Conventional distributed arithmetic (DA) is popular in application-specific integrated circuit (ASIC) design, and it features on-chip ROM to achieve high speed and regularity. In this paper, a new DA architecture called NEDA is proposed, aimed at reducing the cost metrics of power and area while maintaining high speed and accuracy in digital signal processing (DSP) applications. Mathematical analysis proves that DA can implement inner product of vectors in the form of two's complement numbers using only additions, followed by a small number of shifts at the final stage. Comparative studies show that NEDA outperforms widely used approaches such as multiply/accumulate (MAC) and DA in many aspects. Being a high-speed architecture free of ROM, multiplication, and subtraction, NEDA can also expose the redundancy existing in the adder array consisting of entries of 0 and 1. A hardware compression scheme is introduced to generate a butterfly structure with minimum number of additions. NEDA-based architectures for 8 /spl times/ 8 discrete cosine transform (DCT) core are presented as an example. Savings exceeding 88% are achieved, when the compression scheme is applied along with NEDA. Finite word-length simulations demonstrate the viability and excellent performance of NEDA.

Malaria is a life-threatening disease caused by parasites that are transmitted to people through the bites of infected mosquitoes. Automation of the diagnosis process will enable accurate diagnosis of the disease and hence holds the promise of delivering reliable health-care to resource-scarce areas. Machine learning technologies have been used for automated diagnosis of malaria. We present some of our recent progresses on highly accurate classification of malaria-infected cells using deep convolutional neural networks. First, we describe image processing methods used for segmentation of red blood cells from wholeslide images. We then discuss the procedures of compiling a pathologists-curated image dataset for training deep neural network, as well as data augmentation methods used to significantly increase the size of the dataset, in light of the overfitting problem associated with training deep convolutional neural networks. We will then compare the classification accuracies obtained by deep convolutional neural networks through training, validating, and testing with various combinations of the datasets. These datasets include the original dataset and the significantly augmented datasets, which are obtained using direct interpolation, as well as indirect interpolation using automatically extracted features provided by stacked autoencoders. This chapter ends with a discussion of further research.

This paper addresses the problem of efficient predictive lossless compression on the regions of interest (ROIs) in the hyperspectral images with no-data regions. We propose a two-stage prediction scheme, where a context-similarity-based weighted average prediction is followed by recursive least square filtering to decorrelate the hyperspectral images for compression. We then propose to apply separate Golomb-Rice codes for coding the prediction residuals of the full-context pixels and boundary pixels, respectively. To study the coding gains of this separate coding scheme, we introduce a mixture geometric model to represent the residuals associated with various combinations of the full-context pixels and boundary pixels. Both information-theoretic analysis and simulations on synthetic data confirm the advantage of the separate coding scheme over the conventional coding method based on a single underlying geometric distribution. We apply the aforementioned prediction and coding methods to four publicly available hyperspectral image data sets, attaining significant improvements over several other state-of-the-art methods, including the shape-adaptive JPEG 2000 method.

Wi-Fi is the standard protocol for wireless networks used extensively in US critical infrastructures. Since the Wired Equivalency Privacy (WEP) security protocol was broken, the Wi-Fi Protected Access (WPA) protocol has been considered the secure alternative compatible with hardware developed for WEP. However, in November 2008, researchers developed an attack on WPA, allowing forgery of Address Resolution Protocol (ARP) packets. Subsequent enhancements have enabled ARP poisoning, cryptosystem denial of service, and man-in-the-middle attacks. Open source systems and methods (OSSM) have long been used to secure networks against such attacks. This article reviews OSSMs and the results of experimental attacks on WPA. These experiments re-created current attacks in a laboratory setting, recording both wired and wireless traffic. The article discusses methods of intrusion detection and prevention in the context of cyberphysical protection of critical Internet infrastructure. The basis for this research is a specialized (and undoubtedly incomplete) taxonomy of Wi-Fi attacks and their adaptations to existing countermeasures and protocol revisions. Ultimately, this article aims to provide a clearer picture of how and why wireless protection protocols and encryption must achieve a more scientific basis for detecting and preventing such attacks.