1,720,979 research outputs found
VIPDA: a visually driven point cloud denoising algorithm based on anisotropic point cloud filtering
Get PDFPoint clouds (PCs) provide fundamental tools for digital representation of 3D surfaces, which have a growing interest in recent applications, such as e-health or autonomous
means of transport. However, the estimation of 3D coordinates on the surface as well as the signal defined on the surface points (vertices) is affected by noise. The presence of
perturbations can jeopardize the application of PCs in real scenarios. Here, we propose a novel visually driven point cloud denoising algorithm (VIPDA) inspired by visually driven filtering approaches. VIPDA leverages recent results on local harmonic angular filters extending image processing tools to the PC domain. In more detail, the VIPDA method applies a harmonic angular analysis of the PC shape so as to associate each vertex of the PC to suit a set of neighbors and to drive the denoising in accordance with the local PC variability. The performance of VIPDA is assessed by numerical simulations on synthetic and real data corrupted by Gaussian noise. We also compare our results with state-of-theart methods, and we verify that VIPDA outperforms the others in terms of the signal-tonoise ratio (SNR). We demonstrate that our method has strong potential in denoising the point clouds by leveraging a visually driven approach to the analysis of 3D surfaces
Enabling IoT for In-Home Rehabilitation: Accelerometer Signals Classification Methods for Activity and Movement Recognition
No full textRehabilitation and elderly monitoring for active aging can benefit from Internet of Things (IoT) capabilities in particular for in-home treatments. In this paper, we consider two functions useful for such treatments: 1) activity recognition (AR) and 2) movement recognition (MR). The former is aimed at detecting if a patient is idle, still, walking, running, going up/down the stairs, or cycling; the latter individuates specific movements often required for physical rehabilitation, such as arm circles, arm presses, arm twist, curls, seaweed, and shoulder rolls. Smartphones are the reference platforms being equipped with an accelerometer sensor and elements of the IoT. The work surveys and compares accelerometer signals classification methods to enable IoT for the aforementioned functions. The considered methods are support vector machines (SVMs), decision trees, and dynamic time warping. A comparison of the methods has been proposed to highlight their performance: all the techniques have good recognition accuracies and, among them, the SVM-based approaches show an accuracy above 90% in the case of AR and above 99% in the case of MR
A Television Channel Real-Time Detector using Smartphones
No full textRecently, people have been interested in sharing what they are watching on TV, allowing the development of Social TV
Applications often based on mobile devices. In this context, this paper proposes IRTR (Improved Real-Time TV-channel Recognition): a new method aimed at recognizing in real time (live) what people are watching on TV without any active user interaction. IRTR uses the audio signal of the TV program recorded by smartphones and is performed through two steps: i) fingerprint extraction and ii) TV channel real-time identification. Step i) is based on the computation of the Audio Fingerprint (AF). The AF computation has been taken from the literature and has been improved in terms of power consumption and computation speed to make the smartphone implementation feasible by using an ad hoc cost function aimed at selecting the best set of AF parameters. Step ii) is aimed at deciding the TV channel the user is watching. This step is performed using a likelihood estimation algorithm proposed in this paper. The consumed power, computation and response time, and correct decision rate of IRTR, evaluated through experimental measures, show very satisfying results such as a correct decision rate of about 95%, about 2s of computation time, and above 90% power saving with respect to the literatur
A simple ultrasonic Indoor/Outdoor detector for mobile devices
No full textContext information is fundamental for mobile application. A system able to detect Indoor/Outdoor state switching can give useful information to upper-level applications permitting to improve their performance or reduce the computational load and consequently the lifetime of the smartphone. A localization application may exploit the Indoor/Outdoor information to smartly decide if using GPS (that performs well outdoors but poorly indoors) or other localization methods. In this paper we present an ultrasonic-signal-based Indoor/Outdoor detector for smartphones. The phone plays an ultrasonic ping by using its in-built speakers and records the echoes by using its microphone, therefore, no specific hardware is required to be added to the smartphone. The proposed detector shows good performance in terms of accuracy and latency
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
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