Separation-Based Joint Decoding in Compressive Sensing

We introduce a joint decoding method for compressive sensing that can simultaneously exploit sparsity of individual components of a composite signal. Our method can significantly reduce the total number of variables decoded jointly by separating variables of large magnitudes in one domain and using only these variables to represent the domain. Furthermore, we enhance the separation accuracy by using joint decoding across multiple domains iteratively. This separation-based approach improves the decoding time and quality of the recovered signal. We demonstrate these benefits analytically and by presenting empirical results.Engineering and Applied Science

The Effect Of Accounting Conservatism And Life-Cycle Stages On Firm Valuation

This paper investigates how accounting conservatism affects the value-relevance of accounting information under different economic attributes. A firm’s value is driven by the underlying economics, such as its production function, investment opportunity set, and risk. The corporate life-cycle stage can capture general differences in these underlying economics. From the perspective of the Feltham and Ohlson (1995)’s valuation model, this suggests that firms in different life-cycle stages have different financial characteristics that affect the value-relevance of the accounting information. Their valuation model depicts theoretically that, under conservative accounting, the expected growth in net operating assets affects a firm’s market valuation. This paper predicts that the pricing multiples of the value components of the valuation model will differ in different corporate life-cycle stages and accounting conservatism will have a joint effect with the life-cycle stage on the value-relevance of accounting information. This study conducts its hypothesis tests using comprehensive proxies such as conservatism estimates from the valuation model and corporate life-cycle stages.  These enable this study to examine the overall effects of accounting information, accounting conservatism as well as economic attributes on firm value. According to those comprehensive proxies, sample firms are classified into two conservatism groups, and three life-cycle stages. The results of this study provide evidence that accounting conservatism has a joint effect with the life-cycle stage on the value-relevance of accounting information.&nbsp

Wireless Inference-based Notification (WIN) without Packet Decoding

We consider ultra-energy-efficient wireless transmission of notifications in sensor networks. We argue that the usual practice where a receiver decodes packets sent by a remote node to acquire its state or message is suboptimal in energy use. We propose an alternative approach where a receiver first (1) performs physical-layer matched filtering on arrived packets without actually decoding them at the link layer or higher layer, and then (2) based on the matching results infers the sender's state or message from the time-series pattern of packet arrivals. We show that hierarchical multi-layer inference can be effective for this purpose in coping with channel noise. Because packets are not required to be decodable by the receiver, the sender can reach a farther receiver without increasing the transmit power or, equivalently, a receiver at the same distance with a lower transmit power. We call our scheme Wireless Inference-based Notification (WIN) without Packet Decoding. We demonstrate by analysis and simulation that WIN allows a sender to multiply its notification distance. We show how senders can realize these energy-efficiency benefits with unchanged system and protocols; only receivers, which normally are larger systems than senders and have ample computing and power resources for WIN-related processing.Engineering and Applied Science

Wren’s Walk-Off Bests No. 23 Clemson to Even the Series

Wren’s Walk-Off Bests No. 23 Clemson to Even the Series Junior right fielder hits a bases loaded single with two outs in the nint

Compressed Statistical Testing and Application to Radar

We present compressed statistical testing (CST) with an illustrative application to radar target detection. We characterize an optimality condition for a compressed domain test to yield the same result as the corresponding test in the uncompressed domain. We demonstrate by simulation that under high SNR, a likelihood ratio test with compressed samples at 3.3x or even higher compression ratio can achieve detection performance comparable to that with uncompressed data. For example, our compressed domain Sample Matrix Inversion test for radar target detection can achieve constant false alarm rate (CFAR) performance similar to the corresponding test in the raw data domain. By exploiting signal sparsity in the target and interference returns, compressive sensing based CST can incur a much lower processing cost in statistical training and decision making, and can therefore enable a variety of distributed applications such as target detection on resource limited mobile devices.Engineering and Applied Science

