Implementation of a Biometric Interface in Voice Controlled Wheelchairs

In order to assist physically handicapped persons in their movements,we developed an embedded isolated word speech recognition system(ASR)applied to voice control of smart wheelchairs.However,in spite of the existence in the industrial market of several kinds of electric wheelchairs,the problem remains the need to manually control this device by hand via joystick;which limits their use especially by people with severe disabilities.Thus,a significant number of disabled people cannot use a standard electric wheelchair or drive it with difficulty.The proposed solution is to use the voice to control and drive the wheelchair instead of classical joysticks.The intelligent chair is equipped with an obstacle detection system consisting of ultrasonic sensors,a moving navigation algorithm and a speech acquisition and recognition module for voice control embedded in a DSP card.The ASR architecture consists of two main modules.The first one is the speech parameterization module(features extraction)and the second module is the classifier which identifies the speech and generates the control word to motors power unit.The training and recognition phases are based on Hidden Markov Models(HMM),K-means,Baum-Welch and Viterbi algorithms.The database consists of 39 isolated speaker words(13 words pronounced 3 times under different environments and conditions).The simulations are tested under Matlab environment and the real-time implementation is performed by C language with code composer studio embedded in a TMS 320 C6416 DSP kit.The results and experiments obtained gave promising recognition ratio and accuracy around 99%in clean environment.However,the system accuracy decreases considerably in noisy environments,especially for SNR values below 5 dB(in street:78%,in factory:52%).

Automated Burned Scar Mapping Using Sentinel-2 Imagery

The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. However, the high detail and volume of information provided actually encumbers the automation of the mapping process, at least for the level of automation required to map systematically wildfires on a national level. This paper proposes a fully automated methodology for mapping burn scars using Sentinel-2 data. Information extracted from a pair of Sentinel-2 images, one pre-fire and one post-fire, is jointly used to automatically label a set of training patterns via two empirical rules. An initial pixel-based classification is derived using this training set by means of a Support Vector Machine (SVM) classifier. The latter is subsequently smoothed following a multiple spectral-spatial classification (MSSC) approach, which increases the mapping accuracy and thematic consistency of the final burned area delineation. The proposed methodology was tested on six recent wildfire events in Greece, selected to cover representative cases of the Greek ecosystems and to present challenges in burned area mapping. The lowest classification accuracy achieved was 92%, whereas Matthews correlation coefficient (MCC) was greater or equal to 0.85.