Search Results (17)

In order to achieve low-cost scalability, the same-wavelength bidirectional (SWB) fiber communication system is a better solution. We present a detailed investigation of the performance of the different orders Raman amplifiers in same-wavelength bidirectional fiber communication systems. We discuss how to suppress the main factor affecting system performance which is Rayleigh scattering noise (RSN). By using different Raman amplifiers to construct different quasi-lossless transmission, the performance changes in the same-wavelength bidirectional fiber optic communication system were studied. On this basis, multi-channel and same-wavelength single fiber bidirectional system experiments were conducted to compare the performance of second-order Raman systems and first-order Raman systems. The results indicate that the Rayleigh scattering suppression effect of second-order Raman systems is better, and compared to first-order Raman systems, the average signal-to-noise ratio (SNR) can be increased by 2.88 dB. Full article

This article presents the implementation and the evaluation of a transmission–reception system for Physical Downlink Control Channel (PDCCH) within the context of fifth-generation cellular networks (5G/NR). The research focuses on characterizing the behavior of the PDCCH channel concerning different User Equipment (UE) positions through simulations conducted in MATLAB. Two scenarios are considered: a lossless system and a system with losses, where the outcomes are presented in terms of Bit Error Rate (BER) vs. Signal to Noise Ratio (SNR) and BER vs. Transmit Power, respectively. One contribution of the paper is the explanation transmission–reception system for PDCCH in MATLAB, while the second contribution is the presentation of simulation results. From results, it was deduced that higher UE height corresponds to improved channel performance, greater UE elevation angle leads to enhanced channel performance, and increased carrier frequency results in reduced performance of the physical control PDCCH channel. Full article

In recent years, underwater acoustic applications have attracted much attention, for example, for underwater environmental monitoring, underwater exploration, etc. Hydrophones play a particularly important role. Although hydrophone design has been in multifarious application forms, it still needs to consider increasing demand for low-cost, low-consumption, and multiple-function devices, as well as issues around miniaturization, lossless data collection, etc. In this paper, we design a compact underwater acoustic device that has the capability of underwater acoustic signal storage, underwater acoustic signal transmission via the Internet, and decoding based on the direct sequences spread spectrum (DSSS). The key problem is how to implement multiple functions in only one micro-controller unit (MCU). The hardware and software of the proposed multi-function hydrophone are described in detail. In particular, the MCU, the pre-amplifier with gain control, and the analog-to-digital integrated chip are introduced. Moreover, underwater acoustic data storage, underwater acoustic transmission, and the DSSS receiver are depicted in terms of software. The different functions of the hydrophone are verified in sea trial experiments. The results show that the proposed multi-function hydrophone is able to sample underwater acoustic data at high quality. In addition, to demonstrate configurable parameters, the DSSS receiver with different carrier frequencies is provided. The proposed multi-function hydrophone realizes zero bit error rate (BER) when carrier frequency $f_c=9$ kHz, and the BER with ${10}^{-3}$ order of magnitude when carrier frequency $f_c=15.5$ kHz. The results show that the proposed multi-function hydrophone has great potential to explore the ocean. Full article

Heart-related ailments have become a significant cause of death around the globe in recent years. Due to lifestyle changes, people of almost all age brackets face these issues. Preventing and treating heart-related issues require the electrocardiogram (ECG) monitoring of patients. The study of patients’ ECG signals helps doctors identify abnormal heart rhythm patterns by which screening problems like arrhythmia (irregular heart rhythm), myocardial infarction (heart attacks), and myocarditis (heart inflammation) is possible. The need for 24 h heart rate monitoring has led to the development of wearable devices, and the constant monitoring of ECG data leads to the generation of a large amount of data since wearable systems are resource-constrained regarding energy, memory, size, and computing capabilities. The optimization of biomedical monitoring systems is required to increase their efficiency. This paper presents an ECG compression system to reduce the amount of data generated, which reduces the energy consumption in the transceiver, which is a significant part of the overall energy consumed. The proposed system uses hybrid Golomb–Rice coding for data compression, a lossless data compression technique. The data compression is performed on the MIT BIH arrhythmia database; the achieved compression ratio of the compression system is 2.75 and 3.14 for average and maximum values, which, compared to the raw ECG samples, requires less transmission cost in terms of power consumed. Full article

