Sharif Digital Repository

Price movement prediction has always been one of the traders’ concerns in the field of financial market prediction. In order to increase the profit of the trades, the traders can process the historical data and predict the movement. The large size of the data and complex relations between them lead us to use algorithmic trading and artificial intelligence.The stock and Cryptocurrency markets are two common markets attracting traders. This thesis aims to offer an approach using Twin-Delayed DDPG (TD3) and daily close price in order to achieve a trading strategy. Unlike the previous studies using a discrete action space reinforcement learning algorithm, TD3 is a continuous one offering both... 

The main purpose of the thesis is Digital image Processing using the sparsity of image. The issue could be described in two ways. First is reconstruction of missed blocks of an image based on the sparsity of image in the transform domain and second is impulsive noise removal using the sparsity of noise in the spatial domain. In the first approach we will review Guleryuz method and simulate and analyze it. In the second approach, two new methods named RDE and Knockout will be introduced. These methods try two remove the impulsive noise of an image using its sparseness. RDE method is a development of conventional methods, but Knockout has a completely new idea. We will show that Knockout is... 

In  this  dissertation,  we  investigate  the  frequency  hopped  spread  spectrum  systems  which are extensively used in many communication systems. We derive the capacity region  of  Synchronous  Frequency  Hopped  Multiple  Access  (FHMA)  channels  (for  any  kind of noisy or nonselective fading environment) by using the side information which exists in the multi‐user detection. We assume that random hopping patterns are used by all the transmitters, and the common receiver knows the hopping patterns. We also compute  the  capacities  of  FHMA  with  and  without  power  control  for  AWGN  and  noiseless MFSK (in particular BFSK) modulated systems and compare the ... 

Digital images degrade during transmittion via noisy channels. The goal of this thesis is to propose new methods for image recovery from random and block losses In the first part of the thesis, various techniques for image recovery from random losses will be reviewd and then a method will be proposed based on the correlation among image pixels in the spatial domain. The method is fast, efficient and robust against Gaussian noise. Also a technique will be developed for quality estimation in the recipient. The second part of the thesis devotes image recovery from block losses. After a brief survey for image inpainting techniques we intoduce the concept of image reconstruction using the... 

The two central topics of information theory are the compression and the transmission of data. Shannon, in his seminal work, formalized both these problems and determined their fundamental limits. Since then the main goal of coding theory has been to find practical schemes that approach these limits.Polar codes, recently invented by Arıkan, are the first practical codes that are known to achieve the capacity for a large class of channels. Their code construction is based on a phenomenon called “channel polarization”.
Since the performance of polar code degrades when the block length is small, several kinds of Reed – Solomon concatenation is considered in order to improve the... 

CDMA is one of the main multiple access techniques. In this thesis, we study the channel capacity and the optimum codes for CDMA systems under different conditions. The optimality has different meaning for each studied scenario. The discussed scenarios differ from the aspects of the alphabet of the system, the ability of getting active or inactive for users, and the random fluctuation of the user powers. The first scenario is the systems with real input alphabet. In this case, by optimum codes, we mean a code that maximizes the sum channel capacity of the system. The second scenario is the systems with binary input alphabets and binary signatures. In this section, optimum codes are the codes... 

The emerging field of compressed sensing deals with the techniques of combining the two blocks of sampling and compression into a single unit without compromising the performance. Clearly, this is not feasible for any general signal; however, if we restrict the signal to be sparse, it becomes possible. There are two main challenges in compressed sensing, namely the sampling process and the reconstruction methods. In this thesis, we will focus only on the deterministic sampling process as opposed to the random sampling. The sampling methods discussed in the literature are mainly linear, i.e., a matrix is used as the sampling operator. Here, we first consider linear sampling methods and... 

In this thesis, we aim to explore the recovery of sparse signals from their compressive or random samples. At first, the Compressed Sensing (CS) recovery is considered and an iterative method with adaptive thresholding has been suggested which has superior performance compared to its counterparts in both reconstruction quality and simplicity. Then, random sampling, a special kind of compressive sensing, is investigated which is practically more efficient to be implemented than the compressive sampling scheme. A number of random sampling recovery techniques are offered based on sparsity which has very low computational complexity in a way that largedimensional signals can efficiently be... 

