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The monitoring of a cutting tool is needed for the prediction of impending faults and estimating its Remaining Useful Life (RUL). Implementing a robust Prognostic and Health Management (PHM) system for a high speed milling CNC cutter remains a challenge for various industries to reach improved quality, reduced downtime, increased system safety and lower production costs. The purpose of the present...
In Japan, which has become a very aged society, the increasing burden of nursing care is an issue. Services and systems related to automatic recording of healthcare management of elderly people have been proposed in order to reduce the burden of nursing care. Water intake is one of the items necessary for healthcare management of elderly people. However, it is not currently automated, which is a burden...
Image processing is an inevitable tool for visual tracking. Visual object tracking is a very hot area of research in the computer vision. Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and in general, deal with the extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the...
In this paper, we perform beamforming with a speech recognition-level criterion. A beamformer is usually designed by optimizing signal-level criteria, e.g., by minimizing the beamformer output covariance or by maximizing the signal-to-noise ratio (SNR). Such signal-level criteria do not always guarantee that the optimized beamformer is the best for noise robust automatic speech recognition. Recently,...
The ability to automatically detect the extent of agreement or disagreement a person expresses is an important indicator of inter-personal relations and emotion expression. Most of existing methods for automated analysis of human agreement from audio-visual data perform agreement detection using either audio or visual modality of human interactions. However, this is suboptimal as expression of different...
In this work, a system based on a Bayesian approach, for the off-line recognition of handwritten arabic words, is proposed. Different structural features such as ascenders, descenders, loops and diacritic, are extracted from word's image, tacking into account the morphology of handwritten arabic words. For accurate features extraction, we proposed a novel method to estimate the word's baseline and...
Many artificial speech bandwidth extension (ABE) approaches perform source-filter decomposition of the input narrowband speech, with subsequent computation of upper frequency band (UB) spectral envelope posteriors. In this paper we perform a direct comparison of HMM- and deep neural network (DNN)-based modeling of likelihoods or posteriors for ABE UB envelope estimation. DNN-based approaches turn...
In this paper we present a system for offline recognition cursive Arabic handwritten text based on Hidden Markov Models (HMMs). The system is analytical without explicit segmentation used embedded training to perform and enhance the character models. Extraction features preceded by baseline estimation are statistical and geometric to integrate both the peculiarities of the text and the pixel distribution...
During routine sleep diagnostic procedure, sleep is broadly divided into three states: rapid eye movement (REM), non-REM (NREM) states, and wake, frequently named macro-sleep stages (MSS). In this study, we present a pioneering attempt for MSS detection using full night audio analysis. Our working hypothesis is that there might be differences in sound properties within each MSS due to breathing efforts...
The building, being one of the largest energy consumers, accounts for over 40% of the global energy consumption. The traditional Heating, Ventilation and Air Conditioning (HVAC) and lighting systems cause considerable energy wastes in the building since they cannot adapt to the time-varying occupancy levels, which is expensive to measure with dedicated sensing systems. We propose an indirect method...
In this paper, we propose the deep neural network - switching Kalman filter (DNN-SKF) based frameworks for both single modal and multi-modal continuous affective dimension estimation. The DNN-SKF framework firstly models the complex nonlinear relationship between the input (audio, visual, or lexical) features and the affective dimensions via the non-recurrent DNN, then models the temporal dynamics...
In general for any speech processing, represented speech signals are pre-processed for some features at front end and some estimation are performed at back end. Hidden Markov Model is exclusively used for modeling time-varying vector sequences due to its simplicity. It also provides high accuracy in non-stationary environment. In this paper, HTK (Hidden Markov model Tool-Kit) toolkit is used for compiling...
Gait recognition is an emerging biometric technology due to the widespread use of closed-circuit television (CCTV) camera. Owing to the non-cooperative nature of CCTV setting, gait appears to be a valuable cue that can be extracted from the video footage. The gait feature extracted from the video can be used for several applications such as person authentication for security access control and walking...
We present a novel approach to automated estimation of agreement intensity levels from facial images. To this end, we employ the MAHNOB Mimicry database of subjects recorded during dyadic interactions, where the facial images are annotated in terms of agreement intensity levels using the Likert scale (strong disagreement, disagreement, neutral, agreement and strong agreement). Dynamic modelling of...
In this paper we propose the deep bidirectional long short-term memory recurrent neural network (DBLSTM-RNN) based single modal and multi-modal affect recognition frameworks. In the single modal framework DBLSTM with moving average (MA), audio or visual features are input into the DBLSTM-RNN model, whose output estimations of a dimension are smoothed by the moving average filter. After the smoothed...
In the present work, we introduce a new probabilistic model for the task of estimating beat positions in a musical audio recording, instantiating the Conditional Random Field (CRF) framework. Our approach takes its strength from a sophisticated temporal modeling of the audio observations, accounting for local tempo variations which are readily represented in the CRF model proposed using well-chosen...
In the Prognostics and Health Management domain, estimating the remaining useful life (RUL) of critical machinery is a challenging task. Various research topics as data acquisition and processing, fusion, diagnostics, prognostivs and decision are involved in this domain. This paper presents an approach for estimating the Remaining Useful Life (RUL) of equipments based on shapelet extraction and characterization...
This paper presents an automatic translation method from time-series driving behavior into natural language with contextual information. Nowadays, various advanced driver-assistance systems (ADASs) have been developed to reduce the number of traffic accidents and multiple ADASs are required to reduce further accidents. For such multiple ADASs, considering the context of driving and selecting appropriate...
Automatic detection of the event of switching-on an electric load, estimation of that switch-on instant and subsequent automatic identification of that electric load using current transient signal around the switch-on instant are studied in this paper. The time variation of the current harmonics is used to first detect the event of switching-on an electric load and then spectral features extracted...
Fault prognosis of sensor is vital for measurement system, or even the whole system to work safely, maintenance and repair. We adopted HMM (Hidden Markov Model) to solve the problem of the sensor fault prediction, established the basic structure of sensor fault prognosis system and HMM model, used Bayesian Toolbox in Mat lab for simulation and data sample for training model parameters, obtained the...
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