Novel CBIR System Based on Ripplet Transform Using Interactive Neuro-Fuzzy Technique

Content Based Image Retrieval (CBIR) system is an emerging research area in effective digital data management and retrieval paradigm. In this article, a novel CBIR system based on a new Multiscale Geometric Analysis (MGA)-tool, called Ripplet Transform Type-I (RT) is presented. To improve the retrieval result and to reduce the computational complexity, the proposed scheme utilizes a Neural Network (NN) based classifier for image pre-classification, similarity matching using Manhattan distance measure and relevance feedback mechanism (RFM) using fuzzy entropy based feature evaluation technique. Extensive experiments were carried out to evaluate the effectiveness of the proposed technique. The performance of the proposed CBIR system is evaluated using a 2 £ 5-fold cross validation followed by a statistical analysis. The experimental results suggest that the proposed system based on RT, performs better than many existing CBIR schemes based on other transforms, and the difference is statistically significant

Simulation Study of an Autonomous Ground Vehicle Model

The focus of the paper is to understand what an Autonomous Vehicle (AV) is, and realising a simulation model of intended vehicle for coarse testing of autonomous guidance navigation and control (AGNC) algorithm. MATLAB and SIMULINK are used as platform for development of this model. The model is developed to calculate the next position and direction of the vehicle based on the steering angle as commanded by the AGNC algorithm. This would lead towards the design of an scaled down model of AV using a modified radio control car chassis. The AV would then be equipped with a GPS and ultrasonic or infrared sensors to navigate it to a predetermined geographical location with obstacle avoidance. Keywords: Autonomous, Control, GPS, Model, Simulink, Vehicl

Exploring the Electromagnetically Interacting Dark Matter at the International Linear Collider

Dark Matter being electrically neutral does not participate in electromagnetic interactions at leading order. However, we discuss here fermionic dark matter (DM) with permanent magnetic and electric dipole moment that interacts electromagnetically with photon at loop-level through a dimension-5 operator. We discuss the search prospect of the dark matter at the proposed International Linear Collider (ILC) and constrain the parameter space in the plane of the DM mass and the cutoff scale $\Lambda$. At the 500 GeV ILC with $4$ ab$^{-1}$ of integrated luminosity we probed the mono-photon channel and utilizing the advantages of beam polarization we obtained an upper bound on the cutoff scale that reaches up to $\Lambda = 3.72$ TeV.Comment: 8 pages, 5 figures, 3 table

Quantum yield of Cl<SUP>&#8727;</SUP> (<SUP>2</SUP>P<SUB>&#189;</SUB>) production in the gas phase photolysis of CCl<SUB>4</SUB> in the ultraviolet

In this paper, we have probed the dynamics of chlorine atom production from the gas phase photodissociation of carbon tetrachloride at 222 and 235 nm. The quantum yield, &#966;&#8727; of Cl&#8727; (2P&#189;) production has been determined by probing the nascent concentrations of both excited (2P&#189;) and ground state (2P3/2) chlorine atoms by suitable resonance-enhanced multiphoton ionization (REMPI) detection schemes. Although at the photolysis wavelengths the absorption of carbon tetrachloride is weak, significant amounts of Cl&#8727; are produced. Surprisingly, the quantum yield of Cl&#8727; production does not follow the absorption spectrum closely, which gives rise to the possibility of an indirect dissociation mechanism present in CCl4 along with direct dissociation at these ultraviolet wavelengths

Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question Answering

We study visual question answering in a setting where the answer has to be mined from a pool of relevant and irrelevant images given as a context. For such a setting, a model must first retrieve relevant images from the pool and answer the question from these retrieved images. We refer to this problem as retrieval-based visual question answering (or RETVQA in short). The RETVQA is distinctively different and more challenging than the traditionally-studied Visual Question Answering (VQA), where a given question has to be answered with a single relevant image in context. Towards solving the RETVQA task, we propose a unified Multi Image BART (MI-BART) that takes a question and retrieved images using our relevance encoder for free-form fluent answer generation. Further, we introduce the largest dataset in this space, namely RETVQA, which has the following salient features: multi-image and retrieval requirement for VQA, metadata-independent questions over a pool of heterogeneous images, expecting a mix of classification-oriented and open-ended generative answers. Our proposed framework achieves an accuracy of 76.5% and a fluency of 79.3% on the proposed dataset, namely RETVQA and also outperforms state-of-the-art methods by 4.9% and 11.8% on the image segment of the publicly available WebQA dataset on the accuracy and fluency metrics, respectively.Comment: Accepted to IJCAI 202