Deep Learning Approach for Dynamic Sampling for High-Throughput Nano-DESI MSI

Mass Spectrometry Imaging (MSI) extracts molecular mass data to form visualizations of molecular spatial distributions. The involved scanning procedure is conducted by moving a probe across and around a rectilinear grid, as in the case of nanoscale Desorption Electro-Spray Ionization (nano-DESI) MSI, where singular measurements can take up to ~5 seconds to acquire high-resolution (better than 10 μm) results. This temporal expense creates a high inefficiency in sample processing and throughput. For example, in a high-resolution nano-DESI study, a single mouse uterine tissue section (2.5 mm by 1.7 mm) had an acquisition time of ~4 hours to acquire 104,400 pixels. Anywhere from ~25-30% of those pixels were outside the actual tissue, and a further portion of those locations lacked relevant information. An existing method, a Supervised Learning Approach for Dynamic Sampling (SLADS), utilizes information obtained during an active scan to infer, using a least-squares regression, regions of interest that most likely contain meaningful information, and a computationally inexpensive weighted mean interpolation to perform sparse sample reconstruction. This approach could potentially be used to significantly improve throughput in this and other biological tissue scanning applications. However, existing SLADS implementations were neither designed nor optimized for leveraging or handling the 3rd dimension in MSI of molecular spectra. Further, integrating more recent advances in machine learning since the last SLADS publication issuance, such as Convolutional Neural Network (CNN) architectures, offers additional performance gains. The objective of this research is the updating, re-design, and optimization of the SLADS methodology, to form a Deep Learning Approach for Dynamic Sampling (DLADS) for high-resolution biological tissues and integration with nano-DESI MSI instrumentation

Classification Of Rotating Machinery Fault Using Vibration Signal

Rotating machinery are critical instruments in the manufacturing sectors that are continually operated to fulfill their productivity objective. To reduce the risk of catastrophic failure and unwanted breakdown, it is crucial to ensure that these machines operate within their quality standards. Waste is undesirable to such sectors that directly affect manufacturing price. Maintenance intervention must be efficient, else it is deemed as waste. It is estimated that businesses are losing billions of dollars worldwide due to inadequate maintenance and poor management. It is, therefore, crucial to carry out effective maintenance actions. Since condition-based monitoring method recommends maintenance only when necessary, this approach can avoid unnecessary plan maintenance costs. Condition-based approach, along with the different faults detecting and correcting approach can become handy for the smooth operation of the machine in the industries. Out of various approaches, the vibration parameters-based condition monitoring approach has been proposed in this work. The significance of the proposed method is that it can correctly identify and classify the condition of the equipment as normal, misaligned, unbalanced, and cracked. Using the information of local harmonic acceleration amplitude, instead of harmonic acceleration amplitude, fault detecting, and classifying method is proposed. Then, the phase plane diagram-based fault classification technique is also proposed using the information of all the accelerometer data. Similarly, the Fuzzy Logic method is also used for fault detection and classification purpose. The obtained results signify the effectiveness of these proposed methods

