“A healthy mind lives in a healthy body”. To maintain a healthy body, one has to follow a healthy lifestyle. Fitness trackers are excellent companions that help people meet their fitness goals and keep a healthy lifestyle. Thus, fitness tracking is becoming an integral part of the human lifestyle. But, most available fitness trackers nowadays require wearing/mounting on the body, which makes them unattractive for widespread use. To the contrary, mmwave radar sensors can accurately capture micro-motion parameters in a contactless configuration. In this paper, we propose and experimentally demonstrate the use of an mmwave radar sensor for real-time contactless fitness tracking using deep convolutional neural networks (CNNs). In this work, we created a setup to capture the body movements during various exercises by an mmwave radar. The radar data was processed and passed onto a deep CNN for training. Post-training results show that the system is able to classify different exercises in real-time with very high accuracy. The proposed system is computationally inexpensive and has the potential to become an effective alternative to body wearable fitness trackers for indoor fitness activities.

An mmWave Radar Based Real-Time Contactless Fitness Tracker Using Deep CNNs

The increasing bandwidth requirement of new wireless applications has lead to standardization of the millimeter wave spectrum for high-speed wireless communication The millimeter wave spectrum is part of 5G and covers frequencies between 30 and 300 GHz that correspond to wavelengths ranging from 10 to 1 mm Although millimeter wave is often considered as a communication medium, it has also proved to be an excellent ‘sensor’, thanks to its narrow beams, operation across a wide bandwidth, and interaction with atmospheric constituents In this paper, which is to the best of our knowledge the first review that completely covers millimeter wave sensing application pipelines, we provide a comprehensive overview and analysis of different basic application pipeline building blocks, including hardware, algorithms, analytical models, and model evaluation techniques The review also provides a taxonomy that highlights different millimeter wave sensing application domains By performing a thorough analysis, complying with the systematic literature review methodology and reviewing 165 papers, we not only extend previous investigations focused only on communication aspects of the millimeter wave technology and using millimeter wave technology for active imaging, but also highlight scientific and technological challenges and trends, and provide a future perspective for applications of millimeter wave as a sensing technology

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Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

Information Systems, increasingly present in a world that goes towards complete digitalisation, can be seen as complex systems at the base of which is the hardware. When dealing with the security of these systems to stop possible intrusions and malicious uses, the analysis must necessarily include the possible vulnerabilities that can be found at the hardware level, since their exploitation can make all defences implemented at web or software level ineffective. In this paper, we propose a meaningful and comprehensive taxonomy for the vulnerabilities affecting the hardware and the attacks that exploit them to compromise the system, also giving a definition of Hardware Security, in order to clarify a concept often confused with other domains, even in the literature.

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Hardware security, vulnerabilities, and attacks: a comprehensive taxonomy

Taint-propagation and X-propagation analyses are important tools for enforcing circuit design properties such as security and reliability. Fundamental to these tools are effective models for accurately measuring the propagation of information and calculating metadata. In this work, we formalize a unified model for reasoning about taint- and X-propagation behaviors and verifying design properties related to these behaviors. Our model are developed from the perspective of information flow and can be described using standard hardware description language (HDL), which allows formal verification of both taint-propagation (i.e., security) and X-propagation (i.e., reliability) related properties using standard electronic design automation (EDA) verification tools. Experimental results show that our formal model can be used to prove both security and reliability properties in order to uncover unintended design flaw, timing channel and intentional malicious undocumented functionality in circuit designs.

A Unified Formal Model for Proving Security and Reliability Properties

The advancement of the data communication technologies has increased the traffic of data exchange over the internet and at the same time created the opportunity of data attack by various party.  This paper present  Time Performance Analysis Of Advanced Encryption Standard And Data Encryption Standard  in Data Security Transaction . In this study we proposed an AES algorithm with  different key size, and different file format. Our aim is to safely to transfer the file for using the AES algorithm. Proposed algorithm has done by analyzing the different time taken for both AES and DES, experiments were done by three different file  format which were text, image, and voice. Each file format type was tested with five different file sizes. The result of each experiments were analysed and it was confirmed that  the AES algorithm have better performance in term of time taken as compared to DES.

