Sparsity constraint is a priori knowledge of the signal, which means that in some properly chosen basis only small percentage total number signal components nonzero. has been used and image processing for long time. Recent publications have shown can be to achieve super-resolution optical sparse objects beyond diffraction limit. In this paper we present quantum theory establishes limits objects. The key idea our use discrete prolate spheroidal sequences (DPSS) as sensing basis. We...

This work deals with the direction of arrival (DOA) estimation multiple radar targets present in mainlobe a rotating antenna using sparse signal reconstruction methods. We exploit knowledge main beam pattern and fact that mechanical scanning impresses an amplitude modulation on signals backscattered by targets. Having studied Basis Pursuit (BP) traditional Orthogonal Matching (OMP) algorithm, improved approach named Sensing (Sensing OMP) is proposed which utilizes sensing dictionary to...

Abstract Objectives This work intended to identify candidate C2H2 genes participating in low-temperature conditioning (LTC)-alleviated postharvest chilling injury of peach fruit. Materials and Methods For LTC treatment, fruit were pre-stored at 10 °C for 5 d then transferred 0 storage. Fruit firmness was measured by a hardness tester. H2O2 content determined luminosity measurement model using multifunctional enzyme labeler. Identification family members performed HMMSCAN according genome....

With the evolution of smart cities, images are used in a wide range services such as healthcare and surveillance. How to ensure that transmitted shared securely is paramount importance for cities. To this end, secure efficient scheme image transmission proposed paper, which uses sparse signal transformation (SST) parallel compressive sensing (CS). The primary employed techniques (SST), CS, diffusion-permutation operation. compression performance achieved by whereas encryption derived from...

The compressed sensing (CS) can acquire and reconstruct a sparse signal from relatively fewer measurements than the classical Nyquist sampling. Practical ADCs not only sample but also quantize each measurement to finite number of bits; moreover, there is an inverse relationship between achievable sampling rate bit depth. quantized CS has been studied recently it demonstrated that accurate stable acquisition still possible even when just single bit. Many algorithms have proposed for 1-bit...

The theory of Compressed Sensing (CS) enables reconstruction sparse or compressible signals from a small number linear measurements, relative to the dimension signal space. Rather than uniformly sampling, compressive sensing computes inner products with randomized dictionary test functions. is then recovered by convex optimization. One main challenges in CS find support set observations. In this paper we consider case 1-bit which preserve only sign information random measurements. Although...

Range estimation is an important issue of radar system, and its performance mainly limited by the transmitting bandwidth. To improve range resolution, existing approaches often either occupied wider bandwidth or employed some non-linear techniques which cause high complexity. The has not only more network resource takes but also easier radio frequency interference (RFI) introduced. authors propose a new system utilising jointly compressive sensing (CS) proposed virtual expanding technique,...

Abstract In this paper, the generation method of medial axis in arbitrary quadrilateral surface is proposed. It can provide a solution for simplification complex fillet feature and mesh model. By using locus associated with moving Frenet frame, we realize simple fast algorithm generating axis. As engineering problem, B-rep 3D solid models clear boundary definition are mostly applied; information vertex, side model, which clearly stored model file, be used to simplify traditional axis, order...

This paper studies the problem of recovering an arbitrarily distributed sparse matrix from its one-bit (1-bit) compressive measurements. We propose a sketching based binary method iterative hard thresholding (MSBIHT) algorithm by combining two dimensional version BIHT (2DBIHT) and method, to solve recovery in form. In contrast traditional one-dimensional (BIHT), proposed can reduce computational complexity. Besides, MSBIHT also improve performance comparing 2DBIHT method. A brief theoretical...

In the article, issue of multiple frequencies estimation from compressive phase-only data is addressed. A frequency receiver scheme based on sensing technique presented first. Then, we propose two reconstruction algorithms: one convex; and other hybrid, which robust optimisation iterative hard threshold. With these methods, accomplished by reconstructing Fourier transform complex-valued time signal finding peaks in domain. Simulation experiments illustrate its advantages both with noiseless...

We discuss the ultimate quantum limits in compressed sensing, a new technique which allows for accurate reconstruction of so-called "sparse" signals even when sampling rate is far below Nyquist limit.

The Compressive Sensing (CS) is an effective method on data collection, transmission and processing in wireless sensor networks. One of hot research points CS to design a kind measurement matrix that satisfies Restricted Isometry Property (RIP). In this paper, designed depending the analysis sparsity features sensing nodes. effort sparse with least incurred computational cost less storage space when it maintains quality signal recovery. approach based properties combinations. And, optimized...

Abstract The accuracy of the compressed sensing theory reconstruction algorithm is important for signal recovery. Sparsity Adaptive Matching Pursuit (SAMP) has a fixed step size in each iterative process reconstruction, which significant impact on actual use, often leading to overestimation and underestimation. In order solve this problem, combined with advantages regular backtracking variable size, article introduces idea retrospective atom selection stage. Then, atoms are inspected using...

Compressed Sensing (CS) is a new method of signal and image processing which allows for exact recovery an from number samples much smaller than that required by the Nyquist/Shannon theorem. uses priori information about object called "sparsity", means only small are nonzero. We have analyzed superresolution behavior CS taking into account quantum fluctuations in image. Our analysis to characterize ultimate capabilities imposed nature light.