1,721,040 research outputs found
A new asset tracking architecture integrating RFID, Bluetooth Low Energy tags and ad hoc smartphone applications
No full textThe paper describes an original architecture aimed at tracking assets within construction sites. The main components are Radio Frequency IDentification (RFID), Bluetooth Low Energy (BLE) tags and smartphones. The core functions are performed by two Android applications, which implement asset tracking and searching. The main merits of the architecture are its ability to maximize smartphone battery lifetime, that can reach an entire working shift, very satisfactory accuracy of BLE tag-smartphone distance estimation (with a mean error around 2 [m]), high probability of detecting all the tags present in the construction site, as well as a suitably short Aging time
Cell-ID Meter App: a Tester for Coverage Maps Localization Proofs in Forensic Investigations
No full textSmartphone-centric ambient assisted living platform for patients suffering from co-morbidities monitoring
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Performance Evaluation and Analysis of Drone-based Vehicle Detection Techniques From Deep Learning Perspective
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Smart probabilistic fingerprinting for WiFi-based indoor positioning with mobile devices
No full textDifferent positioning schemes are based on the probability p(o|l) to have an observation vector o at a Reference Point (RP) l, based on Gaussian probabilities. This paper presents an approach to speed-up the p(o|l) computation without any approximation. The consequent positioning scheme is called Smart P-FP. The comparison between Traditional (without any p(o|l) acceleration) and Smart P-FP is performed over different smartphones. The saved energy is about 90% for a large number of Access Points (APs) but is significant even with few APs: more than 86% with 3 APs. The proposed p(o|l) computation is beneficial to any p(o|l)-based positioning scheme
Computational complexity closed-form upper bounds derivation for fingerprint-based Point-Of-Interest recognition algorithms
No full textPerformance Comparison of a Probabilistic Fingerprint-based Indoor Positioning System over Different Smartphones
No full textSmart and Robust Speaker Recognition for Context-Aware In-Vehicle Applications
No full textThe importance of robust audio speech processing has rapidly increased in the latest years, as the number of smart and connected devices is growing. This effect is strongly related to the Internet of Things framework, introducing concepts such as connected vehicles and future smart cities. Context-aware applications are fundamental in this evolving environment, enabling smart and custom-tailored services for a variety of users. The use of on-board speaker recognition systems can play a key role in enhancing the customization of in-vehicle applications, by identifying the actual users and personalizing services based on their identity. Driven by this motivation, in this paper we present a performance study of a Speaker Recognition (SR) system, designed to face typical challenging conditions of an in-vehicle environment. We propose the design of a robust speaker identification algorithm embedding a smart pre-processing method based on Voice Activity Detection (VAD), which can effectively reduce the influence of noise and distance on classification. Results show that our solution is able to efficiently improve the correct classification rate, even in the case of distant audio acquisition and in a variety of noisy environments
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