Model Predictive Control for Autonomous Driving Based on Time Scaled Collision Cone

In this paper, we present a Model Predictive Control (MPC) framework based on path velocity decomposition paradigm for autonomous driving. The optimization underlying the MPC has a two layer structure wherein first, an appropriate path is computed for the vehicle followed by the computation of optimal forward velocity along it. The very nature of the proposed path velocity decomposition allows for seamless compatibility between the two layers of the optimization. A key feature of the proposed work is that it offloads most of the responsibility of collision avoidance to velocity optimization layer for which computationally efficient formulations can be derived. In particular, we extend our previously developed concept of time scaled collision cone (TSCC) constraints and formulate the forward velocity optimization layer as a convex quadratic programming problem. We perform validation on autonomous driving scenarios wherein proposed MPC repeatedly solves both the optimization layers in receding horizon manner to compute lane change, overtaking and merging maneuvers among multiple dynamic obstacles.Comment: 6 page

Large Rashba splittings in bulk and monolayer of BiAs

Two-dimensional materials with Rashba split bands near the Fermi level are key to developing upcoming next-generation spintronics. They enable generating, detecting, and manipulating spin currents without an external magnetic field. Here, we propose BiAs as a novel layered semiconductor with large Rashba splitting in bulk and monolayer forms. Using first-principles calculations, we determined the lowest energy structure of BiAs and its basic electronic properties. Bulk BiAs has a layered crystal structure with two atoms in a rhombohedral primitive cell, similar to the parent Bi and As elemental phases. It is a semiconductor with a narrow and indirect band gap. The spin-orbit coupling leads to Rashba-Dresselhaus spin splitting and characteristic spin texture around the L-point in the Brillouin zone of the hexagonal conventional unit cell, with Rashba energy and Rashba coupling constant for valence (conduction) band of $E_R$= 137 meV (93 meV) and $\alpha_R$= 6.05 eV\AA~(4.6 eV{\AA}). In monolayer form (i.e., composed of a BiAs bilayer), BiAs has a much larger and direct band gap at $\Gamma$, with a circular spin texture characteristic of a pure Rashba effect. The Rashba energy $E_R$= 18 meV and Rashba coupling constant $\alpha_R$= 1.67 eV{\AA} of monolayer BiAs are quite large compared to other known 2D materials, and these values are shown to increase under tensile biaxial strain.Comment: 15pages,9figure

Blind image quality evaluation using perception based features

This paper proposes a novel no-reference Perception-based Image Quality Evaluator (PIQUE) for real-world imagery. A majority of the existing methods for blind image quality assessment rely on opinion-based supervised learning for quality score prediction. Unlike these methods, we propose an opinion unaware methodology that attempts to quantify distortion without the need for any training data. Our method relies on extracting local features for predicting quality. Additionally, to mimic human behavior, we estimate quality only from perceptually significant spatial regions. Further, the choice of our features enables us to generate a fine-grained block level distortion map. Our algorithm is competitive with the state-of-the-art based on evaluation over several popular datasets including LIVE IQA, TID & CSIQ. Finally, our algorithm has low computational complexity despite working at the block-level

Predicting band gaps and band-edge positions of oxide perovskites using DFT and machine learning

Density functional theory within the local or semilocal density approximations (DFT-LDA/GGA) has become a workhorse in electronic structure theory of solids, being extremely fast and reliable for energetics and structural properties, yet remaining highly inaccurate for predicting band gaps of semiconductors and insulators. Accurate prediction of band gaps using firstprinciples methods is time consuming, requiring hybrid functionals, quasi-particle GW, or quantum Monte Carlo methods. Efficiently correcting DFT-LDA/GGA band gaps and unveiling the main chemical and structural factors involved in this correction is desirable for discovering novel materials in high-throughput calculations. In this direction, we use DFT and machine learning techniques to correct band gaps and band-edge positions of a representative subset of ABO3 perovskite oxides. Relying on results of HSE06 hybrid functional calculations as target values of band gaps, we find a systematic band gap correction of ~1.5 eV for this class of materials, where ~1 eV comes from downward shifting the valence band and ~0.5 eV from uplifting the conduction band. The main chemical and structural factors determining the band gap correction are determined through a feature selection procedure

