19 papers · ranked by Valyu relevance
Junhao Cai, Taegun An, Chengjun Jin, Sung Il Choi + 2 more
Distributed multi-stage image compression—where visual content traverses multiple processing nodes under varying quality requirements—poses challenges. Progressive methods enable bitstream truncation but underutilize available compute resources; successive compression repeats costly pixel-domain operations and suffers…
Kumar Viswanatha, Sharadh Ramaswamy, Ankur Saxena, Emrah Akyol + 1 more
'Kenneth Rose'] This paper considers the problem of distributed source coding for a large network. A major obstacle that poses an existential threat to practical deployment of conventional approaches to distributed coding is the exponential growth of the decoder complexity with the number of sources and the encoding…
Chong Han, Songtao Zhang, Biao Zhang, Jian Zhou + 1 more
As an emerging technology, edge computing will enable traditional sensor networks to be effective and motivate a series of new applications. Meanwhile, limited battery power directly affects the performance and survival time of sensor networks. As an extension application for traditional sensor networks, the energy…
Songze Li, Mohammad Ali Maddah-Ali, A. Salman Avestimehr
—Communication overhead is one of the major performance bottlenecks in large-scale distributed computing systems, in particular for machine learning applications. Conventionally, compression techniques are used to reduce the load of communication by combining intermediate results of the same computation task as much as…
Derya Malak, Ali Tajer
Kolmogorov's representation theorem provides a framework for decomposing any arbitrary realvalued, multivariate, and continuous function into a two-layer nested superposition of a finite number of functions. The functions at these two layers, are referred to as the inner and outer functions with the key property that…
Yann-Aël Le Borgne, Sylvain Raybaud, Gianluca Bontempi
The Principal Component Analysis (PCA) is a data dimensionality reduction tech-nique well-suited for processing data from sensor networks. It can be applied to tasks like compression, event detection, and event recognition. This technique is based on a linear trans-form where the sensor measurements are projected on a…
Metin Aktas, Murat Kuscu, Ergin Dinc, Ozgur B. Akan + 1 more
'Kim-Kwang Raymond Choo'] Spatial correlation between densely deployed sensor nodes in a wireless sensor network (WSN) can be exploited to reduce the power consumption through a proper source coding mechanism such as distributed source coding (DSC). In this paper, we propose the Decoding Delay-based Distributed Source…
Rui Cao, John H. Bladon, Stephen J. Charczynski, Michael E. Hasselmo + 1 more
The Weber-Fechner law proposes that our perceived sensory input increases with physical input on a logarithmic scale. Hippocampal “time cells” carry a record of recent experience by firing sequentially during a circumscribed period of time after a triggering stimulus. Different cells have “time fields” at different…
Peng Jiang, Sheng-Qiang Li
In many wireless sensor network applications, the possibility of exceptions occurring is relatively small, so in a normal situation, data obtained at sequential time points by the same node are time correlated, while, spatial correlation may exist in data obtained at the same time by adjacent nodes. A great deal of…
Feng Liu, Cheng-yi Yang, Jie Yang, De-li Kong + 3 more
'Jia-yin Qi' 'Zhi-bin Li'] As a distributed storage scheme, the blockchain network lacks storage space has been a long-term concern in this field. At present, there are relatively few research on algorithms and protocols to reduce the storage requirement of blockchain, and the existing research has limitations such as…
Victoria Erofeeva, Oleg Granichin, Vikentii Pankov, Zeev Volkovich + 1 more
'Ayesha Maqbool'] The paper presents a decentralized, real-time clustering method designed for large-scale, distributed environments such as the Internet of Things (IoT). The approach combines compressed sensing for dimensionality reduction with a consensus protocol for distributed aggregation, enabling each node to…
Andrew A. Chen, Chongliang Luo, Yong Chen, Russell T. Shinohara + 1 more
Challenges in clinical data sharing and the need to protect data privacy have led to the development and popularization of methods that do not require directly transferring patient data. In neuroimaging, integration of data across multiple institutions also introduces unwanted biases driven by scanner differences.…
Senik Matinyan, Jan Pieter Abrahams
High-throughput data collection in crystallography poses significant challenges in handling massive amounts of data. Here, we present TERSE, a novel lossless compression algorithm specifically designed for diffraction data. We compare TERSE with the established lossless compression algorithms implemented in gzip, CBF…
Herbert J. Bernstein, Alexei S. Soares, Kimberly Horvat, Jean Jakoncic
New higher-count-rate, integrating, large area X-ray detectors with framing rates as high as 17,400 images per second are beginning to be available. These will soon be used for specialized MX experiments but will require optimal lossy compression algorithms to enable systems to keep up with data throughput. Some…
Anders Andreasen, Maria Bonto, Fernando Montero
– This paper presents a framework for optimisation and techno-economic analysis of various pressurisation pathways for CO2 pipeline transportation. The pressurisation pathways include a conventional compression only case from initial to final pressure, a sub-critical compression part followed by cooling, liquefaction…
Anas Al-okaily, Abdelghani Tbakhi
Data compression is a challenging and increasingly important problem. As the amount of data generated daily continues to increase, efficient transmission and storage has never been more critical. In this study, a novel encoding algorithm is proposed, motivated by the compression of DNA data and associated…
Authors not listed
Real-world datasets in chemical engineering and bioengineering processes--such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials--can often be unlabelled or disorganized, rendering the training of existing supervised learning models ineffective at learning the…
Songze Li, Mohammad Ali Maddah-Ali, A. Salman Avestimehr
More specifically, a general distributed computing framework, motivated by commonly used structures like MapReduce, is considered, where the overall computation is decomposed into computing a set of "Map" and "Reduce" functions distributedly across multiple computing nodes. A coded scheme, named "Coded Distributed…
Noah Lewis, Harshvardhan Gazula, Sergey M. Plis, Vince D. Calhoun
In this age of big data, large data stores allow researchers to compose robust models that are accurate and informative. In many cases, the data are stored in separate locations requiring data transfer between local sites, which can cause various practical hurdles, such as privacy concerns or heavy network load. This…