Visual Speech Recognition using Histogram of Oriented Displacements

Lip reading is the recognition of spoken words from the visual information of lips. It has been of considerable interest in the Computer Vision and Speech Recognition communities to automate this process using computer algorithms. In this thesis, we have developed a novel method involving describing visual features using fixed length descriptors called Histogram of Oriented Displacements to which we apply Support Vector Machines for recognition of spoken words. Using this method on the CUAVE database we have achieved a recognition rate of 81%

Small Bowel Perforation in Acute Abdomen - Redefining Its Cause

BACKGROUND: Small bowel perforation is a common problem seen in acute abdomen. The commonest cause being typhoid fever followed by tuberculosis and other cause. AIMS & OBJECTIVES: To analyse and compare the cause of small bowel perforation in acute abdomen. METHODS: The study was conducted in Institute of General Surgery, Madras Medical College, Chennai from MAY 2017 TO OCTOBER 2018. A minimum of 103 patients of Small bowel perforations included in the study. Patients with traumatic perforations, Large bowel perforation duodenal perforation (D1 AND D2) and irradiated abdomen have been excluded. Factors were tabulated and statistically analysed to study their contributions. RESULTS: The study concludes the commonest cause of Small bowel perforation was typhoid followed by non specific causes. Perforation commonly occurred in the third and fourth decade of life with patients between the ages of 30 and 50. Pneumoperitoneum in chest x-ray and erect abdominal x-ray was seen in 80% of patients. CONCLUSION: Typhoid is the most common cause of small perforation, followed by non-specific perforation. Other Causes of small bowel perforation include non-specific, TB, Widal test is helpfull in the diagnosis of typhoid fever

A theory of (almost) zero resource speech recognition

Automatic speech recognition has matured into a commercially successful technology, enabling voice-based interfaces for smartphones, smart TVs, and many other consumer devices. The overwhelming popularity, however, is still limited to languages such as English, Japanese, and German, where vast amounts of labeled training data are available. For most other languages, it is prohibitively expensive to 1) collect and transcribe the speech data required to learn good acoustic models; and 2) acquire adequate text to estimate meaningful language models. A theory of unsupervised and semi-supervised techniques for speech recognition is therefore essential. This thesis focuses on HMM-based sequence clustering and examines acoustic modeling, language modeling, and applications beyond the components of an ASR, such as anomaly detection, from the vantage point of PAC-Bayesian theory. The first part of this thesis extends standard PAC-Bayesian bounds to address the sequential nature of speech and language signals. A novel algorithm, based on sparsifying the cluster assignment probabilities with a Renyi entropy prior, is shown to provably minimize the generalization error of any probabilistic model (e.g. HMMs). The second part examines application-specific loss functions such as cluster purity and perplexity. Empirical results on a variety of tasks -- acoustic event detection, class-based language modeling, and unsupervised sequence anomaly detection -- confirm the practicality of the theory and algorithms developed in this thesis

An Efficient Model for Forest Fire Detection using Deep Convolutional Neural Networks

Forest fires are a significant natural disaster that causes extensive damage to both human and wildlife habitats. Early detection and management of forest fires are critical in preventing potential losses. In recent years, deep learning-based approaches have emerged as promising solutions for forest fire detection. This paper proposes a deep learning-based approach for forest fire detection using SqueezeNet model.The proposed approach utilizes still images captured from forest areas under different weather conditions to classify whether an image contains a fire or not. The models were trained and tested using accuracy, precision, and recall metrics. The experimental results show that SqueezeNet achieve high precision, and recall in detecting forest fires.SqueezeNet is a Convolutional Neural Networks (CNN) architecture designed to reduce the number of parameters and computations required in a deep learning model while maintaining high accuracy in image classification tasks.