The rapidly growing requirement for data has put forward Compressed Sensing (CS) to realize low-ratio sampling and to reconstruct complete signals. With the intensive development of Deep Neural Network (DNN) methods, performance in image reconstruction from CS measurements is constantly increasing. Currently, many network structures pay less attention to the relevance of before- and after-stage results and fail to make full use of relevant information in the compressed domain to achieve interblock information fusion and a great receptive field. Additionally, due to multiple resamplings and several forced compressions of information flow, information loss and network structure redundancy inevitably result. Therefore, an Information Enhancement and Fusion Network for CS reconstruction (IEF-CSNET) is proposed in this work, and a Compressed Information Extension (CIE) module is designed to fuse the compressed information in the compressed domain and greatly expand the receptive field. The Error Comprehensive Consideration Enhancement (ECCE) module enhances the error image by incorporating the previous recovered error so that the interlink among the iterations can be utilized for better recovery. In addition, an Iterative Information Flow Enhancement (IIFE) module is further proposed to complete the progressive recovery with loss-less information transmission during the iteration. In summary, the proposed method achieves the best effect, exhibits high robustness at this stage, with the peak signal-to-noise ratio (PSNR) improved by 0.59 dB on average under all test sets and sampling rates, and presents a greatly improved speed compared with the best algorithm. Full article

The use of sensor applications has been steadily increasing, leading to an urgent need for efficient data compression techniques to facilitate the storage, transmission, and processing of digital signals generated by sensors. Unlike other sequential data such as text sequences, sensor signals have more complex statistical characteristics. Specifically, in every signal point, each bit, which corresponds to a specific precision scale, follows its own conditional distribution depending on its history and even other bits. Therefore, applying existing general-purpose data compressors usually leads to a relatively low compression ratio, since these compressors do not fully exploit such internal features. What is worse, partitioning a bit stream into groups with a preset size will sometimes break the integrity of each signal point. In this paper, we present a lossless data compressor dedicated to compressing sensor signals which is built upon a novel recurrent neural architecture named multi-channel recurrent unit (MCRU). Each channel in the proposed MCRU models a specific precision range of each signal point without breaking data integrity. During compressing and decompressing, the mirrored network will be trained on observed data; thus, no pre-training is needed. The superiority of our approach over other compressors is demonstrated experimentally on various types of sensor signals. Full article

The JPEG format, consisting of a set of image compression techniques, is one of the most commonly used image coding standards for both lossy and lossless image encoding. In this format, various techniques are used to improve image transmission and storage. In the final step of lossy image coding, JPEG uses either arithmetic or Huffman entropy coding modes to further compress data processed by lossy compression. Both modes encode all the 8 × 8 DCT blocks without filtering empty ones. An end-of-block marker is coded for empty blocks, and these empty blocks cause an unnecessary increase in file size when they are stored with the rest of the data. In this paper, we propose a modified version of the JPEG entropy coding. In the proposed version, instead of storing an end-of-block code for empty blocks with the rest of the data, we store their location in a separate buffer and then compress the buffer with an efficient lossless method to achieve a higher compression ratio. The size of the additional buffer, which keeps the information of location for the empty and non-empty blocks, was considered during the calculation of bits per pixel for the test images. In image compression, peak signal-to-noise ratio versus bits per pixel has been a major measure for evaluating the coding performance. Experimental results indicate that the proposed modified algorithm achieves lower bits per pixel while retaining quality. Full article

The electrocardiogram (ECG) electrode, as a sensor, is an important part of the wearable ECG monitoring device. Natural leather is rarely used as the electrode substrate. In this paper, wearable flexible silver electrodes based on cowhide were prepared by sputtering and brush-painting. A signal generator, oscilloscope, impedance test instrument, and ECG monitor were used to build the test platform evaluating the performance of electrodes with six subjects. The lossless waveform transmission can be achieved with our electrodes. Therefore, the Pearson’s correlation coefficient calculated with input waveform and output waveform of the electrodes based on the top grain layer (GLE) and the split layer (SLE) of cowhide were 0.997 and 0.998 at 0.1 Hz respectively. The skin electrode impedance (Z) was tested, and the parameters of the equivalent circuit model of the skin electrode interface were calculated by a fitting method, indicating that the Z of the prepared electrodes was comparable with the standard gel electrode when the skin is moist enough. The signal-to-noise ratio of the ECG of the GLE and the SLE were 1.148 and 1.205 times that of the standard electrode in the standing posture, which meant the ECG measured by our electrodes was basically consistent with that measured by the standard electrode. Full article