Processor Microarchitecture Security

As computer systems grow more and more complicated, various optimizations can unintentionally introduce security vulnerabilities in these systems. The vulnerabilities can lead to user information and data being compromised or stolen. In particular, the ending of both Moore\u27s law and Dennard scaling motivate the design of more exotic microarchitectural optimizations to extract more performance -- further exacerbating the security vulnerabilities. The performance optimizations often focus on sharing or re-using of hardware components within a processor, between different users or programs. Because of the sharing of the hardware, unintentional information leakage channels, through the shared components, can be created. Microarchitectural attacks, such as the high-profile Spectre and Meltdown attacks or the cache covert channels that they leverage, have demonstrated major vulnerabilities of modern computer architectures due to the microarchitectural~optimizations. Key components of processor microarchitectures are processor caches used for achieving high memory bandwidth and low latency for frequently accessed data. With frequently accessed data being brought and stored in caches, memory latency can be significantly reduced when data is fetched from the cache, as opposed to being fetched from the main memory. With limited processor chip area, however, the cache size cannot be very large. Thus, modern processors adopt a cache hierarchy with multiple levels of caches, where the cache close to processor is faster but smaller, and the cache far from processor is slower but larger. This leads to a fundamental property of modern processors: {\em the latency of accessing data in different cache levels and in main memory is different}. As a result, the timing of memory operations when fetching data from different cache levels, e.g., the timing of fetching data from closest-to-processor L1 cache vs. from main memory, can reveal secret-dependent information if attacker is able to observe the timing of these accesses and correlate them to the operation of the victim\u27s code. Further, due to limited size of the caches, memory accesses by a victim may displace attacker\u27s data from the cache, and with knowledge, or reverse-engineering, of the cache architecture, the attacker can learn some information about victim\u27s data based on the modifications to the state of the cache -- which can be observed by the timing~measurements. Caches are not only structures in the processor that can suffer from security vulnerabilities. As an essential mechanism to achieving high performance, cache-like structures are used pervasively in various processor components, such as the translation lookaside buffer (TLB) and processor frontend. Consequently, the vulnerabilities due to timing differences of accessing data in caches or cache-like structures affect many components of the~processor. The main goal of this dissertation is the {\em design of high performance and secure computer architectures}. Since the sophisticated hardware components such as caches, TLBs, value predictors, and processor frontend are critical to ensure high performance, realizing this goal requires developing fundamental techniques to guarantee security in the presence of timing differences of different processor operations. Furthermore, effective defence mechanisms can be only developed after developing a formal and systematic understanding of all the possible attacks that timing side-channels can lead to. To realize the research goals, the main main contributions of this dissertation~are: \begin{itemize}[noitemsep] \item Design and evaluation of a novel three-step cache timing model to understand theoretical vulnerabilities in caches \item Development of a benchmark suite that can test if processor caches or secure cache designs are vulnerable to certain theoretical vulnerabilities. \item Development of a timing vulnerability model to test TLBs and design of hardware defenses for the TLBs to address newly found vulnerabilities. \item Analysis of value predictor attacks and design of defenses for value predictors. \item Evaluation of vulnerabilities in processor frontends based on timing differences in the operation of the frontends. \item Development of a design-time security verification framework for secure processor architectures, using information flow tracking methods. \end{itemize} \newpage This dissertation combines the theoretical modeling and practical benchmarking analysis to help evaluate susceptibility of different architectures and microarchitectures to timing attacks on caches, TLBs, value predictors and processor frontend. Although cache timing side-channel attacks have been studied for more than a decade, there is no evidence that the previously-known attacks exhaustively cover all possible attacks. One of the initial research directions covered by this dissertation was to develop a model for cache timing attacks, which can help lead towards discovering all possible cache timing attacks. The proposed three-step cache timing vulnerability model provides a means to enumerate all possible interactions between the victim and attacker who are sharing a cache-like structure, producing the complete set of theoretical timing vulnerabilities. This dissertation also covers new theoretical cache timing attacks that are unknown prior to being found by the model. To make the advances in security not only theoretical, this dissertation also covers design of a benchmarking suite that runs on commodity processors and helps evaluate their cache\u27s susceptibility to attacks, as well as can run on simulators to test potential or future cache designs. As the dissertation later demonstrates, the three-step timing vulnerability model can be naturally applied to any cache-like structures such as TLBs, and the dissertation encompasses a three-step model for TLBs, uncovering of theoretical new TLB attacks, and proposals for defenses. Building on success of analyzing caches and TLBs for new timing attacks, this dissertation then discusses follow-on research on evaluation and uncovering of new timing vulnerabilities in processor frontends. Since security analysis should be applied not just to existing processor microarchitectural features, the dissertation further analyzes possible future features such as value predictors. Although not currently in use, value predictors are actively being researched and proposed for addition into future microarchitectures. This dissertation shows, however, that they are vulnerable to attacks. Lastly, based on findings of the security issues with existing and proposed processor features, this dissertation explores how to better design secure processors from ground up, and presents a design-time security verification framework for secure processor architectures, using information flow tracking methods

Image Processing Using FPGAs

This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs

Towards the automatic evaluation of stylistic quality of natural texts: constructing a special-­purpose corpus of stylistic edits from the Wikipedia revision history

