Construction of Anytime Algorithms for Robust Speech Recognition

2015 ◽  
Vol 1117 ◽  
pp. 265-268
Author(s):  
Gabriella Nagy
Keyword(s):  
Speech Recognition ◽  
Real Time ◽  
Assisted Living ◽  
High Probability ◽  
Ambient Assisted Living ◽  
Time Constraints ◽  
Robust Speech Recognition ◽  
Noisy Environment ◽  
Anytime Algorithms ◽  
Rule Out

Reliability of voice-controlled Ambient Assisted Living (AAL) systems depends mostly on real-time recognition of speech commands coming from a noisy environment. This paper discusses the aspects of constructing anytime algorithms for robust speech recognition in AAL systems. In this case, command recognition is a speaker-dependent classification on a limited set of isolated phrases. An interruptible search can rule out every command that is not a possible solution according to various parameters of speech, and can identify a spoken command with a sufficiently high probability, while keeping time constraints in mind.

scholarly journals Environmentally-Powered WSN for Urban-Scale Mapping and Assessment of Air Quality in Qatar

2020 ◽  
Author(s):  
Hasan Tariq ◽  
Farid Touati
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Assisted Living ◽  
Quality Index ◽  
Electrochemical Sensors ◽  
Ambient Assisted Living ◽  
Quality Measurement ◽  
Air Quality Index ◽  
Cross Sensitivity ◽  
Index Mapping

Environmental monitoring has gained significant importance in outdoor air quality measurement and assessment for fundamental survival as well as ambient assisted living. In real-time outdoor urban scale, instantaneous air quality index estimation, the electrochemical sensors warm-up time, cross-sensitivity computation-error, geo-location typography, instantaneous capacity or back up time; and energy efficiency are the six major challenges. These challenges lead to real-time gradient anomalies that effect the accuracy and pro-longed lags in air quality index mapping campaigns for state and environmental/meteorological agencies. In this work, a gradient-aware, multi-variable air quality-sensing node is proposed with event-triggered sensing based on position, gas magnitudes, and cross-sensitivity interpolation. In this approach, temperature, humidity, pressure, geo-position, photovoltaic power, volatile organic compounds, particulate matter (2.5), ozone, Carbon mono-oxide, Nitrogen dioxide, and Sulphur dioxide are the principle variables. Results have shown that the proposed system optimized the real-time air quality monitoring for the chosen geo-spatial cluster (Qatar University).

scholarly journals A Low-Dimensional Radial Silhouette-Based Feature for Fast Human Action Recognition Fusing Multiple Views

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Alexandros Andre Chaaraoui ◽  
Francisco Flórez-Revuelta
Keyword(s):  
Real Time ◽  
Action Recognition ◽  
Assisted Living ◽  
Learning Algorithm ◽  
Ambient Assisted Living ◽  
Human Action Recognition ◽  
Human Action ◽  
Sequence Matching ◽  
Low Dimensional ◽  
Video Frequency

This paper presents a novel silhouette-based feature for vision-based human action recognition, which relies on the contour of the silhouette and a radial scheme. Its low-dimensionality and ease of extraction result in an outstanding proficiency for real-time scenarios. This feature is used in a learning algorithm that by means of model fusion of multiple camera streams builds a bag of key poses, which serves as a dictionary of known poses and allows converting the training sequences into sequences of key poses. These are used in order to perform action recognition by means of a sequence matching algorithm. Experimentation on three different datasets returns high and stable recognition rates. To the best of our knowledge, this paper presents the highest results so far on the MuHAVi-MAS dataset. Real-time suitability is given, since the method easily performs above video frequency. Therefore, the related requirements that applications as ambient-assisted living services impose are successfully fulfilled.

scholarly journals SMS: A Secure Healthcare Model for Smart Cities

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1135
Author(s):  
Gautami Tripathi ◽  
Mohd Abdul Ahad ◽  
Sara Paiva
Keyword(s):  
Real Time ◽  
Assisted Living ◽  
Smart City ◽  
Smart Cities ◽  
Ambient Assisted Living ◽  
Critical Time ◽  
Personal Data ◽  
Healthcare Management ◽  
Real Time Analysis ◽  
Healthcare Model

Technological innovations have enabled the realization of a utopian world where all objects of everyday life, as well as humans, are interconnected to form an “Internet of Things (IoT).” These connected technologies and IoT solutions have led to the emergence of smart cities where all components are converted into a connected smart ecosystem. IoT has envisioned several areas of smart cities including the modern healthcare environment like real-time monitoring, patient information management, ambient-assisted living, ambient-intelligence, anomaly detection, and accelerated sensing. IoT has also brought a breakthrough in the medical domain by integrating stake holders, medical components, and hospitals to bring about holistic healthcare management. The healthcare domain is already witnessing promising IoT-based solutions ranging from embedded mobile applications to wearable devices and implantable gadgets. However, with all these exemplary benefits, there is a need to ensure the safety and privacy of the patient’s personal and medical data communicated to and from the connected devices and systems. For a smart city, it is pertinent to have an accessible, effective, and secure healthcare system for its inhabitants. This paper discusses the various elements of technology-enabled healthcare and presents a privacy-preserved and secure “Smart Medical System (SMS)” framework for the smart city ecosystem. For providing real-time analysis and responses, this paper proposes to use the concept of secured Mobile Edge Computing (MEC) for performing critical time-bound computations on the edge itself. In order to protect the medical and personal data of the patients and to make the data tamper-proof, the concept of blockchain has been used. Finally, this paper highlights the ways to capture and store the medical big data generated from IoT devices and sensors.

A NEURAL NETWORK APPROACH TO REAL-TIME PATTERN RECOGNITION

2001 ◽  
Vol 15 (06) ◽  
pp. 937-947 ◽  
Author(s):  
JINWEN MA
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Real Time ◽  
Error Probability ◽  
Time Constraints ◽  
Network Approach ◽  
Noisy Environment ◽  
Neural Network Approach ◽  
Time Pattern ◽  
Perceptive Field

This paper presents a new neural network approach to real-time pattern recognition on a given set of binary (or bipolar) sample patterns. The perceptive neuron of a binary pattern is defined and constructed as a binary neuron with a neighborhood perceptive field. Letting its hidden units be the respective perceptive neurons of the patterns, a three-layer forward neural network is constructed to recognize these patterns with minimum error probability in a noisy environment. The theoretical and simulation analyses show that the network is effective for pattern recognition and can be under strict real-time constraints.

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