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Autonomous Surface Vehicles (ASVs) rely on two layers of guidance for effective path planning: global and local methods. Global methods require a macroscopic view of the mission's goals in order to efficiently navigate an environment. For this research, different global path planner algorithms are explored both in simulation and in field tests to observe path planner qualities for modularity, practicality,...
Human re-identification is an important component in many application domains especially the automatic surveillance system. This paper proposes a robust method to re-identify persons using their face shapes based on the Active Shape Model (ASM) and the Procrustes Shape Analysis (PSA). The ASM-based technique is used to extract landmark points of each face image, as the feature. Then, the Procrustes...
We describe a general technique that yields the first Statistical Query lower bounds} fora range of fundamental high-dimensional learning problems involving Gaussian distributions. Our main results are for the problems of (1) learning Gaussian mixture models (GMMs), and (2) robust (agnostic) learning of a single unknown Gaussian distribution. For each of these problems, we show a super-polynomial...
To robustly estimate the pose, classical methods assume some geometrical and temporal assumptions (SfM: Structure from Motion, SLAM: Simultaneous Localization and mapping). These approaches take a pair of images as input and establish correspondences based on global strategy (using the whole image information) or sparse strategy (using key-points features). These correspondences allow solving a set...
In this paper, we propose a scale-invariant framework based on Convolutional Neural Networks (CNNs). The network exhibits robustness to scale and resolution variations in data. Previous efforts in achieving scale invariance were made on either integrating several variant-specific CNNs or data augmentation. However, these methods did not solve the fundamental problem that CNNs develop different feature...
New and unseen network attacks pose a great threat to the signature-based detection systems. Consequently, machine learning-based approaches are designed to detect attacks, which rely on features extracted from network data. The problem is caused by different distribution of features in the training and testing datasets, which affects the performance of the learned models. Moreover, generating labeled...
Object-branch coverage (OBC) is often used as a measure of the thoroughness of tests suites, augmenting or substituting source-code based structural criteria such as branch coverage and modified condition/decision coverage (MC/DC). In addition, with the increasing use of third-party components for which source-code access may be unavailable, robust object-code coverage criteria are essential to assess...
Recently, sparse representation based classifiers (SRC) and collaborative representation based classifiers (CRC) have been shown to give very good performance under controlled scenarios. However, in practical applications, face recognition often encounters variations in illumination, expression, noise and occlusion, which cause severe performance degradation (due to the outliers in testing). In this...
This work presents an image watermarking method with blind detection based on amplitude modulation. Basically, the watermark embedding is performed by modifying the pixel values in the blue channel of an image, while the watermark retrieval is achieved by using a prediction technique based on a linear combination of nearby pixel values around the embedded pixels. The experimental results obtained...
With the introduction of touch-screens for an increasing number of applications such as smart phones, vending machines and automotive displays, the need for testing input possibilities for such displays arose. For many different test scenarios not only synthetic, but also human-like inputs are desired. This paper describes an approach capable of creating human-like finger-traces for the input method...
Human action recognition has been extensively studied with a lot of real life application. Many methods have been proposed and achieved promising results when the input video captured from the same viewpoints. However, their accuracy decreases significantly under viewpoint changing. The reason is that action appearance is quite different when looking from a different angle. To overcome this problem,...
This paper details the design of an autonomous vehicle CAD toolchain, which captures formal descriptions of driving scenarios in order to develop a safety case for an autonomous vehicle (AV). Rather than focus on a particular component of the AV, like adaptive cruise control, the toolchain models the end-to-end dynamics of the AV in a formal way suitable for testing and verification. First, a domain-specific...
In this paper, a target detection procedure with global error control is proposed. The novelty of this approach consists in taking into account spatial structures of the target while ensuring proper error control over pixelwise errors. A generic framework is discussed and a method based on this framework is implemented. Results on simulated data show conclusive gains in detection power for a nominal...
In this paper, we develop a robust generalization of the Gaussian quasi score test (GQST) for composite binary hypothesis testing. The proposed test, called measure-transformed GQST (MT-GQST), is based on a transformation applied to the probability distribution of the data. The considered transform is structured by a non-negative function, called MT-function, that weights the data points. By appropriate...
Test point insertion methods to reduce the number of test patterns at register transfer level are required for the adaptability of traditional VLSI design flows and the reduction of time to search test point locations. In this paper, we propose a design-for-testability method at register transfer level to enable operational units as many as possible to be concurrently tested in scan testing. Using...
Physical Unclonable Function (PUF) has broad application prospects in the field of hardware security. Arbiter PUF is a typical PUF, but is threatened by modeling attacks. To resist attack, XOR arbiter PUF employs multiple basic arbiter PUFs and XOR their response bits to generate the final response bit. However, its low reliability not only limits its applications, but also leaks information to enhance...
MTJ-based logic-in-memory architecture, where MTJ memory elements with spin-injection write capability are distributed over a logic-circuit plane, is attractive design template to realize ultra-low-power and reduced interconnection delay. Moreover, because of advantages of MTJ cells i.e., large resistance ratio, virtually unlimited endurance, fast read/write accessibility, scalability, CMOS-process...
Aggressive technology and supply voltage scaling has led to increasing concern for reliability. Optimizing power and energy with sub-threshold (sub-VT) operation exponentially increases the occurrences of both static and dynamic failures. With smaller node capacitances with each technology and supply scaling node, radiation-induced Single Event Upset (SEU) has become a critical design metric for Ultra-Low-Power...
Deep neural networks (DNNs) achieve excellent performance on standard classification tasks. However, under image quality distortions such as blur and noise, classification accuracy becomes poor. In this work, we compare the performance of DNNs with human subjects on distorted images. We show that, although DNNs perform better than or on par with humans on good quality images, DNN performance is still...
Integrated circuits used in high-reliability applications must demonstrate low failure rates and high-levels of fault detection coverage. Safety Integrity Level (SIL) metrics indicated by the general IEC 61508 standard and the derived Automotive Safety Integrity Level (ASIL) specified by the ISO 26262 standard specify specific failure (FIT) rates and fault coverage metrics (e.g. SPFM and LFM) that...
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