Transmission Of Compressed Data Research Articles

Design of Compressed Data Transmission for Mobile IoT Devices

Design of Compressed Data Transmission for Mobile IoT Devices

Spatiotemporal compression‐transmission strategies for energy‐harvesting wireless sensor networks

In energy-harvesting wireless sensor networks (EHWSNs), sensors perform many functions such as sensing, compression, and transmission. It is known that the sensing and transmission processes consume most of the energy by the sensors. The maximisation of the lifetime by balancing energy acquisition and consumption has been the focus of many research activities. This study focuses on the data compression-transmission optimisation problem of the EHWSNs in the presence of energy input and output processes, wherein compressive sensing is employed as the compression scheme for data transmission. Both transmission and energy dynamics are modelled, and the Markov process of data transmission is assumed. The jointly optimised compression and transmission strategies are formulated using the spatial-temporal feature via a Lagrangian relaxation approach, and the theoretical results of the optimal modelling structure are derived. Finally, extensive simulations are presented to validate the effectiveness of the proposed algorithm in comparison with existing algorithms.

Design of wavelet transform based electrocardiogram monitoring system

The new age advancements in information technology due to materials and integrated circuit (IC) technologies and their applications in biomedical sciences have made the healthcare facilities more compact and affordable for the aging population. Market trends in healthcare and related devices indicate a sharp rise in their demand. Hence the researchers have converged the efforts on designing more smart and advanced medical devices using IC technology. Among these devices, cardiac pacemakers have become a recurrent biomedical device which is engrafted in the human body to detect and monitor a person's heart beating rate. The data thus generated is processed for various medical usages and devices via wireless methods. Cardiovascular diseases (CVDs) or diseases related to the heart are due to abnormalities or disorders of the heart and blood vessels. Till date, limited literature is available which focuses on a single technique that can perform all of the ECG signal denoising, ECG detection, lossless data compression and wireless transmission. In this work, a joint approach for denoising, detection, compression, and wireless transmission of ECG signal is proposed. The modified biorthogonal wavelet transform is used for denoising, detection and lossless compression of ECG signal. To reduce the circuit complexity, biorthogonal wavelet transform is realized using linear phase structure. Further, it is found in this work that the usage of modified biorthogonal wavelet transform increases the detection accuracy and CR of the proposed design. Also, in this work, the Wi-Fi-based wireless protocol is used for compressed data transmission. The proposed ECG detector achieves the highest sensitivity and positive predictivity of 99.95% and 99.92%, respectively, with the MIT-BIH arrhythmia database. The use of modified biorthogonal 3.1 wavelet transform and run-length encoding (RLE) for the compression of ECG data achieves a higher compression ratio (CR) of 6.271. To justify the effectiveness of the proposed algorithm, which uses modified biorthogonal wavelet 3.1transform, the results are compared with the existing methods, namely, Huffman coding/simple predictor, Huffman coding/adaptive, and slope predictor/fixed length packaging.

A comprehensive optimization strategy for real-time spatial feature sharing and visual analytics in cyberinfrastructure

ABSTRACTFor geospatial cyberinfrastructure-enabled web services, the ability of rapidly transmitting and sharing spatial data over the Internet plays a critical role to meet the demands of real-time change detection, response and decision-making. Especially for vector datasets which serve as irreplaceable and concrete material in data-driven geospatial applications, their rich geometry and property information facilitates the development of interactive, efficient and intelligent data analysis and visualization applications. However, the big-data issues of vector datasets have hindered their wide adoption in web services. In this research, we propose a comprehensive optimization strategy to enhance the performance of vector data transmitting and processing. This strategy combines: (1) pre- and on-the-fly generalization, which automatically determines proper simplification level through the introduction of appropriate distance tolerance speed up simplification efficiency; (2) a progressive attribute transmission method to reduce data size and, therefore, the service response time; (3) compressed data transmission and dynamic adoption of a compression method to maximize the service efficiency under different computing and network environments. A cyberinfrastructure web portal was developed for implementing the proposed technologies. After applying our optimization strategies, substantial performance enhancement is achieved. We expect this work to facilitate real-time spatial feature sharing, visual analytics and decision-making.

