A UWB-2PPM Reconstruction Algorithm without a Priori Knowledge of Pilot

2014 ◽  
Vol 556-562 ◽  
pp. 3545-3548
Author(s):  
Hai Bo Yin ◽  
Jun An Yang ◽  
Jie Gong ◽  
Wei Dong Wang
Keyword(s):  
Theoretical Analysis ◽  
A Priori ◽  
Sampling Rate ◽  
Reconstruction Algorithm ◽  
A Priori Knowledge ◽  
Reconstruction Algorithms ◽  
High Sampling Rate ◽  
The Common ◽  
Transmitted Signal ◽  
Complex Channel

Compressed Sensing is very efficient in reducing the relatively high sampling rate. But when it comes to the channel estimation of uncooperative communication, the common CS reconstruction algorithms seem impractical to implement since a pilot is required, which is difficult for uncooperative communication. In this paper, we combine the sparsity transform dictionary, which is formed by a sequence of delays of the template signal, together with the idea of alternative minimization to improve the traditional CoSaMP algorithm to reconstruct under-sampled UWB-2PPM signal transmitted by unkown complex channel without a knowledge of pilot. The theoretical analysis and simulations show that the proposed algorithm is capable of reconstructing the original transmitted signal without a pilot.

A posteriori

2018 ◽  
Author(s):  
Paul K. Moser
Keyword(s):  
A Priori ◽  
Sensory Experience ◽  
A Priori Knowledge ◽  
Empirical Knowledge ◽  
A Posteriori ◽  
Critique Of Pure Reason ◽  
The Common ◽  
And Mathematics ◽  
Common Understanding ◽  
Priori Knowledge

A prominent term in theory of knowledge since the seventeenth century, ‘a posteriori’ signifies a kind of knowledge or justification that depends on evidence, or warrant, from sensory experience. A posteriori truth is truth that cannot be known or justified independently of evidence from sensory experience, and a posteriori concepts are concepts that cannot be understood independently of reference to sensory experience. A posteriori knowledge contrasts with a priori knowledge, knowledge that does not require evidence from sensory experience. A posteriori knowledge is empirical, experience-based knowledge, whereas a priori knowledge is non-empirical knowledge. Standard examples of a posteriori truths are the truths of ordinary perceptual experience and the natural sciences; standard examples of a priori truths are the truths of logic and mathematics. The common understanding of the distinction between a posteriori and a priori knowledge as the distinction between empirical and non-empirical knowledge comes from Kant’s Critique of Pure Reason (1781/1787).

Modified meta-heuristic-oriented compressed sensing reconstruction algorithm for bio-signals

2019 ◽  
Vol 17 (05) ◽  
pp. 1950031
Author(s):  
Ashok Naganath Shinde ◽  
Sanjay L. Lalbalwar ◽  
Anil B. Nandgaonkar
Keyword(s):  
Compressed Sensing ◽  
Signal Reconstruction ◽  
Sampling Rate ◽  
Reconstruction Algorithm ◽  
Haar Wavelet ◽  
Evaluation Procedure ◽  
Grey Wolf Optimizer ◽  
Reconstruction Algorithms ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization

In signal processing, several applications necessitate the efficient reprocessing and representation of data. Compression is the standard approach that is used for effectively representing the signal. In modern era, many new techniques are developed for compression at the sensing level. Compressed sensing (CS) is a rising domain that is on the basis of disclosure, which is a little gathering of a sparse signal’s linear projections including adequate information for reconstruction. The sampling of the signal is permitted by the CS at a rate underneath the Nyquist sampling rate while relying on the sparsity of the signals. Additionally, the reconstruction of the original signal from some compressive measurements can be authentically exploited using the varied reconstruction algorithms of CS. This paper intends to exploit a new compressive sensing algorithm for reconstructing the signal in bio-medical data. For this purpose, the signal can be compressed by undergoing three stages: designing of stable measurement matrix, signal compression and signal reconstruction. In this, the compression stage includes a new working model that precedes three operations. They are signal transformation, evaluation of [Formula: see text] and normalization. In order to evaluate the theta ([Formula: see text]) value, this paper uses the Haar wavelet matrix function. Further, this paper ensures the betterment of the proposed work by influencing the optimization concept with the evaluation procedure. The vector coefficient of Haar wavelet function is optimally selected using a new optimization algorithm called Average Fitness-based Glowworm Swarm Optimization (AF-GSO) algorithm. Finally, the performance of the proposed model is compared over the traditional methods like Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Firefly (FF), Crow Search (CS) and Glowworm Swarm Optimization (GSO) algorithms.

