UWB Pulse Radar for Human Imaging and Doppler Detection Applications

We were motivated to develop new technologies capable of identifying human life through walls. Our goal is to pinpoint multiple people at a time, which could pay dividends during military operations, disaster rescue efforts, or assisted-living. Such system requires the combination of two features in one platform: seeing-through wall localization and vital signs Doppler detection. Ultra-wideband (UWB) radar technology has been used due to its distinct advantages, such as ultra-low power, fine imaging resolution, good penetrating through wall characteristics, and high performance in noisy environment. Not only being widely used in imaging systems and ground penetrating detection, UWB radar also targets Doppler sensing, precise positioning and tracking, communications and measurement, and etc. A robust UWB pulse radar prototype has been developed and is presented here. The UWB pulse radar prototype integrates seeing-through imaging and Doppler detection features in one platform. Many challenges existing in implementing such a radar have been addressed extensively in this dissertation. Two Vivaldi antenna arrays have been designed and fabricated to cover 1.5-4.5 GHz and 1.5-10 GHz, respectively. A carrier-based pulse radar transceiver has been implemented to achieve a high dynamic range of 65dB. A 100 GSPS data acquisition module is prototyped using the off-the-shelf field-programmable gate array (FPGA) and analog-to-digital converter (ADC) based on a low cost solution: equivalent time sampling scheme. Ptolemy and transient simulation tools are used to accurately emulate the linear and nonlinear components in the comprehensive simulation platform, incorporated with electromagnetic theory to account for through wall effect and radar scattering. Imaging and Doppler detection examples have been given to demonstrate that such a “Biometrics-at-a-glance” would have a great impact on the security, rescuing, and biomedical applications in the future

Impacts of different SNLS3 light-curve fitters on cosmological consequences of interacting dark energy models

We explore the cosmological consequences of interacting dark energy (IDE) models using the SNLS3 supernova samples. In particular, we focus on the impacts of different SNLS3 light-curve fitters (LCF) (corresponding to "SALT2", "SiFTO", and "Combined" sample). Firstly, making use of the three SNLS3 data sets, as well as the Planck distance priors data and the galaxy clustering data, we constrain the parameter spaces of three IDE models. Then, we study the cosmic evolutions of Hubble parameter $H(z)$, deceleration diagram $q(z)$, statefinder hierarchy $S^{(1)}_3(z)$ and $S^{(1)}_4(z)$, and check whether or not these dark energy diagnosis can distinguish the differences among the results of different SNLS3 LCF. At last, we perform high redshift cosmic age test using three old high redshift objects (OHRO), and explore the fate of the Universe. We find that, the impacts of different SNLS3 LCF are rather small, and can not be distinguished by using $H(z)$, $q(z)$, $S^{(1)}_3(z)$, $S^{(1)}_4(z)$, and the age data of OHRO. In addition, we infer, from the current observations, how far we are from a cosmic doomsday in the worst case, and find that the "Combined" sample always gives the largest 2$\sigma$ lower limit of the time interval between "big rip" and today, while the results given by the "SALT2" and the "SiFTO" sample are close to each other. These conclusions are insensitive to a specific form of dark sector interaction. Our method can be used to distinguish the differences among various cosmological observations.Comment: 12 pages, 7 figures, 2 tables, accepted for publication in Astronomy and Astrophysic

A quasi-current representation for information needs inspired by Two-State Vector Formalism

Recently, a number of quantum theory (QT)-based information retrieval (IR) models have been proposed for modeling session search task that users issue queries continuously in order to describe their evolving information needs (IN). However, the standard formalism of QT cannot provide a complete description for users’ current IN in a sense that it does not take the ‘future’ information into consideration. Therefore, to seek a more proper and complete representation for users’ IN, we construct a representation of quasi-current IN inspired by an emerging Two-State Vector Formalism (TSVF). With the enlightenment of the completeness of TSVF, a “two-state vector” derived from the ‘future’ (the current query) and the ‘history’ (the previous query) is employed to describe users’ quasi-current IN in a more complete way. Extensive experiments are conducted on the session tracks of TREC 2013 & 2014, and show that our model outperforms a series of compared IR models

