International Institute of Information Technology, Hyderabad

About: International Institute of Information Technology, Hyderabad is a education organization based out in Hyderabad, India. It is known for research contribution in the topics: Computer science & Authentication. The organization has 2048 authors who have published 3677 publications receiving 45319 citations. The organization is also known as: IIIT Hyderabad & International Institute of Information Technology (IIIT).

Software quality models: a systematic mapping study

Abstract: Quality Models play a critical role in assuring quality and have evolved over 40+ years. They provide support for defining quality attributes, building and measuring the quality of the resulting product. Each quality model adopts a critical view on quality in terms of a set of model elements and relationships between them. This study aims to provide an overview of the state-of-the-art research on quality models with a focus on encompassing model elements and their support to architecting quality. The study was conducted using systematic mapping as the research methodology. A total of 238 primary papers were classified based on the type of research, standards usage, and publication trends. We identified that 17% (40) of papers belong to quality models. These 40 models were analyzed for the underlying meta-model elements and their support for a quality architecture using Bayer's reference architecture framework. The architecture phase mapping analysis shows that quality planning phase is 100% supported, quality assessment is 75% supported, quality documentation is included in 40% models and quality realization aspect is barely considered in 13% models. Quality realization happens through software processes and patterns, and it is necessary to evolve quality models and software process architectures that correlate quality definitions and quality realization mechanisms. Future research is expected in this direction.

Solving Inverse Computational Imaging Problems Using Deep Pixel-Level Prior

Abstract: Signal reconstruction is a challenging aspect of computational imaging as it often involves solving ill-posed inverse problems. Recently, deep feed-forward neural networks have led to state-of-the-art results in solving various inverse imaging problems. However, being task specific, these networks have to be learned for each inverse problem. On the other hand, a more flexible approach would be to learn a deep generative model once and then use it as a signal prior for solving various inverse problems. In this paper, we show that among the various state-of-the-art deep generative models, autoregressive models are especially suitable for our purpose for the following reasons. First, they explicitly model the pixel level dependencies and hence are capable of reconstructing low-level details, such as texture patterns and edges better. Second, they provide an explicit expression for the image prior, which can then be used for MAP-based inference along with the forward model. Third, they can model long range dependencies in images which make them ideal for handling global multiplexing as encountered in various compressive imaging systems. We demonstrate the efficacy of our proposed approach in solving three computational imaging problems: Single Pixel Camera, LiSens, and FlatCam. For both real and simulated cases, we obtain better reconstructions than the state-of-the-art methods in terms of perceptual and quantitative metrics.

An Output Feedback Based Robust Saturated Controller Design for Pavement Sweeping Self-Reconfigurable Robot

Abstract: Mobile robots play a crucial role in cleaning, maintenance, and surveillance applications. This article advocates for the use of a novel robust output feedback based path following controller, for a class of self-reconfigurable mobile robot under actuator saturation. The reconfigurability property of such platforms is captured via an uncertain Euler–Lagrange dynamics. The proposed control framework estimates the unmeasurable states and the uncertain dynamics terms through two extended high gain observers, whereas the actuator limits are honored via a fast dynamic compensator. The closed-loop stability is analyzed via contraction theory, which, compared to the conventional Lyapunov based approaches, avoids the requirement of arbitrarily large controller and observer gains. Such a feature is of particular interest in view of actuator saturation. The experimental results with PANTHERA self-reconfigurable robot validate the effectiveness of the proposed technique over the state of the art.

Artificial-Delay Adaptive Control for Under-actuated Euler-Lagrange Robotics

Abstract: Artificial-delay control is a method in which state and input measurements collected at an immediate past time instant (i.e. artificially delayed) are used to compensate the uncertain dynamics affecting the system at the current time. This work formulates an artificial-delay control method with adaptive gains in the presence of nonlinear (Euler-Lagrange) under-actuation. The appeal of studying Euler-Lagrange dynamics is to capture many robotics applications of practical interest, as demonstrated via stability and robustness analysis and via robotic ship and robotic aerial vehicle test cases.

Vowel onset point detection for noisy speech using spectral energy at formant frequencies

Abstract: In this paper, we propose a method for robust detection of the vowel onset points (VOPs) from noisy speech. The proposed VOP detection method exploits the spectral energy at formant frequencies of the speech segments present in glottal closure region. In this work, formants are extracted by using group delay function, and glottal closure instants are extracted by using zero frequency filter based method. Performance of the proposed VOP detection method is compared with the existing method, which uses the combination of evidence from excitation source, spectral peaks energy and modulation spectrum. Speech data from TIMIT database and noise samples from NOISEX database are used for analyzing the performance of the VOP detection methods. Significant improvement in the performance of VOP detection is observed by using proposed method compared to existing method.