CS Scheme Research Articles

Highly accelerated intracranial time-of-flight magnetic resonance angiography using wave-encoding.

To develop an accelerated 3D intracranial time-of-flight (TOF) magnetic resonance angiography (MRA) sequence with wave-encoding (referred to as 3D wave-TOF) and to evaluate two variants: wave-controlled aliasing in parallel imaging (CAIPI) and compressed-sensing wave (CS-wave). A wave-TOF sequence was implemented on a 3 T clinical scanner. Wave-encoded and Cartesian k-space datasets from six healthy volunteers were retrospectively and prospectively undersampled with 2D-CAIPI sampling and variable-density Poisson disk sampling. 2D-CAIPI, wave-CAIPI, standard CS, and CS-wave schemes were compared at various acceleration factors. Flow-related artifacts in wave-TOF were investigated, and a set of practicable wave parameters was developed. Quantitative analysis of wave-TOF and traditional Cartesian TOF MRA was performed by comparing the contrast-to-background ratio between the vessel and background tissue in source images, and the structural similarity index measure (SSIM) between the maximum intensity projection images from accelerated acquisitions and their respective fully sampled references. Flow-related artifacts caused by the wave-encoding gradients in wave-TOF were eliminated by properly chosen parameters. Images from wave-CAIPI and CS-wave acquisitions had a higher SNR and better-preserved contrast than traditional parallel imaging (PI) and CS methods. Maximum intensity projection images from wave-CAIPI and CS-wave acquisitions had a cleaner background, with vessels that were better depicted. Quantitative analyses indicated that wave-CAIPI had the highest contrast-to-background ratio, SSIM, and vessel-masked SSIM among the sampling schemes studied, followed by the CS-wave acquisition. 3D wave-TOF improves the capability of accelerated MRA and provides better image quality at higher acceleration factors compared to traditional PI- or CS-accelerated TOF, suggesting the potential use of wave-TOF in cerebrovascular disease.

Experimental comparison of DSB and CS-DSB mmW formats over a hybrid fiber and FSO fronthaul network for 5G.

The telecommunication world is experiencing the 5th generation (5G) networks deployment including the use of millimeter wave (mmW) frequency bands to satisfy capacity demands. This leads to the extensive use of optical communications, especially the optical fiber connectivity at the last mile access and the edge networks. In this paper we outline fiber and free space optics (FSO) technologies for use as part of the 5G optical fronthaul network. We investigate two different mmW transmission schemes based on (i) the conventional analog radio over fiber transmission using one Mach-Zehnder modulator (MZM) with double sideband (DSB) optical modulation, and (ii) an optical-based frequency doubling with one MZM biased at the null point to introduce carrier suppression DSB (CS DSB) transmission and second MZM used for data modulation. Both systems are assessed in terms of the error vector magnitude, signal-to-noise ratio, dynamic range and phase noise. We consider a configuration for the fronthaul network in the frequency range 2 (FR2) at 27 and 39 GHz with the scale of bandwidth up to 400 MHz with M-quadrature amplitude modulation and quadrature phase shift keying. Results are also shown for FR1 at 3.5 GHz. Moreover, we investigate for the first time the 5G new radio signal transmission under strong turbulence conditions and show the turbulence-induced FSO link impairment. We finally demonstrate the CS DSB scheme performs well under chromatic dispersion-induced fading for the frequency up to 40 GHz and single mode fiber length of 30 km, whereas the DSB format seems more appropriate for an antenna seamless transmission.

Physical Layer Intercept Probability in Wireless Sensor Networks over Fisher–Snedecor \({\mathcal{F}}\) Fading Channels

In this paper, we analyze the physical layer security (PLS) of an arbitrarily dimensioned wireless sensor network (WSN) in the presence of an unauthorized attacker. Various scheduling schemes have been exploited in order to enhance the secure transmission of reliable links impaired by Fisher–Snedecor F fading. The path loss among active nodes is also considered. The exact intercept probability expressions are derived recalling an optimal scheduling scheme (OS), a scheduling policy based on a specific cumulative distribution function (CS), and round-robin scheduling as a baseline. The asymptotic behavior of the intercept metric is also presented in a simpler form with acceptable accuracy. The secrecy diversity orders are defined and the security–reliability tradeoff of WSN is specified. Numerical results are provided to demonstrate the interplay of various main/wiretap channel conditions, the distances among nodes, the number of active sensors, and the average main-to-eavesdropper’s signal ratio in order to upgrade the quality of the WSN secrecy performance. Additionally, the impact of the outage probability on the intercept probability is defined for a variety of scenarios under which either the CS or OS scheme could be selected as suitable for PLS enhancement. The obtained results are verified by independent Monte Carlo simulations.

Exploiting Restricted Boltzmann Machines and Deep Belief Networks in Compressed Sensing

This paper proposes a CS scheme that exploits the representational power of restricted Boltzmann machines and deep learning architectures to model the prior distribution of the sparsity pattern of signals belonging to the same class. The determined probability distribution is then used in a maximum a posteriori (MAP) approach for the reconstruction. The parameters of the prior distribution are learned from training data. The motivation behind this approach is to model the higher-order statistical dependencies between the coefficients of the sparse representation, with the final goal of improving the reconstruction. The performance of the proposed method is validated on the Berkeley Segmentation Dataset and the MNIST Database of handwritten digits.

