Extended 16x16 Play-Fair Algorithm for Secure Key Exchange Using RSA Algorithm

With the world entering in the 21st century rigorous efforts are being made to secure data and flow of information among the users. Though with the advancements are fast and efficient the third party intervention and security threats has also increased many folds. The algorithms being used to encrypt and decrypt data needs to be strong enough to secure the data but also simple enough for a user to handle the process. With this article a novel, practical approach is presented which not only makes the information more secured but also being based on RSA algorithm is easy enough for users to understand and implement into the systems

Memex: a browsing assistant for collaborative archiving and mining of surf trails

Keyword indices, topic directories and link-based rankings are used to search and structure the rapidly growing Web today. Surprisingly little use is made of years of browsing experience of millions of people. Indeed, this information is routinely discarded by browsers. Even deliberate bookmarks are stored in a passive and isolated manner. All this goes against Vannevar Bush’s dream of the Memex: An enhanced supplement to personal and community memory. We propose to demonstrate the beginnings of a ‘Memex’ for the Web: A browsing assistant for individuals and groups with focused interests. Memex blurs the artificial distinction between browsing history and deliberate bookmarks. The resulting glut of data is analyzed in a number of ways at the individual and community levels. Memex constructs a topic directory customized to the community, mapping their interests naturally to nodes in this directory. This lets the user recall topic-based browsing contexts by asking questions like “What trails was I following when I was last surfing about classical music?” and “What are some popular pages in or near my community’s recent trail graph related to music?

Machine Learning for Microcontroller-Class Hardware -- A Review

The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering their direct deployment on ultra resource-constrained microcontrollers. This paper highlights the unique requirements of enabling onboard machine learning for microcontroller class devices. Researchers use a specialized model development workflow for resource-limited applications to ensure the compute and latency budget is within the device limits while still maintaining the desired performance. We characterize a closed-loop widely applicable workflow of machine learning model development for microcontroller class devices and show that several classes of applications adopt a specific instance of it. We present both qualitative and numerical insights into different stages of model development by showcasing several use cases. Finally, we identify the open research challenges and unsolved questions demanding careful considerations moving forward.Comment: Accepted for publication at IEEE Sensors Journa

Eagle: End-to-end Deep Reinforcement Learning based Autonomous Control of PTZ Cameras

Existing approaches for autonomous control of pan-tilt-zoom (PTZ) cameras use multiple stages where object detection and localization are performed separately from the control of the PTZ mechanisms. These approaches require manual labels and suffer from performance bottlenecks due to error propagation across the multi-stage flow of information. The large size of object detection neural networks also makes prior solutions infeasible for real-time deployment in resource-constrained devices. We present an end-to-end deep reinforcement learning (RL) solution called Eagle to train a neural network policy that directly takes images as input to control the PTZ camera. Training reinforcement learning is cumbersome in the real world due to labeling effort, runtime environment stochasticity, and fragile experimental setups. We introduce a photo-realistic simulation framework for training and evaluation of PTZ camera control policies. Eagle achieves superior camera control performance by maintaining the object of interest close to the center of captured images at high resolution and has up to 17% more tracking duration than the state-of-the-art. Eagle policies are lightweight (90x fewer parameters than Yolo5s) and can run on embedded camera platforms such as Raspberry PI (33 FPS) and Jetson Nano (38 FPS), facilitating real-time PTZ tracking for resource-constrained environments. With domain randomization, Eagle policies trained in our simulator can be transferred directly to real-world scenarios.Comment: 20 pages, IoTD

Exploring body composition metrics: Comparing percentage body fat, BMI, and body fat mass in college students

Purpose: The study's objective was to compare the chosen Physiological variables indicating Obesity Status among Physical Education Students and Humanities Students. Methodology: For the purpose of the study, 21 participants (8 Physical Education Students and 13 Humanities Students) of age 20-24 years were chosen from Department of Physical Education and Sports and Department of Sociology of Central University of Haryana, Mahendargarh. To achieve the study's goals, simple random sampling technique was used, Body Composition Analyzer a leading Physiological assessment tool was used for measuring parameters named Body Mass Index (BMI), Body Fat Mass (BFM) & Percent Body Fat (PBF), of students of Physical Education and Humanities. As a statistical method, the independent sample "T" test was used. Findings: A 0.05 alpha level was chosen. Because the t value was insignificant (p>0.05), the statistical analysis of the results and comparison of the two groups revealed no statistically significant  difference in mean Body Fat Mass (BFM), Percent Body Fat (PBF), and Body Mass Index  (BMI). The outcome demonstrated that the similarity between these parameters was either due to similar Diet provided by the University to both the groups in the University Hostel Mess or the daily long-distance walking done by the Physical Education as well as the Humanities students from the Hostels to their respective classes

Synergistic association of STX1A and VAMP2 with cryptogenic epilepsy in North Indian population

Introduction “Common epilepsies”, merely explored for genetics are the most frequent, nonfamilial, sporadic cases in hospitals. Because of their much debated molecular pathology, there is a need to focus on other neuronal pathways including the existing ion channels. Methods For this study, a total of 214 epilepsy cases of North Indian ethnicity comprising 59.81% generalized, 40.19% focal seizures, and based on epilepsy types, 17.29% idiopathic, 37.38% cryptogenic, and 45.33% symptomatic were enrolled. Additionally, 170 unrelated healthy individuals were also enrolled. Here, we hypothesize the involvement of epilepsy pathophysiology genes, that is, synaptic vesicle cycle, SVC genes (presynapse), ion channels and their functionally related genes (postsynapse). An interactive analysis was initially performed in SVC genes using multifactor dimensionality reduction (MDR). Further, in order to understand the influence of ion channels and their functionally related genes, their interaction analysis with SVC genes was also performed. Results A significant interactive two-locus model of STX1A_rs4363087|VAMP2_rs2278637 (presynaptic genes) was observed among SVC variants in all epilepsy cases (P1000-value = 0.054; CVC = 9/10; OR = 2.86, 95%CI = 1.88–4.35). Further, subgroup analysis revealed stronger interaction for the same model in cryptogenic epilepsy patients only (P1000-value = 0.012; CVC = 10/10; OR = 4.59, 95%CI = 2.57–8.22). However, interactive analysis of presynaptic and postsynaptic genes did not show any significant association. Conclusions Significant synergistic interaction of SVC genes revealed the possible functional relatedness of presynapse with pathophysiology of cryptogenic epilepsy. Further, to establish the clinical utility of the results, replication in a large and similar phenotypic group of patients is warranted

Comparative study of magnetic and magnetotransport properties of Sm0.55Sr0.45MnO3 thin films grown on different substrates

Highly oriented polycrystalline SSMO thin films deposited on single crystal substrates by ultrasonic nebulized spray pyrolysis have been studied. The film on LAO is under compressive strain while LSAT and STO are under tensile strain. The presence of a metamagnetic state akin to cluster glass formed due to coexisting FM and antiferromagnetic/charge order (AFM/CO) clusters. All the films show colossal magnetoresistance but its temperature and magnetic field dependence are drastically different. In the lower temperature region the magnetic field dependent isothermal resistivity also shows signature of metamagnetic transitions. The observed results have been explained in terms of the variation of the relative fractions of the coexisting FM and AFM/CO phases as a function of the substrate induced strain and oxygen vacancy induced quenched disorder.Comment: 21 page