Sharif Digital Repository

The development of emerging smart grid technologies has led to more and more penetration of renewable energy resources and electric energy storage in the residential sectors. Besides, owing to the significant evolution of power electronic devices, there is a rapid growth in penetration of DC loads and generations, such as PV and electric vehicles (EVs), into the buildings and homes as a building block of the future smart cities. This is despite the fact that the electricity infrastructure of the conventional buildings is designed based on AC electricity and as a result, there would be a lot of losses due to the frequent power conversion from AC to DC and vice versa. Besides, according to a... 

The distribution system's economic operation is significantly impacted by the management of distributed generation (DG) resources, energy storage (ES), and controllable loads. The paper employs a smart distribution system that incorporates dispatchable and non-dispatchable DG resources, as well as battery storage, in addition to the demand response (DR) scheme. New modelling was performed in hourly steps to achieve the optimal unit commitment. In smart homes, appliances are prioritized and classified into four types: adjustable, interruptible, shiftable, and uncontrollable loads. Load reduction in smart homes is also considered based on load prioritization and customer participation in the... 

With the advent of the Internet of Things (IoT), the development of self-powered sensors has received much attention. Introducing triboelectric nanogenerators (TENGs) as a power source that converts mechanical movement into electrical signals has been admired recently. Moreover, the monitoring of humidity has become enormously essential in several technological contexts from environment monitoring to biomedical applications, thus joining these two subjects provides a huge benefit in achieving self-powered humidity sensors. Here, in this research, facile, low-priced and self-powered humidity sensors are fabricated utilizing transition-metal dichalcogenides (TMD) nanosheets. Semi-vertical SnS2... 

CPU caches are powerful sources of information leakage. To develop practical cache-based attacks, the need for automation of the process of finding exploitable cachebased side-channels in computer systems is felt more than ever. Cache template attack is a generic technique that utilizes Flush+Reload attack in order to automatically exploit cache vulnerability of Intel platforms. Cache template attack on the T-table-based AES implementation consists of two phases including the profiling phase and key exploitation phase. Profiling is a preprocessing phase to monitor dependencies between the secret key and behavior of the cache memory. In addition, the addresses of T-tables can be obtained... 

ARX algorithms are a class of symmetric-key algorithms constructed by Addition, Rotation, and XOR. To evaluate the resistance of an ARX cipher against differential and impossible-differential cryptanalysis, the recent automated methods employ constraint satisfaction solvers to search for optimal characteristics or impossible differentials. The main difficulty in formulating this search is finding the differential models of the non-linear operations. While an efficient bit-vector differential model was obtained for the modular addition with two variable inputs, no differential model for the modular addition by a constant has been proposed so far, preventing ARX ciphers including this... 

Smart home is a small but an important energy segment that has a significant potential to implement authentic energy policies, where human is a major decision-maker in the home energy management dilemma. Therefore, humans’ emotions and tendencies plays a vital role as the End-User's daily decisions. In this paper, we develop a behavioral home energy management model based on time-driven prospect theory incorporating energy storage devices, distributed energy resources, and smart flexible home appliances. The conventional prospect theory is then modified by considering the psychological time discount effect in the value function. The non-flexible load and photovoltaic generation uncertainties... 

The main contribution of this paper is to present the systematic and low-complexity translation techniques between a class of hybrid systems referred to as automaton-based Discrete-time Hybrid Automata (DHA) and piecewise affine (PWA) systems. As a starting point, the general modeling framework of the automaton-based DHA is represented, which models the controlled and uncontrolled switching phenomena between linear continuous dynamics including discrete and continuous states, inputs and outputs. The basic theoretical definitions on the state trajectories of the proposed DHA with forward and backward evolutions that yield forward and backward piecewise affine (FPWA and BPWA) systems are... 

Battery energy systems are playing significant roles in smart homes, e.g., absorbing the uncertainty of solar energy from root-top photovoltaic, supplying energy during a power outage, and responding to dynamic electricity prices. For the safe and economic operation of batteries, an optimal battery-management system (BMS) is required. One of the most important features of a BMS is state-of-charge (SoC) estimation. This article presents a robust central-difference Kalman filter (CDKF) method for the SoC estimation of on-site lithium-ion batteries in smart homes. The state-space equations of the battery are derived based on the equivalent circuit model. The battery model includes two RC... 

Simultaneous recording of activity across brain regions can contain additional information compared to regional recordings done in isolation. In particular, multivariate pattern analysis (MVPA) across voxels has been interpreted as evidence for distributed coding of cognitive or sensorimotor processes beyond what can be gleaned from a collection of univariate effects (UVE) using functional magnetic resonance imaging (fMRI). Here, we argue that regardless of patterns revealed, conventional MVPA is merely a decoding tool with increased sensitivity arising from considering a large number of ‘weak classifiers’ (i.e., single voxels) in higher dimensions. We propose instead that ‘real’ multivoxel... 

