Journal articles on the topic 'Time-vertex signal processing'
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Stanković, Ljubiša, Jonatan Lerga, Danilo Mandic, Miloš Brajović, Cédric Richard, and Miloš Daković. "From Time–Frequency to Vertex–Frequency and Back." Mathematics 9, no. 12 (June 17, 2021): 1407. http://dx.doi.org/10.3390/math9121407.
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The paper presents an analysis and overview of vertex–frequency analysis, an emerging area in graph signal processing. A strong formal link of this area to classical time–frequency analysis is provided. Vertex–frequency localization-based approaches to analyzing signals on the graph emerged as a response to challenges of analysis of big data on irregular domains. Graph signals are either localized in the vertex domain before the spectral analysis is performed or are localized in the spectral domain prior to the inverse graph Fourier transform is applied. The latter approach is the spectral form of the vertex–frequency analysis, and it will be considered in this paper since the spectral domain for signal localization is well ordered and thus simpler for application to the graph signals. The localized graph Fourier transform is defined based on its counterpart, the short-time Fourier transform, in classical signal analysis. We consider various spectral window forms based on which these transforms can tackle the localized signal behavior. Conditions for the signal reconstruction, known as the overlap-and-add (OLA) and weighted overlap-and-add (WOLA) methods, are also considered. Since the graphs can be very large, the realizations of vertex–frequency representations using polynomial form localization have a particular significance. These forms use only very localized vertex domains, and do not require either the graph Fourier transform or the inverse graph Fourier transform, are computationally efficient. These kinds of implementations are then applied to classical time–frequency analysis since their simplicity can be very attractive for the implementation in the case of large time-domain signals. Spectral varying forms of the localization functions are presented as well. These spectral varying forms are related to the wavelet transform. For completeness, the inversion and signal reconstruction are discussed as well. The presented theory is illustrated and demonstrated on numerical examples.
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Grassi, Francesco, Andreas Loukas, Nathanael Perraudin, and Benjamin Ricaud. "A Time-Vertex Signal Processing Framework: Scalable Processing and Meaningful Representations for Time-Series on Graphs." IEEE Transactions on Signal Processing 66, no. 3 (February 1, 2018): 817–29. http://dx.doi.org/10.1109/tsp.2017.2775589.
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Jiang, Junzheng, Hairong Feng, David B. Tay, and Shuwen Xu. "Theory and Design of Joint Time-Vertex Nonsubsampled Filter Banks." IEEE Transactions on Signal Processing 69 (2021): 1968–82. http://dx.doi.org/10.1109/tsp.2021.3064984.
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Skulrattanakulchai, San. "Δ-List vertex coloring in linear time." Information Processing Letters 98, no. 3 (May 2006): 101–6. http://dx.doi.org/10.1016/j.ipl.2005.12.007.
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Fan, Tiffany, David I. Shuman, Shashanka Ubaru, and Yousef Saad. "Spectrum-Adapted Polynomial Approximation for Matrix Functions with Applications in Graph Signal Processing." Algorithms 13, no. 11 (November 13, 2020): 295. http://dx.doi.org/10.3390/a13110295.
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We propose and investigate two new methods to approximate f(A)b for large, sparse, Hermitian matrices A. Computations of this form play an important role in numerous signal processing and machine learning tasks. The main idea behind both methods is to first estimate the spectral density of A, and then find polynomials of a fixed order that better approximate the function f on areas of the spectrum with a higher density of eigenvalues. Compared to state-of-the-art methods such as the Lanczos method and truncated Chebyshev expansion, the proposed methods tend to provide more accurate approximations of f(A)b at lower polynomial orders, and for matrices A with a large number of distinct interior eigenvalues and a small spectral width. We also explore the application of these techniques to (i) fast estimation of the norms of localized graph spectral filter dictionary atoms, and (ii) fast filtering of time-vertex signals.
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Dereniowski, Dariusz. "Maximum vertex occupation time and inert fugitive: Recontamination does help." Information Processing Letters 109, no. 9 (April 2009): 422–26. http://dx.doi.org/10.1016/j.ipl.2008.12.022.
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Carrabs, F., R. Cerulli, M. Gentili, and G. Parlato. "A linear time algorithm for the minimum Weighted Feedback Vertex Set on diamonds." Information Processing Letters 94, no. 1 (April 2005): 29–35. http://dx.doi.org/10.1016/j.ipl.2004.12.008.
