Sharif Digital Repository

Abstract: Commercially pure titanium and titanium alloys have been among the most commonly used materials for biomedical applications since the 1950s. Due to the excellent mechanical tribological properties, corrosion resistance, biocompatibility, and antibacterial properties of titanium, it is getting much attention as a biomaterial for implants. Furthermore, titanium promotes osseointegration without any additional adhesives by physically bonding with the living bone at the implant site. These properties are crucial for producing high-strength metallic alloys for biomedical applications. Titanium alloys are manufactured into the three types of α, β, and α + β. The scientific and clinical... 

The development of active, durable, cost-effective, and stable electrocatalysts is an urgent need for various industrial fields including renewable energy systems. The state-of-the-art catalysts suffer from poor water splitting activity in alkaline media due to their sluggish kinetics, high cost, and scarcity on earth to be scaled up. Herein, we present an electrochemical analysis of a nanostructured electrocatalyst based on a face-centered cubic (FCC) copper-rich Cu-Ni-Fe-Cr-Co alloy with a quasi-spherical morphology electrodeposited on a highly porous nickel substrate. Electrochemical studies determine the enhanced electrocatalytic activity toward both hydrogen and oxygen reactions in an... 

Mixed-criticality (MC) systems have recently been devised to address the requirements of real-time systems in industrial applications, where the system runs tasks with different criticality levels on a single platform. In some workloads, a high-critically task might overrun and overload the system, or a fault can occur during the execution. However, these systems must be fault tolerant and guarantee the correct execution of all high-criticality (HC) tasks by their deadlines to avoid catastrophic consequences, in any situation. Furthermore, in these MC systems, the peak-power consumption of the system may increase, especially in an overload situation and exceed the processor thermal design... 

The purpose of this research is to provide power grid energy efficiency solutions. In this paper, a comprehensive review and its optimal solution is proposed considering the various challenges of smart grid demand-side management. The main technique is based on a novel idea in the Smart Grid—demand response optimization which enables autonomous energy management on the demand side for a wide variety of customers. The first section of this research examines the smart grid issue and evaluates the state-of-the-art load management techniques in terms of the work’s scope. The demand-side load management architecture consists of three primary levels, two of them in line planning and low-cost... 

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... 

Embedded Systems (ESs) have migrated from special-purpose hardware to commodity hardware. These systems have also tended to Mixed-Criticality (MC) implementations, executing applications of different criticalities upon a shared platform. Multi-cores, which are commonly used to design MC Systems (MCSs), bring out new challenges due to the Process Variation (PV). Power and frequency asymmetry affects the predictability of ESs. In this work, variation-aware techniques are explored to not only improve the reliability of MCSs, but also aid the scheduling and energy saving of them. We leverage the Core-to-Core (C2C) variations to protect high-criticality tasks and provide full service for a high... 

Due to the battery-operated nature of some embedded Mixed-Criticality Systems, simultaneous energy and reliability management is a crucial issue in designing these systems. We propose two comprehensive schemes, MC-2S and MC-4S, which exploit the standby-sparing technique to tolerate permanent faults through inherent redundancy of multicore systems and maintain the system's reliability against transient faults with low energy overhead. In these schemes, two copies of each high-criticality task are scheduled on different cores to guarantee their timeliness in case of permanent fault occurrence. To guarantee the quality of service of low-criticality tasks, in the MC-2S scheme, one backup copy... 

Energy harvesting systems powered by renewable energy sources employ hybrid volatile-nonvolatile memory to enhance energy efficiency and forward progress. These systems have unreliable power sources and energy buffers with limited capacity, so they complete long-running applications across multiple power outages. However, a power outage might cause data inconsistency, because the data in nonvolatile memories are persistent, while the data in volatile memories are unsteady. State of the art studies proposed various memory architectures and compiler-based techniques to tackle the data inconsistency in these systems. These approaches impose too many unnecessary check-points on the system to... 

Increasing power densities in future technology nodes is a crucial issue in multicore platforms. As the number of cores increases in them, power budget constraints may prevent powering all cores simultaneously at full performance level. Therefore, chip manufacturers introduce a power budget constraint as Thermal Design Power (TDP) for chips. Meanwhile, multicore platforms are suitable for the implementation of fault-tolerance techniques to achieve high reliability. Task Replication is a well-known technique to tolerate transient faults. However, careless task replication may lead to significant peak power consumption. In this article, we consider the problem of achieving a given reliability... 

Embedded systems have migrated from special-purpose hardware to commodity hardware. These systems have also tended to Mixed-Criticality (MC) implementations, executing applications of different criticalities upon a shared platform. Multi-core processors, which are commonly used to design MC systems, bring out new challenges due to the process variations. Power and frequency asymmetry affects the predictability of embedded systems. In this work, variation-aware techniques are explored to not only improve the reliability of MC systems, but also aid the scheduling and energy saving of them. We leverage the core-to-core (C2C) variations to protect high-criticality tasks and provide full service... 

