overcomplete dictionaries, compressed sensing, and decoding real field codes. Contrary to previous methods, which usually solve this problem by minimizing the ℓ1 norm using linear programming (LP) techniques, our algorithm tries to directly minimize the ℓ0 norm. It is experimentally shown that the proposed algorithm is about two to three orders of magnitude faster than the... 

We present a Bayesian approach for Sparse Component Analysis (SCA) in the noisy case. The algorithm is essentially a method for obtaining sufficiently sparse solutions of underdetermined systems of linear equations with additive Gaussian noise. In general, an underdetermined system of linear equations has infinitely many solutions. However, it has been shown that sufficiently sparse solutions can be uniquely identified. Our main objective is to find this unique solution. Our method is based on a novel estimation of source parameters and maximum a posteriori (MAP) estimation of sources. To tackle the great complexity of the MAP algorithm (when the number of sources and mixtures become large),...