Low Resolution Thermal Imaging Dataset of Sign Language Digits

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MADG: Margin-based Adversarial Learning for Domain Generalization

Domain Generalization (DG) techniques have emerged as a popular approach to address the challenges of domain shift in Deep Learning (DL), with the goal of generalizing well to the target domain unseen during the training. In recent years, numerous methods have been proposed to address the DG setting, among which one popular approach is the adversarial learning-based methodology. The main idea behind adversarial DG methods is to learn domain-invariant features by minimizing a discrepancy metric. However, most adversarial DG methods use 0-1 loss based $\mathcal{H}\Delta\mathcal{H}$ divergence metric. In contrast, the margin loss-based discrepancy metric has the following advantages: more informative, tighter, practical, and efficiently optimizable. To mitigate this gap, this work proposes a novel adversarial learning DG algorithm, MADG, motivated by a margin loss-based discrepancy metric. The proposed MADG model learns domain-invariant features across all source domains and uses adversarial training to generalize well to the unseen target domain. We also provide a theoretical analysis of the proposed MADG model based on the unseen target error bound. Specifically, we construct the link between the source and unseen domains in the real-valued hypothesis space and derive the generalization bound using margin loss and Rademacher complexity. We extensively experiment with the MADG model on popular real-world DG datasets, VLCS, PACS, OfficeHome, DomainNet, and TerraIncognita. We evaluate the proposed algorithm on DomainBed's benchmark and observe consistent performance across all the datasets

Classification of Targets Using Statistical Features from Range FFT of mmWave FMCW Radars

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Self-Powered IoT Device for Indoor Applications

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Lack of association of the CIITA -168A→G promoter SNP with myasthenia gravis and its role in autoimmunity

<p>Abstract</p> <p>Background</p> <p>The major histocompatibility complex class II transactivator (CIITA) regulates MHC class II gene expression. A promoter SNP -168A→G (rs3087456) has previously been shown to be associated with susceptibility to several immune mediated disorders, including rheumatoid arthritis (RA), multiple sclerosis (MS) and myocardial infarction (MI). Myasthenia gravis (MG) is an autoimmune disorder which has previously been shown to be associated with polymorphisms of several autoimmune predisposing genes, including <it>IL-1</it>, <it>PTPN22</it>, <it>TNF-α </it>and the <it>MHC</it>. In order to determine if allelic variants of rs3087456 increase predisposition to MG, we analyzed this SNP in our Swedish cohort of 446 MG patients and 1866 controls.</p> <p>Results</p> <p>No significant association of the SNP with MG was detected, neither in the patient group as a whole, nor in any clinical subgroup. The vast majority of previous replication studies have also not found an association of the SNP with autoimmune disorders.</p> <p>Conclusions</p> <p>We thus conclude that previous findings with regard to the role of the <it>CIITA </it>-168A→G SNP in autoimmunity may have to be reconsidered.</p

Ultrasonic measurements in ethylacetate and n-butanol

13-19The ultrasonic velocity and density measurements have been carried out at 2MH frequency in the binary mixture of ethyl acetate and n-butanol over the entire range of mole fractions at 30 and 40°C temperature. The Pulse echo overlap (PEO) technique is used for measuring the ultrasonic velocities (v). The measured values of velocity and density are utilised to compute the <span style="font-size:11.0pt;line-height:115%;font-family:Calibri;mso-fareast-font-family: Calibri;mso-bidi-font-family:" times="" new="" roman";color:black;mso-ansi-language:="" en-us;mso-fareast-language:en-us;mso-bidi-language:ar-sa"="">thermodynamic parameters such as Lf, V and Ks, for an understanding of their intermolecular interaction.</span

Low Resolution Thermal Imaging Dataset of Sign Language Digits

The dataset contains low resolution thermal images corresponding to various sign language digits represented by hand and captured using the Omron D6T thermal camera. The resolution of the camera is [Formula: see text] pixels. Because of the low resolution of the images captured by this camera, machine learning models for detecting and classifying sign language digits face additional challenges. Furthermore, the sensor’s position and quality have a significant impact on the quality of the captured images. In addition, it is affected by external factors such as the temperature of the surface in comparison to the temperature of the hand. The dataset consists of 3200 images corresponding to ten sign digits, 0–9. Thus, each sign language digit consists of 320 images collected from different persons. The hand is oriented in various ways to capture all of the variations in the dataset

Abatement of mixture of volatile organic compounds (VOCs) in a catalytic non-thermal plasma reactor

Total oxidation of mixture of dilute volatile organic compounds was carried out in a dielectric barrier discharge reactor with various transition metal oxide catalysts integrated in-plasma. The experimental results indicated the best removal efficiencies in the presence of metal oxide catalysts, especially MnOx, whose activity was further improved with AgOx deposition. It was confirmed water vapor improves the efficiency of the plasma reactor, probably due to the formation of hydroxyl species, whereas, in situ decomposition of ozone on the catalyst surface may lead to nascent oxygen. It may be concluded that non-thermal plasma approach is beneficial for the removal of mixture of volatile organic compounds than individual VOCs, probably due to the formation of reactive intermediates like aldehydes, peroxides, etc