In this paper, we present and evaluate a hardware implementation for user-driven and packet-loss tolerant image compression, especially designed to enable low-power image compression and communication over wireless sensors networks (WSNs). The proposed compression scheme, presented as a CMOS circuit, is intended to be embedded in the camera sensor.It will be considered as a co-processor for tasks related with image compression and data packetization, which unloads the main microcontroller so that it will spend less time in active mode. The interest of our solution is twofold. First, compression settings can be changed at runtime (upon reception of a request message sent by an end-user or according to the internal state of the camera sensor node). Second, the image compression chain includes a (block of) pixel interleaving scheme which significantly improves the robustness against packet loss in image communication. The main part of this paper focuses on the specification and the performances analysis of this solution when implemented on FPGA and ASIC circuits.
We propose in this work a method of electrocardiogram (ECG) signal pretreatment by the application of Discreet Wavelet Transform DWT by automatically determining the optimal order of decomposition. After the purification of the original signal, we describe an algorithm to detect R waves based on the Dyadic Wavelet Transform DyWT by applying a windowing process. This algorithm is validated on a sample of synthesis ECG signal with and without noise which we have proposed and on real data. Finally, once the R peaks of real data are detected, we use three methods of RR intervals analysis by calculating the standard deviation of heart rate and applying the Fast Fourier Transform FFT and the Wavelet Transform on detected RR intervals to study the Heart Rate Variability (HRV). A comparative study between the analysis results of detected RR intervals in healthy and diseased subjects through the application of the FFT and the Wavelet Transform will be given.
The present work develops a novel hybrid method for ocular and muscular artifact removal from electroencephalography (EEG) signals, EFICA-TQWT. It is a combination of efficient fast independent component analysis (EFICA) method with the tunable Q-factor wavelet transform (TQWT). The main contribution of this paper is to apply the 3D interpolation method in the filtering system. Three EEG datasets are used in this work, two healthy and one epileptic. The choice of subjects for each dataset is made with the help of an expert in physiology. The selection criterion adopted is the presence of muscular and ocular artifacts in the processed recordings. First, a noisy channel automatic classification is performed by the support vector machine (SVM) with radial basis function in order to delete the signal(s) corresponding to the noisiest channel(s) from each EEG recording. The results of the automatic classification by the SVM were compared with those found by the expert’s classification. An accuracy of 97.45%, a sensitivity of 86.66% and a 100% specificity are provided by the SVM classification. The hybrid method of artifact removal will be applied on the rest of the EEG channels of international 10/20 system for each subject. Then, a reconstruction of the eliminated channel signal(s) will be performed in order to obtain a well-filtered signal. The proposed filtering process is evaluated by calculating the mean squared error (MSE) and the signal to noise ratio (SNR). Both for the healthy and pathological EEG datasets, a comparative study of the proposed method (EFICA-TQWT) and other filtering techniques (Fast-ICA, DWT, TQWT and EFICA) is generated. The EFICA-TQWT method gave the best results with a minimum of MSE and a maximum of SNR, more particularly in the case of the application of the 3D interpolation method. Besides, in order to optimize the computing time of the proposed system, a parallel implementation of this filtering system is developed based on graphical processing units using compute unified device architecture.
The emergence of internet of things allows the integration of health systems by enabling real-time monitoring with a low cost. Therefore, one of the essential targets in this work is the realization of a new smart real-time electrocardiogram remote monitoring system based on cloud networks. This health wireless system allows the acquisition of electrocardiogram signal with the temperature and acceleration measurement of the patient's body using the inertial measurement unit module sensor. A strong access schemes is employed to transfer the data from sensors to cloud environment by keeping the protection of e-health information. The second objective in this chapter is designing a flexible and stretchable health circuit basing on design considerations, aiming the combination of flexible, elastic, and rigid materials around minimal constraints and maximum mechanical dependability in the structures. The flexible fabrication part was inspired from the biocompatible process technology.
Abstract-With recent progress in the medical signals processing, the EEG allows to study the Brain functioning with a high temporal and spatial resolution. This approach is possible by combining the standard processing algorithms of cortical brain waves with characterization and interpolation methods. First, a new vector of characteristics for each EEG channel was introduced using the Extended Kalman filter (EKF). Next, the spherical spline interpolation technique was applied in order to rebuild other vectors corresponding to virtual electrodes. The temporal variation of these vectors was restored by applying the EKF. Finally, the accuracy of the method has been estimated by calculating the error between the actual and interpolated signal after passing by the characterization method with the Root Mean Square Error algorithm (RMSE).
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