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Among the components that can fortify computer security there is a relatively new entrant known as the memristor (contraction of memory resistor).ABSTRACT | Information security has emerged as an important system and application metric. Classical security solutions use algorithmic mechanisms that address a small subset of emerging security requirements, often at high-energy and performance overhead. Further, emerging side-channel and physical attacks can compromise classical security solutions. Hardware security solutions overcome many of these limitations with less energy and performance overhead. Nanoelectronics-based hardware security preserves these advantages while enabling conceptually new security primitives and applications. This tutorial paper shows how one can develop hardware security primitives by exploiting the unique characteristics such as complex device and system models, bidirectional operation, and nonvolatility of emerging nanoelectronic devices. This paper then explains the security capabilities of several emerging nanoelectronic devices: memristors, resistive random-access memory, contact-resistive random-access memory, phase change memories, spin torque-transfer random-access memory, orthogonal spin transfer random access memory, graphene, carbon nanotubes, silicon nanowire field-effect transistors, and nanoelectronic mechanical switches. Further, the paper describes hardware security primitives for authentica-tion, key generation, data encryption, device identification, digital forensics, tamper detection, and thwarting reverse engineering. Finally, the paper summarizes the outstanding challenges in using emerging nanoelectronic devices for security.In this paper, we refer a non-CMOS, nanoscale, emerging technology device as a nanoelectronic device. 0018-9219

Abstract-Wearable sensing systems have facilitated a variety of applications in Wireless Health. Due to the considerable number of sensors and their constant monitoring these systems are often expensive and power hungry. Traditional approaches to sensor selection in large multisensory arrays attempt to alleviate these issues by removing redundant sensors while maintaining overall sensor predictability. However, predicting sensors is unnecessary if ultimately the system needs only to quantify diagnostic measurements specific to the application domain. We propose a new method for optimizing the design of medical sensor systems through diagnostic-based bottom-up sensor selection. We reduce the original sensor array from ninety nine to twelve sensors while maintaining a prediction error rate of less than 5% over all diagnostic metrics in our testing dataset.

We propose a novel approach using representation learning for tackling the problem of extracting structured information from form-like document images. We propose an extraction system that uses knowledge of the types of the target fields to generate extraction candidates, and a neural network architecture that learns a dense representation of each candidate based on neighboring words in the document. These learned representations are not only useful in solving the extraction task for unseen document templates from two different domains, but are also interpretable, as we show using loss cases.

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