Adaptive Rate Sampling Research Articles

Adaptive Compressed Sampling Based on EMD for Wireless Sensor Networks

Image acquisition and communication in wireless sensor networks (WSNs) are key issues for some applications that require image sensing. Lowering the sampling rate and reducing the amount of data to be transmitted can greatly save hardware costs and power consumption to overcome communication bottlenecks. To serve this purpose, block compressed sampling (CS) can be used. For block CS, the sparsity of the signal to be sampled is an important parameter. On the sampling side of sensor nodes, we cannot obtain the complete digital signal. Therefore, it is challenging to perform adaptive rate sampling for block CS. In this article, a novel rate adaptive allocation block CS scheme based on empirical mode decomposition (EMD) is proposed. First, we calculate an energy matrix of measurement results (EMMR) and perform EMD on EMMR. Considering the high computational complexity of 2-D EMD, which will increase the burden on the sampling side, we propose a 1-D EMD based on zigzag scanning to decompose EMMR. Then, we can obtain an energy distribution map (EDM) of high-frequency components of EMMR. Finally, the adaptive allocation of sampling rate is carried out according to the EDM of high-frequency components. On the reconstruction side of WSN, we propose a reconstruction algorithm based on residual measurement results, which can further improve the reconstruction accuracy of images. The experiment results show that the proposed scheme has improved the accuracy in general compared with the previous schemes, and the subjective quality of the reconstructed images is also improved.

Epileptic seizure classification using level-crossing EEG sampling and ensemble of sub-problems classifier

Epilepsy is a disorder of the brain characterized by seizures and requires constant monitoring particularly in serious patients. Electroencephalogram (EEG) signals are frequently used in epilepsy diagnosis and monitoring. A new paradigm of battery packed wearable gadgets has recently gained popularity which constantly monitor a patient’s signals. These gadgets acquire the data and transmit it to the cloud for further processing. Power consumption due to data transmission is a major issue in these devices. Moreover, in a constant monitoring environment, the number of classes to be identified are usually higher and overlapping. Existing techniques either require the entire data to be transmitted, such as in deep learning, or suffer from reduced accuracy. In this context, we propose a new framework for EEG based epilepsy detection which requires a low data transmission while maintaining high accuracy for multiclass classification. At the device-end, we use a preprocessing mechanism that uses adaptive rate sampling, modified activity selection, filtering, and wavelet decomposition to extract only a handful of highly discriminatory features to be transmitted instead of the entire EEG waveform. For multiclass classification, we propose a novel ensemble of sub-problems-based classification paradigm to achieve high accuracy using the reduced data. Our proposed solution shows many-fold increase in computational gains and an accuracy of 100% and 99.38% on the 2-class problem when tested on the popular University of Bonn and CHB-MIT datasets, respectively. An accuracy of 99.6% on 3-class, 96% on 4-class, and 92% on 5-class problems is obtained for the University of Bonn dataset.

An Adaptive Rate Blocked Compressive Sensing Method for Video.

An adaptive rate Compressive Sensing (CS) method for video signals is proposed. The Blocked Compressive Sensing (BCS) scheme is adopted in this method. Firstly, each video frame is blocked and measured by the BCS scheme, and then the mean and variance of each image block are estimated by observing the CS measurement results. Using the mean and variance of each image block, the sparsity of the block is estimated and then the block can be classified. Adaptive rate sampling is realized by assigning different sampling rates to different classes. At the same time, in order to make better use of the correlation between video frames, a reference block subtraction method is also designed in this paper, which uses the estimates of the sparsity of image blocks as the basis for the reference block update. All operations of the proposed method only depend on the CS measurement results of image blocks and all calculations are simple. Thus, the proposed method is suitable for implementation in CS sampling devices with limited computational performance. Experiment results show that, compared with the actual values, the sparsity estimates and block classification results of the proposed method are accurate. Compared with the latest adaptive Compressive Video Sensing methods, the reconstructed image quality of the proposed method is better.

Adaptive Rate Sampling and Filtering Based on Level Crossing Sampling

The recent sophistications in areas of mobile systems and sensor networks demand more and more processing resources. In order to maintain the system autonomy, energy saving is becoming one of the most difficult industrial challenges, in mobile computing. Most of efforts to achieve this goal are focused on improving the embedded systems design and the battery technology, but very few studies target to exploit the input signal time-varying nature. This paper aims to achieve power efficiency by intelligently adapting the processing activity to the input signal local characteristics. It is done by completely rethinking the processing chain, by adopting a non conventional sampling scheme and adaptive rate filtering. The proposed approach, based on the LCSS (Level Crossing Sampling Scheme) presents two filtering techniques, able to adapt their sampling rate and filter order by online analyzing the input signal variations. Indeed, the principle is to intelligently exploit the signal local characteristics--which is usually never considered--to filter only the relevant signal parts, by employing the relevant order filters. This idea leads towards a drastic gain in the computational efficiency and hence in the processing power when compared to the classical techniques.

Reliable Speech Transmission Using Combined Adaptive-rate Sampling (ARS) and Channel Coding

A novel speech transmission technique over noisy channel condition based on combined ARS and channel coding schemes is described. The proposed scheme is suitable for time varying channels with some form of ARQ facilities. It is shown that under an AWGN channel condition, up to 3.8 dB gain relative to a conventional 32 kbits/s transmission can be provided using the combined technique.

Speech scrambling employing adaptive rate sampling

A new speech scrambling technique is described. The speech is spectrally scrambled at the transmitter to produce a defined multiple bandpass structure. The reconstruction ‘key’ comprises a knowledge of the spectral frequency bands of the scrambled elements together with a unique sampling rate which will allow correct recombination of these elements. To prevent unauthorised reconstruction, the bandpass structure and sampling rate can be changed irregularly according to an appropriate key sequence.

Error-Free Recovery of Signals from Irregularly Spaced Samples

Error free recovery of signals from irregularly spaced samples in terms of completeness of sets of nonharmonic exponentials