Data compression in an underwater acoustic image on demand system

Data compression techniques in IoT-enabled wireless body sensor networks: A systematic literature review and research trends for QoS improvement

Underwater acoustic imaging systems are useful in providing underwater observation and inspection capabilities whenever optical systems cannot. Underwater acoustic imaging is a part of the larger field of acoustic imaging, and as such, has its own peculiarities and attributes as compared with the rest of the acoustic imaging field. In this report, the general characteristics of underwater acoustic imaging are presented. Three basic techniques of acoustic imaging that are appropriate for underwater imaging are discussed (focused, beamformed, and holographic) and examples of each of them are described. Then the limitations and promise for the future in underwater acoustic imaging are explored.

A underwater acoustic imaging system capable of working in extremely turbid conditions and producing high resolution dimensioned images has been developed. It is suitable for inspecting pipelines and offshore structures. A large development program has been carried out in Australia as a collaborative effort between a number of organisations to overcome the traditional problems with underwater imaging. The system uses megahertz frequency sound, sparse array technology, one-bit digitisation and chirp illumination. Acoustic three dimensional images with range and orthogonal dimensions have been obtained from a prototype underwater acoustic imaging system and examples are presented. Resolutions of millimeters have been obtained. This high resolution, large image (103x103x103 voxels) system is a new-to-the-world capability. This paper is a step in diffusing the new technology so that underwater acoustic imaging may become an innovation in offshore inspection and measurement. Introduction A new technology has been demonstrated for generating three dimensional near video quality images using underwater acoustics. There are two technologies for producing focused acoustic images, lenses and beam forming from twodimensional arrays. The lens has been demonstrated for two dimensional images by Belcher (1998) using a onedimensional array in the focal plane. The lens system places an array of acoustic sensors at the focal plane of the system and avoids the processing load of beam forming. Erikson (1997) discusses a more capable two-dimensional focal plane array. Beam formed images rely on the ability of the signals from an appropriately spaced array of acoustic sensors to be delayed in time and added to make the reflected pressure signals in phase for a particular voxel. For a large imaging volume this can become a very large processing load. The Underwater Acoustic Imaging technology is founded on five elements. Sparse arrays, one-bit digitisation, chirp ensonification, economical acoustic tiles and correlation flattening. While all of these elements have been used in some context before, it took the implementation of them all in a single system to produce a potential innovation. The sonar equation sets limits to performance that are determined by the source level and the system noise. While the source level is felt to be limited by the cavitation pressure, even before that magnitude the acoustic signal is nonlinear. Non linear acoustic signals distort during propagation and make replicate correction more difficult. System noise limits the range at which an object of given target strength can be seen above this noise. Arrays do not focus exactly on a single voxel but allow acoustic energy from adjacent voxels to leak into the picture. Replicate correlation of the received signals with the chirp ensonification, which provides focusing in the range direction, does the same. The clutter from elements of the target limits the size of objects that can be viewed clearly by restricting the contrast. Technology For an active acoustic imaging system such as the present, the acoustic pressure on the face of the receive array depends on the source level, the range of the target, its target strength and the absorption and scattering of water.

The rapid growth of smart grid systems has resulted in an exponential increase in data volume. This has led to significant challenges for efficient storage and transmission, thereby necessitating the development of advanced data compression techniques. Traditional techniques often struggle to maintain a balance between compression efficiency and data integrity, particularly when dealing with diverse and large datasets. To address this issue, this paper presents an adaptive data compression algorithm based on discrete wavelet transform (DWT) and Horse Herd Optimization (HHO). The proposed technique significantly improves storage by dynamically optimizing key performance metrics, such as signal-to-noise ratio (SNR), mean square error (MSE), and compression ratio (CR). By employing a robust optimization algorithm, it effectively addresses the computational challenges of processing large-scale and real-time data. This adaptability ensures the algorithm is well-suited for dynamic smart grid environments, providing a scalable and reliable solution to modern data management demands. This technique uses HHO to find the optimal thresholding for smart grid data compression. In general, DWT-based data compression is carried out using a universal threshold for ignoring particular wavelet coefficients. But, the performance of data compression varies for different threshold values. Hence, selecting an optimal threshold is a challenging task for data compression. Therefore, to solve this issue, an effective optimization algorithm is needed. In this work, a Multi-Objective Horse Herd Optimization (MO-HHO) algorithm is proposed to find the optimal threshold. The suggested MO-HHO algorithm accurately determines the global optimum threshold. Hence, it maintains a good compromise between SNR, MSE and CR. The effectiveness of the proposed algorithm is examined using three different data sets. Various datasets from the IEEE Power quality wave data, Household Electric Power Consumption data and Real-time dataset from an experimental set-up were used to test the proposed algorithm. The outcome demonstrates that the suggested MO-HHO algorithm performs better than the other conventional methods.