scholarly journals An Image Reconstruction Algorithm based on Sparse Representation for Image Compressed Sensing

2021 ◽  
Vol 15 ◽  
pp. 511-518
Author(s):  
Shuyao Tian ◽  
Liancheng Zhang ◽  
Yajun Liu
Keyword(s):  
Image Reconstruction ◽  
Sparse Representation ◽  
Sampling Rate ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Step Size ◽  
Sampling Process ◽  
Artifact Suppression ◽  
Reconstruction Time ◽  
Improved Algorithm

It is difficult to control the balance between artifact suppression and detail preservation. In addition, the information contained in the reconstructed image is limited. For achieving the purpose of less lost information and lower computational complexity in the sampling process, this paper proposed a novel algorithm to realize the image reconstruction using sparse representation. Firstly, the principle of algorithm for sparse representation is introduced, and then the current commonly used reconstruction algorithms are described in detail. Finally, the algorithm can still process the image when the sparsity is unknown by introducing the sparsity theory and dynamically changing the step size to approximate the sparsity. The results explain that the improved algorithm can not only reconstruct the image with unknown sparsity, but also has advantages over other algorithms in reconstruction time. In addition, compared with other algorithms, the reconstruction time of the improved algorithm is the shortest under the same sampling rate.

scholarly journals Experimental Assessment of Linear Sampling and Factorization Methods for Microwave Imaging of Concealed Targets

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
M. N. Akıncı ◽  
T. Çağlayan ◽  
S. Özgür ◽  
U. Alkaşı ◽  
M. Abbak ◽  
...  
Keyword(s):  
A Priori ◽  
Surrounding Medium ◽  
Shape Reconstruction ◽  
A Priori Knowledge ◽  
Reconstruction Algorithms ◽  
Electrical Parameters ◽  
Reconstruction Methods ◽  
Factorization Methods ◽  
Priori Knowledge ◽  
Linear Sampling

Shape reconstruction methods are particularly well suited for imaging of concealed targets. Yet, these methods are rarely employed in real nondestructive testing applications, since they generally require the electrical parameters of outer object as a priori knowledge. In this regard, we propose an approach to relieve two well known shape reconstruction algorithms, which are the linear sampling and the factorization methods, from the requirement of the a priori knowledge on electrical parameters of the surrounding medium. The idea behind this paper is that if a measurement of the reference medium (a medium which can approximate the material, except the inclusion) can be supplied to these methods, reconstructions with very high qualities can be obtained even when there is no information about the electrical parameters of the surrounding medium. Taking the advantage of this idea, we consider that it is possible to use shape reconstruction methods in buried object detection. To this end, we perform several experiments inside an anechoic chamber to verify the approach against real measurements. Accuracy and stability of the obtained results show that both the linear sampling and the factorization methods can be quite useful for various buried obstacle imaging problems.

scholarly journals Sparse reconstruction for fluorescence molecular tomography via a fast iterative algorithm

2014 ◽  
Vol 07 (03) ◽  
pp. 1450008 ◽  
Author(s):  
Jingjing Yu ◽  
Jingxing Cheng ◽  
Yuqing Hou ◽  
Xiaowei He
Keyword(s):  
Iterative Algorithm ◽  
Large Scale ◽  
Learning Strategy ◽  
Imaging Modality ◽  
A Priori ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Fluorescence Molecular Tomography ◽  
Ill Posed ◽  
Priori Information