The deep-acceptor nature of the chalcogen vacancies in 2D transition-metal dichalcogenides

Chalcogen vacancies in the semiconducting monolayer transition-metal dichalcogenides (TMDs) have frequently been invoked to explain a wide range of phenomena, including both unintentional p-type and n-type conductivity, as well as sub-band gap defect levels measured via tunneling or optical spectroscopy. These conflicting interpretations of the deep versus shallow nature of the chalcogen vacancies are due in part to shortcomings in prior first-principles calculations of defects in the semiconducting two-dimensional (2D) TMDs that have been used to explain experimental observations. Here we report results of hybrid density functional calculations for the chalcogen vacancy in a series of monolayer TMDs, correctly referencing the thermodynamic charge transition levels to the fundamental band gap (as opposed to the optical band gap). We find that the chalcogen vacancies are deep acceptors and cannot lead to n-type or p-type conductivity. Both the (0/$-1$) and ($-$1/$-$2) transition levels occur in the gap, leading to paramagnetic charge states S=1/2 and S=1, respectively, in a collinear-spin representation. We discuss trends in terms of the band alignments between the TMDs, which can serve as a guide to future experimental studies of vacancy behavior

AUTOMATED SYSTEM AND METHOD OF RETAINING IMAGES BASED ON A USER'S FEEDBACK ON IMAGE QUALITY

An automated system and method for retaining images in a smart phone are disclosed . The system may then determine a no - reference quality score of the image using a PIQUE module . The PIQUE module utilizes block level features of the image to determine the no - reference quality score . The system may present the image and the no - reference quality score to the user and accept a feedback towards quality of the image .The system may utilize a supervised learning model for continually learning a user ' s perception of quality of the image , the no -reference quality score determined by the PIQUE module , and the user feedback . Based on the learning , the supervised learning model may adapt the no - reference quality score and successively the image may either be retained or isolated for deletion , based on the adapted quality score and a predefined threshold rang

Building an interpretable fuzzy rule base from data using Orthogonal Least Squares Application to a depollution problem

In many fields where human understanding plays a crucial role, such as bioprocesses, the capacity of extracting knowledge from data is of critical importance. Within this framework, fuzzy learning methods, if properly used, can greatly help human experts. Amongst these methods, the aim of orthogonal transformations, which have been proven to be mathematically robust, is to build rules from a set of training data and to select the most important ones by linear regression or rank revealing techniques. The OLS algorithm is a good representative of those methods. However, it was originally designed so that it only cared about numerical performance. Thus, we propose some modifications of the original method to take interpretability into account. After recalling the original algorithm, this paper presents the changes made to the original method, then discusses some results obtained from benchmark problems. Finally, the algorithm is applied to a real-world fault detection depollution problem.Comment: pre-print of final version published in Fuzzy Sets and System

A fuzzy feature fusion method for auto-segmentation of gliomas with multi-modality diffusion and perfusion magnetic resonance images in radiotherapy

The difusion and perfusion magnetic resonance (MR) images can provide functional information about tumour and enable more sensitive detection of the tumour extent. We aimed to develop a fuzzy feature fusion method for auto-segmentation of gliomas in radiotherapy planning using multi-parametric functional MR images including apparent difusion coefcient (ADC), fractional anisotropy (FA) and relative cerebral blood volume (rCBV). For each functional modality, one histogram-based fuzzy model was created to transform image volume into a fuzzy feature space. Based on the fuzzy fusion result of the three fuzzy feature spaces, regions with high possibility belonging to tumour were generated automatically. The auto-segmentations of tumour in structural MR images were added in fnal autosegmented gross tumour volume (GTV). For evaluation, one radiation oncologist delineated GTVs for nine patients with all modalities. Comparisons between manually delineated and auto-segmented GTVs showed that, the mean volume diference was 8.69% (±5.62%); the mean Dice’s similarity coefcient (DSC) was 0.88 (±0.02); the mean sensitivity and specifcity of auto-segmentation was 0.87 (±0.04) and 0.98 (±0.01) respectively. High accuracy and efciency can be achieved with the new method, which shows potential of utilizing functional multi-parametric MR images for target defnition in precision radiation treatment planning for patients with gliomas