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Il’ev, A. V., and V. P. Il’ev. "ALGORITHMS FOR SOLVING SYSTEMS OF EQUATIONS OVER VARIOUS CLASSES OF FINITE GRAPHS." Prikladnaya Diskretnaya Matematika, no. 53 (2021): 89–102. http://dx.doi.org/10.17223/20710410/53/6.
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The aim of the paper is to study and to solve finite systems of equations over finite undirected graphs. Equations over graphs are atomic formulas of the language L consisting of the set of constants (graph vertices), the binary vertex adjacency predicate and the equality predicate. It is proved that the problem of checking compatibility of a system of equations S with k variables over an arbitrary simple n-vertex graph Γ is N P-complete. The computational complexity of the procedure for checking compatibility of a system of equations S over a simple graph Γ and the procedure for finding a general solution of this system is calculated. The computational complexity of the algorithm for solving a system of equations S with k variables over an arbitrary simple n-vertex graph Γ involving these procedures is O(k 2n k/2+1(k + n) 2 ) for n > 3. Polynomially solvable cases are distinguished: systems of equations over trees, forests, bipartite and complete bipartite graphs. Polynomial time algorithms for solving these systems with running time O(k 2n(k + n) 2 ) are proposed.
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Lin, Min-Sheng, and Yung-Jui Chen. "Linear time algorithms for counting the number of minimal vertex covers with minimum/maximum size in an interval graph." Information Processing Letters 107, no. 6 (August 2008): 257–64. http://dx.doi.org/10.1016/j.ipl.2008.03.008.
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FUKUOKA, T. "A Linear Time Algorithm for Bi-Connectivity Augmentation of Graphs with Upper Bounds on Vertex-Degree Increase." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E88-A, no. 4 (April 1, 2005): 954–63. http://dx.doi.org/10.1093/ietfec/e88-a.4.954.
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Silva, Gabriel A. "The Effect of Signaling Latencies and Node Refractory States on the Dynamics of Networks." Neural Computation 31, no. 12 (December 2019): 2492–522. http://dx.doi.org/10.1162/neco_a_01241.
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We describe the construction and theoretical analysis of a framework derived from canonical neurophysiological principles that model the competing dynamics of incident signals into nodes along directed edges in a network. The framework describes the dynamics between the offset in the latencies of propagating signals, which reflect the geometry of the edges and conduction velocities, and the internal refractory dynamics and processing times of the downstream node receiving the signals. This framework naturally extends to the construction of a perceptron model that takes into account such dynamic geometric considerations. We first describe the model in detail, culminating with the model of a geometric dynamic perceptron. We then derive upper and lower bounds for a notion of optimal efficient signaling between vertex pairs based on the structure of the framework. Efficient signaling in the context of the framework we develop here means that there needs to be a temporal match between the arrival time of the signals relative to how quickly nodes can internally process signals. These bounds reflect numerical constraints on the compensation of the timing of signaling events of upstream nodes attempting to activate downstream nodes they connect into that preserve this notion of efficiency. When a mismatch between signal arrival times and the internal states of activated nodes occurs, it can cause a breakdown in the signaling dynamics of the network. In contrast to essentially all of the current state of the art in machine learning, this work provides a theoretical foundation for machine learning and intelligence architectures based on the timing of node activations and their abilities to respond rather than necessary changes in synaptic weights. At the same time, the theoretical ideas we developed are guiding the discovery of experimentally testable new structure-function principles in the biological brain.
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Novak, Roman, Andrej Hrovat, Michael D. Bedford, and Tomaž Javornik. "Geometric Simplifications of Natural Caves in Ray-Tracing-Based Propagation Modelling." Electronics 10, no. 23 (November 25, 2021): 2914. http://dx.doi.org/10.3390/electronics10232914.
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Natural caves show some similarities to human-made tunnels, which have previously been the subject of radio-frequency propagation modelling using deterministic ray-tracing techniques. Since natural caves are non-uniform because of their inherent concavity and irregular limestone formations, detailed 3D models contain a large number of small facets, which can have a detrimental impact on the ray-tracing computational complexity as well as on the modelling accuracy. Here, we analyse the performance of ray tracing in repeatedly simplified 3D descriptions of two caves in the UK, i.e., Kingsdale Master Cave (KMC) Roof Tunnel and Skirwith Cave. The trade-off between the size of the reflection surface and the modelling accuracy is examined. Further, by reducing the number of facets, simulation time can be reduced significantly. Two simplification methods from computer graphics were applied: Vertex Clustering and Quadric Edge Collapse. We compare the ray-tracing results to the experimental measurements and to the channel modelling based on the modal theory. We show Edge Collapse to be better suited for the task than Vertex Clustering, with larger simplifications being possible before the passage becomes entirely blocked. The use of model simplification is predominantly justified by the computational time gains, with the acceptable simplified geometries roughly halving the execution time given the laser scanning resolution of 10 cm.
