Problem Of Event Detection Research Articles

Mobile Networks and Applications (MONET) Special Issue on Sensor Systems and Software

Wireless sensor networks introduce innovative and interesting application scenarios that may support a variety of different application domains including environmental monitoring, disaster prevention, building automation, object tracking, nuclear reactor control, fire detection, agriculture, healthcare, and traffic monitoring. The widespread acceptance of these new services can be improved by the definition of frameworks and architectures that have the potential to radically simplify software development for wireless sensor network based applications. The aim of these new architectures is to support flexible, scalable programming of applications based on adaptive middleware. As a consequence, wireless sensor networks require novel programming paradigms and technologies. Moreover the design of new complex systems, characterized by the interaction of different and heterogeneous resources, will allow the development of innovative applications that meet high performance goals. As a consequence, wireless sensor networks require contributions from many fields such as embedded systems, distributed systems, data management, system security and applications. This Special Issue on Sensor Systems and Software provides a venue to report on recent developments in addressing the research challenges facing system development and software support for wireless sensor networks based systems. In particular, it places emphasis on layers well above the traditional MAC and routing, and transport layer protocols. In the paper Robust Image Transmission over Wireless Sensor Networks, Boluk, Baydere and Harmanci consider the problem of ubiquitous multimedia applications over wireless sensor networks. In particular, they attempt to address the problem of distortions caused on the image data due to channel and instant node failures. They propose two techniques, which they relate to multipath transmission, as a means to compensate for the occurrence of the above problems at the expense of increasing energy consumption and bandwidth requirements. The first technique is based on watermarking and aims to conceal errors by utilizing the discrete wavelet transform to embedding minute replicas of the original image into itself. The second technique is a modification of Reed-Solomon coding appropriate for this context. Through extensive simulations, they discover that error concealment with multipath fusion provides superior performance. Chen, Gonzalez, Vasilakos, Cao and Leung provide a survey in the very active area of Body Area Networks (BANs). Such networks are widely recognised as a core ingredient of next generation healthcare monitoring applications. The survey specifically considers BAN communication modes and their costs and benefits and identify the main design challenges and open issues that need to be addressed before BANs become competitive in applications. Finally, Zoumboulakis and Roussos consider the problem of Complex Event Detection in Extremely ResourceS. Sicari (*) Dipartimento di informatica e comunicazione, Universita degli studi dell’Insubria, Varese, Italy e-mail: sabrina.sicari@uninsubria.it

Computing reachable sets for uncertain nonlinear hybrid systems using interval constraint-propagation techniques

We investigate solution techniques for numerical constraint-satisfaction problems and validated numerical set integration methods for computing reachable sets of nonlinear hybrid dynamical systems in the presence of uncertainty. To use interval simulation tools with higher-dimensional hybrid systems, while assuming large domains for either initial continuous state or model parameter vectors, we need to solve the problem of flow/sets intersection in an effective and reliable way. The main idea developed in this paper is first to derive an analytical expression for the boundaries of continuous flows, using interval Taylor methods and techniques for controlling the wrapping effect. Then, the event detection and localization problems underlying flow/sets intersection are expressed as numerical constraint-satisfaction problems, which are solved using global search methods based on branch-and-prune algorithms, interval analysis and consistency techniques. The method is illustrated with hybrid systems with uncertain nonlinear continuous dynamics and nonlinear invariants and guards.

Unusual event detection and prediction in real-life scenes

In this paper, we consider unusual event detection problem in a novel viewpoint and provide an algorithm to solve the problem. The actions or events in the scene is usual or not will eventually be reflected on the changes of some basic features. We summarize these basic event features and propose special representation for each of them. Thus we can model these features in a uniform mode using adaptive Gaussian mixture model. Supervised and unsupervised unusual event detection algorithm can be designed to fit various situations based on this model. The superiority of our model is that it can detect unusual event automatically without to know the determinate model of unusual events. In conclusion, we provide two applications to verify the effectiveness of our model.

Unusual event detection and prediction based on sectional contextual edit distance

We redefine the unusual event detection problem from a different point of view. Several fundamental event features are investigated and adopted. These features are redescribed in a uniform model. Thus, using this model, supervised/unsupervised unusual event detection algorithms can be designed to fit various situations. Trajectory is treated as the most important feature. To more accurately measure the similarity of different moving object trajectories, a novel distance measurement, the sectional contextual edit distance (SCED), is developed. In the SCED, cost functions are designed according to contextual information and trajectories are segmented into subsections automatically, based on the relevant contexts. Velocity and orientation are also taken into account in cost functions to build an integrated distance similarity measurement. Experimental results demonstrate better performance using the newly proposed similarity measurement while being compared with the existing methods, and some cases of the unusual event detection problem are also demonstrated.