Progressive Streaming of Video Data for Traffic Surveillance

Traffic surveillance has been one of the essential attributes in smart city concept. Nowadays, in such applications rotating camera is preferred over static camera. Motivation behind this substitution is to reduce the cost of data transmission and Total of cost of ownership. To design an optimal and performant wireless ‘smart city area network’ for video surveillance systems, this paper focuses on some key areas, namely, transmission efficiency, lossless video data coding, data congestion, edge computing at transmission nodes. The end objective is to achieve high quality received video stream in spite of compressed data transmission. Some research initiatives in this domain are pertinent. For example, Structural Similarity Index (SSIM) based rate distortion optimization is an effective tool in enhancing the perceptual video quality in wireless environments. However, prevailing system does not consider the network congestion conditions, affecting quality of received video. Also, effect of distortion introduced by ‘channel noise’ is unattended. This motivated us to propose a new dual metric traffic control mechanism, wherein both metrics i.e. distortion and data congestion are considered. It is based on an ‘improvised SSIM’ method which incorporates the ‘Rate of allocation’ algorithm as a function. Experimental results unveil that the proposed traffic control using similarity index under noise diversity can achieve better video quality and more data throughput.

Multimedia Data Hiding using Fourier series on an Experimental Basis

The compressed data transmission to be implemented in this work with the help of Fourier series information hiding mechanism. In the proceeding step, the necessity requirement is the problem statement formulation for designing the system. It mainly focuses on the partitions or segmentations of the multimedia content into a compressed one. The compressed content will be hided to be maintained the originality during the data transmission over the network.

On Integration of Error Concealment and Authentication in JPEG2000 Coded Images

Nowadays, it is widely understood that data compression is not only essential to speed up the transmission rate but also to provide other gains like low storage. In order to counter data manipulations and tampering during transmission, the image authentication has turned out to be equally important. But the drawback of compressed data transmission is that the compressed data are susceptible to channel impairments. In this paper, an error concealment approach is integrated with low cost image authentication scheme to benefit better visual quality as well as content author and user satisfaction. The image authentication includes content based digital signature that is watermarked and diffused in the whole image before JPEG2000 coding. To tackle noise, the error detection and concealment technology is examined to include edge information as part of error concealment approach. The edge image is sent along with JPEG2000 coded image to determine corrupted coefficients. The simulation results are conducted on test images for different values of bit error rate to judge confidence in noise concealment within the received images .

Research on Low Speed Serial Links Transmission Optimization Technology

In order to overcome the low speed serial links of the bandwidth low, easy to produce decline, low rate of transmission characteristics, This paper design a transmission optimization system, In the system, this paper proposes a forward error correction and data compression transmission optimization combined method, Transmission optimization technology in recent years has been widely used in the field of network communication. Transmission optimization technology has been shown to improve the utilization rate of bandwidth, improve transmission efficiency. In this paper, the transmission optimization of the forward error correction technology has carried on the simulation test, And the analysis of experimental data were compared before and after the optimization, The experimental results show that the proposed transmission optimization method for low speed serial links the data transmission efficiency has been obviously increased . Transmission Optimization Technology Transmission optimization techniques include forward error correction, data compression, and adaptive protocol of three parts, combined guarantee bad message transmission efficiency in transmission environment. This paper mainly studies the transmission on the wireless communication channel environment serial links optimization techniques. Bandwidth Compression Technology. Bandwidth compression is transmitted through compression technology after traditional smaller packets, such as real time transmission of packets is more than the number of packets before compression, so as to realize the goal of improving transmission speed. Bandwidth compression technology is not really to data compression, because the network of packet size and length size has strict rules, if the size of the data has changed, and is also change his nature. So the true meaning of bandwidth compression is part of the data in other ways to replace, including cache corresponding not change information data or with short data instead of long, Then after receiving inverse substitution process, complete the purpose of saving bandwidth. The most commonly used three kinds of compression algorithm: Huffinan coding, dictionary coding and arithmetic coding [.