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Ascoli, Alon, Martin Weiher, Melanie Herzig, Stefan Slesazeck, Thomas Mikolajick, and Ronald Tetzlaff. "Graph Coloring via Locally-Active Memristor Oscillatory Networks." Journal of Low Power Electronics and Applications 12, no. 2 (April 18, 2022): 22. http://dx.doi.org/10.3390/jlpea12020022.
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This manuscript provides a comprehensive tutorial on the operating principles of a bio-inspired Cellular Nonlinear Network, leveraging the local activity of NbOx memristors to apply a spike-based computing paradigm, which is expected to deliver such a separation between the steady-state phases of its capacitively-coupled oscillators, relative to a reference cell, as to unveal the classification of the nodes of the associated graphs into the least number of groups, according to the rules of a non-deterministic polynomial-hard combinatorial optimization problem, known as vertex coloring. Besides providing the theoretical foundations of the bio-inspired signal-processing paradigm, implemented by the proposed Memristor Oscillatory Network, and presenting pedagogical examples, illustrating how the phase dynamics of the memristive computing engine enables to solve the graph coloring problem, the paper further presents strategies to compensate for an imbalance in the number of couplings per oscillator, to counteract the intrinsic variability observed in the electrical behaviours of memristor samples from the same batch, and to prevent the impasse appearing when the array attains a steady-state corresponding to a local minimum of the optimization goal. The proposed Memristor Cellular Nonlinear Network, endowed with ad hoc circuitry for the implementation of these control strategies, is found to classify the vertices of a wide set of graphs in a number of color groups lower than the cardinality of the set of colors identified by traditional either software or hardware competitor systems. Given that, under nominal operating conditions, a biological system, such as the brain, is naturally capable to optimise energy consumption in problem-solving activities, the capability of locally-active memristor nanotechnologies to enable the circuit implementation of bio-inspired signal processing paradigms is expected to pave the way toward electronics with higher time and energy efficiency than state-of-the-art purely-CMOS hardware.
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Balossino, Ilaria, Fabio Cossio, Riccardo Farinelli, and Lia Lavezzi. "The CGEM-IT: An Upgrade for the BESIII Experiment." Symmetry 14, no. 5 (April 28, 2022): 905. http://dx.doi.org/10.3390/sym14050905.
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The BESIII experiment has been collecting data since 2009 at the e+e− collider BEPCII in Beijing, a charm-τ factory characterized by high statistics and high precision. The discovery of exotic charmonium-like states and the still open questions in low-energy QCD led to an extension of the experimental program, with several upgrades. This review focuses on the CGEM-IT, the innovative solution proposed to replace the current inner tracker, which is aging. It consists of three, co-axial, cylindrical triple-GEM detectors and will be the first cylindrical GEM operating inside a 1 T magnetic field with analogue readout. For this purpose, a dedicated mixed-signal ASIC for the readout of CGEM-IT signals and FPGA-based electronics for data processing have been developed. The simultaneous measurement of both ionization charge and time distribution enables three reconstruction algorithms, to cope with the asymmetry of the electron avalanche in the magnetic field and with non-orthogonal incident tracks. The CGEM-IT will not only restore the design efficiency but also improve the secondary vertex reconstruction and the radiation tolerance. The gas mixture and gain settings were chosen to optimize the position resolution to ∼130 µm in the transverse plane and better than 350 µm along the beam direction. This paper addresses the innovative aspects in terms of construction, readout, and software, employed to achieve the design goals as well as the experimental measurements performed during the development and commissioning of the CGEM-IT.
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Ding, Sheng, Huazheng Du, Na Xia, Shaojie Li, and Yongtang Yu. "Study on Gibbs Optimization-Based Resource Scheduling Algorithm in Data Aggregation Networks." Electronics 11, no. 11 (May 26, 2022): 1695. http://dx.doi.org/10.3390/electronics11111695.
