A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

Improving the security of secure direct communication based on secret transmitting order of particles

We analyzed the security of the secure direct communication protocol based on secret transmitting order of particles recently proposed by Zhu, Xia, Fan, and Zhang [Phys. Rev. A 73, 022338 (2006)], and found that this scheme is insecure if an eavesdropper, say Eve, wants to steal the secret message with Trojan horse attack strategies. The vital loophole in this scheme is that the two authorized users check the security of their quantum channel only once. Eve can insert another spy photon, an invisible photon or a delay one in each photon which the sender Alice sends to the receiver Bob, and capture the spy photon when it returns from Bob to Alice. After the authorized users check the security, Eve can obtain the secret message according to the information about the transmitting order published by Bob. Finally, we present a possible improvement of this protocol.Comment: 4 pages, no figur

Optical fiber sensors for coal mine shaft integrity and equipment condition monitoring

Shaft is an important structure of mine. Deep mining increases mine pressure, induces shaft deformation and affects mine normal lifting. How to improve the inspection efficiency, reduce the maintenance cost and ensure the normal operation of the shaft is an important problem facing the mine. The paper introduces the optical fiber sensing technology to monitor the equipment status of the main shaft, puts forward the implementation scheme of the optical fiber monitoring of shaft deformation, and sets up a shaft equipment condition monitoring system based on the optical fiber sensing technology. It can realize equipment displacement monitoring, strain monitoring and vibration signal monitoring in the process of shaft operation. Comprehensive on-line monitoring of shaft running state can be realized, which opens up a new method for shaft deformation monitoring technology. Fiber optic sensing monitoring technology is of great significance to the safe operation of shaft

Adaptive Fuzzy Game-based Energy Efficient Localization in 3D Underwater Sensor Networks

Numerous applications in 3D underwater sensor networks (UWSNs), such as pollution detection, disaster prevention, animal monitoring, navigation assistance, and submarines tracking, heavily rely on accurate localization techniques. However, due to the limited batteries of sensor nodes and the di!culty for energy harvesting in UWSNs, it is challenging to localize sensor nodes successfully within a short sensor node lifetime in an unspeci"ed underwater environment. Therefore, we propose the Adaptive Energy-E!cient Localization Algorithm (Adaptive EELA) to enable energy-e!cient node localization while adapting to the dynamic environment changes. Adaptive EELA takes a fuzzy game-theoretic approach, whereby Stackelberg game is used to model the interactions among sensor and anchor nodes in UWSNs and employs the adaptive neuro-fuzzy method to set the appropriate utility functions. We prove that a socially optimal Stackelberg–Nash Equilibrium is achieved in Adaptive EELA. Through extensive numerical simulations under various environmental scenarios, the evaluation results show that our proposed algorithm accomplishes a signi"cant energy reduction, e.g., 66% lower compared to baselines, while achieving a desired performance level in terms of localization coverage, error, and delay

High-capacity quantum secure direct communication based on quantum hyperdense coding with hyperentanglement

We present a quantum hyperdense coding protocol with hyperentanglement in polarization and spatial-mode degrees of freedom of photons first and then give the details for a quantum secure direct communication (QSDC) protocol based on this quantum hyperdense coding protocol. This QSDC protocol has the advantage of having a higher capacity than the quantum communication protocols with a qubit system. Compared with the QSDC protocol based on superdense coding with $d$-dimensional systems, this QSDC protocol is more feasible as the preparation of a high-dimension quantum system is more difficult than that of a two-level quantum system at present.Comment: 5 pages, 2 figur

Acoustic Tweezing Cytometry Induces Rapid Initiation of Human Embryonic Stem Cell Differentiation.

Mechanical forces play critical roles in influencing human embryonic stem cell (hESC) fate. However, it remains largely uncharacterized how local mechanical forces influence hESC behavior in vitro. Here, we used an ultrasound (US) technique, acoustic tweezing cytometry (ATC), to apply targeted cyclic subcellular forces to hESCs via integrin-bound microbubbles (MBs). We found that ATC-mediated cyclic forces applied for 30 min to hESCs near the edge of a colony induced immediate global responses throughout the colony, suggesting the importance of cell-cell connection in the mechanoresponsiveness of hESCs to ATC-applied forces. ATC application generated increased contractile force, enhanced calcium activity, as well as decreased expression of pluripotency transcription factors Oct4 and Nanog, leading to rapid initiation of hESC differentiation and characteristic epithelial-mesenchymal transition (EMT) events that depend on focal adhesion kinase (FAK) activation and cytoskeleton (CSK) tension. These results reveal a unique, rapid mechanoresponsiveness and community behavior of hESCs to integrin-targeted cyclic forces

Specifying and Verifying Concurrent Algorithms with Histories and Subjectivity

We present a lightweight approach to Hoare-style specifications for fine-grained concurrency, based on a notion of time-stamped histories that abstractly capture atomic changes in the program state. Our key observation is that histories form a partial commutative monoid, a structure fundamental for representation of concurrent resources. This insight provides us with a unifying mechanism that allows us to treat histories just like heaps in separation logic. For example, both are subject to the same assertion logic and inference rules (e.g., the frame rule). Moreover, the notion of ownership transfer, which usually applies to heaps, has an equivalent in histories. It can be used to formally represent helping---an important design pattern for concurrent algorithms whereby one thread can execute code on behalf of another. Specifications in terms of histories naturally abstract granularity, in the sense that sophisticated fine-grained algorithms can be given the same specifications as their simplified coarse-grained counterparts, making them equally convenient for client-side reasoning. We illustrate our approach on a number of examples and validate all of them in Coq.Comment: 17 page