Randomized methods in lossless compression of hyperspectral data

We evaluate recently developed randomized matrix decomposition methods for fast lossless compression and reconstruction of hyperspectral imaging (HSI) data. The simple random projection methods have been shown to be effective for lossy compression without severely affecting the performance of object identification and classification. We build upon these methods to develop a new double-random projection method that may enable security in data transmission of compressed data. For HSI data, the distribution of elements in the resulting residual matrix, i.e., the original data subtracted by its low-rank representation, exhibits a low entropy relative to the original data that favors high-compression ratio. We show both theoretically and empirically that randomized methods combined with residual-coding algorithms can lead to effective lossless compression of HSI data. We conduct numerical tests on real large-scale HSI data that shows promise in this case. In addition, we show that randomized techniques can be applicable for encoding on resource-constrained on-board sensor systems, where the core matrix-vector multiplications can be easily implemented on computing platforms such as graphic processing units or field-programmable gate arrays.

Lossless Compression Method for Medical Image Sequences Using Super-Spatial Structure Prediction and Inter-frame Coding

Space research organizations, hospitals and military air surveillance activities, among others, produce a huge amount of data in the form of images hence a large storage space is required to record this information. In hospitals, data produced during medical examination is in the form of a sequence of images and are very much correlated; because these images have great importance, some kind of lossless image compression technique is needed. Moreover, these images are often required to be transmitted over the network. Since the availability of storage and bandwidth is limited, a compression technique is required to reduce the number of bits to store these images and take less time to transmit them over the network. For this purpose, there are many state-of the-art lossless image compression algorithms like CALIC, LOCO-I, JPEG-LS, JPEG20000; Nevertheless, these compression algorithms take only a single file to compress and cannot exploit the correlation among the sequence frames of MRI or CE images. To exploit the correlation, a new algorithm is proposed in this paper. The primary goals of the proposed compression method are to minimize the memory resource during storage of compressed data as well as minimize the bandwidth requirement during transmission of compressed data. For achieving these goals, the proposed compression method combines the single image compression technique called super spatial structure prediction with inter-frame coding to acquire grater compression ratio. An efficient compression method requires elimination of redundancy of data during compression; therefore, for elimination of redundancy of data, initially, the super spatial structure prediction algorithm is applied with the fast block matching approach and later Huffman coding is applied for reducing the number of bits required for transmitting and storing single pixel value. Also, to speed up the block-matching process during motion estimation, the proposed method compares those blocks that have identical sum and leave the others; therefore, the time taken by the block-matching process is reduced by minimizing the unnecessary overhead during the blockmatching process. Thus, in the proposed fast lossless compression method for medical image sequences, the twostage redundant data elimination process ultimately reduces the memory resource required for storing and transmission. The method is tested on the sequences of MRI and CE images and produces an improved compression rate.

Magnetic Resonance Imaging Research in Sub-Saharan Africa: Challenges and Satellite-Based Networking Implementation

As part of an NIH-funded study of malaria pathogenesis, a magnetic resonance (MR) imaging research facility was established in Blantyre, Malaŵi to enhance the clinical characterization of pediatric patients with cerebral malaria through application of neurological MR methods. The research program requires daily transmission of MR studies to Michigan State University (MSU) for clinical research interpretation and quantitative post-processing. An intercontinental satellite-based network was implemented for transmission of MR image data in Digital Imaging and Communications in Medicine (DICOM) format, research data collection, project communications, and remote systems administration. Satellite Internet service costs limited the bandwidth to symmetrical 384 kbit/s. DICOM routers deployed at both the Malaŵi MRI facility and MSU manage the end-to-end encrypted compressed data transmission. Network performance between DICOM routers was measured while transmitting both mixed clinical MR studies and synthetic studies. Effective network latency averaged 715 ms. Within a mix of clinical MR studies, the average transmission time for a 256 × 256 image was ~2.25 and ~6.25 s for a 512 × 512 image. Using synthetic studies of 1,000 duplicate images, the interquartile range for 256 × 256 images was [2.30, 2.36] s and [5.94, 6.05] s for 512 × 512 images. Transmission of clinical MRI studies between the DICOM routers averaged 9.35 images per minute, representing an effective channel utilization of ~137% of the 384-kbit/s satellite service as computed using uncompressed image file sizes (including the effects of image compression, protocol overhead, channel latency, etc.). Power unreliability was the primary cause of interrupted operations in the first year, including an outage exceeding 10 days.

