Sharif Digital Repository

Deep neural networks are currently the best solution for road and traffic scene interpretation for autonomous and self-driving vehicles. Compared to the autonomous cars, motorcycles have significant flexibility and advantages in crowded traffic situations and especially in non-urban and off-road areas. Many off-road tracks especially for agriculture and environment management tasks are only traversable with motorcycles. In this paper, a deep neural network is used for design and implementation of the vision system for navigation of an autonomous motorcycle. The proposed framework is evaluated using real world scenarios captured by a real motorcycle in various complex situations. The... 

A phase-coherent technique for multiple all-digital phase-locked loops (ADPLLs) is presented and developed in this paper to target a 57-63-GHz multiple-input multiple-output (MIMO) transmitter (TX) with a digital beam-steering capability. The ADPLL TX chains are first fabricated in nanoscale CMOS and then time-synchronized and frequency-phase locked by a field-programmable gate array (FPGA) evaluation board. The calibration approach for phase alignment is carried out using a cancellation method to acquire the out-of-phase state within two ADPLLs. The accuracy of beam steering and phase alignment is investigated and analyzed based on a time-domain model for ADPLL to consider the impact of... 

This paper introduces a novel approach for power system fault diagnosis based on synchronized phasor measurements during the fault. The synchronized measurements are obtained in real time from Phasor Measurement Units (PMUs) and compared with offline thresholds determined by decision trees (DTs) to diagnose the fault. The DTs have already been trained offline using detailed power system analysis for different fault cases. While the traditional methods for fault diagnosis use the status of protective relays (PRs) and circuit breakers (CBs) to infer the fault section in the power system, the proposed method uses the available signals following the fault and thus can be trusted even in case of... 

Cardiovascular disease is one of the most rampant causes of death around the world and was deemed as a major illness in Middle and Old ages. Coronary artery disease, in particular, is a widespread cardiovascular malady entailing high mortality rates. Angiography is, more often than not, regarded as the best method for the diagnosis of coronary artery disease; on the other hand, it is associated with high costs and major side effects. Much research has, therefore, been conducted using machine learning and data mining so as to seek alternative modalities. Accordingly, we herein propose a highly accurate hybrid method for the diagnosis of coronary artery disease. As a matter of fact, the...