Влияние параллельного слоям магнитного поля на фототок в GaAs/AlAs p-i-n-структурах

The behavior of the photocurrent in GaAs / AlAs p-i-n heterostructures is studied in a magnetic field parallel to the heterolayers in the wavelength range from 395 to 650 nm. A strong dependence of the non-oscillating component of the photocurrent on the radiation wavelength associated with the suppression of the diffusion current by the magnetic field was found. It is shown that the behavior of the oscillating component of the photocurrent in a magnetic field does not depend on the wavelength of light and is determined by the transfer of electrons through the dimensional quantization level in a triangular near-barrier well. It is shown that the suppression of the oscillating component by the magnetic field is due to the smearing of the level in the triangular well due to the motion of electrons parallel to the walls of the well and perpendicular to the magnetic field.

Detection for All Zero Coefficient Blocks in HEVC Based on Uniform Quantizer

Transform and quantization are adopted in HEVC. There are lots of all zero coefficient blocks in transform and quantization. By detecting all zero coefficient blocks, the complexity of transform or quantization can be greatly reduced. All zero coefficient blocks for uniform quantizer can be efficiently detected by comparing the float quantization level of the estimated coefficients with an explicit threshold. The experimental result shows that 50% complexity of transform or quantization for uniform quantizer can be reduced with negligible loss of video coding efficiency.

A New Real-time Lossless Data Compression Algorithm for ECG and PPG Signals

<i>Objective:</i> Data 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) signal is proposed, maintaining all dominant features. <i>Methods: </i>The single channel data are first quantized using optimal quantization level, so that lesser 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. <i>Results:</i> This 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. <i>Discussion:</i> This 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. <i>Significance:</i> A comparative study of 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.

A New Real-time Lossless Data Compression Algorithm for ECG and PPG Signals

<i>Objective:</i> Data 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) signal is proposed, maintaining all dominant features. <i>Methods: </i>The single channel data are first quantized using optimal quantization level, so that lesser 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. <i>Results:</i> This 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. <i>Discussion:</i> This 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. <i>Significance:</i> A comparative study of 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.

Optimization Research on Deep Learning and Temporal Segmentation Algorithm of Video Shot in Basketball Games

The analysis of the video shot in basketball games and the edge detection of the video shot are the most active and rapid development topics in the field of multimedia research in the world. Video shots’ temporal segmentation is based on video image frame extraction. It is the precondition for video application. Studying the temporal segmentation of basketball game video shots has great practical significance and application prospects. In view of the fact that the current algorithm has long segmentation time for the video shot of basketball games, the deep learning model and temporal segmentation algorithm based on the histogram for the video shot of the basketball game are proposed. The video data is converted from the RGB space to the HSV space by the boundary detection of the video shot of the basketball game using deep learning and processing of the image frames, in which the histogram statistics are used to reduce the dimension of the video image, and the three-color components in the video are combined into a one-dimensional feature vector to obtain the quantization level of the video. The one-dimensional vector is used as the variable to perform histogram statistics and analysis on the video shot and to calculate the continuous frame difference, the accumulated frame difference, the window frame difference, the adaptive window’s mean, and the superaverage ratio of the basketball game video. The calculation results are combined with the set dynamic threshold to optimize the temporal segmentation of the video shot in the basketball game. It can be seen from the comparison results that the effectiveness of the proposed algorithm is verified by the test of the missed detection rate of the video shots. According to the test result of the split time, the optimization algorithm for temporal segmentation of the video shot in the basketball game is efficiently implemented.

High-Precision Tracking of Periodic Signals: a Macro-Micro Approach with Quantized Feedback

<div>This paper proposes a novel control design method for high-precision positioning systems. The method aims to eliminate the tracking error caused by measurement quantization present in positioning systems with optical encoders. By employing a combined internal model based feedback and quantized feedforward design, we are able to make the output of the positioning system asymptotically track any input signal with one or more sinusoidal components of known frequencies and a possible constant component. When combined with a micro actuator, the resulting dual-stage positioning system is able to track any continuous periodic signal with a known period. Besides theoretical guarantees, the proposed design is validated experimentally and proved able to achieve asymptotic tracking error below ?1 ?m when subject to a sensor quantization level of 5 ?m.</div>

High-Precision Tracking of Periodic Signals: a Macro-Micro Approach with Quantized Feedback

<div>This paper proposes a novel control design method for high-precision positioning systems. The method aims to eliminate the tracking error caused by measurement quantization present in positioning systems with optical encoders. By employing a combined internal model based feedback and quantized feedforward design, we are able to make the output of the positioning system asymptotically track any input signal with one or more sinusoidal components of known frequencies and a possible constant component. When combined with a micro actuator, the resulting dual-stage positioning system is able to track any continuous periodic signal with a known period. Besides theoretical guarantees, the proposed design is validated experimentally and proved able to achieve asymptotic tracking error below ?1 ?m when subject to a sensor quantization level of 5 ?m.</div>

An Adaptive Reversible Data Hiding Scheme Using AMBTC and Quantization Level Difference

Hiding a message in compression codes can reduce transmission costs and simultaneously make the transmission more secure. This paper presents an adaptive reversible data hiding scheme that is able to provide large embedding capacity while improving the quantity of modified images. The proposed scheme employs the quantization level difference (QLD) and interpolation technique to adaptively embed the secret information into pixels of each absolute moment block truncation coding (AMBTC)-compressed block, except for the positions of two replaced quantization levels. The values of QLD tend to be much larger in complex areas than in smooth areas. In other words, our proposed method can obtain good performance for embedding capacity and still meets the requirement for better modified image quality when the image is complex. The performance of the proposed approach was compared to previous image hiding methods. The experimental results show that our approach outperforms referenced approaches.