Generating Information-Diverse Microwave Speckle Patterns Inside a Room at a Single Frequency With a Dynamic Metasurface Aperture

We demonstrate that dynamic metasurface apertures (DMAs) are capable of generating a multitude of highly uncorrelated speckle patterns in a typical residential environment at a single frequency. We use a DMA implemented as an electrically-large cavity excited by a single port and loaded with many individually-addressable tunable metamaterial radiators. We placed such a DMA in one corner of a plywood-walled L-shape room transmitting microwave signals at 19 GHz as we changed the tuning states of the metamaterial radiators. In another corner, in the non-line-of-sight of the DMA, we conducted a scan of the field generated by the DMA. For comparison, we also performed a similar test where the DMA was replaced by a simple dipole antenna with fixed pattern but generating a signal that spanned 19-24 GHz. Using singular value decomposition of the scanned data, we demonstrate that the DMA can generate a multitude of highly uncorrelated speckle patterns at a single frequency. In contrast, a dipole antenna with a fixed pattern can only generate such a highly uncorrelated set of patterns when operating over a large bandwidth. The experimental results of this paper suggest that DMAs can be used to capture a diversity of information at a single frequency which can be used for single frequency computational imaging systems, NLOS motion detection, gesture recognition systems, and more

DISTROY: Detecting Integrated Circuit Trojans with Compressive Measurements

Detecting Trojans in an integrated circuit (IC) is an important but hard problem. A Trojan is malicious hardware it can be extremely small in size and dormant until triggered by some unknown circuit state. To allow wake-up, a Trojan could draw a minimal amount of power, for example, to run a clock or a state machine, or to monitor a triggering event. We introduce DISTROY (Discover Trojan), a new approach that can effciently and reliably detect extremely small background power leakage that a Trojan creates and as a result, we can detect the Trojan. We formulate our method based on compressive sensing, a recent advance in signal processing, which can recover a signal using the number of measurements approximately proportional to its sparsity rather than size. We argue that circuit states in which the Trojan background power consumption stands out are rare, and thus sparse, so that we can apply compressive sensing. We describe how this is done in DISTROY so as to afford suffcient measurement statistics to detect the presence of Trojans. Finally, we present our initial simulation results that validate DISTROY and discuss the impact of our work in the field of hardware security.Engineering and Applied Science

Generating Information-Diverse Microwave Speckle Patterns Inside a Room at a Single Frequency With a Dynamic Metasurface Aperture

We demonstrate that dynamic metasurface apertures (DMAs) are capable of generating a multitude of highly uncorrelated speckle patterns in a typical residential environment at a single frequency. We use a DMA implemented as an electrically-large cavity excited by a single port and loaded with many individually-addressable tunable metamaterial radiators. We placed such a DMA in one corner of a plywood-walled L-shape room transmitting microwave signals at 19 GHz as we changed the tuning states of the metamaterial radiators. In another corner, in the non-line-of-sight of the DMA, we conducted a scan of the field generated by the DMA. For comparison, we also performed a similar test where the DMA was replaced by a simple dipole antenna with fixed pattern but generating a signal that spanned 19-24 GHz. Using singular value decomposition of the scanned data, we demonstrate that the DMA can generate a multitude of highly uncorrelated speckle patterns at a single frequency. In contrast, a dipole antenna with a fixed pattern can only generate such a highly uncorrelated set of patterns when operating over a large bandwidth. The experimental results of this paper suggest that DMAs can be used to capture a diversity of information at a single frequency which can be used for single frequency computational imaging systems, NLOS motion detection, gesture recognition systems, and more

Belle II Technical Design Report

The Belle detector at the KEKB electron-positron collider has collected almost 1 billion Y(4S) events in its decade of operation. Super-KEKB, an upgrade of KEKB is under construction, to increase the luminosity by two orders of magnitude during a three-year shutdown, with an ultimate goal of 8E35 /cm^2 /s luminosity. To exploit the increased luminosity, an upgrade of the Belle detector has been proposed. A new international collaboration Belle-II, is being formed. The Technical Design Report presents physics motivation, basic methods of the accelerator upgrade, as well as key improvements of the detector.Comment: Edited by: Z. Dole\v{z}al and S. Un

