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Unsupervised semantic segmentation in the time series domain is a much-studied problem due to its potential to detect unexpected regularities and regimes in poorly understood data. However, the current techniques have several shortcomings, which have limited the adoption of time series semantic segmentation beyond academic settings for three primary reasons. First, most methods require setting/learning...
One of the major concern of Wireless Sensor Networks (WSNs) is to minimize the energy consumption of the sensor nodes. In multi-hop clustering, sensor nodes closer to the Base Station (BS) deplete their energy faster as compared to far away nodes. Transmission of own data as well as other nodes data by the nearer nodes is the prime cause for this uneven energy consumption. Hence, the nodes closer...
Coverage issue in directional sensor networks (DSNs) is different from traditional omni-directional wireless sensor networks (WSNs) due to the limited angle of view and adjustable working direction. This paper sets up a model of monitoring area and a model of sensing area according to the unique characteristics of directional sensor and then derives an optimization model for area coverage ratio maximization...
A common scenario in Search and Rescue robotics is to map and patrol a disaster site to assess the situation and plan potential missions of rescue teams. Particular importance has to be given to changes in the environment as these may correspond to critical events like building collapses, movement of objects, etc. This paper presents a change detection pipeline for LiDAR-equipped robots to assist...
A cluster formation algorithm is proposed to save the wastage of energy in cooperative spectrum sensing (CSS), in which small number of groups called clusters are made using fuzzy c-means (FCM). Based on spatial correlation, only limited number of SUs are selected from each cluster, whose sensing information is forwarded to their cluster head (CHs). The primary goal of cognitive radio network is spectrum...
One of the most well-known clustering methods for wireless sensor network is, no doubt, the so-called low energy adaptive clustering hierarchy (LEACH) because it is simple and easy to implement. Although LEACH tries to provide a fair selection mechanism by randomly selecting a number of sensors as the cluster-heads, it does not take into account the distribution of sensors, the main reason that LEACH...
With the growing public interest in health today, people are rapidly increasing their use of sleep sensing devices and smartphone apps in their daily lives to check and manage their sleeping health. However, some of the current sleep monitoring services are void of technical reliability in terms of data collection and analytic methodologies. In this research, for the purpose of robust representativeness,...
Various types of sensors are required to drive autonomous vehicles in order to perceive surroundings on behalf of human beings. Among the various sensors for obtaining the distance information of obstacles, the light detection and ranging (lidar) sensor is chosen as a representative. The quality and amount of information that can be obtained depends on the number and location of lidar sensors. It...
The Internet of Things (IoT) is a very promising concept that by connecting numerous devices to the internet and extracting large sums of information (BigData) can enable the realisation of various futuristic scenarios. In order to develop and assess future applications and services, it is necessary the availability of datasets that can be used to train, test and cross validate. Project SCoT (Smart...
An accurate detection of spectrum opportunities is a key factor in governing the efficient spectrum usage in a cognitive radio (CR) system. Energy detection based spectrum sensing has been widely used due to its ease of implementation with lower computational complexity; however, its robustness and performance are highly affected by the noise uncertainty. In the present work, a real time hardware...
Pedestrian detection is an important component for safety of autonomous vehicles, as well as for traffic and street surveillance. There are extensive benchmarks on this topic and it has been shown to be a challenging problem when applied on real use-case scenarios. In purely image-based pedestrian detection approaches, the state-of-the-art results have been achieved with convolutional neural networks...
In recent technological advancement such as smart grid applications, security surveillance & border protection, internet of things, disaster management & other smart home applications exhilarate the deployment of autonomous, self-configured, large-scale wireless sensor networks. Efficient power conservation is crucial concerns for sensor networks to operate in the hostile environment. Therefore...
Road accident is a widespread problem all over the world. The number of the vehicle is increasing rapidly. Therefore, the probability of an accident is also increasing. So in this paper, we propose an IoT-based system for providing safe driving. It will collect data using smartphone and show the driver about the condition of the road. We have developed an Android based application which will collect...
A wireless sensor network (WSN) can provide a low cost and flexible solution to sensing and monitoring for large distributed applications. To save energy and prolong the network lifetime, the WSN is often partitioned into a set of spatial clusters. Each cluster includes sensor nodes with similar sensing data, and only a few sensor nodes (samplers) report their sensing data to a base node. Then the...
Cooperative detection system, which combines selforganizing network and single radar system, improves the ability of cooperative localization by distributed multi-node using multi-direction scattering power of target. Multiplehypothesis (MH)-Based Algorithm for Target Localization finds all possible targets using multi-path echo information with unknown number of targets, and realizes multi-target...
In this paper, we propose a superpixel generation method for synthetic aperture radar (SAR) images by using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The pixels is firstly grouped to generate initial superpixels by using probabilistic patch-based (PPB) dissimilarity. Then, small clusters are combined into their neighbor superpixels to get final results through...
The Euclid distance based K-means clustering is among the hard classification algorithms. When dealing with deterministic remote sensing data, it is difficult to gain satisfactory classification results using K-means algorithm. The traditional K-means clustering algorithm is faced with several shortcomings such as locally converged optimization, being sensitive to initial clustering centers, etc....
Tracking single or multiple maneuvering targets is an urgent need for defense. In order to meet the military requirement, we propose a modified clustering-based Rao-Blackwellized particle filter (CBRBPF) to track single or multiple maneuvering targets with observations received by single or multiple sensors. The modified RBPF is basing on the clustering-based data association method. We partition...
Radio environment maps can be a powerful tool for achieving efficient context-aware resource allocation in 5G heterogeneous networks. In this paper, we consider an heterogeneous network formed by a traditional cellular network and a wireless sensor network. The role of the wireless sensor network is to estimate the radio environment map of the cell using a geostatistical interpolation technique named...
Golf is a popular sport for exercise or socializing. It affects an increasing number of patients. Because of these reasons the researchers decided to focus on this problem. We presented the analysis golf swing using K-Means Clustering with Two-Sided Confidence Intervals and the Closest Pair of Points Problem. The raw data were clustered by K-Means Clustering. The boundaries of subgroups processed...
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