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In data aggregation networks (WSNs, ad hoc, mesh, etc., it is key to schedule the network resources, such as channels and TDMA time slots, to minimize the communication conflict and optimize the network data-gathering performance. In this paper, the resources scheduling problem is formulated as a vertex coloring problem in graph theory. Then, a multi-channel TDMA scheduling algorithm based on the Gibbs optimization is proposed. By defining the Gibbs energy expression according to the objective function of the problem, the joint probability of channel and time slot can be computed for the optimized selection of channels and time slots. This algorithm is low-complexity and its convergence performance can be proven. Experiments with different network parameters demonstrate that the proposed algorithm can reduce the communication conflict, improve the network throughput, and effectively reduce the network transmission delay and scheduling length for the data aggregation networks.
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Shakeel, Shafaq, Adeel Anjum, Alia Asheralieva, and Masoom Alam. "k-NDDP: An Efficient Anonymization Model for Social Network Data Release." Electronics 10, no. 19 (October 8, 2021): 2440. http://dx.doi.org/10.3390/electronics10192440.
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With the evolution of Internet technology, social networking sites have gained a lot of popularity. People make new friends, share their interests, experiences in life, etc. With these activities on social sites, people generate a vast amount of data that is analyzed by third parties for various purposes. As such, publishing social data without protecting an individual’s private or confidential information can be dangerous. To provide privacy protection, this paper proposes a new degree anonymization approach k-NDDP, which extends the concept of k-anonymity and differential privacy based on Node DP for vertex degrees. In particular, this paper considers identity disclosures on social data. If the adversary efficiently obtains background knowledge about the victim’s degree and neighbor connections, it can re-identify its victim from the social data even if the user’s identity is removed. The contribution of this paper is twofold. First, a simple and, at the same time, effective method k–NDDP is proposed. The method is the extension of k-NMF, i.e., the state-of-the-art method to protect against mutual friend attack, to defend against identity disclosures by adding noise to the social data. Second, the achieved privacy using the concept of differential privacy is evaluated. An extensive empirical study shows that for different values of k, the divergence produced by k-NDDP for CC, BW and APL is not more than 0.8%, also added dummy links are 60% less, as compared to k-NMF approach, thereby it validates that the proposed k-NDDP approach provides strong privacy while maintaining the usefulness of data.
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Ozen, Mustafa, Goran Lesaja, and Hua Wang. "Globally Optimal Dense and Sparse Spanning Trees, and their Applications." Statistics, Optimization & Information Computing 8, no. 2 (May 27, 2020): 328–45. http://dx.doi.org/10.19139/soic-2310-5070-855.
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Finding spanning trees under various constraints is a classic problem with applications in many fields. Recently, a novel notion of dense ( sparse ) tree, and in particular spanning tree (DST and SST respectively), is introduced as the structure that have a large (small) number of subtrees, or small (large) sum of distances between vertices. We show that finding DST and SST reduces to solving the discrete optimization problems. New and efficient approaches to find such spanning trees is achieved by imposing certain conditions on the vertex degrees which are then used to define an objective function that is minimized over all spanning trees of the graph under consideration. Solving this minimization problem exactly may be prohibitively time consuming for large graphs. Hence, we propose to use genetic algorithm (GA) which is one of well known metaheuristics methods to solve DST and SST approximately. As far as we are aware this is the first time GA has been used in this context.We also demonstrate on a number of applications that GA approach is well suited for these types of problems both in computational efficiency and accuracy of the approximate solution. Furthermore, we improve the efficiency of the proposed method by using Kruskal s algorithm in combination with GA. The application of our methods to several practical large graphs and networks is presented. Computational results show that they perform faster than previously proposed heuristic methods and produce more accurate solutions. Furthermore, the new feature of the proposed approach is that it can be applied recursively to sub-trees or spanning trees with additional constraints in order to further investigate the graphical properties of the graph and/or network. The application of this methodology on the gene network of a cancer cell led to isolating key genes in a network that were not obvious from previous studies.
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Waselius, Tomi, Jan Wikgren, Hanna Halkola, Markku Penttonen, and Miriam S. Nokia. "Learning by heart: cardiac cycle reveals an effective time window for learning." Journal of Neurophysiology 120, no. 2 (August 1, 2018): 830–38. http://dx.doi.org/10.1152/jn.00128.2018.