This paper describes a voting-based approach for the fast automatic recognition of man-made objects and related attitude estimation in underwater acoustic images generated by forward-looking sonars or acoustic cameras. In general, the continuous analysis of sequences of images is a very heavy task for human operators and this is due to the poor quality of acoustic images. Hence, algorithms able to recognize an object on the basis of a priori knowledge of the model and to estimate its attitude with reference to a global coordinate system are very useful to facilitate underwater operations like object manipulation or vehicle navigation. The proposed method is capable of recognizing objects and estimating their two-dimensional attitude by using information coming from boundary segments and their angular relations. It is based on a simple voting approach directly applied to the edge discontinuities of underwater acoustic images, whose quality is usually affected by some undesired effects such as object blurring, speckle noise, and geometrical distortions degrading the edge detection. The voting approach is robust, with respect to these effects, so that good results are obtained even with images of very poor quality. The sequences of simulated and real acoustic images are presented in order to test the validity of the proposed method in terms of average estimation error and computational load.

Research interface on a programmable ultrasound scanner

This paper provides different kinds of techniques for lossless data compression and comparison between them. By eliminating redundant bits, data compression decreases the file size. In order to reduce the capacity needed for that data, it decreases the redundant bits in data representation and thus uses the bandwidth effectively to reduce the communication cost. Compression of data saves file volume, network bandwidth and speeds up the transfer speed as well. Lossless and Lossy are the two techniques for data compression. Lossless compression maintains the data properly.

Underwater acoustic imaging is a part of the larger field of acoustic imaging, and as such, has its own peculiarities and attributes as compared with the rest of the acoustic imaging field. Such systems are useful in providing underwater observation and inspection capabilities whenever optical systems cannot. Three basic techniques of acoustic imaging that are appropriate for underwater imaging use (focused, beamformed, and holographic) and examples of each of them are described. Then the limitations and promise for the future in underwater acoustic imaging is explored.

In the scientific domain, extremely large amounts of data are generated by large-scale high performance computing (HPC) simulations. Storing and sending such vast volumes of data poses serious scalability and performance issues, which can be considerably mitigated by data compression techniques which significantly reduced storage size and data movement burdens. Since scientific data are being shared by scientists more and more frequently, data security methods that ensure the confidentiality, integrity, and availability of data itself are becoming increasingly important. As such, combing compression and encryption is critical to storing large-scale datasets securely. In this work, we explore how to integrate data compression and cryptography techniques as efficiently as possible for big scientific datasets in the HPC field. We perform thorough experiments using different scientific datasets with the state-of-the-art error-bounded lossy compressor - SZ - on a real-world supercomputing environment. Experiments verify that performing encryption before lossy compression (a.k.a., encr-cmpr method) may invalidate the advantage of compression algorithms. By contrast, executing encryption after lossy compression (a.k.a., cmpr-encr method) keeps not only high compression ratios but high overall execution speed. Experiments also verify that the encryption overhead under the cmpr-encr method decreases with increasing compression ratios, which means very good scalability.

