5,843 research outputs found
Deep Room Recognition Using Inaudible Echos
Recent years have seen the increasing need of location awareness by mobile
applications. This paper presents a room-level indoor localization approach
based on the measured room's echos in response to a two-millisecond single-tone
inaudible chirp emitted by a smartphone's loudspeaker. Different from other
acoustics-based room recognition systems that record full-spectrum audio for up
to ten seconds, our approach records audio in a narrow inaudible band for 0.1
seconds only to preserve the user's privacy. However, the short-time and
narrowband audio signal carries limited information about the room's
characteristics, presenting challenges to accurate room recognition. This paper
applies deep learning to effectively capture the subtle fingerprints in the
rooms' acoustic responses. Our extensive experiments show that a two-layer
convolutional neural network fed with the spectrogram of the inaudible echos
achieve the best performance, compared with alternative designs using other raw
data formats and deep models. Based on this result, we design a RoomRecognize
cloud service and its mobile client library that enable the mobile application
developers to readily implement the room recognition functionality without
resorting to any existing infrastructures and add-on hardware.
Extensive evaluation shows that RoomRecognize achieves 99.7%, 97.7%, 99%, and
89% accuracy in differentiating 22 and 50 residential/office rooms, 19 spots in
a quiet museum, and 15 spots in a crowded museum, respectively. Compared with
the state-of-the-art approaches based on support vector machine, RoomRecognize
significantly improves the Pareto frontier of recognition accuracy versus
robustness against interfering sounds (e.g., ambient music).Comment: 29 page
EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design
The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application
Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention
Deep neural networks, including recurrent networks, have been successfully
applied to human activity recognition. Unfortunately, the final representation
learned by recurrent networks might encode some noise (irrelevant signal
components, unimportant sensor modalities, etc.). Besides, it is difficult to
interpret the recurrent networks to gain insight into the models' behavior. To
address these issues, we propose two attention models for human activity
recognition: temporal attention and sensor attention. These two mechanisms
adaptively focus on important signals and sensor modalities. To further improve
the understandability and mean F1 score, we add continuity constraints,
considering that continuous sensor signals are more robust than discrete ones.
We evaluate the approaches on three datasets and obtain state-of-the-art
results. Furthermore, qualitative analysis shows that the attention learned by
the models agree well with human intuition.Comment: 8 pages. published in The International Symposium on Wearable
Computers (ISWC) 201
Ambient Intelligence in Healthcare: A State-of-the-Art
Information technology advancement leads to an innovative paradigm called Ambient Intelligence (AmI). A digital environment is employed along with AmI to enable individuals to be aware to their behaviors, needs, emotions and gestures. Several applications of the AmI systems in healthcare environment attract several researchers. AmI is considered one of the recent technologies that support hospitals, patients, and specialists for personal healthcare with the aid of artificial intelligence techniques and wireless sensor networks. The improvement in the wearable devices, mobile devices, embedded software and wireless technologies open the doors to advanced applications in the AmI paradigm. The WSN and the BAN collect medical data to be used for the progress of the intelligent systems adapted inevitably. The current study outlines the AmI role in healthcare concerning with its relational and technological nature. Health
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