Computing reachable sets for uncertain nonlinear hybrid systems using interval constraint propagation techniques

We investigate solution techniques for numerical constraint satisfaction problems and validated numerical set integration methods for computing reachable sets of nonlinear hybrid dynamical systems in presence of uncertainty. To use interval simulation tools with higher dimensional hybrid systems, while assuming large domains for either initial continuous state or model parameter vectors, we need to solve the problem of flow/sets intersection in an effective and reliable way. The main idea developed in this paper is first to derive an analytical expression for the boundaries of continuous flows, using interval Taylor methods and techniques for controlling the wrapping effect. Then, the event detection and localization problems underlying flow/sets intersection are expressed as numerical constraint satisfaction problems, which are solved using global search methods based on branch-and-prune algorithms, interval analysis and consistency techniques. The method is illustrated with a hybrid system with uncertain nonlinear continuous dynamics and nonlinear invariants and guards.

Learning, detection and representation of multi-agent events in videos

In this paper, we model multi-agent events in terms of a temporally varying sequence of sub-events, and propose a novel approach for learning, detecting and representing events in videos. The proposed approach has three main steps. First, in order to learn the event structure from training videos, we automatically encode the sub-event dependency graph, which is the learnt event model that depicts the conditional dependency between sub-events. Second, we pose the problem of event detection in novel videos as clustering the maximally correlated sub-events using normalized cuts. The principal assumption made in this work is that the events are composed of a highly correlated chain of sub-events that have high weights (association) within the cluster and relatively low weights (disassociation) between the clusters. The event detection does not require prior knowledge of the number of agents involved in an event and does not make any assumptions about the length of an event. Third, we recognize the fact that any abstract event model should extend to representations related to human understanding of events. Therefore, we propose an extension of CASE representation of natural languages that allows a plausible means of interface between users and the computer. We show results of learning, detection, and representation of events for videos in the meeting, surveillance, and railroad monitoring domains.

Event detection in real time on a personal camera phone image collection

Grouping photos of the same event together is extremely useful for the management of personal photo collections. However, most methods cannot be applied to the problem of online event detection in embedded devices because they do not consider hardware constraints or a user’s photo-taking behavior. In this paper, we propose an efficient and effective event detection algorithm for managing personal photo collections in camera phones or digital cameras. The proposed algorithm fuses time and location information, which is deemed the most important information for personal photo management, and works in real time in embedded devices. We model event occurrences in a user’s photo-taking behavior as a Poisson process by imposing certain constraints on calculating the elapsed time. Location information is incorporated into event detection when confidence in a decision based on the Poisson process is not high enough. The algorithm is user-centric because it provides the unique capabilities of accepting and adjusting to user feedback. Our experiment results show that the proposed event detection method has the potential to support emerging multimedia applications in embedded devices.

Application of the wavelet transform to automatic seismic signal detection

The paper is devoted to such a priority direction of digital seismology as the detection of seismic signals in a noisy medium. Various formulations of the problem of seismic event detection are analyzed. The reasons for the ineffectiveness of the classical formulation of the detection problem are revealed, and an adequate general formulation of the problem in the frequency-time space is proposed. A criterion for the automatic determination of the length of a seismic signal is proposed. The time-scale STA/LTA (Short Time Average to Long Time Average) detector, utilizing the expansion of a seismogram in a packet of wavelets, is proposed. The method eliminates the shortcomings of the standard scheme and enables the detection of seismic events with a low signal-to-noise ratio. The problems of the choice of the mother wavelet and the decomposition level of seismograms are discussed from the standpoint of physical properties of seismic wave fields.

Real-Time Seismic Signal Enhancement Utilizing a Hybrid Rao–Blackwellized Particle Filter and Hidden Markov Model Filter

This letter outlines a novel and robust algorithm for identifying seismic events within low signal-to-noise ratio (SNR) passive seismic data in real time. Since the event detection problem is a continuous, real-time process which has nonlinear mathematical representations, a Rao-Blackwellized particle filter (RBPF) is utilized. In this algorithm, a jump Markov linear Gaussian system (JMLGS) is defined where changes (i.e., jumps) in the state-space system and measurement equations are due to the occurrences and losses of events within the measurement noise. The RBPF obtains optimal estimates of the possible seismic events by individually weighting and subsequently summing a bank of Kalman filters (KFs). These KFs are specified and updated by samples drawn from a Markov chain distribution which defines the probability of the individual dynamical systems which compose the JMLGS. In addition, a hidden Markov model filter is utilized within the RBPF filter formulation so that real-time estimates of the phase of the seismic event can be obtained. The filter is demonstrated to provide up to an 80-fold improvement in the SNR when processing simulated seismic data with Gauss-Markov measurement noise.

Decidability and complexity of event detection problems for ODEs

Decidability and complexity of event detection problems for ODEs

Undecidable event detection problems for ODEs of dimension one and two

The ability of dynamical systems of various kinds to simulate Turing machines and thus manifest at universat computation power (and beyond) has gathered a lot of interest lately, see e.g. [16]. |5|. |6| and |4|. A similar line of investigation for ordinary differential equations was startest in [11] and continued in [12] and [13]. In this context the minimum dimension required for universal computation is of interest. The dynamical systems in [5] and [6] are of small dimension and the topic of [4] is to find the smallest dimension for certain types of dynamical systems. The results in this paper show that for ODEs dimension two can be reached and, allowing somewhat complicated events, even dimension one.