This thesis proposes an approach to automatic evaluation of the stylistic quality of natural texts through data-driven methods of Natural Language Processing. Advantages of data driven methods and their dependency on the size of training data are discussed. Also the advantages of using Wikipedia as a source for textual data mining are presented. The method in this project crucially involves a program for quick automatic extraction of sentences edited by users from the Wikipedia Revision History. The resulting edits have been compiled in a large-scale corpus of examples of stylistic editing. The complete modular structure of the extraction program is described and its performance is analyzed. Furthermore, the need to separate stylistic edits stylistic edits from factual ones is discussed and a number of Machine Learning classification algorithms for this task are proposed and tested. The program developed in this project was able to process approximately 10% of the whole Russian Wikipedia Revision history (200 gigabytes of textual data) in one month, resulting in the extraction of more than two millions of user edits. The best algorithm for the classification of edits into factual and stylistic ones achieved 86.2% cross-validation accuracy, which is comparable with state-of-the-art performance of similar models described in published papers.Master i Datalingvistikk og språkteknologiMAHF-DASPDASP35

Vision Based Extraction of Nutrition Information from Skewed Nutrition Labels

An important component of a healthy diet is the comprehension and retention of nutritional information and understanding of how different food items and nutritional constituents affect our bodies. In the U.S. and many other countries, nutritional information is primarily conveyed to consumers through nutrition labels (NLs) which can be found in all packaged food products. However, sometimes it becomes really challenging to utilize all this information available in these NLs even for consumers who are health conscious as they might not be familiar with nutritional terms or find it difficult to integrate nutritional data collection into their daily activities due to lack of time, motivation, or training. So it is essential to automate this data collection and interpretation process by integrating Computer Vision based algorithms to extract nutritional information from NLs because it improves the user’s ability to engage in continuous nutritional data collection and analysis. To make nutritional data collection more manageable and enjoyable for the users, we present a Proactive NUTrition Management System (PNUTS). PNUTS seeks to shift current research and clinical practices in nutrition management toward persuasion, automated nutritional information processing, and context-sensitive nutrition decision support. PNUTS consists of two modules, firstly a barcode scanning module which runs on smart phones and is capable of vision-based localization of One Dimensional (1D) Universal Product Code (UPC) and International Article Number (EAN) barcodes with relaxed pitch, roll, and yaw camera alignment constraints. The algorithm localizes barcodes in images by computing Dominant Orientations of Gradients (DOGs) of image segments and grouping smaller segments with similar DOGs into larger connected components. Connected components that pass given morphological criteria are marked as potential barcodes. The algorithm is implemented in a distributed, cloud-based system. The system’s front end is a smartphone application that runs on Android smartphones with Android 4.2 or higher. The system’s back end is deployed on a five node Linux cluster where images are processed. The algorithm was evaluated on a corpus of 7,545 images extracted from 506 videos of bags, bottles, boxes, and cans in a supermarket. The DOG algorithm was coupled to our in-place scanner for 1D UPC and EAN barcodes. The scanner receives from the DOG algorithm the rectangular planar dimensions of a connected component and the component’s dominant gradient orientation angle referred to as the skew angle. The scanner draws several scan lines at that skew angle within the component to recognize the barcode in place without any rotations. The scanner coupled to the localizer was tested on the same corpus of 7,545 images. Laboratory experiments indicate that the system can localize and scan barcodes of any orientation in the yaw plane, of up to 73.28 degrees in the pitch plane, and of up to 55.5 degrees in the roll plane. The videos have been made public for all interested research communities to replicate our findings or to use them in their own research. The front end Android application is available for free download at Google Play under the title of NutriGlass. This module is also coupled to a comprehensive NL database from which nutritional information can be retrieved on demand. Currently our NL database consists of more than 230,000 products. The second module of PNUTS is an algorithm whose objective is to determine the text skew angle of an NL image without constraining the angle’s magnitude. The horizontal, vertical, and diagonal matrices of the (Two Dimensional) 2D Haar Wavelet Transform are used to identify 2D points with significant intensity changes. The set of points is bounded with a minimum area rectangle whose rotation angle is the text’s skew. The algorithm’s performance is compared with the performance of five text skew detection algorithms on 1001 U.S. nutrition label images and 2200 single- and multi-column document images in multiple languages. To ensure the reproducibility of the reported results, the source code of the algorithm and the image data have been made publicly available. If the skew angle is estimated correctly, optical character recognition (OCR) techniques can be used to extract nutrition information