When detecting micro-distortion of lidar scanning signals, current hardwires and algorithms have low compatibility, resulting in slow detection speed, high energy consumption, and poor performance against interference. A geometric statistics-based micro-distortion detection technology for lidar scanning signals was proposed. The proposed method built the overall framework of the technology, used TCD1209DG (made by TOSHIBA, Tokyo, Japan) to implement a linear array CCD (charge-coupled device) module for photoelectric conversion, signal charge storage, and transfer. Chip FPGA was used as the core component of the signal processing module for signal preprocessing of TCD1209DG output. Signal transmission units were designed with chip C8051, FT232, and RS-485 to perform lossless signal transmission between the host and any slave. The signal distortion feature matching algorithm based on geometric statistics was adopted. Micro-distortion detection of lidar scanning signals was achieved by extracting, counting, and matching the distorted signals. The correction of distorted signals was implemented with the proposed method. Experimental results showed that the proposed method had faster detection speed, lower detection energy consumption, and stronger anti-interference ability, which effectively improved micro-distortion correction. Full article

Modern daily life activities result in a huge amount of data, which creates a big challenge for storing and communicating them. As an example, hospitals produce a huge amount of data on a daily basis, which makes a big challenge to store it in a limited storage or to communicate them through the restricted bandwidth over the Internet. Therefore, there is an increasing demand for more research in data compression and communication theory to deal with such challenges. Such research responds to the requirements of data transmission at high speed over networks. In this paper, we focus on deep analysis of the most common techniques in image compression. We present a detailed analysis of run-length, entropy and dictionary based lossless image compression algorithms with a common numeric example for a clear comparison. Following that, the state-of-the-art techniques are discussed based on some bench-marked images. Finally, we use standard metrics such as average code length (ACL), compression ratio (CR), pick signal-to-noise ratio (PSNR), efficiency, encoding time (ET) and decoding time (DT) in order to measure the performance of the state-of-the-art techniques. Full article

In the wild, wireless multimedia sensor network (WMSN) communication has limited bandwidth and the transmission of wildlife monitoring images always suffers signal interference, which is time-consuming, or sometimes even causes failure. Generally, only part of each wildlife image is valuable, therefore, if we could transmit the images according to the importance of the content, the above issues can be avoided. Inspired by the progressive transmission strategy, we propose a hierarchical coding progressive transmission method in this paper, which can transmit the saliency object region (i.e. the animal) and its background with different coding strategies and priorities. Specifically, we firstly construct a convolution neural network via the MobileNet model for the detection of the saliency object region and obtaining the mask on wildlife. Then, according to the importance of wavelet coefficients, set partitioned in hierarchical tree (SPIHT) lossless coding is utilized to transmit the saliency image which ensures the transmission accuracy of the wildlife region. After that, the background region left over is transmitted via the Embedded Zerotree Wavelets (EZW) lossy coding strategy, to improve the transmission efficiency. To verify the efficiency of our algorithm, a demonstration of the transmission of field-captured wildlife images is presented. Further, comparison of results with existing EZW and discrete cosine transform (DCT) algorithms shows that the proposed algorithm improves the peak signal to noise ratio (PSNR) and structural similarity index (SSIM) by 21.11%, 14.72% and 9.47%, 6.25%, respectively. Full article