Recent Advances in the Molecular Effects of Biostimulants in Plants: An Overview

As the world develops and population increases, so too does the demand for higher agricultural output with lower resources. Plant biostimulants appear to be one of the more prominent sustainable solutions, given their natural origin and their potential to substitute conventional methods in agriculture. Classified based on their source rather than constitution, biostimulants such as humic substances (HS), protein hydrolysates (PHs), seaweed extracts (SWE) and microorganisms have a proven potential in improving plant growth, increasing crop production and quality, as well as ameliorating stress effects. However, the multi-molecular nature and varying composition of commercially available biostimulants presents challenges when attempting to elucidate their underlying mechanisms. While most research has focused on the broad effects of biostimulants in crops, recent studies at the molecular level have started to unravel the pathways triggered by certain products at the cellular and gene level. Understanding the molecular influences involved could lead to further refinement of these treatments. This review comprises the most recent findings regarding the use of biostimulants in plants, with particular focus on reports of their molecular influence

Power Loss Modelling of GaN HEMT based 3L ANPC Three Phase Inverter for different PWM Techniques

The paper presents a straightforward modelling approach to compute the power loss distribution in GaN HEMT based three phase and three level (3L) active neutral point clamped (ANPC) inverters, for different pulse width modulated techniques. Conduction and switching losses averaged over each PWM switching period are analytically computed by starting from the operating conditions of the AC load and data of GaN power devices. The accuracy of the proposed analytical approach is evaluated through a circuit based power electronics simulation tool, applied to different carrier-based PWM strategies.Comment: 10 pages, 13 figures, 24th European Conference on Power Electronics and Applications ( IEEE EPE 2022 ECCE Europe). This work has been carried out in the framework of the ECSEL-JU Project GaN4AP (Gallium Nitride for Advanced Power Applications) - Grant Agreement No.10100731

Building-Blocks for Performance Oriented DSLs

Domain-specific languages raise the level of abstraction in software development. While it is evident that programmers can more easily reason about very high-level programs, the same holds for compilers only if the compiler has an accurate model of the application domain and the underlying target platform. Since mapping high-level, general-purpose languages to modern, heterogeneous hardware is becoming increasingly difficult, DSLs are an attractive way to capitalize on improved hardware performance, precisely by making the compiler reason on a higher level. Implementing efficient DSL compilers is a daunting task however, and support for building performance-oriented DSLs is urgently needed. To this end, we present the Delite Framework, an extensible toolkit that drastically simplifies building embedded DSLs and compiling DSL programs for execution on heterogeneous hardware. We discuss several building blocks in some detail and present experimental results for the OptiML machine-learning DSL implemented on top of Delite.Comment: In Proceedings DSL 2011, arXiv:1109.032

Coronary artery fistula: A case series with review of the literature

SummaryCoronary artery fistula (CAF) is an anomalous connection between a coronary artery and a major vessel or cardiac chamber. Most of the coronary fistulas are discovered incidentally during angiographic evaluation for coronary vascular disorder. The management of CAF is complicated and recommendations are based on anecdotal cases or very small retrospective series. We present three cases of CAF, two of which were symptomatic due to hemodynamically significant coronary steal phenomenon. They underwent successful transcatheter coil embolization, leading to resolution of their symptoms. Percutaneous closure offers a safe and effective way for the management of symptomatic patients. CAFs are rare cardiac anomalies but can give rise to a variety of symptoms because of their hemodynamic consequences or complications. They should be part of cardiac differential diagnosis particularly in patients without other risk factors. Correction of CAF is indicated if the patients are symptomatic or if other secondary complications develop

Molecular priming as an approach to induce tolerance against abiotic and oxidative stresses in crop plants

Abiotic stresses, including drought, salinity, extreme temperature, and pollutants, are the main cause of crop losses worldwide. Novel climate-adapted crops and stress tolerance-enhancing compounds are needed increasingly to counteract the negative effects of unfavorable stressful environments. A number of natural products and synthetic chemicals can protect model and crop plants against abiotic stresses through the ectopic induction of molecular and physiological defense mechanisms, a process known as molecular priming. In addition to their stress-protective effect, some of these compounds can also stimulate plant growth. Here, we provide an overview of the known physiological and molecular mechanisms behind the compounds that induce molecular priming, together with a survey of approaches to discover and functionally study new stress-alleviating chemicals