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Cardiac cycle phase is known to modulate processing of simple sensory information. This effect of the heartbeat on brain function is likely exerted via baroreceptors, the neurons sensitive for changes in blood pressure. From baroreceptors, the signal is conveyed all the way to the forebrain and the medial prefrontal cortex. In the two experiments reported, we examined whether learning, as a more complex form of cognition, can be modulated by the cardiac cycle phase. Human participants ( experiment 1) and rabbits ( experiment 2) were trained in trace eyeblink conditioning while neural activity was recorded. The conditioned stimulus was presented contingently with either the systolic or diastolic phase of the cycle. The tone used as the conditioned stimulus evoked amplified responses in both humans (electroencephalogram from “vertex,” Cz) and rabbits (hippocampal CA1 local field potential) when its onset was timed at systole. In humans, the cardiac cycle phase did not affect learning, but rabbits trained at diastole learned significantly better than those trained at a random phase of the cardiac cycle. In summary, our results suggest that neural processing of external stimuli and also learning can be affected by targeting stimuli on the basis of cardiac cycle phase. These findings might be useful in applications aimed at maximizing or minimizing the effects of external stimulation. NEW & NOTEWORTHY It has been shown that rapid changes in bodily states modulate neural processing of external stimulus in brain. In this study, we show that modulation of neural processing of external stimulus and learning about it depends on the phase of the cardiac cycle. This is a novel finding that can be applied to optimize associative learning.
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Reskó, Barna, Ádám B. Csapó, and Péter Baranyi. "Cognitive Vision Inspired Contour and Vertex Detection." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 4 (July 20, 2006): 527–33. http://dx.doi.org/10.20965/jaciii.2006.p0527.
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This paper presents a visual cortex inspired cognitive model for contour and vertex detection. The model is strongly based on the receptive field characteristics of cortical neurons of the visual cortex. As a step forward compared to the previous version of the model, a new dimension has been added, which replaces the binary signals and operations by operations on real values. The resulting system yields a better approximation of the biological system, as well as provides stronger and more distinct contour lines and vertices. The contour detection and vertex extraction is performed by a vast network of simple units of computation simultaneously processing the visual data. The computational units are organized in a special structure, the Visual Feature Array (VFA), which allows the structural representation of complex operations. The goal of the model is to extract abstract information from an image, which in turn may be used as input for the recognition process of even more abstract visual objects. In order to achieve constant time execution of the model, the aspects of hardware implementation are also treated in this paper.
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Delle Monache, Sergio, Francesco Lacquaniti, and Gianfranco Bosco. "Differential contributions to the interception of occluded ballistic trajectories by the temporoparietal junction, area hMT/V5+, and the intraparietal cortex." Journal of Neurophysiology 118, no. 3 (September 1, 2017): 1809–23. http://dx.doi.org/10.1152/jn.00068.2017.
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The ability to catch objects when transiently occluded from view suggests their motion can be extrapolated. Intraparietal cortex (IPS) plays a major role in this process along with other brain structures, depending on the task. For example, interception of objects under Earth’s gravity effects may depend on time-to-contact predictions derived from integration of visual signals processed by hMT/V5+ with a priori knowledge of gravity residing in the temporoparietal junction (TPJ). To investigate this issue further, we disrupted TPJ, hMT/V5+, and IPS activities with transcranial magnetic stimulation (TMS) while subjects intercepted computer-simulated projectile trajectories perturbed randomly with either hypo- or hypergravity effects. In experiment 1, trajectories were occluded either 750 or 1,250 ms before landing. Three subject groups underwent triple-pulse TMS (tpTMS, 3 pulses at 10 Hz) on one target area (TPJ | hMT/V5+ | IPS) and on the vertex (control site), timed at either trajectory perturbation or occlusion. In experiment 2, trajectories were entirely visible and participants received tpTMS on TPJ and hMT/V5+ with same timing as experiment 1. tpTMS of TPJ, hMT/V5+, and IPS affected differently the interceptive timing. TPJ stimulation affected preferentially responses to 1-g motion, hMT/V5+ all response types, and IPS stimulation induced opposite effects on 0-g and 2-g responses, being ineffective on 1-g responses. Only IPS stimulation was effective when applied after target disappearance, implying this area might elaborate memory representations of occluded target motion. Results are compatible with the idea that IPS, TPJ, and hMT/V5+ contribute to distinct aspects of visual motion extrapolation, perhaps through parallel processing. NEW & NOTEWORTHY Visual extrapolation represents a potential neural solution to afford motor interactions with the environment in the face of missing information. We investigated relative contributions by temporoparietal junction (TPJ), hMT/V5+, and intraparietal cortex (IPS), cortical areas potentially involved in these processes. Parallel organization of visual extrapolation processes emerged with respect to the target’s motion causal nature: TPJ was primarily involved for visual motion congruent with gravity effects, IPS for arbitrary visual motion, whereas hMT/V5+ contributed at earlier processing stages.