Multiple human 3D pose estimation is a useful but challenging task in computer vison applications. The ambiguities in estimation of 2D and 3D poses of multiple persons can be verified by using multi-view frames, in which the occluded or self-occluded body parts of some persons might be visible in other camera views. But, when cameras are moving and uncalibrated, estimating the association of multiple human body parts among different camera views is a challenging task. This paper presents novel methods for multiple human 3D pose estimation and pose association in multi-view camera frames in an uncalibrated camera setup using an adversarial learning framework. The generator is a 3D pose... 

Three novel methods, named α, ζ and ϵ, are suggested in this paper to recover the performance loss during switching in the gas turbine control systems. The Minimum Command Selection (MCS) in the gas turbine control systems prompts this performance loss. Any step towards more productivity with less aging factors have a great impact on the gas turbine's lifetime profit and vice versa. Although many hardware upgrades have been studied and applied to accomplish this, in many cases a low-risk manipulation in the software may yield equivalent achievement. State of the art gas turbine control systems are supposed to handle various forms of disturbances, several operation modes and relatively high... 

Deep Neural networks (DNNs) are getting deeper and larger which intensify the data movement and compute demands. Prior work focuses on reducing data movements and computation through exploiting sparsity and similarity. However, none of them exploit input similarity and only focus on sparsity and weight similarity. Synergistically analysing the similarity and sparsity of inputs and weights, we show that memory accesses and computations can be reduced by 5.7× and 4.1×, more than what can be decreased by exploiting only sparsity, and 3.9× and 2.1×, more than what can be decreased by exploiting only weight similarity. We propose a new data-aware compression approach, called DANA, to effectively... 

Due to the automatic feature extraction procedure via multi-layer nonlinear transformations, the deep learning-based visual trackers have recently achieved a great success in challenging scenarios for visual tracking purposes. Although many of those trackers utilize the feature maps from pre-trained convolutional neural networks (CNNs), the effects of selecting different models and exploiting various combinations of their feature maps are still not compared completely. To the best of our knowledge, all those methods use a fixed number of convolutional feature maps without considering the scene attributes (e.g., occlusion, deformation, and fast motion) that might occur during tracking. As a... 

We consider the problem of tracking an unknown small target from aerial videos of medium to high altitudes. This is a challenging problem, which is even more pronounced in unavoidable scenarios of drastic camera motion and high density. To address this problem, we introduce a context-aware IoU-guided tracker (COMET) that exploits a multitask two-stream network and an offline reference proposal generation strategy. The proposed network fully exploits target-related information by multi-scale feature learning and attention modules. The proposed strategy introduces an efficient sampling strategy to generalize the network on the target and its parts without imposing extra computational... 

In recent years, visual tracking methods that are based on discriminative correlation filters (DCFs) have been very promising. However, most of these methods suffer from a lack of robust scale estimation skills. Although a wide range of recent DCF-based methods exploit the features that are extracted from deep convolutional neural networks (CNNs) in their translation model, the scale of the visual target is still estimated by hand-crafted features. Whereas the exploitation of CNNs imposes a high computational burden, this paper exploits pre-trained lightweight CNNs models to propose two efficient scale estimation methods, which not only improve the visual tracking performance but also... 

Semantic segmentation is one of the basic, yet essential scene understanding tasks for an autonomous agent. The recent developments in supervised machine learning and neural networks have enjoyed great success in enhancing the performance of the state-of-the-art techniques for this task. However, their superior performance is highly reliant on the availability of a large-scale annotated dataset. In this letter, we propose a novel fully unsupervised semantic segmentation method, the so-called Information Maximization and Adversarial Regularization Segmentation (InMARS). Inspired by human perception which parses a scene into perceptual groups, rather than analyzing each pixel individually, our... 

This paper proposes a novel 3D action recognition technique that uses time-series information extracted from depth image sequences for use in systems of human daily activity monitoring. To this end, each action is represented as a multi-dimensional time series, where each dimension represents the position variation of one skeleton joint over time. The time series is then mapped onto a vector space using Dynamic Time Warping (DTW) distance. Furthermore, to employ the correlation-distinctiveness relationship of the sequences in recognition, this vector space is remapped onto a discriminative space using the regularized Fisher method, where final decisions about the actions are made. Unlike... 

The multiple uncertainties in a microgrid, such as limited photovoltaic generations, ups and downs in the market price, and controlling different loads, are challenging points in managing campus energy with multiple microgrid systems and are a hot topic of research in the current era. Microgrids deployed at multiple campuses can be successfully operated with an exemplary energy management system (EMS) to address these challenges, offering several solutions to minimize the greenhouse gas (GHG) emissions, maintenance costs, and peak load demands of the microgrid infrastructure. This literature survey presents a comparative analysis of multiple campus microgrids’ energy management at different... 

Due to the battery-operated nature of embedded Mixed-Criticality Systems, simultaneous energy and reliability management is a cru-cial issue in designing these systems. We propose two comprehensive schemes, MC-2S and MC-4S, which tolerate permanent faults through exploiting the inherent redundancy of multicore systems for applying standby-sparing technique and maintaining the system re-liability against transient faults with low energy overhead. In these schemes, two copies of each high-criticality task are scheduled on different cores to guarantee their timeliness in case of permanent fault occurrence. In order to guarantee the quality of service of low-criticality tasks, in the MC-2S...