Automated Real-time Anomaly Detection in Human Trajectories using Sequence to Sequence Networks

Detection of anomalous trajectories is an important problem with potential applications to various domains, such as video surveillance, risk assessment, vessel monitoring and high-energy physics. Modeling the distribution of trajectories with statistical approaches has been a challenging task due to the fact that such time series are usually non stationary and highly dimensional. However, modern machine learning techniques provide robust approaches for data-driven modeling and critical information extraction. In this paper, we propose a Sequence to Sequence architecture for real-time detection of anomalies in human trajectories, in the context of risk-based security. Our detection scheme is tested on a synthetic dataset of diverse and realistic trajectories generated by the ISL iCrowd simulator. The experimental results indicate that our scheme accurately detects motion patterns that deviate from normal behaviors and is promising for future real-world applications.Comment: AVSS 201

Genetic Algorithms in Antennas and Smart Antennas Design Overview: Two Novel Antenna Systems for Triband GNSS Applications and a Circular Switched Parasitic Array for WiMax Applications Developments with the Use of Genetic Algorithms

Genetic algorithms belong to a stochastic class of evolutionary techniques, whose robustness and global search of the solutions space have made them extremely popular among researchers. They have been successfully applied to electromagnetic optimization, including antenna design as well as smart antennas design. In this paper, extensive reference to literature related antenna design efforts employing genetic algorithms is taking place and subsequently, three novel antenna systems are designed in order to provide realistic implementations of a genetic algorithm. Two novel antenna systems are presented to cover the new GPS/Galileo band, namely, L5 (1176 MHz), together with the L1 GPS/Galileo and L2 GPS bands (1575 and 1227 MHz). The first system is a modified PIFA and the second one is a helical antenna above a ground plane. Both systems exhibit enhanced performance characteristics, such as sufficient front gain, input impedance matching, and increased front-to-back ratio. The last antenna system is a five-element switched parasitic array with a directional beam with sufficient beamwidth to a predetermined direction and an adequate impedance bandwidth which can be used as receiver for WiMax signals

Optimal and Suboptimal Distributed Decision Fusion

The problem of decision fusion in distributed sensor systems is considered. Distributed sensors pass their decisions about the same hypotheses to a fusion center that combines then into a final decision. Assuming that the sensor decisions are independent from each other conditioned on each hypothesis, we provide a general proof that the optimal decision scheme that maximizes the probability of detection for fixed probability of false alarm at the fusion, is the Neymann-Pearson test at the fusion and Likelihood-Ratio tests at the sensors. The optimal set of thresholds is given via a set of nonlinear, coupled equations that depend on the decision policy but not on the priors. The nonlinear threshold equations cannot be solved in general. We provide a suboptimal algorithm for solving for the sensor thresholds through a one dimensional minimization. The algorithm applies to arbitrary type of similar or disimilar sensors. Numerical results have shown that the algorithm yields solutions that are extremely close to the optimal solutions in all the tested cases, and it does not fail in singular cases

Probabilistic Error Modeling and Topology-Based Smoothing of Indoor Localization and Tracking Data, Based on the IEEE 802.15.4a Chirp Spread Spectrum Specification

Location awareness is a core capability in many context-aware computing platforms. Multiple existing systems either provide inadequate accuracy or require extensive calibration or preexisting measurements in order to be functional. This work presents an extensive study of indoor tracking based on the chirp spread spectrum (CSS) specification and an associated analytical framework that allows comparisons to be made between different deployments. CSS provides resilience to fading, while being rapidly deployable. Wireless CSS modules are used to provide time of arrival measurements, necessary to infer the coordinates of a mobile user through trilateration. CSS resilience is tested in four deployments: an indoor space where line of sight (LoS) conditions are always satisfied, an indoor site that includes concrete, nonreflective obstructions, an industrial space with metallic, reflective obstacles, and a Tunnel. Empirical data are discussed in conjunction with the geometric dilution of precision (GDoP) metric, which depends on the system's deployment topology. The probabilistic modeling of the normalized localization error provides insight into the underlying distribution and is utilized in the context of a novel topology-based smoothing technique. Results indicate that CSS can provide accurate tracking. The application of the smoothing algorithm, however, further reduces the normalized error by a considerable amount

Signal Clustering Using Self-Organizing Neural Networks with Adaptive Thresholds

This paper presents an adaptive signal clustering technique using a self-organizing neural network (NN) algorithm, Dignet. The neural network Dignet performs self-organized clustering on input patterns without supervised learning procedures. The number of clusters is automatically generated by adaptive learning and convergence of parameters on the network. The initial threshold value used in Dignet can be specifically determined from a desired lower-bound of tolerable signal-to-noise ratio (SNR), which is useful for systematic analysis of the clustering performance. The threshold value in the algorithm can be self-adjusted from the learning of new patterns to adapt to the changes in the environment. On the application of signal clustering, the NN model Dignet has the adaptability and flexibility to achieving fast and accurate processing. The algorithm can be easily adopted and implemented by massively parallel architectures. The Dignet models have been used in areas of signal detection..

SCENE CATEGORIZATION USING LOW-LEVEL VISUAL FEATURES

Abstract: In this paper, we have built two binary classifiers for indoor/outdoor and city/landscape categories, respectively. The proposed classifiers consist of robust visual feature extraction that feeds a support vector classification. In the case of indoor/outdoor classification, we combine color and texture information using the first three moments of RGB color space components and the low order statistics of the energy wavelet coefficients from a two-level wavelet pyramid. In the case of city/landscape classification, we combine the first three moments of L*a*b color space components and structural information (line segment orientation). Experimental results show that a high classification accuracy is achieved.