Covariogram-Based Compressive Sensing for Environmental Wireless Sensor Networks

In this paper, we propose covariogram-based compressive sensing (CB-CS), a spatio-temporal compression algorithm for environmental wireless sensor networks. CB-CS combines a novel sampling mechanism along with an original covariogram-based approach for the online estimation of the covariance structure of the signal and leverages the signal's spatio-temporal correlation structure through the Kronecker CS framework. CB-CS's performance is systematically evaluated in the presence of synthetic and real signals, comparing it against a number of compression methods from the literature, based on linear approximations, Fourier transforms, distributed source coding, and against several approaches based on CS. CB-CS is found superior to all of them and able to effectively and promptly adapt to changes in the underlying statistical structure of the signal, while also providing compression versus energy tradeoffs that approach those of idealized CS schemes (where the signal correlation structure is perfectly known at the receiver).

Flux conservation principle on construction of residual restriction operators for multigrid method

Multigrid method is one of the most efficient iterative algorithms to accelerate the convergence rate of both linear and non-linear algebraic equations. It has been indicated by many researches that the actual performance of multigrid method is influenced by various factors, among which the most important one is the implementation of residual restriction. For different discretized forms of the same partial differential governing equation, it is proposed in the present paper that the multigrid method could behave most effectively only in the case that the implementation of residual restriction satisfies the flux conservation principle of the finite volume method. Taking the correction scheme (CS scheme) of the multigrid method into consideration, when the flux conservation principle is well satisfied, residual restriction operators for different algebraic equations are derived. Corresponding numerical studies are made via four cases of pure diffusion and convective-diffusion problems. It is found that the acceleration of the convergence rate of the multigrid method is most desirable when the residual restriction satisfies the flux conservation principle.

Compressive image acquisition in modern CMOS IC design

SummaryCompressive sampling (CS) offers bandwidth, power, and memory size reduction compared to conventional (Nyquist) sampling. These are very attractive features for the design of modern complementary metal‐oxide semiconductor (CMOS) image sensors, cameras, and camera systems. However, very few integrated circuit (IC) designs based on CS exist because of the missing link between the well‐established CS theory on one side, and the practical aspects/effects related to physical IC design on the other side. This paper focuses on the application of compressed image acquisition in CMOS image sensor integrated circuit design. A new CS scheme is proposed, which is suited for hardware implementation in CMOS IC design. All the main physical non‐idealities are explained and carefully modeled. Their influences on the acquired image quality are analyzed in the general case and quantified for the case of the proposed CS scheme. The presented methodology can also be used for different CS schemes and as a general guideline in future CS based CMOS image sensor designs. Copyright © 2013 John Wiley & Sons, Ltd.

Blind Compressive Sensing Dynamic MRI

We propose a novel blind compressive sensing (BCS) frame work to recover dynamic magnetic resonance images from undersampled measurements. This scheme models the dynamic signal as a sparse linear combination of temporal basis functions, chosen from a large dictionary. In contrast to classical compressed sensing, the BCS scheme simultaneously estimates the dictionary and the sparse coefficients from the undersampled measurements. Apart from the sparsity of the coefficients, the key difference of the BCS scheme with current low rank methods is the nonorthogonal nature of the dictionary basis functions. Since the number of degrees-of-freedom of the BCS model is smaller than that of the low-rank methods, it provides improved reconstructions at high acceleration rates. We formulate the reconstruction as a constrained optimization problem; the objective function is the linear combination of a data consistency term and sparsity promoting l1 prior of the coefficients. The Frobenius norm dictionary constraint is used to avoid scale ambiguity. We introduce a simple and efficient majorize-minimize algorithm, which decouples the original criterion into three simpler subproblems. An alternating minimization strategy is used, where we cycle through the minimization of three simpler problems. This algorithm is seen to be considerably faster than approaches that alternates between sparse coding and dictionary estimation, as well as the extension of K-SVD dictionary learning scheme. The use of the l1 penalty and Frobenius norm dictionary constraint enables the attenuation of insignificant basis functions compared to the l0 norm and column norm constraint assumed in most dictionary learning algorithms; this is especially important since the number of basis functions that can be reliably estimated is restricted by the available measurements. We also observe that the proposed scheme is more robust to local minima compared to K-SVD method, which relies on greedy sparse coding. Our phase transition experiments demonstrate that the BCS scheme provides much better recovery rates than classical Fourier-based CS schemes, while being only marginally worse than the dictionary aware setting. Since the overhead in additionally estimating the dictionary is low, this method can be very useful in dynamic magnetic resonance imaging applications, where the signal is not sparse in known dictionaries. We demonstrate the utility of the BCS scheme in accelerating contrast enhanced dynamic data. We observe superior reconstruction performance with the BCS scheme in comparison to existing low rank and compressed sensing schemes.

Congestion control using dynamic routing and flow control

In this paper, the congestion control in store and forward computer com-munication networks is studied, the method employed consists of a combination of a dynamic routing policy that minimizes the delay, and buffer nianagement schemes to control the flow of data. Simulation using liner programming has been carried out, under balanced and transient traffic conditions, on a simple 3–node network , the relative merits and drawbacks of each approach are discussed. Irland [2], showed that with static routing, the SQRT scheme improves the switch performance. In this paper it is shown that in the case of dynamic routing, the SQRT is an excellent solution to remedy the performance degradation caused by CP and CS schemes

Making Community Service into Service for the Community

Community service can be relevant both to the offender and the community. Here the authors describe the operation of a CS scheme which underlines the importance of positive thinking and related practice.