Abstract

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Abstract

Quality assessment of agricultural produce is a crucial step in minimizing food stock wastage. However, this is currently done manually and often requires expert supervision, especially in smaller seeds like corn. We propose a novel computer vision-based system for automating this process. We build a novel seed image acquisition setup, which captures both the top and bottom views. Dataset collection for this problem has challenges of data annotation costs/time and class imbalance. We address these challenges by i.) using a Conditional Generative Adversarial Network (CGAN) to generate real-looking images for the classes with lesser images and ii.) annotate a large dataset with minimal expert human intervention by using a Batch Active Learning (BAL) based annotation tool. We benchmark different image classification models on the dataset obtained. We are able to get accuracies of up to 91.6% for testing the physical purity of seed samples.

Abstract

Malaria is a deadly disease caused by the plasmodium parasites. Approximately 210 million people get affected by malaria every year resulting in half a million deaths. Among several species of the parasite, Plasmodium falciparum is the primary cause of severe infection and death. Several drugs are available for malaria treatment in the market but plasmodium parasites have successfully developed resistance against many drugs over the years. This poses a serious threat to efficacy of the treatments and continuing discovery of new drug is necessary to tackle the situation, especially due to failure in designing an effective vaccine. People are now trying to design new drugs for malaria using AI technologies which can substantially reduce the time and cost required in classical drug discovery programs. In this chapter, we provide a comprehensive overview of a road map for several AI based computational techniques which can be implemented in a malaria drugs discovery program. Classical computers has limiting computing power. So, researchers are also trying to harness quantum machine learning to speed up the drug discovery processes.

Abstract

The identification of plant species by looking at their leaves, flowers, and seeds is a crucial component in the conservation of endangered plants. Traditional identification methods are manual and time consuming and require domain knowledge to operate. Owing to an increased interest in the automated plant identification system, we propose one that utilizes modern convolutional neural network architectures. This approach helps in the recognition of leaf images and can be integrated into mobile platforms like smartphones. Such a system can also be employed in aiding plant-related education, promoting ecotourism, and creating a digital heritage for plant species, among many others. Our proposed solution achieves a state-of-the-art performance for plant classification in the wild. An exhaustive set of experiments are performed to classify 112 species of plants from the challenging Indic-Leaf dataset. The best-performing model gives Top 1 precision of 90.08 and Top 5 precision of 96.90. We discuss and elaborate on our crowdsourcing web application that is used to collect and regulate data. We explain how the automated plant identification system can be integrated into a smartphone by detailing the flow of our mobile application.

Abstract

This paper describes a new method for speech activity detection (SAD) based on the recently proposed single frequency filtering (SFF) analysis of speech signals and a neural network model. The SFF analysis gives instantaneous spectrum of the speech signal at each sampling instant. The frequency resolution of the spectrum is decided by the number of frequencies used in the SFF analysis, which in turn depends on the frequency spacing. Using a frequency spacing of 10 Hz and a sampling frequency of 8 kHz, a 401 dimensional spectrum, covering 0–4 kHz, is obtained at each sampling instant. This is used as a feature vector to train an artificial neural network (ANN) model to discriminate (noisy) speech and nonspeech (mostly noise). The output of the trained ANN model for a given test utterance gives speech/nonspeech decision at every sampling instant. Post processing of the decision is used for SAD. The system generated SAD is evaluated on the Apollo corpus for SAD task in terms of detection cost function (DCF). The DCF values of the proposed system on the development and evaluation datasets are 3.1% and 4.6%, respectively, whereas the DCF values of the reported baseline system are 8.6% and 11.7%, respectively.

Abstract

Sparse arrays are special geometrical arrangements of sensors which overcome some of the drawbacks associated with dense uniform arrays and require fewer sensors. For direction finding applications, sparse arrays with the same number of sensors can resolve more sources while providing higher resolution than a dense uniform array. This has been verified numerically and with real data for one-dimensional microphone arrays. In this study the use of nested and co-prime arrays is examined with sparse Bayesian learning (SBL), which is a compressive sensing algorithm, for estimating sparse vectors and support. SBL is an iterative parameter estimation method and can process multiple snapshots as well as multiple frequency data within its Bayesian framework. A multi-frequency variant of SBL is proposed, which accounts for non-flat frequency spectra of the sources. Experimental validation of azimuth and elevation [two-dimensional (2D)] direction-of-arrival (DOA)estimation are provided using sparse arrays and real data acquired in an anechoic chamber with a rectangular array. Both co-prime and nested arrays are obtained by sampling this rectangular array. The SBL method is compared with conventional beamforming and multiple signal classification for 2D DOA estimation of experimental data.

Abstract

In a developing country like India, agriculture provides large scale employment in rural areas, thus serving as the backbone of an economic system. For farmers it is important to decide which crop to grow, what Government schemes benefit them the most and what is the best selling price of the crop. In this paper we described the Speech to Speech interaction between farmer and government through an application using speech recognition system for Telugu language that will form the interface to the webpage providing information about the government schemes and commodity prices through voice. The proposed Kisan Information System (KIS) is integration of Speech recognition, Dialogue Manager, and Speech Synthesis modules. The performance of the KIS is better compared with existing one for Telugu language.

Abstract

We derive some formulas for the Riemann zeta value  ( k ) in terms of nested sums of depth k and depth k

Abstract

It is critical for aerial robots flying in city scale urban environments to make very quick estimates of a building depth with respect to itself. It should be done in a matter of few views to navigate itself, avoiding collisions with such a towering structure. As such, no one has attacked this problem. We bring together several modules combining deep learning and 3D vision to showcase a quick reconstruction in a few views. We exploit the inherent planar structure in the buildings (facades, windows) for this purpose. We evaluate the efficacy of our pipeline with various constraints and errors from multi-view geometry using ablation studies. We then retrieve the skyline of the buildings in synthetic as well as real-world scenes.

Abstract

Wharfs and jetties are the key structures in port transportation systems that support export and import activities. Wharf structures during a seismic event are susceptible to severe damage and may have an adverse effect on port operations. Historical earthquakes such as 1995 Kobe earthquake (Mw = 6.9; Japan) Chang [1], 1999 Chi-Chi earthquake (Mw = 7.3; Taiwan), 2004 the Great Indian Ocean earthquake (Mw = 9.1; Sumatra) and Haiti earthquake (2010) DesRoches [2] damaged major port structures, such as caissons, quays and pile-supported wharfs [3]. These ports suffered severe economic losses due to the collapse of wharfs leading to downtime in port operations. This attracted the attention of researchers towards seismic analysis and design of port structures, especially pile-supported wharfs. In the present study, a wharf structure from Vishakhapatnam port (Andhra Pradesh, India) has been chosen for pushover analysis. For the structural model of the pile-supported wharf considered, shell elements were used for modelling the deck. Winkler model has been used for representation of the pile–soil system, in which the piles were represented by beam elements and soils were represented by springs. Pushover analysis has been carried out with distribution of lateral loads according to the fundamental modal shape of the wharf structure to derive the capacity curve of the wharf structure. Capacity spectrum method which is a nonlinear static procedure has been used to efficiently construct a response matrix of the wharf. Nonlinear static pushover analysis has been performed, and it has been observed that the hinge sequence obtained for the wharf in transverse direction in the present study matches with the