Sharif Digital Repository

In nanometer-scale process technologies, the effects of process variations are observed in Multiprocessor System-on-Chips (MPSoC) in terms of variations in frequencies and leakage powers among the processors on the same chip as well as across different chips of the same design. Traditionally, worst-case values are assumed for these parameters and then a deterministic optimization technique is applied to the MPSoC application under design. We show that such worst-case-based approaches are not optimal with the increasing variation observed at system-level, and instead, statistical approaches should be employed. We consider the problem of simultaneously choosing MPSoC architecture and task... 

In this paper, the performance of a novel HDH solar desalination system equipped with a combination of heat pipe (HP), evacuated tube collector (ETC) and air bubble column humidifier is experimentally investigated. This novel HDH system uses advantages of ETC-HP as a highly efficient thermal absorption and conductor device, and at the same time employs the advantages of an air bubble column humidifier, i.e. high interface area and effective mixing in order to heat the water and humidify the air, respectively. The effects of various parameters including incoming air flow rate into the humidifier, initial depth of water in the humidifier, and adding fluids such as oil and water in the space... 

Image fusion means to integrate information from one image to another image. Medical images according to the nature of the images are divided into structural (such as CT and MRI) and functional (such as SPECT, PET). This article fused MRI and PET images and the purpose is adding structural information from MRI to functional information of PET images. The images decomposed with Nonsubsampled Contourlet Transform and then two images were fused with applying fusion rules. The coefficients of the low frequency band are combined by a maximal energy rule and coefficients of the high frequency bands are combined by a maximal variance rule. Finally, visual and quantitative criteria were used to... 

Echocardiography as a common place device has been widely used for diagnosis. Segmentation of left ventricle just using the standard level set formulation based on the magnitude of intensity cannot restrict the contour evolution completely due to the lack of sharp edges. This paper aims to introduce a robust method for segmentation of three-dimensional echocardiography images based on two boundary maps namely distance map and probabilistic map as stopping criteria of contour evolution which are extracted using two different approaches. The distance map is extracted by applying Free Form Deformation (FFD) method on manually determined landmarks of initial spherical volume to register the... 

In this paper, an efficient approach for ultrasonic object segmentation with special application for left ventricle segmentation in echocardiography images is proposed. At first, an efficient hybrid trend for ultrasonic image edge detection is suggested. Then, a modified level set approach is introduced based on the extracted edges and the computed probabilistic map as the stopping criteria for the contour evolution. Both synthetic and clinical images are utilized as validation measures with respect to the prior techniques which indicate outperform results quantitatively and qualitatively. Left ventricle segmentation using proposed method illustrates expert-approved performance, providing a... 

The substitution of conventional mathematical models with fast and accurate modeling tools can result in the further development of desalination technologies and tackling the need for freshwater. Due to the great capability of data-driven methods in analyzing complex systems, several attempts have been made to study various desalination systems using data-driven approaches. In this state-of-the-art review, the application of various artificial intelligence and design of experiment data-driven methods for analyzing different desalination technologies have been thoroughly investigated. According to the applications of data-driven methods in the field of desalination, the reviewed... 

Corner-case analysis is a well-known technique to cope with occasional deviations occurring during the manufacturing process of semiconductors. However, the increasing amount of process variation in nanometer technologies has made it inevitable to move toward statistical analysis methods, instead of deterministic worst-case-based techniques, at all design levels. We show that by statically considering statistical effects of random and systematic process variation on performance and power consumption of a Multiprocessor System-on-Chip (MPSoC), significant power improvement can be achieved by static software-level optimizations such as task and communication scheduling. Moreover, we analyze... 

New technologies such as smart homes and appliances and renewable energy production have been attracting much attention in recent years. In practice, the management of these technologies to get the minimum household payment has become a challenge due to the time-varying electricity price. In this paper, we propose a real time energy management system to manage the appliances and storages. The solar energy is charged in the storages and used together with the grid electricity to supply the appliances. The proposed management system uses an ILP-based Home Energy Management (ILPHEM) optimization engine. ILPHEM schedules appliances requests and storage usage based on updated real time input data... 

Increasing delay and power variation has become a major challenge to designing high performance Multiprocessor System-On-Chips (MPSoC) in deep sub-micron technologies. As a result, a paradigm shift from deterministic to statistical design methodology at all levels of the design hierarchy is inevitable. In this paper, we propose a static variation-aware task scheduling and power mode selection algorithm for MPSoCs. The proposed algorithm is able to maximize the total power yield of the chip under a given performance yield constraint by searching for the optimal task scheduling and power mode selection policy for a specified multiprocessor platform. Experimental results are gathered by... 

Parameter variations reveal themselves as different frequency and leakage powers per instances of the same MPSoC. By the increasing variation with technology scaling, worst-case-based scheduling algorithms result in either increasingly less optimal schedules or otherwise more lost yield. To address this problem, this paper introduces a variationaware task and communication scheduling algorithm for multiprocessor system-on-chip (MPSoC). We consider both delay and leakage power variations during the process of finding the best schedule so that leakier processors are less utilized and can be more frequently put in sleep mode to reduce power. Our algorithm takes advantage of event tables to... 

Automatic extraction of the varying regions of magnetic resonance images is required as a prior step in a diagnostic intelligent system. The sparsest representation and high-dimensional feature are provided based on learned dictionary. The classification is done by employing the technique that computes the reconstruction error locally and non-locally of each pixel. The acquired results from the real and simulated images are superior to the best MRI segmentation method with regard to the stability advantages. In addition, it is segmented exactly through a formula taken from the distance and sparse factors. Also, it is done automatically taking sparse factor in unsupervised clustering methods... 

Sparse representation is a powerful tool for image processing, including noise removal. It is an effective method for Gaussian noise removal by taking advantage of a fixed and learned dictionary. In this study, the variable distribution of Rician noise is reduced in magnetic resonance (MR) images by sparse representation based on reconstruction error sets. Standard deviation of Gaussian noise is used to find these errors locally. The proposed method represents two formulas for local error calculation using standard deviation of noise. The acquired results from the real and simulated images are comparable, and in some cases, better than the best Rician noise removal method due to the... 

A novel combined power and refrigeration cycle is introduced and analyzed from the thermodynamic standpoint. The proposed cycle is an integration of a Kalina cycle equipped with an ejector (EKalina) and an ejector refrigeration cycle (ERC). In order to enhance the overall cycle performance, the possibility of employing two-phase ejectors in combined power and refrigeration cycles is studied. A parametric analysis is conducted to investigate the effects of the key thermodynamic parameters namely turbine inlet pressure, split ratio, basic ammonia concentration, heat source and evaporator temperatures on the cycle performance. The developed model is also verified with available data and it is... 

Segmentation of a scene to the part made is a challenging work. In this paper a graphical model is used for this task. The methods based on geometrical derivatives such as curvature and normal often haven't good result in segmentation of geometrically-complex architecture and lead to over-segmentation and even failure. Proposed method for segmentation contains two steps. At first region growing based on curvature, normal and color is used for growing region. This segmented cloud is used for unary potential in graphical model. Fully connected graph for Conditional Random Field with Gaussian kernel for pair wise potentials is used for correcting this segmentation. Gaussian kernels are based on...