Ranking-based Deep Cross-modal Hashing

Cross-modal hashing has been receiving increasing interests for its low storage cost and fast query speed in multi-modal data retrievals. However, most existing hashing methods are based on hand-crafted or raw level features of objects, which may not be optimally compatible with the coding process. Besides, these hashing methods are mainly designed to handle simple pairwise similarity. The complex multilevel ranking semantic structure of instances associated with multiple labels has not been well explored yet. In this paper, we propose a ranking-based deep cross-modal hashing approach (RDCMH). RDCMH firstly uses the feature and label information of data to derive a semi-supervised semantic ranking list. Next, to expand the semantic representation power of hand-crafted features, RDCMH integrates the semantic ranking information into deep cross-modal hashing and jointly optimizes the compatible parameters of deep feature representations and of hashing functions. Experiments on real multi-modal datasets show that RDCMH outperforms other competitive baselines and achieves the state-of-the-art performance in cross-modal retrieval applications

Network Proactive Defense Model Based on Immune Danger Theory

Recent investigations into proactive network defense have not produced a systematic methodology and structure; in addition, issues including multi-source information fusion and attacking behavior analysis have not been resolved. Borrowing ideas of danger sensing and immune response from danger theory, a proactive network defense model based on danger theory is proposed. This paper defines the signals and antigens in the network environment as well as attacking behavior analysis algorithm, providing evidence for future proactive defense strategy selection. The results of preliminary simulations demonstrate that this model can sense the onset of varied network attacks and corresponding endangered intensities, which help to understand the attack methods of hackers and assess the security situation of the current network, thus a better proactive defense strategy can be deployed. Moreover, this model possesses good robustness and accuracy

Coarse embeddings at infinity and generalized expanders at infinity

We introduce a notion of coarse embedding at infinity into Hilbert space for metric spaces, which is a weakening of the notion of fibred coarse embedding and a far generalization of Gromov's concept of coarse embedding. It turns out that a residually finite group admits a coarse embedding into Hilbert space if and only if one (or equivalently, every) box space of the group admits a coarse embedding at infinity into Hilbert space. Moreover, we introduce a concept of generalized expander at infinity and show that it is an obstruction to coarse embeddability at infinity.Comment: 20 page

Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical Transformer

Automated segmentation of large volumes of medical images is often plagued by the limited availability of fully annotated data and the diversity of organ surface properties resulting from the use of different acquisition protocols for different patients. In this paper, we introduce a more promising few-shot learning-based method named Region-enhanced Prototypical Transformer (RPT) to mitigate the effects of large intra-class diversity/bias. First, a subdivision strategy is introduced to produce a collection of regional prototypes from the foreground of the support prototype. Second, a self-selection mechanism is proposed to incorporate into the Bias-alleviated Transformer (BaT) block to suppress or remove interferences present in the query prototype and regional support prototypes. By stacking BaT blocks, the proposed RPT can iteratively optimize the generated regional prototypes and finally produce rectified and more accurate global prototypes for Few-Shot Medical Image Segmentation (FSMS). Extensive experiments are conducted on three publicly available medical image datasets, and the obtained results show consistent improvements compared to state-of-the-art FSMS methods. The source code is available at: https://github.com/YazhouZhu19/RPT.Comment: Accepted by MICCA

A Bott periodicity theorem for ℓp\ell^p-spaces and the coarse Novikov conjecture at infinity

We formulate and prove a Bott periodicity theorem for an $\ell^p$-space ($1\leq p<\infty$). For a proper metric space $X$ with bounded geometry, we introduce a version of $K$-homology at infinity, denoted by $K_*^{\infty}(X)$, and the Roe algebra at infinity, denoted by $C^*_{\infty}(X)$. Then the coarse assembly map descents to a map from $\lim_{d\to\infty}K_*^{\infty}(P_d(X))$ to $K_*(C^*_{\infty}(X))$, called the coarse assembly map at infinity. We show that to prove the coarse Novikov conjecture, it suffices to prove the coarse assembly map at infinity is an injection. As a result, we show that the coarse Novikov conjecture holds for any metric space with bounded geometry which admits a fibred coarse embedding into an $\ell^p$-space. These include all box spaces of a residually finite hyperbolic group and a large class of warped cones of a compact space with an action by a hyperbolic group.Comment: 55 page