Distilled Sensing: Adaptive Sampling for Sparse Detection and Estimation

Adaptive sampling results in dramatic improvements in the recovery of sparse signals in white Gaussian noise. A sequential adaptive sampling-and-refinement procedure called Distilled Sensing (DS) is proposed and analyzed. DS is a form of multi-stage experimental design and testing. Because of the adaptive nature of the data collection, DS can detect and localize far weaker signals than possible from non-adaptive measurements. In particular, reliable detection and localization (support estimation) using non-adaptive samples is possible only if the signal amplitudes grow logarithmically with the problem dimension. Here it is shown that using adaptive sampling, reliable detection is possible provided the amplitude exceeds a constant, and localization is possible when the amplitude exceeds any arbitrarily slowly growing function of the dimension.Comment: 23 pages, 2 figures. Revision includes minor clarifications, along with more illustrative experimental results (cf. Figure 2

Sequential joint signal detection and signal-to-noise ratio estimation

The sequential analysis of the problem of joint signal detection and signal-to-noise ratio (SNR) estimation for a linear Gaussian observation model is considered. The problem is posed as an optimization setup where the goal is to minimize the number of samples required to achieve the desired (i) type I and type II error probabilities and (ii) mean squared error performance. This optimization problem is reduced to a more tractable formulation by transforming the observed signal and noise sequences to a single sequence of Bernoulli random variables; joint detection and estimation is then performed on the Bernoulli sequence. This transformation renders the problem easily solvable, and results in a computationally simpler sufficient statistic compared to the one based on the (untransformed) observation sequences. Experimental results demonstrate the advantages of the proposed method, making it feasible for applications having strict constraints on data storage and computation.Comment: 5 pages, Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 201

Predict to Detect: Prediction-guided 3D Object Detection using Sequential Images

Recent camera-based 3D object detection methods have introduced sequential frames to improve the detection performance hoping that multiple frames would mitigate the large depth estimation error. Despite improved detection performance, prior works rely on naive fusion methods (e.g., concatenation) or are limited to static scenes (e.g., temporal stereo), neglecting the importance of the motion cue of objects. These approaches do not fully exploit the potential of sequential images and show limited performance improvements. To address this limitation, we propose a novel 3D object detection model, P2D (Predict to Detect), that integrates a prediction scheme into a detection framework to explicitly extract and leverage motion features. P2D predicts object information in the current frame using solely past frames to learn temporal motion features. We then introduce a novel temporal feature aggregation method that attentively exploits Bird's-Eye-View (BEV) features based on predicted object information, resulting in accurate 3D object detection. Experimental results demonstrate that P2D improves mAP and NDS by 3.0% and 3.7% compared to the sequential image-based baseline, illustrating that incorporating a prediction scheme can significantly improve detection accuracy.Comment: ICCV 202

One-Class Conditional Random Fields for Sequential Anomaly Detection

Sequential anomaly detection is a challenging problem due to the one-class nature of the data (i.e., data is collected from only one class) and the temporal dependence in sequential data. We present One-Class Conditional Random Fields (OCCRF) for sequential anomaly detection that learn from a one-class dataset and capture the temporal dependence structure, in an unsupervised fashion. We propose a hinge loss in a regularized risk minimization framework that maximizes the margin between each sequence being classified as "normal" and "abnormal." This allows our model to accept most (but not all) of the training data as normal, yet keeps the solution space tight. Experimental results on a number of real-world datasets show our model outperforming several baselines. We also report an exploratory study on detecting abnormal organizational behavior in enterprise social networks.United States. Defense Advanced Research Projects Agency (W911NF-12-C-0028)United States. Office of Naval Research (N000140910625)National Science Foundation (U.S.) (IIS-1018055

Sequential Transient Detection for RF Fingerprinting

In this paper, a sequential transient detection method for radio frequency (RF) fingerprinting used in the identification of wireless devices is proposed. To the best knowledge of the authors, sequential detection of transient signals for RF fingerprinting has not been considered in the literature. The proposed method is based on an approximate implementation of the generalized likelihood ratio algorithm. The method can be implemented online in a recursive manner with low computational and memory requirements. The transients of wireless transmitters are detected by using the likelihood ratio of the observations without the requirement of any a priori knowledge about the transmitted signals. The performance of the method was evaluated using experimental data collected from 16 Wi-Fi transmitters and compared to those of two existing methods. The experimental test results showed that the proposed method can be used to detect the transient signals with a low detection delay. Our proposed method estimates transient starting points 20-times faster compared to an existing robust method, as well as providing a classification performance of a mean accuracy close to 95%

Parallel Toolkit for Measuring the Quality of Network Community Structure

Many networks display community structure which identifies groups of nodes within which connections are denser than between them. Detecting and characterizing such community structure, which is known as community detection, is one of the fundamental issues in the study of network systems. It has received a considerable attention in the last years. Numerous techniques have been developed for both efficient and effective community detection. Among them, the most efficient algorithm is the label propagation algorithm whose computational complexity is O(|E|). Although it is linear in the number of edges, the running time is still too long for very large networks, creating the need for parallel community detection. Also, computing community quality metrics for community structure is computationally expensive both with and without ground truth. However, to date we are not aware of any effort to introduce parallelism for this problem. In this paper, we provide a parallel toolkit to calculate the values of such metrics. We evaluate the parallel algorithms on both distributed memory machine and shared memory machine. The experimental results show that they yield a significant performance gain over sequential execution in terms of total running time, speedup, and efficiency.Comment: 8 pages; in Network Intelligence Conference (ENIC), 2014 Europea

Experimental validation of a quasi-realtime human respiration detection method via UWB radar

In this paper, we propose a quasi-realtime human respiration detection method via UWB radar system in through-wall or similar condition. With respect to the previous proposed automatic detection method, the new proposed method assures competitive performance in the human respiration motion detection and effective noise/clutter rejection, which have been proved by experimental results in actual scenario. This new method has also been implemented in a UWB through-wall life-detection radar prototype, and its time consuming is about 2 s, which can satisfy the practical requirement of quasi-realtime for through-wall sequential vital sign detection. Therefore, it can be an alternative for through-obstacles static human detection in antiterrorism or rescue scenarios