AN ADAPTIVE SAR IMAGE DESPECKLING ALGORITHM USING STATIONARY WAVELET TRANSFORM

In this paper, we present a Stationary Wavelet Transform (SWT) based method for the purpose of despeckling the Synthetic Aperture radar (SAR) images by applying a maximum a posteriori probability (MAP) condition to estimate the noise free wavelet coefficients. The solution of the MAP estimator is based on the assumption that the wavelet coefficients have a known distribution. Rayleigh distribution is used for modeling the speckle noise and Laplacian distribution for modeling the statistics of the noise free wavelet coefficients for the purpose of designing the MAP estimator. Rayleigh distribution is used for modeling the speckle noise since speckle noise can be well described by it. The parameters required for MAP estimator is determined by the technique used for parameter estimation after SWT. The experimental results show that the proposed despeckling algorithm efficiently removes speckle noise from the SAR images

Detection of one-horned rhino using multispectral images

Animal detection and surveillance is an important field of research to address the needs for protection of endangered species among others. The challenges in animal detection include low-contrast and poor image quality which is commonly observed during night time. Researchers have mostly worked on day-light, low-contrast and thermal images. To handle the challenges of detection during night time, multispectral images in combination with deep architectures may be used for better detection performance. In the present work, one-horned rhino is considered for detection because they are getting endangered for reasons like poaching, natural calamities and diseases. A novel multispectral one-horned rhino dataset is introduced and the multispectral data is obtained by combining the channels of color images and the corresponding thermal images. Instance segmentation based techniques YOLACT and YOLACT++ are used here to detect rhinos with the above multispectral dataset. The performances of the detectors are studied in terms of mAP and FPS

RUASN: A Robust User Authentication Framework for Wireless Sensor Networks

In recent years, wireless sensor networks (WSNs) have been considered as a potential solution for real-time monitoring applications and these WSNs have potential practical impact on next generation technology too. However, WSNs could become a threat if suitable security is not considered before the deployment and if there are any loopholes in their security, which might open the door for an attacker and hence, endanger the application. User authentication is one of the most important security services to protect WSN data access from unauthorized users; it should provide both mutual authentication and session key establishment services. This paper proposes a robust user authentication framework for wireless sensor networks, based on a two-factor (password and smart card) concept. This scheme facilitates many services to the users such as user anonymity, mutual authentication, secure session key establishment and it allows users to choose/update their password regularly, whenever needed. Furthermore, we have provided the formal verification using Rubin logic and compare RUASN with many existing schemes. As a result, we found that the proposed scheme possesses many advantages against popular attacks, and achieves better efficiency at low computation cost

On the Robustness of Explanations of Deep Neural Network Models: A Survey

Explainability has been widely stated as a cornerstone of the responsible and trustworthy use of machine learning models. With the ubiquitous use of Deep Neural Network (DNN) models expanding to risk-sensitive and safety-critical domains, many methods have been proposed to explain the decisions of these models. Recent years have also seen concerted efforts that have shown how such explanations can be distorted (attacked) by minor input perturbations. While there have been many surveys that review explainability methods themselves, there has been no effort hitherto to assimilate the different methods and metrics proposed to study the robustness of explanations of DNN models. In this work, we present a comprehensive survey of methods that study, understand, attack, and defend explanations of DNN models. We also present a detailed review of different metrics used to evaluate explanation methods, as well as describe attributional attack and defense methods. We conclude with lessons and take-aways for the community towards ensuring robust explanations of DNN model predictions.Comment: Under Review ACM Computing Surveys "Special Issue on Trustworthy AI

Weighted delay prediction in mobile ad hoc network using fuzzy time series

Several parameters like routing protocol, mobility pattern, average speed of mobile nodes, path length from source to destination, previous delay, etc., affect the end-to-end packet delay in mobile ad hoc network. But the nature of relationship between end-to-end delay and those parameters is still unclear. In this article, we have tried to establish a relationship among end-to-end delay, path length and previous delay. A regression equation is established between path length and end-to-end delay. The end-to-end delay is also represented as a fuzzy time series. The current end-to-end delay is then obtained by combining delay predicted by path length regression equation and fuzzy time series. The suitable weights of these two predicted delays are also experimentally determined. To the best of our knowledge, comprehensive analysis for packet delay estimation using various network parameters along with fuzzy time series has not been explored earlier. Based on various performance evaluation criterion, we found that by combining the predicted values of delay using path length regression and fuzzy time series gives satisfactory packet delay prediction in ad hoc network

Investigation of the antidiabetic and probiotic properties of lactic acid bacteria isolated from some ethnic fermented foods of Darjeeling District

Abstract Background Indigenous communities residing in the Darjeeling Himalayan region and its adjacent hilly areas have a deeply rooted cultural tradition of consuming a diverse range of vegetable and milk-based fermented products, believed to confer various health advantages. With this traditional knowledge, lactic acid bacteria (LAB) were isolated from popular fermented foods such as Chhurpi (derived from Bos grunniens milk), Gundruk (made from Brassica juncea leaves), Sinki (derived from Raphanus sativus taproots), and Kinema (produced from Glycine max beans). This study aimed to investigate the probiotic properties of the prevalent LABs, including aggregation properties, bile salt hydrolase activities, survival under gastro-inhibitory conditions, safety evaluations, and their potential health-promoting attributes, with a specific focus on inhibiting α-amylase and α-glucosidase enzymes. Results Five of the LAB isolates demonstrated notable viability rates exceeding 85% when exposed to gastro-inhibitory challenges. Based on 16S rRNA gene sequencing, these isolates were identified as Pediococcus pentosaceus (isolate GAD), Lactobacillus plantarum (isolates KAD and CAD), Lactobacillus brevis (isolate SAD), and Lactiplantibacillus plantarum (isolate CMD). These LAB isolates exhibited versatile carbon source utilization, significant auto- and co-aggregation, and bile salt hydrolase (BSH) properties. Auto-aggregation capacity notably increased over time, ranging from 30 to 150 min, with percentage increments from 4.83 ± 1.92% to 67.60 ± 5.93%. L. brevis SAD displayed the highest co-aggregation increment (%) against Staphylococcus aureus, while L. plantarum KAD demonstrated potent antimicrobial activity. In vitro analyses postulated potential health benefits related to antidiabetic properties, particularly inhibiting α-amylase and α-glucosidase enzymes. L. brevis SAD exhibited the highest α-glucosidase inhibitory activity, while L. plantarum KAD displayed the most potent α-amylase inhibitory activity. Comprehensive safety assessments, including antibiotic susceptibility profiling, hemolytic activity evaluation, and in vivo acute toxicity studies, confirmed the suitability of these LAB isolates for human consumption. Conclusions The isolates show promising probiotic characteristics and significant potential in addressing metabolic health. These results carry substantial scientific implications, suggesting the pharmaceutical-based applications of these traditional fermented foods. Further in vivo investigation is recommended to fully elucidate and exploit the health benefits of these LAB isolates, opening avenues for potential therapeutic interventions and the development of functional foods