1,720,995 research outputs found
A Compressive Sampling Data Gathering Approach for Wireless Sensor Networks Using a Sparse Acquisition Matrix With Abnormal Values
No full textApplication of Compressive Sampling (CS) to Wireless Sensor Networks (WSNs), is a very promising field. In particular, CS allows to exactly reconstruct a sparse signal using only a few measurements. Hence, it promises to represent a viable solution for reducing data exchange in WSNs, thus prolonging the network lifetime. On the other hand, natural signals are only approximately sparse and, hence, CS entails a reconstruction error, which limits its applicability in many situations. To cope with this impairment, we first consider a CS scheme based on a sparse acquisition matrix, so that only M over N (M N) randomly chosen nodes in the network send a packet towards the sink. Then, we propose to use a distributed estimation scheme to locally detect whether the data must be forced to transmit or not, thus highly improving the reconstruction quality. © 2012 IEEE
A low refresh-rate video sequences compression technique using quadtrees and adaptive spatial sampling
No full text
Real-Time Automatic Detection of Violent-Acts by Low-Level Colour Visual Cues
No full textAutomatic recognition of human activities is important for the development of next generation video-surveillance systems. In this paper we address the specific problem of automatically detecting violent interpersonal acts in monocular colour video streams. Unlikely previous approaches, only little knowledge is assumed about the acquisition setup and about the content of the acquired scenes. So the proposed approach is suitable in a wide range of practical cases. Reliability and general-purpose applicability is achieved by analysing low-level features (like the spatial-temporal behaviour of coloured stains), and by measuring some warping and motion parameters. In this way it is not necessary to extract accurate target silhouettes, that is a critical task because of occlusions and overcrowding that are typical during interpersonal contacts. A suitable index called maximum warping energy (MWE) has been defined to describe the localized spatial-temporal complexity of colour conformations. Our experiments show that aggressive activities give significantly higher MWE values if compared with safe actions like: walking, running, embracing or handshaking. So it is possible to distinguish violent acts from normal behaviours even in presence of many people and crowded environments. Homography is used to improve robustness by verifying the real targets nearness. False interactions because of perspective-induced occlusions are discarded
A robust fuzzy clustering algorithm for the classification of remote sensing images
No full textA new fuzzy clustering algorithm is presented, that permits to group data samples even when the number of clusters is not known or when noise is present. The new algorithm is obtained by replacing the probabilistic constraint that memberships across clusters must sum to one with a composite constraint. The composite constraint allows the algorithm to assign low memberships to uncertain data, thus ensuring higher robustness against noise, and avoiding the need to know the number of cluster contained in the data. The results obtained by applying the algorithm to the construction of a land cover map from remote sensed data (LANDSAT) are reported
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Recurrent backpropagation networks receiver for modulated signals over noisy channels
Full text linkRecurrent Backpropagation networks have been used to build up a neural receiver for GSM signals. The simulations have been carried out considering an AWGN channel corrupted by ISI, fading and Doppler. The experimental results show that the neural receiver performs better than a classic coherent one and it improves its performances when the number of training samples is increased
A multi-layer lorawan infrastructure for smart waste management
Full text linkLong Range Wide Area Network (LoRaWAN) has rapidly become one of the key enabling technologies for the development of Internet of Things (IoT) architectures. A wide range of different solutions relying on this communication technology can be found in the literature: nevertheless, the most part of these architectures focus on single task systems. Conversely, the aim of this paper is to present the architecture of a LoRaWAN infrastructure gathering under the same network different typologies of services within one of the most significant sub-systems of the Smart City ecosystem (i.e., the Smart Waste Management). The proposed architecture exploits the whole range of different LoRaWAN classes, integrating nodes of growing complexity according to the different functions. The lowest level of this architecture is occupied by smart bins that simply collect data about their status. Moving on to upper levels, smart drop-off containers allow the interaction with users as well as the implementation of asynchronous downlink queries. At the top level, Video Surveillance Units (VSUs) are provided with machine learning capabilities for the detection of the presence of fire nearby bins or drop-off containers, thus fully implementing the Edge Computing paradigm. The proposed network infrastructure and its subsystems have been tested in a laboratory and in the field. This study has enhanced the readiness level of the proposed technology to Technology Readiness Level (TRL) 3
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