Machine-to-Machine (M2M) communication is an important type of communication in the Internet-of-Things (IoT). How to send data in these high-density communications using relay selection can help improve the performance of this type of communication in various applications. In addition, the possibility of simultaneous use of different Radio Frequency (RF) interfaces helps to use the spectrum more efficiently. In this work, we try to further use machine communication RF equipment and improve the average data rate of networks in some applications such as the IoT, which have their own bandwidth requirements. Therefore, we provide an optimization algorithm for relay selection as well as the... 

In Iranian Sign Language (ISL), alongside the movement of fingers/arms, the dynamic movement of lips is also essential to perform/recognize a sign completely and correctly. In a follow up of our previous studies in empowering the RASA social robot to interact with individuals with hearing problems via sign language, we have proposed two automated lip-reading systems based on DNN architectures, a CNN-LSTM and a 3D-CNN, on the robotic system to recognize OuluVS2 database words. In the first network, CNN was used to extract static features, and LSTM was used to model temporal dynamics. In the second one, a 3D-CNN network was used to extract appropriate visual and temporal features from the... 

This paper introduces a cyber-physical system for building automation and control that is developed based on a distributed model predictive control. The implemented distributed method significantly reduces computation overhead with respect to the centralized methods. However, continuous data transfer between subsystems, which are often far from each other, is required when using this method. Information transmission between subsystems is very often subject to the limitations of transmission bandwidth and/or short communication range resulting in significant communication overhead. This causes significant time latency between making measurements and applying control commands, which adversely... 

The reliability of machinery and automated guided vehicle has been one of the most important challenges to enhance production efficiency in several manufacturing systems. Reliability improvement would result in a simultaneous reduction of both production times and transportation costs of the materials, especially in automated guided vehicles. This article aims to conduct a practical multi-objective reliability optimization model for both automated guided vehicles and the machinery involved in a job-shop manufacturing system, where different machines and the storage area through some parallel automated guided vehicles handle materials, parts, and other production needs. While similar machines... 

Human error is one of the leading causes of accidents. Distraction, fatigue, poor visibility, speeding, and other such errors made by drivers can cause accidents. With the rapid advancements in automation technologies, transportation planners have strived to use Intelligent Transportation Systems (ITS) to minimize human error. In this study, the effect of Autonomous Vehicles (AVs) on the number of potential conflicts at two unsignalized intersections is investigated by using a microsimulation model in PTV Vissim software. For human-driven cars, the factor that is considered for calibration is driver distraction mainly caused by reading or writing text messages on a cellphone while driving.... 

Modal identification is a type of system identification, which studies on the modal parameters of systems by using modal test. In the case of using operational or ambient modal analysis, there is no need to measure excitation, and the system output data are adequate for identification purposes. These modal parameters of system are of great importance from the engineering point of view particularly in the area of system identification, damage detection, and condition monitoring. In a fully-automated identification approach, the modal parameters are extracted without intervention of a specialized user. In this study, a Fully Automated Operational Modal Identification algorithm is developed to... 

In this research, we developed a cost-effective automated guided vehicle for small clothing production workshops that ordinary workers in such workplaces can operate. We acquired workers’ opinions in multiple workshops about using the robots (with some social capabilities) and working alongside them. We performed the tests in China and Iran to investigate and compare different preferences and priorities in two countries with different cultural backgrounds. We used the UTAUT questionnaire and conducted two-way ANOVA tests considering two independent factors: Nationality and Gender. The results showed that workers in Iran and China have relatively similar expectations from a social AGV, with... 

This paper is concerned with the tele-operation of autonomous vehicles over analog Additive White Gaussian Noise (AWGN) channel, which is subject to transmission noise and power constraint. The nonlinear dynamic of autonomous vehicles is described by the unicycle model and is cascaded with a bandpass filter acting as encoder. Using the describing function method, the nonlinear dynamic of autonomous vehicles is represented by an approximate linear system. Then, the available results for linear control over analog AWGN channel are extended to account for linear continuous time systems with non-real valued and multiple real valued eigenvalues and for tracking a non-zero reference signal.... 

This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's chest CT scan images. We introduce a new dataset that contains 48,260 CT scan images from 282 normal persons and 15,589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm that analyzes the view of the lung to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel architecture for improving the classification accuracy of convolutional networks on images containing small... 

Background: It is important to improve verbal Working Memory (WM) in reading disability, as it is a key factor in learning. There are commercial verbal WM training programs, which have some short-term effects only on the verbal WM capacity, not reading. However, because of some weaknesses in current verbal WM training programs, researchers suggested designing and developing newly structured programs that particularly target educational functions such as reading skills. In the current double-blind randomized clinical trial study, we designed a new Verbal Working Memory-Balance (VWM-B) program which was carried out using a portable robotic device. The short-term effects of the VWM-B program,... 

Representational similarity analysis (RSA) summarizes activity patterns for a set of experimental conditions into a matrix composed of pairwise comparisons between activity patterns. Two examples of such matrices are the condition-by-condition inner product and correlation matrix. These representational matrices reside on the manifold of positive semidefinite matrices, called the Riemannian manifold. We hypothesize that representational similarities would be more accurately quantified by considering the underlying manifold of the representational matrices. Thus, we introduce the distance on the Riemannian manifold as a metric for comparing representations. Analyzing simulated and real fMRI...