The ability to monitor data in real time has become increasingly important as the industry continues digitalization efforts. Over the past several years this contractor has upgraded the capabilities of a real time monitoring system that has been providing value through improved operational efficiency and visibility of operations across its onshore fleet. These updates open a wide set of alerting opportunities that were previously not possible by distributing the alerting system to local, rig-based devices. An overview of the technology and architecture necessary to make this system possible, as well as the impact on performance will be discussed. This contractor's efforts have focused on converting the system from a cloud-based system to an edge-based system, with all computations and alerts issued at the rig location. The architecture comprises migrating cloud applications to run on the edge and leveraging edge hardware and rig historians to enable alerting on all data available at the rig location. Streamlining and synchronizing the rules/alert definitions and notifying users instantly using cloud infrastructure is a novel implementation. Housed within the edge platform, the system can operate on batches of data in real time without being held back by cloud computing related latency. Preexisting infrastructure made it possible to receive exception-based alerting within 15 minutes of an event occurring. With the new implementation running on the edge, it is possible to receive alerts to the user's phone, email and/or web-based dashboard within a minute. Leveraging increased real-time monitoring capabilities, this contractor has been able to reduce the amount of premature equipment failures as well as improve drilling connection operations. These successes have resulted in a reduction in capital spend and improved spud to release times. The above will be reviewed in detail. This change in data architecture from a cloud-based to a distributed rig-based computing system provides a more efficient way to trigger alerts and opens the possibility of generating alerts locally at the driller's console. Previously, the cloud-based alerting system only had access to data streamed off-location, providing limited insight into equipment health and drilling conditions. As a result of this update, we can provide end users with alert notifications containing detailed messages and specific data from a wide range of local data sources, including thousands of equipment-related data feeds, downhole data, complex artificial intelligence(AI)/machine learning(ML) model outputs, and drilling performance metrics which were not previously synchronized into a system that allowed alerts to be generated across this entire dataset. Quicker computing and notification delivery times will pave the way for well control, drilling dysfunction and more sophisticated equipment health alerts on the driller's console in real time.

Wireless Sensor Networking is a sensor information gathering technology that has a wide range of applications in numerous fields. However due to the fact that sensor nodes have limited battery and are deployed in remote and harsh environments, energy efficient and real time transmission of the information are still open challenges. Since in a wireless sensor network data transmission is the most power consuming task, so far most useful techniques for the purpose of energy efficient and real time transmissions are based on data compression, the majority of them are based on wavelet transform. This paper give performance analysis of different wavelets for energy efficient and real time image data transmission in application like environment monitoring using wireless visual sensor networks. The performance evaluation shows that Haar wavelet is better in terms of energy efficiency and transmission time.

A new numerical procedure for the study of finite temperature quantum dynamics is developed. The method is based on the observation that the real and imaginary time dynamical data contain complementary types of information. Maximum entropy methods, based on a combination of real and imaginary time input data, are used to calculate the spectral densities associated with real time correlation functions. Model studies demonstrate that the inclusion of even modest amounts of short-time real time data significantly improves the quality of the resulting spectral densities over that achievable using either real time data or imaginary time data separately.

It is important to measure and analyze offshore platform motion data for the safety of offshore platforms that are installed in an extreme environmental conditions. Especially, in the Gulf of Mexico, because of the hurricane and loop current that has been strengthened due to changes in the ocean environment, a monitoring system is necessary to measure the offshore platform motion data in real time, as well as simultaneously analyze it for safe operation and accident prevention. In order to develop such a monitoring system, it is extremely critical to obtain accurate data. However, since data measured in real time is not suitable for analysis due to quality problems, the data must be processed. The problem is that the processed data differs depending on the processing methods being employed, and results in entirely different analysis results. Incorrect results can mislead the current state of the offshore platform causing missed maintenance periods or, in the worst case, major accidents. Therefore, verifying both the adequacy of the methods used to process the data and the accuracy of the results is most vital. In this paper, improving reliability of the monitoring system that can measure offshore platform motion data and analyze data in real time, the validity of the method used in measurement data processing and the accuracy of the processed data were verified.

Recent advancements of the Industrial Internet of Things (IIoT) have revolutionized modern urbanization and smart cities. While IIoT data contain rich events and objects of interest, processing a massive amount of IIoT data and making predictions in real-time are challenging. Recent advancements in artificial intelligence (AI) allow processing such a massive amount of IIoT data and generating insights for further decision-making processes. In this article, we propose several key aspects of AI-enabled IIoT data for smart city monitoring. First, we have combined a human-intelligence-enabled crowdsourcing application with that of an AI-enabled IIoT framework to capture events and objects from IIoT data in real time. Second, we have combined multiple AI algorithms that can run on distributed edge and cloud nodes to automatically categorize the captured events and objects and generate analytics, reports, and alerts from the IIoT data in real time. The results can be utilized in two scenarios. In the first scenario, the smart city authority can authenticate the AI-processed events and assign these events to the appropriate authority for managing the events. In the second scenario, the AI algorithms are allowed to interact with humans or IIoT for further processes. Finally, we will present the implementation details of the scenarios mentioned above and the test results. The test results show that the framework has the potential to be deployed within a smart city.

Filtergraph: An interactive web application for visualization of astronomy datasets