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Rudas, Imre J., and János Fodor. "Special Issue: Dedicated to INES 2005 and SISY 2005 Conferences." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 4 (July 20, 2006): 477–78. http://dx.doi.org/10.20965/jaciii.2006.p0477.
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The current issue contains 12 papers presented at the <I>IEEE 9th International Conference on Intelligent Engineering Systems (INES 2005), Cruising on the Mediterranean Sea</I>, on September 16-19, 2005, and <I>3rd Serbian-Hungarian Joint Symposium on Intelligent Systems (SISY 2005), in Subotica, Serbia and Montenegro</I>, on August 31-September 1, 2005. The topics of the two conferences are very close to each other and regard Intelligent Systems both from practical and theoretical point of view. These successful conferences brought together active participants and joined researchers from several countries working on this very quickly developing, more and more important field. After a preliminary selection made by the section chairs and the International Program Committees, we have selected 12 papers to be published in extended form in the current Special Issue of the <I>Journal of Advanced Computational Intelligence and Intelligent Informatics</I>. We would like to express our thanks to our sponsors, the organizers and mainly to the participants, who made these scientific events possible. Also, we express our thanks to the Editors of the Journal of Advanced Computational Intelligence and Intelligent Informatics, for publishing this Special Issue. In the following we briefly describe each paper. A. Almeida, G. Marreiros present a model to support collaborative scheduling in complex dynamic manufacturing environments. This model considers the interaction between an Agent based Scheduling Module and a Group Decision Support Module. P. Baranyi, Z. Petres, P. L. Várkonyi, P. Korondi and Y. Yam study in their paper how the Tensor Product model transformation is capable of determining different types of convex hulls of the Linear Time Invariant models. The study is conducted through the example of the prototypical aeroelastic wing section. B. Bede, H. Nobuhara, J. Fodor and K. Hirota propose the study of the problem if usual sum and product can be substituted by max and product operations in defining approximation operators. In this sense max-product Shepard approximation operators are defined and studied. B. Benyó, P. Somogyi and B. Paláncz address the problem of classification of cerebral blood flow signals in order to identify the disorders of the cerebral circulation. The experimental results provided in the paper confirm the effectiveness of the proposed methods. J. Gáti and Gy. Kártyás propose a model based distance learning in the every day higher education practice in their contribution. They survey some important issues and methodological elements of virtual classrooms in comparison with demands for teaching procedures, programs, and materials. L. Horváth and I. J. Rudas propose a methodology for intelligent communication and change management for engineering modeling. This study motivated by increasing of importance of change management because of continuous product development. M. Maleković and M. Čubrilo describe in their contribution how to incorporate infatuation in multi-agent systems. Infatuation stands for the focusing on a single attractive or desirable characteristic of another agent and then considering the total agent as that one positive characteristic. E. Pap and M. Takács study two dimensional copulas as binary aggregation operators in their paper. Invariant copulas and an application of copulas in the theory of aggregation operators are discussed and a result on approximation of associative copulas by strict and nilpotent triangular norms is obtained. B. Reskó, Á. Csapó and P. Baranyi present in their contribution a visual cortex inspired cognitive model for contour and vertex detection. The contour detection and vertex extraction is performed by a vast network of simple units of computation simultaneously processing the visual data. The computational units are organized in a special structure, the Visual Feature Array. M. Takács addresses investigation of the problem of the approximate reasoning in the fuzzy systems, by reviewing a specific case, where the investigated structure is a real semi-ring with pseudo-operations. It is the investigation of special-type fuzzy sets, special g-generated t-norms and implications in approximate reasoning. J. K. Tar, I. J. Rudas and A. Rontó present in their paper a simple adaptive controller that creates only temporal and situation dependent system model. The temporal model can be built up and maintained step-by-step on the basis of slow elimination of fading information by the use of simple updating rules consisting of finite algebraic steps of lucid geometric interpretation. A. R. Várkonyi-Kóczy, A. Rövid and P. Várlaki present a new fuzzy based tone reproduction pre-processing algorithm which may help in developing the hardly or non-viewable features and content of the images making easier the further processing of it.
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Loukas, Andreas, and Nathanaël Perraudin. "Stationary time-vertex signal processing." EURASIP Journal on Advances in Signal Processing 2019, no. 1 (August 20, 2019). http://dx.doi.org/10.1186/s13634-019-0631-7.
