22 papers · ranked by Valyu relevance
David J. Starling, Joseph Ranalli
Combustion research requires the use of state of the art diagnostic tools, including high energy lasers and gated, cooled CCDs. However, these tools may present a cost barrier for laboratories with limited resources. While the cost of high energy lasers and low-noise cameras continues to decline, new imaging…
Rens Baeyens, Joachim Denil, Jan Steckel, Dennis Laurijssen + 2 more
'Walter Daems' 'Jari Nurmi'] In this paper, a model-based firmware generator is presented towards complex sampling schemes. The framework is capable of automatically generating a fixed-rate Shannon-compliant acquisition scheme, as well as a variable-rate compressive sensing acquisition scheme. The generation starts…
Ramin Ayanzadeh, Milton Halem, Tim Finin
We propose to reduce the original well-posed problem of compressive sensing to weighted-MAX-SAT. Compressive sensing is a novel randomized data acquisition approach that linearly samples sparse or compressible signals at a rate much below the Nyquist-Shannon sampling rate. The original problem of compressive sensing in…
Leslie N. Smith
The potential of compressive sensing (CS) has spurred great interest in the research community and is a fast growing area of research. However, research translating CS theory into practical hardware and demonstrating clear and significant benefits with this hardware over current, conventional imaging techniques has…
Alina L. Machidon, Veljko Pejović
Compressive sensing (CS) is a mathematically elegant tool for reducing the sampling rate, potentially bringing contextawareness to a wider range of devices. Nevertheless, practical issues with the sampling and reconstruction algorithms prevent further proliferation of CS in real world domains, especially among…
Mohammadreza Dadkhah, M. Jamal Deen, Shahram Shirani
The compressive sensing (CS) paradigm uses simultaneous sensing and compression to provide an efficient image acquisition technique. The main advantages of the CS method include high resolution imaging using low resolution sensor arrays and faster image acquisition. Since the imaging philosophy in CS imagers is…
Yong Wang, Zhuoshi Yang, Jianpei Zhang, Feng Li + 4 more
In this paper, we consider the problem of reconstructing the temporal and spatial profile of some physical phenomena monitored by large-scale Wireless Sensor Networks (WSNs) in an energy efficient manner. Compressive sensing is one of the popular choices to reduce the energy consumption of the data collection in WSNs.…
Sandra Costanzo, Álvaro Rocha, Marco Donald Migliore
If information bandwidth less than total bandwidth, then should be able to sample below Nyquist without information loss and recover missing samples by convex optimization. (Emmanuel Candès, “Compressive Sensing-A 25 Minute Tour,” EU-US Frontiers of Engineering Symposium, Cambridge, September 2010) Compressed Sensing…
Jeison Marín Alfonso, Jose Ignacio Martínez Torre, Henry Arguello Fuentes, Leonardo Betancur Agudelo
'Henry Arguello Fuentes' 'Leonardo Betancur Agudelo'] In the process of spectrum sensing applied to wireless communications, it is possible to build interference maps based on acquired power spectral values. This allows the characterization of spectral occupation, which is crucial to take management spectrum decisions.…
Maxime Woringer, Xavier Darzacq, Christophe Zimmer, Mustafa Mir
Three-dimensional fluorescence microscopy based on Nyquist sampling of focal planes faces harsh trade-offs between acquisition time, light exposure, and signal-to-noise. We propose a 3D compressed sensing approach that uses temporal modulation of the excitation intensity during axial stage sweeping and can be adapted…
Michael Sandbichler, Felix Krahmer, Thomas Berer, Peter Burgholzer + 1 more
'Markus Haltmeier'] Speeding up the data acquisition is one of the central aims to advance tomographic imaging. On the one hand, this reduces motion artifacts due to undesired movements, and on the other hand this decreases the examination time for the patient. In this article, we propose a new scheme for speeding up…
Radoje Darmanovic, Tamara Bulatovic, Seid Salkovic
— This paper observes the application of the Compressive Sensing in reconstruction of the under-sampled iris images. Iris recognition represents form of biometric identification whose usage in real applications is growing. Compressive Sensing represents a novel form of sparse signal acquisition and recovering when…
Xiangwei Li, Xuguang Lan, Meng Yang, Jianru Xue + 1 more
Compressive Sensing Imaging (CSI) is a new framework for image acquisition, which enables the simultaneous acquisition and compression of a scene. Since the characteristics of Compressive Sensing (CS) acquisition are very different from traditional image acquisition, the general image compression solution may not work…
Benjamin W. Roop, Benjamin Parrell, Adam C. Lammert
Uncovering cognitive representations is an elusive goal that is increasingly pursued using the reverse correlation method. Employing reverse correlation often entails collecting thousands of stimulus-response pairs from human subjects, a burdensome task that limits the feasibility of many such studies. This…
Hongliang Li, Ke Lu, Jian Xue, Feng Dai + 2 more
'Benoit Vozel'] Compressive Sensing (CS) has proved to be an effective theory in the field of image acquisition. However, in order to distinguish the difference between the measurement matrices, the CS imaging system needs to have a higher signal sampling accuracy. At the same time, affected by the noise of the light…
Jane A. Gargano, Abigail Rice, Divya A. Chari, Benjamin Parrell + 1 more
Reverse correlation is a widely-used and well-established method for probing latent perceptual representations in which subjects render subjective preference responses to ambiguous stimuli. Stimuli are purposefully designed to have no direct relationship with the target representation (e.g., they are…
Marijana Kracunov, Milica Bastica, Jovana Tesovic
—Reducing the number of pixels in video signals while maintaining quality needed for recovering the trace of an object using Compressive Sensing is main subject of this work. Quality of frames, from video that contains moving object, are gradually reduced by keeping different number of pixels in each iteration, going…
Phillip Navarro, Karim Oweiss
Mapping functional connectivity between neurons is an essential step towards probing the neural computations mediating behavior. The ability to consistently and robustly determine synaptic connectivity maps in large populations of interconnected neurons is a significant challenge in terms of yield, accuracy and…
Abbas Kazemipour, Ji Liu, Krystyna Solarana, Daniel A. Nagode + 3 more
Common biological measurements are in the form of noisy convolutions of signals of interest with possibly unknown and transient blurring kernels. Examples include EEG and calcium imaging data. Thus, signal deconvolution of these measurements is crucial in understanding the underlying biological processes. The objective…
Pumiao Yan, Dante G. Muratore, E.J. Chichilnisky, Boris Murmann + 1 more
Scaling neural recording systems to thousands of channels creates extreme bandwidth demands, posing a challenge for resource-constrained, implantable devices. This work introduces an adaptive, multi-stage compression framework for high-bandwidth neural interfaces. The system combines a Wired-OR analog-to-digital…
Authors not listed
This study presents the use of laser-driven microbubbles for micro-patterning Ti3C2TX MXenes on flexible polyethylene terephthalate films, yielding conductive micropatterns without the need for pre- or post-processing. Characterization of the electrical properties under varying strain conditions revealed distinct…
Gbenga Fabusola, Gina Noh, Cory Simon
The cross-sensitivity of a gas sensor can be mitigated by installing a catalytic filter upstream. The objective of the filter is to convert interferents into species that interact weakly with the recognition element of the sensor. Mathematical models may be useful for optimizing the design parameters of the catalytic…