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Time performance analysis of advanced encryption standard and data encryption standard in data security transaction

In recent years, academic focus on side chan-nel analysis has increased due to their effectiveness in leaking information from secure systems. Advanced Encryption Standard or Rinjdael has been the object of scrutiny ever since its inception as a federal standard. Presently, it is one of the most widely used encryption algorithms in the world and has withstood the various efforts to crypt-analyze it. With academic focus on time leaking code implementations increasing in the 90s, and successful crypt-analysis of many algorithms due to side channel data leakage and the fact that improper software implementations can leak information has brought focus on side channel analysis of AES. We shall try and implement the cache timing attack on a modern server and modern implementations and observe the results firsthand. In this paper, the authors try and implement cache timing attack on various AES implementations over modern processors. The practical importance of mounting an attack over a non-idealized system and analyzing these real world results can be considered the primary objectives of this paper.

Timing attack analysis on AES on modern processors

Radar technology has great potential for use as security systems in college campuses with privacy advantages over its visual counterparts. We experimented with the Texas Instruments IWR1642 radar sensor to evaluate the feasibility of using radar systems for security monitoring. We introduce an end-to-end neural architecture which is capable of taking radar data inputs in real time and identify human versus nonhuman targets, and classify various human behavioral motions. The model is real time, robust to noise and displays state-of-the-art results for the problem.

Radar as a Security Measure - Real Time Neural Model based Human Detection and Behaviour Classification

Sparsity learning aims to decrease the computational and memory costs of large deep neural networks (DNNs) via pruning neural connections while simultaneously retaining high accuracy. A large body of work has developed sparsity learning approaches, with recent large-scale experiments showing that two main methods, magnitude pruning and Variational Dropout (VD), achieve similar state-of-the-art results for classification tasks. We propose Adaptive Neural Connections (ANC), a method for explicitly parameterizing fine-grained neuron-to-neuron connections via adjacency matrices at each layer that are learned through backpropagation. Explicitly parameterizing neuron-to-neuron connections confers two primary advantages: 1. Sparsity can be explicitly optimized for via norm-based regularization on the adjacency matrices; and 2. When combined with VD (which we term, ANC-VD), the adjacencies can be interpreted as learned weight importance parameters, which we hypothesize leads to improved convergence for VD. Experiments with ResNet18 show that architectures augmented with ANC outperform their vanilla counterparts.

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Adaptive Neural Connections for Sparsity Learning

Catastrophic forgetting in neural networks is a significant problem for continual learning. A majority of the current methods replay previous data during training, which violates the constraints of an ideal continual learning system. Additionally, current approaches that deal with forgetting ignore the problem of catastrophic remembering, i.e. the worsening ability to discriminate between data from different tasks. In our work, we introduce Relevance Mapping Networks (RMNs) which are inspired by the Optimal Overlap Hypothesis. The mappings reflects the relevance of the weights for the task at hand by assigning large weights to essential parameters. We show that RMNs learn an optimized representational overlap that overcomes the twin problem of catastrophic forgetting and remembering. Our approach achieves state-of-the-art performance across all common continual learning datasets, even significantly outperforming data replay methods while not violating the constraints for an ideal continual learning system. Moreover, RMNs retain the ability to detect data from new tasks in an unsupervised manner, thus proving their resilience against catastrophic remembering.

Understanding Catastrophic Forgetting and Remembering in Continual Learning with Optimal Relevance Mapping

GPS navigation systems are a potential threat to user privacy in case of curious providers, espionage and many other aspects. Users tend to place blind trust into GPS applications without realizing the ease at which the GPS can be spoofed or their position compromised via either the hardware or software. Thus, when a high level of privacy assurance is required, the GPS should be completely switched off. This paper presents an efficient method, a smartphone-based alternative solution, for an outdoor offline navigation system, which works in the absence of GPS, wireless, and cellular signals. The proposed approach exploits the various digital and mathematical resources present to use DEM data and sensor data to minimize errors in the calculated position

Prakhar Kaushik

Papers

Timing attack analysis on AES on modern processors

Radar as a Security Measure - Real Time Neural Model based Human Detection and Behaviour Classification

Adaptive Neural Connections for Sparsity Learning

Understanding Catastrophic Forgetting and Remembering in Continual Learning with Optimal Relevance Mapping

An Offline Outdoor Navigation System with Full Privacy.