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Feng, Hairong, Junzheng Jiang, Haitao Wang, and Fang Zhou. "Design of Time-Vertex Node-Variant Graph Filters." Circuits, Systems, and Signal Processing, September 27, 2020. http://dx.doi.org/10.1007/s00034-020-01548-x.
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Ge, Zirui, Haiyan Guo, Tingting Wang, and Zhen Yang. "The Optimal Joint Time-Vertex Graph Filter Design: From Ordinary Graph Fourier Domains to Fractional Graph Fourier Domains." Circuits, Systems, and Signal Processing, February 8, 2023. http://dx.doi.org/10.1007/s00034-023-02298-2.
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Gou, Limin, Zijian Zhang, Xianjun Zeng, Canping Li, Yulin Zhang, and Lizhi Yan. "Wavefield Characteristics of CSP Gathers and Their Application to Seismic Processing and Interpretation in the Yin'e Basin." GEOPHYSICS, November 22, 2022, 1–50. http://dx.doi.org/10.1190/geo2022-0249.1.
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The Yine Basin, lying in the west of the Inner Mongolia Autonomous Region of China, embodies a complex geological setting involving diverse and deformed strata of sedimentary and igneous rocks. Two-dimensional seismic data, collected in the western portion of the basin for the frontier exploration of oil and gas, are highly complicated with low signal-to-noise ratio and interference of reflected and diffracted waves. Seismic imaging using conventional technologies was poor, and the subsurface structures and strata were unreliable for interpretations. We applied an integrated processing-and-interpretation method based on seismic wavefield characteristics of common scatter point (CSP) gathers to deal with the exploration challenges. First, we enhanced the amplitude of signals and distinguished weak signals from coherent noises by their kinematic characteristics. The signals, including reflected and diffracted waves, appear as scattered waves with highlighted hyperbolic curves, characterized by the vertex of the hyperbola coinciding with subsurface scattered point and the curvature determined by the root-mean-square (RMS) velocity of overlying medium. In contrast, the coherent noises, such as surface and refracted waves, present nearly linear curves with remarkably intensified amplitude in CSP gathers. Then we established an accurate velocity model and investigated the thickness of strata. Finally, we ran a pre-stack time migration (PSTM) for both reflected and diffracted waves. The images were improved by mapping signals to the correct position with accurate velocity, therefore the target strata and the fault planes, with higher lateral resolution, were more visible. With the aid of well control and the velocity model, main faults and target sequences over the surveyed area were interpreted. Our CSP-gather processing technique not only has enabled us to reveal subsurface complexities and reach the exploration target in the area, but also has implications for seismic explorations in similar environments where structures and sequences vary dramatically.
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Arge, Lars, Aaron Lowe, Svend C. Svendsen, and Pankaj K. Agarwal. "1D and 2D Flow Routing on a Terrain." ACM Transactions on Spatial Algorithms and Systems, June 2, 2022. http://dx.doi.org/10.1145/3539660.
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An important problem in terrain analysis is modeling how water flows across a terrain creating floods by forming channels and filling depressions. In this paper we study a number of flow-query related problems: given a terrain Σ , represented as a triangulated xy -monotone surface with n vertices, and a rain distribution R which may vary over time, determine how much water is flowing over a given vertex or edge as a function of time. We develop internal-memory as well as I/O-efficient algorithms for flow queries. This paper contains four main algorithmic results: (i) An internal-memory algorithm for answering terrain-flow queries: preprocess Σ into a linear-size data structure so that given a rain distribution R , the flow-rate functions of all vertices and edges of Σ can be reported quickly. (ii) I/O-efficient algorithms for answering terrain-flow queries. (iii) An internal-memory algorithm for answering vertex-flow queries: preprocess Σ into a linear-size data structure so that given a rain distribution R , the flow-rate function of a vertex under the single-flow direction (SFD) model can be computed quickly. (iv) An efficient algorithm that given a path \(\mathsf {P} \) in Σ and flow rate along \(\mathsf {P} \) , computes the two-dimensional channel along which water flows. Additionally we implement a version of the terrain-flow query and 2D channel algorithms, and examine a number of queries on real terrains.