Fluorescence molecular tomography (FMT) is a fast-developing optical imaging modality that has great potential in early diagnosis of disease and drugs development. However, reconstruction algorithms have to address a highly ill-posed problem to fulfill 3D reconstruction in FMT. In this contribution, we propose an efficient iterative algorithm to solve the large-scale reconstruction problem, in which the sparsity of fluorescent targets is taken as useful a priori information in designing the reconstruction algorithm. In the implementation, a fast sparse approximation scheme combined with a stage-wise learning strategy enable the algorithm to deal with the ill-posed inverse problem at reduced computational costs. We validate the proposed fast iterative method with numerical simulation on a digital mouse model. Experimental results demonstrate that our method is robust for different finite element meshes and different Poisson noise levels.

scholarly journals Sticky Pi, an AI-powered smart insect trap for community chronoecology

2021 ◽  
Author(s):  
Quentin Geissmann ◽  
Paul K Abram ◽  
Di Wu ◽  
Cara H Haney ◽  
Juli Carrillo
Keyword(s):  
A Priori ◽  
Sampling Rate ◽  
Model Organism ◽  
Automatic Identification ◽  
Circadian Activity ◽  
Drosophila Suzukii ◽  
Insect Trap ◽  
Biological Variables ◽  
High Sampling Rate ◽  
Natural Settings

Circadian clocks are paramount to insect survival and drive many aspects of their physiology and behaviour. While insect circadian behaviours have been extensively studied in the laboratory, their circadian activity within natural settings is poorly understood. The study of circadian activity necessitates measuring biological variables (e.g., locomotion) at high frequency (i.e., at least several times per hour) over multiple days, which has mostly confined insect chronobiology to the laboratory. In order to study insect circadian biology in the field, we developed the Sticky Pi, a novel, autonomous, open-source, insect trap that acquires images of sticky cards every twenty minutes. Using custom deep-learning algorithms, we automatically and accurately scored where, when and which insects were captured. First, we validated our device in controlled laboratory conditions with a classic chronobiological model organism, Drosophila melanogaster. Then, we deployed an array of Sticky Pis to the field to characterise the daily activity of an agricultural pest, Drosophila suzukii, and its parasitoid wasps. Finally, we demonstrate the wide scope of our smart trap by describing the sympatric arrangement of insect temporal niches in a community, without targeting particular taxa a priori. Together, the automatic identification and high sampling rate of our tool provide biologists with unique data that impacts research far beyond chronobiology; with applications to biodiversity monitoring and pest control as well as fundamental implications for phenology, behavioural ecology, and ecophysiology. We released the Sticky Pi project as an open community resource on https://doc.sticky-pi.com.

A posteriori

2018 ◽  
Author(s):  
Paul K. Moser
Keyword(s):  
Seventeenth Century ◽  
Perceptual Experience ◽  
A Priori ◽  
Sensory Experience ◽  
A Priori Knowledge ◽  
A Posteriori ◽  
The Common ◽  
And Mathematics ◽  
Common Understanding ◽  
Priori Knowledge

A prominent term in theory of knowledge since the seventeenth century, ‘a posteriori’ signifies a kind of knowledge or justification that depends on evidence, or justification, from sensory experience. A posteriori truth is truth that cannot be known or justified independently of evidence from sensory experience, and a posteriori concepts are concepts that cannot be understood independently of reference to sensory experience. A posteriori knowledge contrasts with a priori knowledge, knowledge that does not require evidence from sensory experience. A posteriori knowledge is empirical, experience-based knowledge, whereas a priori knowledge is nonempirical knowledge. Standard examples of a posteriori truths are the truths of ordinary perceptual experience and the natural sciences; standard examples of a priori truths are the truths of logic and mathematics. The common understanding of the distinction between a posteriori and a priori knowledge as the distinction between empirical and nonempirical knowledge comes from Kant’s Critique of Pure Reason1781/1787.

