Conferences related to Multidimensional signal processing

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2023 Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

All areas of ionizing radiation detection - detectors, signal processing, analysis of results, PET development, PET results, medical imaging using ionizing radiation


ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.


IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

All fields of satellite, airborne and ground remote sensing.


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Periodicals related to Multidimensional signal processing

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Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.


Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


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Most published Xplore authors for Multidimensional signal processing

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Xplore Articles related to Multidimensional signal processing

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Multidimensional signal processing using lower-rank tensor approximation

2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2003

The paper presents a new fast optimal lower rank tensor approximation (FOLRTA) method for lower rank-(R/sub 1/,..., R/sub N/) tensor approximation applied to multidimensional signal processing. It is founded on a new approach which consists of considering multidimensional data as global tensors instead of splitting them into matrices or vectors for later classical second order array processing. Its basic principle ...


Practical solutions for counting scalars and dependences in ATOMIUM-a memory management system for multidimensional signal processing

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 1997

Image and video processing applications involve large multidimensional signals which have to be stored in memory modules. In application-specific architectures for real-time multidimensional signal processing, a significant cost in terms of chip area and power consumption is due to these background memory units. The multidimensional signals are usually modeled in behavioral descriptions with array variables. In the algorithmic specifications of ...


Signal Assignment to Hierarchical Memory Organizations for Embedded Multidimensional Signal Processing Systems

IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2009

The storage requirements of the array-dominated and loop-organized algorithmic specifications running on embedded systems can be significant. Employing a data memory space much larger than needed has negative consequences on the energy consumption, latency, and chip area. Finding an optimized storage of the usually large arrays from these algorithmic specifications is an essential task of memory allocation. This paper proposes ...


Multidimensional signal processing and modeling with neural networks in metal machining: Cutting forces, vibrations, and surface roughness

2016 8th IEEE International Conference on Communication Software and Networks (ICCSN), 2016

Neural networks are a soft computing technique with wide application in signal processing as well as system and process modeling. In the present study, multilayer perceptron (MLP) neural networks were employed to process multidimensional signals generated in metal machining operations (including three-dimensional cutting force signals and three-dimensional cutting vibration signals) and to establish a model for predicting the machined surface ...


A theorem in probability and its applications in multidimensional signal processing

IEEE Transactions on Signal Processing, 1996

In this correspondence, we present a result in probability that does not exist in the literature of probability theory. We show one application of this theorem in multidimensional signal processing, which generalizes some of the existing results.


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Educational Resources on Multidimensional signal processing

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IEEE-USA E-Books

  • Multidimensional signal processing using lower-rank tensor approximation

    The paper presents a new fast optimal lower rank tensor approximation (FOLRTA) method for lower rank-(R/sub 1/,..., R/sub N/) tensor approximation applied to multidimensional signal processing. It is founded on a new approach which consists of considering multidimensional data as global tensors instead of splitting them into matrices or vectors for later classical second order array processing. Its basic principle is to project the initial data tensor into the signal subspace, in each consecutive mode. The developed method is the first analytical solution to the Tucker3 tensor decomposition. We show in a simple example of noise reduction of a color image the efficiency of this method. It can also be applied in seismic, acoustics or multimedia signal processing.

  • Practical solutions for counting scalars and dependences in ATOMIUM-a memory management system for multidimensional signal processing

    Image and video processing applications involve large multidimensional signals which have to be stored in memory modules. In application-specific architectures for real-time multidimensional signal processing, a significant cost in terms of chip area and power consumption is due to these background memory units. The multidimensional signals are usually modeled in behavioral descriptions with array variables. In the algorithmic specifications of our target applications, many of the array references cover large amounts of scalars. Therefore, the efficient handling of array references in the specifications for image and video processing is crucial for obtaining low cost memory allocation solutions. This paper addresses a central problem which arises when handling the array variables in behavioral specifications: the computation of the number of scalars covered by an array reference. This problem is closely related to the computation of dependences in data-flow analysis. The novel algorithms proposed in this paper are embedded in the ATOMIUM environment-a memory management system for multidimensional signal processing.

  • Signal Assignment to Hierarchical Memory Organizations for Embedded Multidimensional Signal Processing Systems

    The storage requirements of the array-dominated and loop-organized algorithmic specifications running on embedded systems can be significant. Employing a data memory space much larger than needed has negative consequences on the energy consumption, latency, and chip area. Finding an optimized storage of the usually large arrays from these algorithmic specifications is an essential task of memory allocation. This paper proposes an efficient algorithm for mapping multidimensional arrays to the data memory. Similarly to [1], it computes bounding windows for live elements in the index space of arrays, but this algorithm is several times faster. More important, since this algorithm works not only for <i>entire</i> arrays, but also <i>parts</i> of arrays - like, for instance, array references or, more general, sets of array elements represented by lattices [2], this signal-to-memory mapping technique can be also applied in hierarchical memory architectures.

