ABSTRACT Blockchain technology has the potential to extend beyond its traditional use in cryptocurrency and make significant strides in critical sectors like healthcare. Clinical research, which plays a pivotal role in enhancing healthcare quality by guiding activities, determining equipment usage, and recommending preferred medications, stands to benefit greatly from blockchain integration. The unique technical capabilities of blockchain offer promising solutions across various phases of clinical research, from study design and patient recruitment to report study findings. By addressing current challenges in the clinical research process, blockchain technology can notably enhance research quality and, consequently, improve patient care. Although conceptual framework studies regarding blockchain technology are in the available literature, practical implementations of this technology remain relatively scarce. Thus, in this study, a private permissioned Hyperledger Fabric blockchain platform was developed to manage clinical research. As a use case, a blockchain‐based distributed framework for counting and reporting COVID‐19 epidemiological parameters and statistics among healthcare centers has been defined in the study. Besides, to make clinical research data findable, accessible, interoperable, and reusable (FAIR), we integrated FAIR principles into the developed blockchain‐based clinical research management system. Additionally, a use case logic has been implemented as a smart contract (chaincode) and invoked on Fabric Network. This study, in general, represents a crucial step towards bridging the gap between theoretical understanding and real‐world application within the domain of blockchain technology. Moreover, the performance of the Fabric Network was evaluated by analyzing the chain code execution performance according to the size of the patient data. By deploying a functioning clinical research network and executing smart contracts, this study contributes to the practical utilization of blockchain technology along with FAIR principles integration to the entire clinical research process, which is a first in the literature.

Abstract This study presents a parallel version of the string matching algorithms research tool (SMART) library, implemented on NVIDIA’s compute unified device architecture (CUDA) platform, and uses general-purpose computing on graphics processing unit (GPGPU) programming concepts to enhance performance and gain insight into the parallel versions of these algorithms. We have developed the CUDA-enhanced SMART (CUSMART) library, which incorporates parallelized iterations of 64 string matching algorithms, leveraging the CUDA application programming interface. The performance of these algorithms has been assessed across various scenarios to ensure a comprehensive and impartial comparison, allowing for the identification of their strengths and weaknesses in specific application contexts. We have explored and established optimization techniques to gauge their influence on the performance of these algorithms. The results of this study highlight the potential of GPGPU computing in string matching applications through the scalability of algorithms, suggesting significant performance improvements. Furthermore, we have identified the best and worst performing algorithms in various scenarios.

Abstract Data management is a crucial requirement due to the autonomous and constrained nature of Unmanned Aerial Vehicles (UAVs), Internet of Things (IoTs), and the aviation domain. The autonomous and restricted nature of these sectors increases the need for a shared, distributed database, strong access control management, consensus in autonomous decision‐making, and effective communication across diverse protocols and devices. This research presents a comprehensive approach and offers a new viewpoint to the field of blockchain while establishing a fundamental baseline for future improvements in data management systems and addressing the shortcomings of previously proposed existing frameworks in order to fulfill the complex needs of secure data management. This study contributes to the advancement of secure and efficient data management systems by implementing robust data monitoring for error detection, ensuring data integrity, and enabling encrypted or anonymous data sharing based on sensitivity levels. Additionally, the integration of diverse devices, enforcement of immutable regulations compliance, and development of permissioned blockchain systems for identity management further enhance the system's capabilities, offering comprehensive solutions for modern data management challenges. In the tests, the proposed framework showed increased successful transactions in all rate controllers. Besides, effect of the validator number on throughput and latency is tested and analyzed thoroughly.

Summary The main objective of this study is to develop real‐time, energy‐efficient embedded computing for machine learning based target detection. Convolutional neural network (CNN) model based detection, a machine learning technique, can provide higher detection accuracy than constant false alarm rate (CFAR) detection techniques even if it results in higher processing costs. In this study, we achieve three significant improvements for real‐time radar target detection by considering computational cost. The first improvement is to reduce the computational cost of the CNN model. The second achievement is the design of heterogeneous computing optimizations. The third of them is to support energy‐efficient computing solutions for mobile sensors. Compared to the initial CNN model, layer improvements decreased the number of operations by 8.5x. Real‐time operations are satisfied by hardware‐specific improvements like vectorization and parallelization. The embedded NVIDIA Jetson GPU and Intel MYRIAD VPU, which have power consumption of 15 Watts and 1 Watt, respectively, have been used to execute the energy‐efficient target detection. The most energy‐efficient solution is achieved by using Jetson AGX Xavier GPU with 32‐bit single precision and 15 Watts of power consumption.

Summary In the direction of arrival (DoA) estimation, typically sensor arrays are used where the number of required sensors can be large depending on the application. With the help of compressed sensing (CS), hardware complexity of the sensor array system can be reduced since reliable estimations are possible by using the compressed measurements where the compression is done by measurement matrices. After the compression, DoAs are reconstructed by using sparsity promoting algorithms such as alternating direction method of multipliers (ADMM). For the given procedure, both the measurement matrix design and the reconstruction algorithm may include computationally intensive operations, which are addressed in this study. The presented simulation results imply the feasibility of the system in real‐time processing with energy efficient implementations. We propose employing parallel programming to satisfy the real‐time processing requirements. While the measurement matrix design has been accelerated 16 with CPU based parallel version with respect to the fastest serial implementation, ADMM based DoA estimation has been improved 1.1 with GPU based parallel version compared to the fastest CPU parallel implementation. In addition, we achieved, to the best of our knowledge, the first energy‐efficient real‐time DoA estimation on embedded Jetson GPGPUs in 15 W power consumption without affecting the DoA accuracy performance.