Face Image and Video Analysis in Biometrics and Health Applications

Computer Vision (CV) enables computers and systems to derive meaningful information from acquired visual inputs, such as images and videos, and make decisions based on the extracted information. Its goal is to acquire, process, analyze, and understand the information by developing a theoretical and algorithmic model. Biometrics are distinctive and measurable human characteristics used to label or describe individuals by combining computer vision with knowledge of human physiology (e.g., face, iris, fingerprint) and behavior (e.g., gait, gaze, voice). Face is one of the most informative biometric traits. Many studies have investigated the human face from the perspectives of various different disciplines, ranging from computer vision, deep learning, to neuroscience and biometrics. In this work, we analyze the face characteristics from digital images and videos in the areas of morphing attack and defense, and autism diagnosis. For face morphing attacks generation, we proposed a transformer based generative adversarial network to generate more visually realistic morphing attacks by combining different losses, such as face matching distance, facial landmark based loss, perceptual loss and pixel-wise mean square error. In face morphing attack detection study, we designed a fusion-based few-shot learning (FSL) method to learn discriminative features from face images for few-shot morphing attack detection (FS-MAD), and extend the current binary detection into multiclass classification, namely, few-shot morphing attack fingerprinting (FS-MAF). In the autism diagnosis study, we developed a discriminative few shot learning method to analyze hour-long video data and explored the fusion of facial dynamics for facial trait classification of autism spectrum disorder (ASD) in three severity levels. The results show outstanding performance of the proposed fusion-based few-shot framework on the dataset. Besides, we further explored the possibility of performing face micro- expression spotting and feature analysis on autism video data to classify ASD and control groups. The results indicate the effectiveness of subtle facial expression changes on autism diagnosis

Beyond the noise : high fidelity MR signal processing

This thesis describes a variety of methods developed to increase the sensitivity and resolution of liquid state nuclear magnetic resonance (NMR) experiments. NMR is known as one of the most versatile non-invasive analytical techniques yet often suffers from low sensitivity. The main contribution to this low sensitivity issue is a presence of noise and level of noise in the spectrum is expressed numerically as “signal-to-noise ratio”. NMR signal processing involves sensitivity and resolution enhancement achieved by noise reduction using mathematical algorithms. A singular value decomposition based reduced rank matrix method, composite property mapping, in particular is studied extensively in this thesis to present its advantages, limitations, and applications. In theory, when the sum of k noiseless sinusoidal decays is formatted into a specific matrix form (i.e., Toeplitz), the matrix is known to possess k linearly independent columns. This information becomes apparent only after a singular value decomposition of the matrix. Singular value decomposition factorises the large matrix into three smaller submatrices: right and left singular vector matrices, and one diagonal matrix containing singular values. Were k noiseless sinusoidal decays involved, there would be only k nonzero singular values appearing in the diagonal matrix in descending order providing the information of the amplitude of each sinusoidal decay. The number of non-zero singular values or the number of linearly independent columns is known as the rank of the matrix. With real NMR data none of the singular values equals zero and the matrix has full rank. The reduction of the rank of the matrix and thus the noise in the reconstructed NMR data can be achieved by replacing all the singular values except the first k values with zeroes. This noise reduction process becomes difficult when biomolecular NMR data is to be processed due to the number of resonances being unknown and the presence of a large solvent peak

A Unified Submodular Framework for Multimodal IC Trojan Detection

Abstract. This paper presents a unified formal framework for inte-grated circuits (IC) Trojan detection that can simultaneously employ multiple noninvasive measurement types. Hardware Trojans refer to modifications, alterations, or insertions to the original IC for adversarial purposes. The new framework formally defines the IC Trojan detection for each measurement type as an optimization problem and discusses the complexity. A formulation of the problem that is applicable to a large class of Trojan detection problems and is submodular is devised. Based on the objective function properties, an efficient Trojan detection method with strong approximation and optimality guarantees is intro-duced. Signal processing methods for calibrating the impact of inter-chip and intra-chip correlations are presented. We propose a number of meth-ods for combining the detections of the different measurement types. Experimental evaluations on benchmark designs reveal the low-overhead and effectiveness of the new Trojan detection framework and provides a comparison of different detection combining methods.