Residual Reconstruction Algorithm Based on Half-Pixel Multi-Hypothesis Prediction for Distributed Compressive Video Sensing

2018 ◽  
Vol 9 (4) ◽  
pp. 16-33
Author(s):  
Ying Tong ◽  
Rui Chen ◽  
Jie Yang ◽  
Minghu Wu
Keyword(s):  
Side Information ◽  
Sampling Rate ◽  
Reconstruction Algorithm ◽  
Video Frame ◽  
Reconstruction Method ◽  
Reconstruction Algorithms ◽  
Directional Motion ◽  
Key Frames ◽  
Video Reconstruction ◽  
Pixel Interpolation

Compressed sensing (CS) provides a method to sample and reconstruct sparse signals far below the Nyquist sampling rate, which has great potential in image/video acquisition and processing. In order to fully exploit the spatial and temporal characteristics of video frame and the coherence between successive frames, we propose a half-pixel interpolation based residual reconstruction method for distributed compressive video sensing (DCVS). At the decoding end, half-pixel interpolation and bi-directional motion estimation helps refine the side information for joint decoding of the non-key-frames. We apply a multi-hypothesis based on residual reconstruction algorithms to reconstruct the non-key-frames. Performance analysis and simulation experiments show that the quality of side information generated by the proposed algorithm is increased by about 1.5dB, with video reconstruction quality increased 0.3~2dB in PSNR, when compared with prior works on DCVS.

Seeing, Knowing, and Doing

2020 ◽  
Author(s):  
Robert Audi
Keyword(s):  
Practical Reasoning ◽  
Practical Knowledge ◽  
A Priori ◽  
Human Action ◽  
A Priori Knowledge ◽  
Rational Action ◽  
Reasons For Action ◽  
Discriminative Response ◽  
Rich Information ◽  
Priori Knowledge

This book provides an overall theory of perception and an account of knowledge and justification concerning the physical, the abstract, and the normative. It has the rigor appropriate for professionals but explains its main points using concrete examples. It accounts for two important aspects of perception on which philosophers have said too little: its relevance to a priori knowledge—traditionally conceived as independent of perception—and its role in human action. Overall, the book provides a full-scale account of perception, presents a theory of the a priori, and explains how perception guides action. It also clarifies the relation between action and practical reasoning; the notion of rational action; and the relation between propositional and practical knowledge. Part One develops a theory of perception as experiential, representational, and causally connected with its objects: as a discriminative response to those objects, embodying phenomenally distinctive elements; and as yielding rich information that underlies human knowledge. Part Two presents a theory of self-evidence and the a priori. The theory is perceptualist in explicating the apprehension of a priori truths by articulating its parallels to perception. The theory unifies empirical and a priori knowledge by clarifying their reliable connections with their objects—connections many have thought impossible for a priori knowledge as about the abstract. Part Three explores how perception guides action; the relation between knowing how and knowing that; the nature of reasons for action; the role of inference in determining action; and the overall conditions for rational action.

How Reality is Reasonable

2018 ◽  
Author(s):  
Donald C. Williams
Keyword(s):  
A Priori ◽  
A Priori Knowledge ◽  
Dimensional Manifold ◽  
The World ◽  
Being There ◽  
Ways Of Being ◽  
Fundamental Entity ◽  
Spatiotemporal Relations ◽  
Priori Knowledge

This chapter begins with a systematic presentation of the doctrine of actualism. According to actualism, all that exists is actual, determinate, and of one way of being. There are no possible objects, nor is there any indeterminacy in the world. In addition, there are no ways of being. It is proposed that actual entities stand in three fundamental relations: mereological, spatiotemporal, and resemblance relations. These relations govern the fundamental entities. Each fundamental entity stands in parthood relations, spatiotemporal relations, and resemblance relations to other entities. The resulting picture is one that represents the world as a four-dimensional manifold of actual ‘qualitied contents’—upon which all else supervenes. It is then explained how actualism accounts for classes, quantity, number, causation, laws, a priori knowledge, necessity, and induction.

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