Cognitive radio (CR) can successfully deal with the growing demand and scarcity of the wireless spectrum. To exploit limited spectrum efficiently, CR technology allows unlicensed users to access licensed spectrum bands. Since licensed users have priorities to use the bands, the unlicensed users need to continuously monitor the licensed users activities to avoid interference and collisions. How to obtain reliable results of the licensed users activities is the main task for spectrum sensing. Based on the sensing results, the unlicensed users should adapt their transmit powers and access strategies to protect the licensed communications. The requirement naturally presents challenges to the... 

This thesis is dedicated to examine performance of compressive sensing based MIMO radar systems. MIMO radars have the ability to achieve higher target detection and parameter estimation as well as better performance in noisy and clutter environments than SISO or phased array radars. The need for high-speed analog to digital converters is one of the weaknesses in implementation of radar systems. With the advent of compressive sensing providing necessary guarantees for reconstruction of sparse signal using fewer samples than what Nyquist thorem describes, the need for such a high-speed converters that are either not available or too expensive is resolved. What allowes us to use compressive... 

There is a vast amount of publications in the literature on various types of tampering detection. In this thesis, we propose two methods for tampering detection and recovery of speech signals with WAV format. Due to redundancy of speech signals, the watermarking technique is utilized for this purpose. In embedder side,the watermark is generated using a similar version of the original signal. In first method, watermark is generated by the downsampled signal. Output of hash generator and some framing headers are used to detect the tampered region automatically. In second method, The original signal is compressed using a source encoder. The output is then packetized to detect the tampered... 

In this correspondence, a non-linear method of convergence accelerating and improving for iteration based algorithms is introduced. After convergence analysis, some enough conditions are proposed to guarantee convergence of the algorithm.For the sake of low complexity implementation of the proposed algorithm, some simple stabilizing methods are suggested. Simulation results show desirable performance of the proposed method and its capability to stabilize the iteration based algorithms. In the literature of missing samples recovery, the proposed method is applied to an Iterative Method (IM) as a general signal reconstruction method,then it is extended to the image recovery problem where... 

One of the funadmental problems in signal processing is finding the best sampling strategy of a continuous time signal, and then finding the best strategy for compressing the obtained signal samples. The sampling and compression steps are designed with the aim of minimizing the distortion of the reconstructed signal. The problem of finding the best sampling strategy has been widely studied in the signal processing literature. In particular, the Nyquist–Shannon sampling theorem gives a sufficient condition for perfect reconstruction of a continuous-time signal of finite bandwidth. Most of the signal processing literature deals with deterministic signals, with relatively less attention paid to... 

In this dissertation, Data analysis and information retrieval from incomplete observations are investigated in different applications. Incomplete observations may be induced by lack of observations or part of data affected by specific noise (quantization noise). Data-driven algorithms are among important hot topics. Our goal is to process the lost information inducing certain assumption on big data structures. Then, the approach is to mathematically model the problem of interest as an optimization problem. Next, the designed algorithms for the optimization problems are proposed trying to cut down on the computational complexity of as well as enhancing recovery accuracy for big data... 

Sensor Networks are set of devices which are distributed throughout an environment and are connected to each other, usually wirelessly, to collect environmental information including temperature, aire pressure, moist, pollution and physiological functions of the human body. Each device consists of a microprocessor, converter and power supply, transmitter and a receiver. In this study we intend to investigate such setup and the measured signals assuming they are sparse. A sparse signal is a discrete time signal most of indices of which are equal to zero. With this assumption at hand, we will be able to reduce the sampling rate and take advantage of sparse signal processing techniques. This... 

Level crossing sampling is a sampling method in which a sample is taken whenever signal crosses predefined and specific levels. In this dissertation, some recommendations are made in order to increase the sampler’s performance in the sampling, data compression, and reconstruction stages.The IMATMirror algorithm is introduced in the sample reconstruction stage. This algorithm is derived from the IMAT reconstruction method. However, additional data processing is done in each iteration, causing the reconstructed signal to satisfy some properties of the Level-Crossing samples.In order to solve the problem of level crossing sampling of extremely bursty signals (ECG signals for example), a sampler...