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GIS Based Procedure to Determine Location of Cascade Small Hydropower Plants Case Study, Nokhan Area
, M.Sc. Thesis Sharif University of Technology ; Shamsai, Abolfazl (Supervisor)
Abstract
Coinciding with the worldwide environmental issues and increasing demand for clean energy sources, hydropower plants have highly attracted decision- makers attention. Low manufacturing costs and pollutions which are made by these plants in comparison with another types of powerhouses, could be reason of that. And so on, hydropower locating and evaluating studies have been highly increased recently, specifically in developing countries. From the point of operation procedure, hydropower plants are generally divided in two types, run-of-he-river and storage projects. This study has concentrated on run-of-he-river projects and due to spotting plant location, Sefidbarg river basin...
Hydropower plant site spotting using geographic information system and a MATLAB based algorithm
, Article Journal of Cleaner Production ; Volume 152 , 2017 , Pages 7-16 ; 09596526 (ISSN) ; Khanian, M ; Shamsai, A ; Sharif University of Technology
2017
Abstract
Meanwhile with increasing demand for energy sources in the world, hydropower plants can be considered as clean energy sources for sustainable development. Hydropower plant environmental impact is almost none. They are easy to construct and operate with much lower cost in comparison with other types of power plants. Currently in Iran, Conventional methods for determining hydropower plant potent location are very complicated and do not always eventuated to the best result. This study focuses on run of river projects and proposes a new methodology to spot hydropower plan best location according to specific engineering criteria, using ArcGIS and an algorithm developed in MATLAB. An economic...
Sentiment-Based Topic Analysis on Product Demand Prediction: Pre-Release and Post-Release Study
, M.Sc. Thesis Sharif University of Technology ; Aslani, Shirin (Supervisor)
Abstract
This thesis examines the impact of electronic word-of-mouth (e-WOM) on product demand forecasting, specifically in the context of video game consoles. This study examines the importance of emotion-based topics and their impact on demand forecasting at two stages of the product life cycle: pre-launch and post-launch. Using sentiment analysis and topic modeling, this study uncovers product sales drivers and captures evolving consumer sentiment throughout the product lifecycle. By integrating insights from diffusion theory and consumer information search theory, this research contributes to the field of demand forecasting using social media data. This research shows that by using the topics...
A Study of Intragroup Block-Trading Incentives on the Tehran Stock Exchange
, M.Sc. Thesis Sharif University of Technology ; Heidari, Mehdi (Supervisor) ; Ebrahimnejad, Ali (Supervisor)
Abstract
Using the data of the Tehran Stock Exchange, we analyze the characteristics and explanatory factors of major intra-group and out-of-group transactions and test the tunneling hypothesis in intra-group transactions. We find that out-of-group transactions can be largely explained by changes in control or management, firm size, and the type of the firm. However, intra-group transaction properties mostly depend on the difference between the parent company's cash flow rights in the buyer and seller companies. Also, in the final analysis, we conclude that many intra-group transactions are made to change the structure of the business groups, especially when investment companies buy shares of listed...
A proper transform for satisfying benford's law and its application to double JPEG image forensics
, Article 2012 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2012, 12 December 2012 through 15 December 2012 ; 2012 , Pages 240-244 ; 9781467356060 (ISBN) ; Razzazi, F ; Behrad, A ; Ahmadi, A ; Babaie-Zadeh, M ; Sharif University of Technology
2012
Abstract
This paper presents a new transform domain to evaluate the goodness of fit of natural image data to the common Benford's Law. The evaluation is made by three statistical fitness criteria including Pearson's chi-square test statistic, normalized cross correlation and a distance measure based on symmetrized Kullback-Leibler divergence. It is shown that the serial combination of variance filtering and block 2-D discrete cosine transform reveals the best goodness of fit for the first significant digit. We also show that the proposed transform domain brings reasonable fit for the second, third and fourth significant digits. As an application, the proposed transform domain is utilized to detect...
A novel forensic image analysis tool for discovering double JPEG compression clues
, Article Multimedia Tools and Applications ; Volume 76, Issue 6 , 2017 , Pages 7749-7783 ; 13807501 (ISSN) ; Razzazi, F ; Behrad, A ; Ahmadi, A ; Babaie Zadeh, M ; Sharif University of Technology
Springer New York LLC
2017
Abstract
This paper presents a novel technique to discover double JPEG compression traces. Existing detectors only operate in a scenario that the image under investigation is explicitly available in JPEG format. Consequently, if quantization information of JPEG files is unknown, their performance dramatically degrades. Our method addresses both forensic scenarios which results in a fresh perceptual detection pipeline. We suggest a dimensionality reduction algorithm to visualize behaviors of a big database including various single and double compressed images. Based on intuitions of visualization, three bottom-up, top-down and combined top-down/bottom-up learning strategies are proposed. Our tool...
Quantization-unaware double JPEG compression detection
, Article Journal of Mathematical Imaging and Vision ; Volume 54, Issue 3 , 2016 , Pages 269-286 ; 09249907 (ISSN) ; Razzazi, F ; Behrad, A ; Ahmadi, A ; Babaie Zadeh, M ; Sharif University of Technology
Springer New York LLC
2016
Abstract
The current double JPEG compression detection techniques identify whether or not an JPEG image file has undergone the compression twice, by knowing its embedded quantization table. This paper addresses another forensic scenario in which the quantization table of a JPEG file is not explicitly or reliably known, which may compel the forensic analyst to blindly reveal the recompression clues. To do this, we first statistically analyze the theory behind quantized alternating current (AC) modes in JPEG compression and show that the number of quantized AC modes required to detect double compression is a function of both the image’s block texture and the compression’s quality level in a fresh...
A novel forensic image analysis tool for discovering double JPEG compression clues
, Article Multimedia Tools and Applications ; Volume 76, Issue 6 , 2017 , Pages 7749-7783 ; 13807501 (ISSN) ; Razzazi, F ; Behrad, A ; Ahmadi, A ; Babaie Zadeh, M ; Sharif University of Technology
2017
Abstract
This paper presents a novel technique to discover double JPEG compression traces. Existing detectors only operate in a scenario that the image under investigation is explicitly available in JPEG format. Consequently, if quantization information of JPEG files is unknown, their performance dramatically degrades. Our method addresses both forensic scenarios which results in a fresh perceptual detection pipeline. We suggest a dimensionality reduction algorithm to visualize behaviors of a big database including various single and double compressed images. Based on intuitions of visualization, three bottom-up, top-down and combined top-down/bottom-up learning strategies are proposed. Our tool...
A part-level learning strategy for JPEG image recompression detection
, Article Multimedia Tools and Applications ; Volume 80, Issue 8 , 2021 , Pages 12235-12247 ; 13807501 (ISSN) ; Razzazi, F ; Behrad, A ; Ahmadi, A ; Babaie Zadeh, M ; Sharif University of Technology
Springer
2021
Abstract
Recompression is a prevalent form of multimedia content manipulation. Different approaches have been developed to detect this kind of alteration for digital images of well-known JPEG format. However, they are either limited in performance or complex. These problems may arise from different quality level options of JPEG compression standard and their combinations after recompression. Inspired from semantic and perceptual analyses, in this paper, we suggest a part-level middle-out learning strategy to detect double compression via an architecturally efficient classifier. We first demonstrate that singly and doubly compressed data with different JPEG coder settings lie in a feature space...