Identification of tumor-associated proteins in oral tongue squamous cell carcinoma by proteomics

Oral tongue carcinoma is an aggressive tumor that particularly affects chronic smokers, drinkers and betel squid chewers. Patients often present symptoms at a late stage, and there is a high recurrence rate after treatment. In this article, we report the first proteomic analysis of oral tongue carcinoma to globally search for tumor related proteins. Apart from helping us to understand the molecular pathogenesis of the carcinoma, these proteins may also have potential clinical applications as biomarkers, enabling the tumor to be identified at an early stage in high risk individuals, treatment response to be predicted, and residual or recurrent carcinoma to be detected sooner after treatment. The protein expression profiles of ten oral tongue squamous cell carcinomas and their matched normal mucosal resection margins were examined by two-dimensional gel electrophoresis and matrix-assisted laser desorption/ionization-time of flight mass spectroscopy. A number of tumor-associated proteins including heat shock protein (HSP)60, HSP27, alpha B-crystalline, ATP synthase beta, calgranulin B, myosin, tropomyosin and galectin 1 were consistently found to be significantly altered in their expression levels in tongue carcinoma tissues, compared with their paired normal mucosae. The expression profile portrays a global protein alteration that appears specific to oral tongue cancer. The potential of utilizing these tumor related proteins for screening cancer and monitoring recurrence warrants further investigation.postprin

Sixty GHz IMPATT diode development

The objective of this program is to develop 60 GHz GaAs IMPATT Diodes suitable for communications applications. The performance goal of the 60 GHz IMPATT is 1W CW output power with a conversion efficiency of 15 percent and 10 year life time. During the course of the program, double drift (DD) GaAs IMPATT Diodes have been developed resulting in the state of the art performance at V band frequencies. A CW output power of 1.12 W was demonstrated at 51.9 GHz with 9.7 percent efficiency. The best conversion efficiency achieved was 15.3 percent. V band DD GaAs IMPATTs were developed using both small signal and large signal analyses. GaAs wafers of DD flat, DD hybrid, and DD Read profiles using molecular beam epitaxy (MBE) were developed with excellent doping profile control. Wafer evaluation was routinely made by the capacitance versus voltage (C-V) measurement. Ion mass spectrometry (SIMS) analysis was also used for more detailed profile evaluation

Gait Recognition Using Encodings With Flexible Similarity Measures

Gait signals detectable by sensors on ubiquitous personal devices such as smartphones can reveal characteristics unique to each individual, and thereby offer a new approach to recognizing users. Conventional pattern matching approaches use inner-product based distance measures which are not robust to common variations in time-series analysis (e.g., shifts and stretching). This is unfortunate given that it is well understood that capturing such variations is paramount for model performance. This work shows how machine learning methods which encode gait signals into a feature space based on a dictionary can use convolution and Dynamic TimeWarping (DTW) similarity measures to improve classification accuracy in a variety of situations common to gait recognition. We also show that data augmentation is crucial in gait recognition, as diverse training data in practical applications is very limited. We validate the effectiveness of these methods empirically, and demonstrate the identification of user gait patterns where shift and stretch variations in measurements are substantial. We present a new gait dataset that contains a complete representation of the variations that can be expected in real-world recognition scenarios. We compare our techniques against the current state of the art gait period detection and normalization schemes on our dataset and show improved classification accuracy under all experimental scenarios.Engineering and Applied Science

A Chip Architecture for Compressive Sensing Based Detection of IC Trojans

We present a chip architecture for a compressive sensing based method that can be used in conjunction with the JTAG standard to detect IC Trojans. The proposed architecture compresses chip output resulting from a large number of test vectors applied to a circuit under test (CUT). We describe our designs in sensing leakage power, computing random linear combinations under compressive sensing, and piggybacking these new functionalities on JTAG. Our architecture achieves approximately a 10× speedup and 1000× reduction in output bandwidth while incurring a small area overhead.Engineering and Applied Science