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Nillo, Ryan M., Iris J. Broce, Besim Uzgil, Nilika S. Singhal, Christine M. Glastonbury, Christopher P. Hess, James A. Barkovich, Rahul S. Desikan, and Leo P. Sugrue. "Longitudinal analysis of regional brain changes in anti-NMDAR encephalitis: a case report." BMC Neurology 21, no. 1 (October 27, 2021). http://dx.doi.org/10.1186/s12883-021-02446-8.
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Abstract Background Anti-NMDA receptor encephalitis is an immune-mediated disorder characterized by antibodies against the GluN1 subunit of the NMDA receptor that is increasingly recognized as a treatable cause of childhood epileptic encephalopathy. In adults, the disorder has been associated with reversible changes in brain volume over the course of treatment and recovery, but in children, little is known about its time course and associated imaging manifestations. Case presentation A previously healthy 20-month-old boy presented with first-time unprovoked seizures, dysautonomia, and dyskinesia. Paraneoplastic workup was negative, but CSF was positive for anti-NMDAR antibodies. The patient’s clinical condition waxed and waned over a 14-month course of treatment with first- and second-line immunotherapies (including steroids, IVIG, rituximab, and cyclophosphamide). Serial brain MRIs scans obtained at 5 time points spanning this same period showed no abnormal signal or enhancement but were remarkable for cycles of reversible regional cortical volume loss. All scans included identical 1-mm resolution 3D T1-weighted sequences obtained on the same 3 T scanner. Using a novel longitudinal processing stream in FreeSurfer6 (Reuter M, et. al, Neuroimage 61:1402–18, 2012) we quantified the rate of change in cortical volume at each vertex (% volume change per month) between consecutive scans and correlated these changes with the time course of the patient’s treatment and clinical response. We found regionally specific changes in cortical volume (up to 7% per month) that preferentially affected the frontal and occipital lobes and paralleled the patient’s clinical course, with clinical decline associated with volume loss and clinical improvement associated with volume gain. Conclusions Our results suggest that reversible cortical volume loss in anti-NMDA encephalitis has a regional specificity that mirrors many of the clinical symptoms associated with the disorder and tracks the dynamics of disease severity over time. This case illustrates how quantitative morphometric techniques can be applied to clinical imaging data to reveal patterns of brain change that may provide insight into disease pathophysiology. More widespread application of this approach might reveal regional and temporal patterns specific to different types of autoimmune encephalitis, providing a tool for diagnosis and a surrogate marker for monitoring treatment response.
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"Preface." Journal of Physics: Conference Series 2290, no. 1 (June 1, 2022): 011001. http://dx.doi.org/10.1088/1742-6596/2290/1/011001.
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Abstract The 2022 3rd International Conference on Electrical, Electronic Information and Communication Engineering (EEICE 2022) was successfully held on April 22-24, 2022. Due to the COVID-19 pandemic around the world, and with the strict travelling rules in China, it is still difficult to take international travel for our attendees abroad. Therefore, EEICE 2022 was held both in physical (Guilin, China) and online (Zoom). EEICE 2022 is to bring together innovative academics and industrial experts in the field of “Electrical”, “Electronic Information”, “Communication Engineering” and “Communication and Information System”, etc. to a common forum. The primary goal of the conference is to promote research and developmental activities in “Electronic Information Technology”, “Communication Engineering” and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches. There were over 160 experts and scholars in the area of Electrical, Electronic Information and Communication Engineering representing different famous universities and institutes around the globe to attend this hybrid conference. During the conference, we were pleased to invite five distinguished experts to present their insightful speeches. Prof. Jizhong Zhu, South China University of Technology, China. His research interest is in the analysis, operation, planning and control of power systems, smart grid, power markets as well as applications of renewable energy. And then we had Prof. Kai Yang, Huazhong University of Science and Technology, China. He performed a wonderful speech: Research on novel hybrid flux permanent magnet motor and its control system. Prof. Ting Yang, School of Electrical and Information Engineering, Tianjin University, China. His main research interests include energy & power, Internet of things, artificial intelligence and intelligent manufacturing. Prof. Xiaohuan Li, Guilin University of Electronic Te c h n o l o g y, China. His current research interests include wireless sensor networks, vehicular networks, U AV networks, and cognitive radios. Our finale keynote speaker, Prof. Junzheng Jiang, Guilin University of Electronic Technology, China. His research interests include graph filter bank, distributed signal processing on graphs, and time-vertex signal processing on graphs. Their insightful speeches had triggered heated discussion. Every participant praised this conference for disseminating useful and insightful knowledge. List of Committee member, Conference Chairman, Organizing Committees, Technical Program Committees are available in this pdf.