Sparse Signal Models for Data Augmentation in Deep Learning ATR

Automatic Target Recognition (ATR) algorithms classify a given Synthetic Aperture Radar (SAR) image into one of the known target classes using a set of training images available for each class. Recently, learning methods have shown to achieve state-of-the-art classification accuracy if abundant training data is available, sampled uniformly over the classes, and their poses. In this paper, we consider the task of ATR with a limited set of training images. We propose a data augmentation approach to incorporate domain knowledge and improve the generalization power of a data-intensive learning algorithm, such as a Convolutional neural network (CNN). The proposed data augmentation method employs a limited persistence sparse modeling approach, capitalizing on commonly observed characteristics of wide-angle synthetic aperture radar (SAR) imagery. Specifically, we exploit the sparsity of the scattering centers in the spatial domain and the smoothly-varying structure of the scattering coefficients in the azimuthal domain to solve the ill-posed problem of over-parametrized model fitting. Using this estimated model, we synthesize new images at poses and sub-pixel translations not available in the given data to augment CNN's training data. The experimental results show that for the training data starved region, the proposed method provides a significant gain in the resulting ATR algorithm's generalization performance.Comment: 12 pages, 5 figures, to be submitted to IEEE Transactions on Geoscience and Remote Sensin

In-situ real time measurements of thermal comfort and comparison with the adaptive comfort theory in Dutch residential dwellings

Indoor thermal comfort is generally assessed using the PMV or the adaptive model. This research presents the results obtained by in-situ real time measurements of thermal comfort and thermal comfort perception in 17 residential dwellings in the Netherlands. The study demonstrates the new possibilities offered by relatively cheap, sensor-rich environments to collect data on clothing, heating, and activities related to thermal comfort, which can be used to improve and validate existing comfort models. The results are analyzed against the adaptive comfort model and its underlying assumptions. Data analysis showed that while indoor temperatures are within the adaptive model’s comfort bandwidth, occupants often reported comfort sensations other than neutral. Furthermore, when indoor temperatures were below the comfort bandwidth, tenants also often reported that they felt ‘neutral’. The adaptive model could overestimate as well as underestimate the occupant’s adaptive capacity towards thermal comfort. Despite the significant outdoors temperature variation, the indoor temperature of the dwellings and the clothing were observed to remain largely constant. Certain actions towards thermal comfort such as ‘turning the thermostat up’ were taking place while tenants were reporting thermal sensation ‘neutral’ or ‘a bit warm’. This indicates that either there is an indiscrimination among the various thermal sensation levels or alliesthesia plays a role and the neutral sensation is not comfortable, or many actions are happening out of habit and not in order to improve one’s thermal comfort. A chi2 analysis showed that only six actions were correlated to thermal sensation in thermally poorly efficient dwellings, and six in thermally efficient dwellings

Lean Implementation on Indian manufacturing firm

In today’s market every manufacturing industry is trying to implement ‘Lean’ in its operations. This emergent need of reducing waste and getting efficient production had created a boom for the Lean Production (LP). Many people from corporate firms and management associates want a productive tool for achieving this task. Value Stream Mapping (VSM) is the solution to such emerging need, it identifies the source of waste practices and tries to scale down them analytically. VSM in this way measures all the value and non-value added processes, to evaluate the origin of wastes, their effect on different operation of industry and the processes in between. In this paper we have implemented VSM technique on a small scale industry to showcase its effect on cost of production, production lead time and the developing some procedure for reduction of root cause of this loss. Considering the current processes of the industries operations Current State map is developed to show how actual production is taking place at the industry before implementing any lean procedure. A Future State Map is finally developed considering the lean behaviours to reduce the waste production and to increase its productivity. This is inspected along with its takt time calculation. Thus, by curbing these wasteful practices we will show how manufacturing performance of the company can be upgraded by employment of VSM

Modified gift box technique for acute compound posttraumatic Achilles tendon repair in young patients

Background: Treatment of Achilles tendon rupture in young active patient remains controversial. Open primary repair remains the mainstay of treatment with prolonged rehabilitation and high wound complication rate (20%). In compound injuries it becomes the default treatment. Newer techniques are being tried to decrease re-rupture rate, decrease local complications and facilitate early rehabilitation. Modified gift box technique of open repair, which has shown higher strength of repair in in-vitro studies and good clinical results in the hands of its inventor. The aim of our study was to evaluate the clinical results of this technique in young active patients with compound Achilles tendon injury.Methods: This is a retrospective study. The parameters recorded at follow up included general demography, ability to single toe raise (on neutral, incline, decline), toe walking for 40 feet, and pain on VAS scale. Achilles tendon total rupture score and modified Rupp score were administered.Results: Out of the 8 patients included in the study, 7 patients had unilateral tear and 1 patient had bilateral tear. The mean age was 27 yrs (20-35) and mean duration of follow up was 17.4 months (08-24 months). Single toe raise and toe walking for 40 ft. was possible in all patients. Two patients complained of grade 2 pain on VAS Scale. The ATRS score was 97.1 (94-99) and modified Rupp score was 28.3 (26-29). Conclusions: Modified gift box technique gives excellent results in young active patients with compound Achilles tendon injury with no re-rupture and return of pre-injury activity

SanskritShala: A Neural Sanskrit NLP Toolkit with Web-Based Interface for Pedagogical and Annotation Purposes

We present a neural Sanskrit Natural Language Processing (NLP) toolkit named SanskritShala (a school of Sanskrit) to facilitate computational linguistic analyses for several tasks such as word segmentation, morphological tagging, dependency parsing, and compound type identification. Our systems currently report state-of-the-art performance on available benchmark datasets for all tasks. SanskritShala is deployed as a web-based application, which allows a user to get real-time analysis for the given input. It is built with easy-to-use interactive data annotation features that allow annotators to correct the system predictions when it makes mistakes. We publicly release the source codes of the 4 modules included in the toolkit, 7 word embedding models that have been trained on publicly available Sanskrit corpora and multiple annotated datasets such as word similarity, relatedness, categorization, analogy prediction to assess intrinsic properties of word embeddings. So far as we know, this is the first neural-based Sanskrit NLP toolkit that has a web-based interface and a number of NLP modules. We are sure that the people who are willing to work with Sanskrit will find it useful for pedagogical and annotative purposes. SanskritShala is available at: https://cnerg.iitkgp.ac.in/sanskritshala. The demo video of our platform can be accessed at: https://youtu.be/x0X31Y9k0mw4.Comment: 7 pages, Accepted at ACL23 (Demo track) to be held at Toronto, Canad