Real-time Lossless Data Compression Research Articles

Notice of Removal: A Triggering-and-Encoding Lossless Compression Scheme for Waveform Capturing Rate Enhancement of Android Bluetooth Oscilloscope

Android-based oscilloscopes have been developed for research and education purpose because they would be able to acquire, transmit, display and analyze any electrical signals and be mobile, operable easily. Due to the speed limitation of wireless transmission channel, Android Oscilloscopes have some limited performances such as bandwidth, waveform capturing rate (WCR) and so on. In this paper, we propose a real-time lossless data compression scheme to solve a bottleneck of continuous data flow from DAQ to smartphone. This compression scheme consists of triggering a signal and encoding differences between waveforms. Also, an advanced structure of Android Bluetooth Oscilloscope is proposed to implement the proposed compression algorithm with FPGA-based hardware and software. To evaluate WCR improvement, we firstly analyze relationship between WCR and compression ratio. And then the compression efficiency is verified by the MATLAB simulations using various waveforms such as simple sinusoidal, complex periodic, square, chirp waveforms. We finally study robustness of this instrument in the range of 0~15% noise amplitude and 0~2rad phase offset. All experimental results show that proposed scheme can enhance WCR and also be applied to the wireless transmission fields collecting and displaying electrical waveforms.

A new real-time lossless data compression algorithm for ECG and PPG signals

ObjectiveData compression is a useful process in tele-monitoring applications, in which lesser number of bits are needed to represent the same data. In this work, a run-time lossless compression of single-channel Electrocardiogram (ECG) and Photoplethysmogram (PPG) signals is proposed, maintaining all dominant features. MethodsThe single-channel data are first quantized using optimal quantization level, so that fewer number of bits are needed to represent it, maintaining low quantization error. Then, second order delta encoding and run-length encoding (RLE) based data compression are proposed in this work. A new approach of using ‘buffer array’ along with RLE is also introduced, so that minimum bits are needed to store. ResultsThis algorithm was tested on various single-lead ECG and PPG signals available in Physionet. An average compression ratio (CR) was achieved of 6.52, 3.82, and 2.49 for 547 PTBDB ECG records, 48 MITDB ECG records, and 53 MIMIC-II PPG records, respectively. This algorithm was also performed on single-channel ECG, collected from 10 healthy volunteers using AD8232 ECG module, with 125 Hz sampling frequency and 10-bit data resolution, which resulted in average CR of 2.34. ConclusionThis algorithm was also performed on a smartphone device that provided user-friendly operation. The low computational complications and standalone operation of data collection, compression, and transmission encouraged its implementation for run-time operation. SignificanceA comparative study of the proposed work with previously published works proved this fact that this algorithm provided better performance in the area of run-time patient health monitoring applications.

Study on the Real-Time Lossless Data Compression Method Used in the Readout System for Micropattern Gas Detector

Nowadays, the data compression technique is fully developed and widely used in many fields, such as communication, multi-media, image processing, and so on. In large physics experiments, especially the ones with micropattern gas detectors (MPGDs), there are always large numbers of readout channels and many data to be transferred. However, the data compression technique is seldom applied in this field. In this paper, a real-time lossless data compression method, which can reduce the data transmission bandwidth while keeping all information of data is proposed and successfully implemented in a general-purpose MPGD readout system. The compression method discussed in this paper mainly consists of two steps: The first step is to preprocess the data according to the signal characteristics and the second step is to compress the preprocessed data using a common lossless compression algorithm. In addition, the whole compression method is implemented in the field-programmable gate array (FPGA) and can run in real time. The compression method is validated with two different signals and the compression ratios reach 43% and 30%, respectively.

KDI: A wireless ECoG recording platform with impedance spectroscopy, electrical stimulation and real-time, lossless data compression.

A power-efficient modular wireless platform has been designed for prototyping and pre-clinical evaluations of neural recording implants. This Kit for Designing Implants (KDI) is separated in function specific modules of 34×34mm which can be assembled as needed. This paper presents the design of new modules for this existing wireless KDI platform. These modules cover the functionalities of electrical stimulation for BCI neurofeedback, impedance spectroscopy for monitoring tissue reaction around implanted electrodes and a real-time lossless data compression algorithm for ECoG signals. This algorithm has been implemented using two different hardware solutions and its performances compared. The design and evaluation of these modules are a first step towards the inclusion of these functionalities into the next generation of WIMAGINE(®) implants.

Design of Real-Time Lossless Compression System Based on DSP and FPGA

The application of the lossless data compression technology in the filed of telemetry system is discussed in this paper. Based on the ARC algorithm, a real-time lossless data compression technology is proposed. By combining the TMS320C6416 and XC3S200AN FPGA, this paper designs a real-time lossless data compression device hardware system. 2048 bytes of some telemetry noise data can be compressed in 5.64ms in this system and the compression removal rate reaches 78%. What’s more, the system has solved the problem of data capacity and speed during the process of data compression , which greatly improves the efficiency of data compression.

Data reduction in the ITMS system through a data acquisition model with self-adaptive sampling rate

Long pulse or steady state operation of fusion experiments require data acquisition and processing systems that reduce the volume of data involved. The availability of self-adaptive sampling rate systems and the use of real-time lossless data compression techniques can help solve these problems. The former is important for continuous adaptation of sampling frequency for experimental requirements. The latter allows the maintenance of continuous digitization under limited memory conditions. This can be achieved by permanent transmission of compressed data to other systems. The compacted transfer ensures the use of minimum bandwidth. This paper presents an implementation based on intelligent test and measurement system (ITMS), a data acquisition system architecture with multiprocessing capabilities that permits it to adapt the system's sampling frequency throughout the experiment. The sampling rate can be controlled depending on the experiment's specific requirements by using an external dc voltage signal or by defining user events through software. The system takes advantage of the high processing capabilities of the ITMS platform to implement a data reduction mechanism based in lossless data compression algorithms which are themselves based in periodic deltas.

Real-time lossless data compression techniques for long-pulse operation

Data logging and data distribution will be two main tasks connected with data handling in ITER. Data logging refers to the recovery and ultimate storage of all data, independent of the data source. Data distribution is related, on the one hand, to the on-line data broadcasting for immediate data availability and, on the other hand, to the off-line data access. Due to the large data volume expected, data compression is a useful candidate to prevent the waste of resources in communication and storage systems. On-line data distribution in a long-pulse environment requires the use of a deterministic approach to be able to ensure a proper response time for data availability. However, an essential feature for all the above purposes is to apply lossless compression techniques. This article reviews different lossless data compression techniques based on delta compression. In addition, the concept of cyclic delta transformation is introduced. Furthermore, comparative results concerning compression rates on different databases (TJ-II and JET) and computation times for compression/decompression are shown. Finally, the validity and implementation of these techniques for long-pulse operation and real-time requirements is also discussed.

A high-performance computing service over the Internet for nonrigid image registration

This paper presents a novel high-performance computing (HPC) system for compute-intensive medical applications. Our system provides surgeons a framework for utilizing remote HPC resources with a secure execution environment by a public key cryptography and a high-speed data transmission by a real-time lossless data compression algorithm. Our implementation on a cluster of 64 off-the-shelf PCs with 128 processors has successfully demonstrated HPC benefits by reducing the turn-around time for the nonrigid registration of liver CT images of 512×512×159 voxels from about 17 h on a sequential system to about 12 min.