In order to meet the increasing demand of the blockchain, it needs to find a solution to the scalability problem. It has been focused on sharding recently to address the scalability problem. In the sharding method, the blockchain is divided into pieces. Instead of a more extensive network, networks with fewer nodes are created. As a result, it becomes more important that each node in the network is reliable. In this study, studies using sharding method have been investigated, and methods for the assigning nodes to shards are proposed. The use of learning-based adaptive methods for this process will contribute to the safe and reliable use of shards. The probability of the shards to deteriorate and influence the whole blockchain will be reduced.

Summary Wideband software‐defined radio (SDR) applications include data and time intensive operations such as wideband spectrum, signal detection, digital down conversion (DDC), and analog demodulation. Each of these processes need to be performed in order to produce the audio signal from the wideband signals. However, serial implementation of SDR applications do not provide necessary rate speed to obtain the sound and speech data in real time. In this work, we propose a real time SDR implementation using a heterogeneous architecture with CPUs and GPUs. To obtain the sound and speech data from the signals received and processed in SDR algorithms in real time, we also provide necessary optimizations. We test the proposed design using both single CPU core, multiple CPU cores, and GPUs. High performance is observed with our proposed algorithm in experimental tests. We also provide test results in a mobile setup where resources such as power and size are limited. For this purpose, we provide test results on NVIDIA Jetson GPUs and portable laptop CPUs. This work shows a proof of concept that the sound of a signal can be detected in the wideband spectrum and can be played back continuously in a real time with the help of the parallel programming suitable for low power consumption.

Advances in wireless communication, digital systems and micro-electronic-mechanical system technologies led to the development of wireless sensor networks (WSNs) which are used in various critical real-world applications. The fact that WSNs are low cost and eliminate the need for infrastructure led to their replacing traditional networks in area/event monitoring and tracking applications. WSNs consist of small and resource-limited sensor nodes, due to which several problems arise in the WSN development process. One of these problems is coverage. Providing the best coverage with a minimum number of sensor nodes is an NP-hard problem known as the maximum coverage sensor deployment problem (MCSDP). Genetic Algorithms (GAs) have been proved effective in solving optimization problems in many different disciplines (increasing coverage in WSNs, image processing, route planning, etc.). In this study, a GPU-based parallel GA solution for increasing the coverage of a given homogeneous WSN topology in a 2-D Euclidean area is proposed which is the first time this technique is used and parallelized on GPUs to the best of our knowledge. Finally, performance results of the proposed algorithm are compared to the previous work with the emphasis on the achieved performance improvement.

In this paper, we describe a novel technique to optimize longest common subsequence (LCS) algorithm for one-to-many matching problem on GPUs by transforming the computation into bit-wise operations and a post-processing step. The former can be highly optimized and achieves more than a trillion operations (cell updates) per second (CUPS)-a first for LCS algorithms. The latter is more efficiently done on CPUs, in a fraction of the bit-wise computation time. The bit-wise step promises to be a foundational step and a fundamentally new approach to developing algorithms for increasingly popular heterogeneous environments that could dramatically increase the applicability of hybrid CPU-GPU environments.

In this paper, we present an algorithm and provide design improvements needed to port the serial Lempel-Ziv-Storer-Szymanski (LZSS), lossless data compression algorithm, to a parallelized version suitable for general purpose graphic processor units (GPGPU), specifically for NVIDIA's CUDA Framework. The two main stages of the algorithm, substring matching and encoding, are studied in detail to fit into the GPU architecture. We conducted detailed analysis of our performance results and compared them to serial and parallel CPU implementations of LZSS algorithm. We also benchmarked our algorithm in comparison with well known, widely used programs, GZIP and ZLIB. We achieved up to 34x better throughput than the serial CPU implementation of LZSS algorithm and up to 2.21x better than the parallelized version.

Increasing needs in efficient storage management and better utilization of network bandwidth with less data transfer have led the computing community to consider data compression as a solution. However, compression introduces extra overhead and performance can suffer. The key elements in making the decision to use compression are execution time and compression ratio. Due to negative performance impact, compression is often neglected. General purpose computing on graphic processing units (GPUs) introduces new opportunities where parallelism is available. Our work targets the use of opportunities in GPU based systems by exploiting parallelism in compression algorithms. In this paper we present an implementation of the Lempel-Ziv-Storer-Szymanski (LZSS) loss less data compression algorithm by using NVIDIA GPUs Compute Unified Device Architecture (CUDA) Framework. Our implementation of the LZSS algorithm on GPUs significantly improves the performance of the compression process compared to CPU based implementation without any loss in compression ratio. This can support GPU based clusters in solving application bandwidth problems. Our system outperforms the serial CPU LZSS implementation by up to 18×, the parallel threaded version up to 3× and the BZIP2 program by up to 6× in terms of compression time, showing the promise of CUDA systems in loss less data compression. To give the programmers an easy to use tool, our work also provides an API for in memory compression without the need for reading from and writing to files, in addition to the version involving I/O.