A target guided subband filter for acoustic event detection in noisy environments using wavelet packets

This paper deals with acoustic event detection (AED), such as screams, gunshots, and explosions, in noisy environments. The main aim is to improve the detection performance under adverse conditions with a very low signal-to-noise ratio (SNR). A novel filtering method combined with an energy detector is presented. The wavelet packet transform (WPT) is first used for time-frequency representation of the acoustic signals. The proposed filter in the wavelet packet domain then uses a priori knowledge of the target event and an estimate of noise features to selectively suppress the background noise. It is in fact a content-aware band-pass filter which can automatically pass the frequency bands that are more significant in the target than in the noise. Theoretical analysis shows that the proposed filtering method is capable of enhancing the target content while suppressing the background noise for signals with a low SNR. A condition to increase the probability of correct detection is also obtained. Experiments have been carried out on a large dataset of acoustic events that are contaminated by different types of environmental noise and white noise with varying SNRs. Results show that the proposed method is more robust and better adapted to noise than ordinary energy detectors, and it can work even with an SNR as low as -15 dB. A practical system for real time processing and multi-target detection is also proposed in this work

Developing an Efficient DMCIS with Next-Generation Wireless Networks

The impact of extreme events across the globe is extraordinary which continues to handicap the advancement of the struggling developing societies and threatens most of the industrialized countries in the globe. Various fields of Information and Communication Technology have widely been used for efficient disaster management; but only to a limited extent though, there is a tremendous potential for increasing efficiency and effectiveness in coping with disasters with the utilization of emerging wireless network technologies. Early warning, response to the particular situation and proper recovery are among the main focuses of an efficient disaster management system today. Considering these aspects, in this paper we propose a framework for developing an efficient Disaster Management Communications and Information System (DMCIS) which is basically benefited by the exploitation of the emerging wireless network technologies combined with other networking and data processing technologies.Comment: 6 page

A Secure Lightweight Approach of Node Membership Verification in Dense HDSN

In this paper, we consider a particular type of deployment scenario of a distributed sensor network (DSN), where sensors of different types and categories are densely deployed in the same target area. In this network, the sensors are associated with different groups, based on their functional types and after deployment they collaborate with one another in the same group for doing any assigned task for that particular group. We term this sort of DSN as a heterogeneous distributed sensor network (HDSN). Considering this scenario, we propose a secure membership verification mechanism using one-way accumulator (OWA) which ensures that, before collaborating for a particular task, any pair of nodes in the same deployment group can verify each other-s legitimacy of membership. Our scheme also supports addition and deletion of members (nodes) in a particular group in the HDSN. Our analysis shows that, the proposed scheme could work well in conjunction with other security mechanisms for sensor networks and is very effective to resist any adversary-s attempt to be included in a legitimate group in the network.Comment: 6 page

A fuzzy-QFD approach for the enhancement of work equipment safety: a case study in the agriculture sector

The paper proposes a design for safety methodology based on the use of the Quality Function Deployment (QFD) method, focusing on the need to identify and analyse risks related to a working task in an effective manner, i.e. considering the specific work activities related to such a task. To reduce the drawbacks of subjectivity while augmenting the consistency of judgements, the QFD was augmented by both the Delphi method and the fuzzy logic approach. To verify such an approach, it was implemented through a case study in the agricultural sector. While the proposed approach needs to be validated through further studies in different contexts, its positive results in performing hazard analysis and risk assessment in a comprehensive and thorough manner can contribute practically to the scientific knowledge on the application of QFD in design for safety activities

Sensing Small Changes in a Wave Chaotic Scattering System

Classical analogs of the quantum mechanical concepts of the Loschmidt Echo and quantum fidelity are developed with the goal of detecting small perturbations in a closed wave chaotic region. Sensing techniques that employ a one-recording-channel time-reversal-mirror, which in turn relies on time reversal invariance and spatial reciprocity of the classical wave equation, are introduced. In analogy with quantum fidelity, we employ Scattering Fidelity techniques which work by comparing response signals of the scattering region, by means of cross correlation and mutual information of signals. The performance of the sensing techniques is compared for various perturbations induced experimentally in an acoustic resonant cavity. The acoustic signals are parametrically processed to mitigate the effect of dissipation and to vary the spatial diversity of the sensing schemes. In addition to static boundary condition perturbations at specified locations, perturbations to the medium of wave propagation are shown to be detectable, opening up various real world sensing applications in which a false negative cannot be tolerated.Comment: 14 pages, 11 figures, as published on J. Appl. Phy

Practical considerations for acoustic source localization in the IoT era: Platforms, energy efficiency, and performance

The rapid development of the Internet of Things (IoT) has posed important changes in the way emerging acoustic signal processing applications are conceived. While traditional acoustic processing applications have been developed taking into account high-throughput computing platforms equipped with expensive multichannel audio interfaces, the IoT paradigm is demanding the use of more flexible and energy-efficient systems. In this context, algorithms for source localization and ranging in wireless acoustic sensor networks can be considered an enabling technology for many IoT-based environments, including security, industrial, and health-care applications. This paper is aimed at evaluating important aspects dealing with the practical deployment of IoT systems for acoustic source localization. Recent systems-on-chip composed of low-power multicore processors, combined with a small graphics accelerator (or GPU), yield a notable increment of the computational capacity needed in intensive signal processing algorithms while partially retaining the appealing low power consumption of embedded systems. Different algorithms and implementations over several state-of-the-art platforms are discussed, analyzing important aspects, such as the tradeoffs between performance, energy efficiency, and exploitation of parallelism by taking into account real-time constraintsThis work was supported in part by the Post-Doctoral Fellowship from Generalitat Valenciana under Grant APOSTD/2016/069, in part by the Spanish Government under Grant TIN2014-53495-R, Grant TIN2015-65277-R, and Grant BIA2016-76957-C3-1-R, and in part by the Universidad Jaume I under Project UJI-B2016-20.Publicad

Drones Detection Using Smart Sensors

Drones are modern and sophisticated technology that have been used in numerous fields. Nowadays, many countries use them in exploration, reconnaissance operations, and espionage in military operations. Drones also have many uses that are not limited to only daily life. For example, drones are used for home delivery, safety monitoring, and others. However, the use of drones is a double-edged sword. Drones can be used for positive purposes to improve the quality of human lives, but they can also be used for criminal purposes and other detrimental purposes. In fact, many countries have been attacked by terrorists using smart drones. Hence, drone detection is an active area of research and it receives the attention of many scholars. Advanced drones are, many times, difficult to detect, and hence they, sometimes, can be life threatening. Currently, most detection methods are based on video, sound, radar, temperature, radio frequency (RF), or Wi-Fi techniques. However, each detection method has several flaws that make them imperfect choices for drone detection in sensitive areas. Our aim is to overcome the challenges that most existing drone detection techniques face. In this thesis, we propose two modeling techniques and compare them to produce an efficient system for drone detection. Specifically, we compare the two proposed models by investigating the risk assessments and the probability of success for each model