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.

Unequal error protection for LZSS compressed data using Reed-Solomon codes

An unequal error protection strategy for minimising the impact of error bursts in the storage or transmission of compressed data has been proposed. The strategy is designed for the LZSS algorithm, which is a dynamic dictionary compression method. Through extensive computer simulations we investigate which information classes within LZSS are more sensitive to burst errors. Based on these results, an unequal error protection strategy that greatly reduces the error propagation during the decompression process has been proposed. Numerical results using text files from a common database are presented.

Efficient transmission of compressed data for remote volume visualization

One of the goals of telemedicine is to enable remote visualization and browsing of medical volumes. There is a need to employ scalable compression schemes and efficient client-server models to obtain interactivity and an enhanced viewing experience. First, we present a scheme that uses JPEG2000 and JPIP (JPEG2000 Interactive Protocol) to transmit data in a multi-resolution and progressive fashion. The server exploits the spatial locality offered by the wavelet transform and packet indexing information to transmit, in so far as possible, compressed volume data relevant to the clients query. Once the client identifies its volume of interest (VOI), the volume is refined progressively within the VOI from an initial lossy to a final lossless representation. Contextual background information can also be made available having quality fading away from the VOI. Second, we present a prioritization that enables the client to progressively visualize scene content from a compressed file. In our specific example, the client is able to make requests to progressively receive data corresponding to any tissue type. The server is now capable of reordering the same compressed data file on the fly to serve data packets prioritized as per the client's request. Lastly, we describe the effect of compression parameters on compression ratio, decoding times and interactivity. We also present suggestions for optimizing JPEG2000 for remote volume visualization and volume browsing applications. The resulting system is ideally suited for client-server applications with the server maintaining the compressed volume data, to be browsed by a client with a low bandwidth constraint.

Development of an ionospheric scintillation monitor using single frequency GPS receiver

Besides their intended use in radio navigation, global positioning system (GPS) satellite signals provide convenient radio beacons for ionospheric studies. Among other propagation phenomena, the ionosphere affects GPS signal propagation through amplitude scintillations that develop after radio waves propagation through ionospheric electron density irregularities. This paper outlines the design, testing, and results of a specialized GPS receiver to monitorL-band scintillations. The scintillation monitor system consists of a commercial GPS receiver development kit with its software designed to log signal strength and carrier phase from up to 12 channels at one sample per second rate. Other prime features of the monitor include the data compression transmission and processing. Here is the fact that they are inexpensive and compact and therefore can be readily proliferated.

A sensitivity matched multicarrier modulation

We develop a multicarrier modulation scheme for transmission of compressed data. The energy allocation among subcarriers is optimized by minimizing a modified symbol error function in either AWGN or Rayleigh fading channels. It is observed that our sensitivity matched multicarrier modulation significantly outperforms the conventional multi carrier modulation. The performance gain in Rayleigh fading channels varies from 0.58 to 3.26 dB depending upon the relative sensitivity of bit groups associated with subchannels.

Testing multimedia transmission systems

Digital television and the convergence of television and telecommunications challenges the television engineer to develop new test methods and new ways of using older methods. Supporting multimedia capabilities makes compressed data transmission an important issue. This article outlines a layered model for systems and testing analogous to the OSI model for communications systems and discusses specific test methods for analysis and generation.

Universal noiseless and noisy data compression techniques — the state of the art

The technique of data compression demonstrates desirable properties that assure performance improvements of advanced network architectures such as ISDN, serial communication channels, data storage and data archiving. A global trend in telecommunications networks is the conversion from analogue to digital transmission. The design goal of these new networks is that voice, data, and images will be transmitted in fundamentally the same way, requiring neither prior knowledge as to the type of data being transmitted nor operator intervention. However, the bandwidth requirements for digital transmission are generally high. Spectrum bandwidth utilization is a design parameter for these systems that will benefit from the transmission of compressed data. The proposed Integrated Services Digital Network (ISDN) combines digital voice, data, and images on the same network. Since this network has limited bandwidth for transmission of information, the available bandwidth must be used as efficiently as possible.