Data transmission of electroencephalography (EEG) signals over Wireless Body Area Network (WBAN) is currently a widely used system that comes together with challenges in terms of efficiency and effectivity. In this study, an effective Very-Large-Scale Integration (VLSI) circuit design of lossless EEG compression circuit is proposed to increase both efficiency and effectivity of EEG signal transmission over WBAN. The proposed design was realized based on a novel lossless compression algorithm which consists of an adaptive fuzzy predictor, a voting-based scheme and a tri-stage entropy encoder. The tri-stage entropy encoder is composed of a two-stage Huffman and Golomb-Rice encoders with static coding table using basic comparator and multiplexer components. A pipelining technique was incorporated to enhance the performance of the proposed design. The proposed design was fabricated using a 0.18 μm CMOS technology containing 8405 gates with 2.58 mW simulated power consumption under an operating condition of 100 MHz clock speed. The CHB-MIT Scalp EEG Database was used to test the performance of the proposed technique in terms of compression rate which yielded an average value of 2.35 for 23 channels. Compared with previously proposed hardware-oriented lossless EEG compression designs, this work provided a 14.6% increase in compression rate with a 37.3% reduction in hardware cost while maintaining a low system complexity. Full article

Compression of hyperspectral imagery increases the efficiency of image storage and transmission. It is especially useful to alleviate congestion in the downlinks of planes and satellites, where these images are usually taken from. A novel compression algorithm is presented here. It first spectrally decorrelates the image using Vector Quantization and Principal Component Analysis (PCA), and then applies JPEG2000 to the Principal Components (PCs) exploiting spatial correlations for compression. We take advantage of the fact that dimensionality reduction preserves more information in the first components, allocating more depth to the first PCs. We optimize the selection of parameters by maximizing the distortion-ratio performance across the test images. An increase of 1 to 3 dB in Signal Noise Ratio (SNR) for the same compression ratio is found over just using PCA + JPEG2000, while also speeding up compression and decompression by more than $10\%$. A formula is proposed which determines the configuration of the algorithm, obtaining results that range from heavily compressed-low SNR images to low compressed-near lossless ones. Full article

An estimated 15 million babies are born prematurely every year worldwide, and suffer from disabilities. Appropriate care of these pre-term babies immediately after birth through telemedicine monitoring is vital. However, problems associated with a limited bandwidth and network overload due to the excessive size of the electromyography (EMG) signal impede the practical application of such medical information systems. Therefore, this research proposes an EMG uterine monitoring transmission solution (EUMTS), a lossless efficient real-time EMG transmission solution that solves such problems through efficient EMG data lossless compression. EMG data samples obtained from the Physionet PhysioBank database were used. Solution performance comparisons were conducted using Lempel-Ziv Welch (LZW) and Huffman methods, in addition to related researches. The LZW and Huffman methods showed CRs of 1.87 and 1.90, respectively, compared to 3.61 for the proposed algorithm. This was relatively high compared to related researches, even when considering that those researches were lossy whereas the proposed research was lossless. The results also showed that the proposed algorithm contributes to a reduction in battery consumption by reducing the wake-up time by 1470.6 ms. Therefore, EUMTS will contribute to providing an efficient wireless transmission environment for the prediction of pre-term delivery, enabling immediate interventions by medical professionals. Another novel point of EUMTS is that it is a lossless algorithm, which will prevent any misjudgement by clinicians because the data will not be distorted. Pre-term babies may receive point-of-care immediately after birth, preventing exposure to the development of disabilities. Full article

Onboard image processing systems for a hyperspectral sensor have been developed in order to maximize image data transmission efficiency for large volume and high speed data downlink capacity. Since more than 100 channels are required for hyperspectral sensors on Earth observation satellites, fast and small-footprint lossless image compression capability is essential for reducing the size and weight of a sensor system. A fast lossless image compression algorithm has been developed, and is implemented in the onboard correction circuitry of sensitivity and linearity of Complementary Metal Oxide Semiconductor (CMOS) sensors in order to maximize the compression ratio. The employed image compression method is based on Fast, Efficient, Lossless Image compression System (FELICS), which is a hierarchical predictive coding method with resolution scaling. To improve FELICS’s performance of image decorrelation and entropy coding, we apply a two-dimensional interpolation prediction and adaptive Golomb-Rice coding. It supports progressive decompression using resolution scaling while still maintaining superior performance measured as speed and complexity. Coding efficiency and compression speed enlarge the effective capacity of signal transmission channels, which lead to reducing onboard hardware by multiplexing sensor signals into a reduced number of compression circuits. The circuitry is embedded into the data formatter of the sensor system without adding size, weight, power consumption, and fabrication cost. Full article