  • Multidimensional signal processing and modeling with neural networks in metal machining: Cutting forces, vibrations, and surface roughness

    Neural networks are a soft computing technique with wide application in signal processing as well as system and process modeling. In the present study, multilayer perceptron (MLP) neural networks were employed to process multidimensional signals generated in metal machining operations (including three-dimensional cutting force signals and three-dimensional cutting vibration signals) and to establish a model for predicting the machined surface roughness. This paper describes in detail our methods of multidimensional signal processing and modeling with MLP neural networks. The MLP neural network model developed in the present study fills an important research gap by taking into account the critical effect of tool-edge radius in machining. As compared to regression models, the MLP neural network model developed in the present study has significantly higher accuracy in predicting the machined surface roughness.

  • A theorem in probability and its applications in multidimensional signal processing

    In this correspondence, we present a result in probability that does not exist in the literature of probability theory. We show one application of this theorem in multidimensional signal processing, which generalizes some of the existing results.

  • Mapping model with Inter-array memory sharing for multidimensional signal processing

    The storage requirements in data-intensive signal processing systems (including applications in video and image processing, artificial vision, medical imaging, real-time 3-D rendering, advanced audio and speech coding) have an important impact on both the system performance and the essential design parameters -the overall power consumption and chip area. This is due to the significant amount of data that must be stored during the execution of the algorithmic specification, as well as due to the amount of data transfers to/from large, energy-consuming, off-chip data memories. This paper addresses the problem of efficiently mapping the multidimensional signals from the algorithmic specification of the system into the physical memory. Different from all the previous mapping models that aim to optimize the memory sharing between the elements of a same array, creating separate windows in the physical memory for distinct arrays, this proposed mapping model is the first one to exploit the possibility of memory sharing between different arrays. As a consequence, this signal-to-memory mapping approach yields significant savings in the amount of data storage resulted after mapping.

  • Multidimensional signal processing on ionitriding techniques

    This paper describes the method of multidimensional signal processing on ionitriding techniques. It focuses on the relations among the multidimensional parameters, the adjustment of these parameters, the adjusting rules, and the composition of the computer system.

  • QHF: A quaternion based a Multidimensional Hash function

    This paper proposes a new method for constructing a multidimensional hash function based on quaternion presentation called QUATERNION hash function QHF. The input data is divided into a sequence of blocks and each block is split into four parts which are mathematically formed as quaternion's parameters. A series of data and quaternion multiplication's processing are implemented to compose a hash function kernel. Each step of data-quaternion processing is carefully scrutinized data's block to maneuver a succession of zeros which might case a possible attack. Computer based simulation is conducted to compute hash values from input quaternion data. Quaternion has non-commutative feature among its multiplication's parameters, therefore, QHF is capable to resist hash collision. Furthermore, numerical examples are given to comprehend the proposed QHF.data- quaternion processing

  • Registration of Geometric Deformations in the Presence of Varying Illumination

    We address the problem of object registration when the observation differs from the object both geometrically and radio-metrically. The geometric deformations being considered are affine. The radiometric deformations are due to the a-priori lack of knowledge regarding the locations and intensities of the light sources. Hence, to solve the registration problem, a joint solution for the radiometric and the geometric deformations must be offered. A direct approach for solving the joint registration problem as an optimization problem leads to a high-dimensional non-convex search problem. In this paper, we treat the images as vector valued measurements, such that each element of the vector provides the intensity at a specific spectral (color) band. By applying a set of operators, derived in the paper, to the vector valued data the original high-dimensional search problem is replaced by an equivalent problem, expressed in terms of two systems of linear equations. Their solution provides an exact solution to the joint problem.

  • Fast Multidimensional Entropy Estimation by $k$-d Partitioning

    We describe a nonparametric estimator for the differential entropy of a multidimensional distribution, given a limited set of data points, by a recursive rectilinear partitioning. The estimator uses an adaptive partitioning method and runs in Theta(_N_ log _N_) time, with low memory requirements. In experiments using known distributions, the estimator is several orders of magnitude faster than other estimators, with only modest increase in bias and variance.



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