Abstract

Diabetic retinopathy (DR) is one of the most common reasons for blindness in the working-age population of world. Diabetic Retinopathy is an eye disease, which occurs with long-standing untreated diabetes. Progression to vision impairment can be slowed down or stopped if DR is detected on time; In detection or screening of DR, automatic methods can play an important role. In this paper, we proposed a novel method to detect hard exudates with high accuracy with respect to lesion level. In the present method we initially detected the possible candidate exudate lesions by using the back ground subtraction methodology. Following the subsequent steps, in the last stage of algorithm we removed the false exudate lesion detections using the de-correlation stretch based method. We tested our algorithm on publicly available DiaretDB database, which contains the ground truth for all images. We achieved high performance results such as sensitivity of 0.87 and F-Score of 0.78 and Positive Predict Value (PPV) of 0.76 for hard exudate lesion level detection, compared to the existing state of art techniques.

Abstract

Forest fires are the most frequent phenomenon during the summer season in India, and especially in the hilly terrains of Uttarakhand forests. Remote sensing sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Visible Infrared Imaging Radiometer Suite (VIIRS) with coarse spatial resolution on board different satellites were used to detect the forest fires across the world. Landsat-8 Operational Land Imager (OLI) data has the better spatial resolution (30m) as compared with the MODIS and VIIRS, therefore useful to detect the smaller fires. Nainital district in Uttarakhand state was severely affected by the massive forest fire events occurred during April-May, 2016. The main objective of the study is to identify the potential of Landsat-8 data in detecting the forest fire for varying topographic region like Nainital. Landsat-8 data acquired on 28 th April 2016 and 1 st May 2016 has been used in this study. The results obtained from Landsat-8 data are compared with the MODIS fire products and showed an improvement in the detection of small fires.

Abstract

We propose flexible-architecture for soft-input soft-output (SISO) decoder with radix-2/4/8 modes to support multiple data-rates. This work presents designs of major internal blocks of SISO decoder using extensive steering logic to support multiple radix operating-modes. These architectures enable efficient clock-gating of our decoder for low-power consumption in different operating modes. Subsequently, we have aggregated eight SISO decoders with quadratic-permutation-polynomial (QPP) interleavers/de-interleavers to design parallel-turbo decoder architecture which can operate in multi-radix mode. Suggested SISO decoder is ASIC synthesized and post-layout simulated in UMC 65 nm-CMOS process. Performance analyses in AWGN channel environment showed that the bit-error-rate (BER) of 10 -4 could be achieved at 5 dB and 0.8 dB for SISO and turbo decoders respectively. Implementation result shows that the suggested SISO decoder could achieve throughput in the range 270-810 Mbps with the corresponding power consumption range of 12.24-37.67 mW. In comparison to the state-of-the-art, our design achieved 38% higher throughput and 61% lower power consumption. Similarly, our multi-radix parallel-turbo decoder is hardware prototyped in 28 nm-CMOS Zynq-FPGA board. It delivers a range of data-rates from 80-320 Mbps operating at 160 MHz of clock frequency for 8 iterations.

Abstract

Emotion evoked by an advertisement plays a key role in influencing brand recall and eventual consumer choices. Automatic ad affect recognition has several useful applications. However, the use of content-based feature representations does not give insights into how affect is modulated by aspects such as the ad scene setting, salient object attributes and their interactions. Neither do such ap- proaches inform us on how humans prioritize visual information for ad understanding. Our work addresses these lacunae by decom- posing video content into detected objects, coarse scene structure, object statistics and actively attended objects identified via eye- gaze. We measure the importance of each of these information channels by systematically incorporating related information into ad affect prediction models. Contrary to the popular notion that ad affect hinges on the narrative and the clever use of linguistic and social cues, we find that actively attended objects and the coarse scene structure better encode affective information as compared to individual scene objects or conspicuous background elements.

Abstract

Ontology engineering aims to specify meaning to computer as accurately as possible since that would enable any kind of application or any amount of analysis required for a given domain. But for that, one needs to first understand the distinction between form and content in reality, for which, understanding the distinction between logical form and onto-logical (ontological) form becomes crucial. In this work, we propose an ontological form - punctuator - in contrast with logical form like proposition, and contend that formal ontology, in its proper sense, is possible only through the ontological form and not the logical form since the former is apt to do the logic of entities whereas the latter is apt to do the logic of sentences about entities. Once we establish this point, we need to think how to specify meaning as descriptively/objectively as possible. One of the best ways to do that is to minimize the amount of human intervention necessary for building the ontology since the objectivity of an ontology (or anything) is inversely proportional to the amount of human intervention in it. For this, we propose a novel concept called ‘Generative Ontology’ where the States-ofAffairs (SOAs) are modeled as graphs, and specify rules to generate potentially infinite number of complex graphs from a finite number of simple graphs. For this, we use the ontological form mentioned above, and we take Vaies. ika ontology, one of the philosophical schools in Indian tradition which focuses on foundational ontology, as a case-inpoint, and specify generation, interpretation as well as parsing rules for its graphs (SOAs) according to the Vaieika worldview and establish the formalness of such a system. Though this work is proposing yet another foundational ontology, its uniqueness lies in its generative nature and the ontological form which it uses to do so.

Abstract

Bug report filing is a major part of software maintenance. Due to the asynchronous nature of the bug filing process, duplicate bug reports are filed. Detecting duplicate bug reports is an important aspect of software maintenance since the same bug should not be assigned to different developers. In this poster, we present Deep Word Embedding Network for computing similarity between two bug reports for the task of duplicate bug report detection. We propose to learn a two step model to calculate similarity between two bug reports by means of word embeddings and a deep neural network. We run experiments on two large datasets of Mozilla Project and Open Office Project and compare the proposed approach with baselines and related approaches. Through this initial work, we show that a combination of word embeddings and deep neural networks can be used to improve duplicate bug report detection.

Abstract

With improved digital literacy and cheaper data connections, crowdsourcing has become more prevalent. Crowdsourcing tasks such as image tagging, language translation, etc. have contributors with varied skill level. Question and answers, software development/testing and product design are more serious tasks and require expert contributions. With increasing security threats, crowdsourcing in 'security' domain is seen as an upcoming trend. In the survey, opportunities and challenges in crowdsourcing security are analyzed based on a survey conducted with 229 participants. The results provide insights on motivation, quality mechanisms and improvement areas for increased participation. 'Question and Answers' that includes information sharing and 'software development and testing' that includes Bug Bounty are identified as top crowdsourcing tasks by security specialists although they are concerned about security and privacy of information. Among quality attributes analyzed, contributor 'credibility' is identified as a key area of improvement for increased participation in crowdsourcing.

Abstract

Permalloy is commonly used to boost the overall permeability of on-chip inductors for radio frequencies. To capacitate the use of Permalloy at high frequencies, its high-frequency behaviour is studied by exploring the role of domain dynamics in RF applications. In this work, Aluminium lines covered with discrete Permalloy films are studied through simulation and fabrication. The effect of domain dynamics due to various domain patterns, applied RF power and DC bias level on high-frequency inductance is thoroughly analysed.

Abstract

Several designer controllable parameters and their effect on various specifications like switching time, sampling speed and input amplitude of all-optical Binary Delta Sigma Modulator (BDSM) are analyzed and possibility of enhancing the performance is discussed.

Abstract

In this paper we discuss the VLSI implementation of the new radix-2 Decimation In Time (DIT) Fast Fourier Transform (FFT) algorithm with reduced arithmetic complexity which is based on scaling the twiddle factor. Some signal processing require high performance FFT processors and to meet these performance requirements, the processor needs to be pipelined and parallelized. An optimized ASIC design is derived from this new radix-2 algorithm with fewer multipliers and adopted a complete parallel and pipelined architecture for hardware implementation of a 64 point FFT. The implementation results show that the proposed architecture significantly reduces the hardware area by 13.74 percent and power consumption by 16 percent when compared to the standard FFT architecture. Simulation of design units is done in Xilinx ISE WebPack 13.1 and synthesized using Cadence Encounter RTL Compiler.