Single-Pixel Image Reconstruction Based on Block Compressive Sensing and Deep Learning

Single-pixel imaging (SPI) is a novel imaging technique whose working principle is based on the compressive sensing (CS) theory. In SPI, data is obtained through a series of compressive measurements and the corresponding image is reconstructed. Typically, the reconstruction algorithm such as basis pursuit relies on the sparsity assumption in images. However, recent advances in deep learning have found its uses in reconstructing CS images. Despite showing a promising result in simulations, it is often unclear how such an algorithm can be implemented in an actual SPI setup. In this paper, we demonstrate the use of deep learning on the reconstruction of SPI images in conjunction with block compressive sensing (BCS). We also proposed a novel reconstruction model based on convolutional neural networks that outperforms other competitive CS reconstruction algorithms. Besides, by incorporating BCS in our deep learning model, we were able to reconstruct images of any size above a certain smallest image size. In addition, we show that our model is capable of reconstructing images obtained from an SPI setup while being priorly trained on natural images, which can be vastly different from the SPI images. This opens up opportunity for the feasibility of pretrained deep learning models for CS reconstructions of images from various domain areas

LOCATION OF A MIXALCO PRODUCTION FACILITY WITH RESPECT TO ECONOMIC VIABILITY

Monte-Carlo simulation modeling is used to perform a feasibility study of alternative locations for a MixAlco production facility. Net present value distributions will be ranked within feasible risk aversion boundaries. If MixAlco is a profitable investment, it would have a major impact on the fuel oxygenate and gasoline markets.Resource /Energy Economics and Policy,

Sensor node localisation using a stereo camera rig

In this paper, we use stereo vision processing techniques to detect and localise sensors used for monitoring simulated environmental events within an experimental sensor network testbed. Our sensor nodes communicate to the camera through patterns emitted by light emitting diodes (LEDs). Ultimately, we envisage the use of very low-cost, low-power, compact microcontroller-based sensing nodes that employ LED communication rather than power hungry RF to transmit data that is gathered via existing CCTV infrastructure. To facilitate our research, we have constructed a controlled environment where nodes and cameras can be deployed and potentially hazardous chemical or physical plumes can be introduced to simulate environmental pollution events in a controlled manner. In this paper we show how 3D spatial localisation of sensors becomes a straightforward task when a stereo camera rig is used rather than a more usual 2D CCTV camera

Automated Pavement Crack Segmentation Using U-Net-based Convolutional Neural Network

Automated pavement crack image segmentation is challenging because of inherent irregular patterns, lighting conditions, and noise in images. Conventional approaches require a substantial amount of feature engineering to differentiate crack regions from non-affected regions. In this paper, we propose a deep learning technique based on a convolutional neural network to perform segmentation tasks on pavement crack images. Our approach requires minimal feature engineering compared to other machine learning techniques. We propose a U-Net-based network architecture in which we replace the encoder with a pretrained ResNet-34 neural network. We use a "one-cycle" training schedule based on cyclical learning rates to speed up the convergence. Our method achieves an F1 score of 96% on the CFD dataset and 73% on the Crack500 dataset, outperforming other algorithms tested on these datasets. We perform ablation studies on various techniques that helped us get marginal performance boosts, i.e., the addition of spatial and channel squeeze and excitation (SCSE) modules, training with gradually increasing image sizes, and training various neural network layers with different learning rates.Comment: Accepted for publication in IEEE Acces

New variables, the gravitational action, and boosted quasilocal stress-energy-momentum

This paper presents a complete set of quasilocal densities which describe the stress-energy-momentum content of the gravitational field and which are built with Ashtekar variables. The densities are defined on a two-surface $B$ which bounds a generic spacelike hypersurface $\Sigma$ of spacetime. The method used to derive the set of quasilocal densities is a Hamilton-Jacobi analysis of a suitable covariant action principle for the Ashtekar variables. As such, the theory presented here is an Ashtekar-variable reformulation of the metric theory of quasilocal stress-energy-momentum originally due to Brown and York. This work also investigates how the quasilocal densities behave under generalized boosts, i. e. switches of the $\Sigma$ slice spanning $B$. It is shown that under such boosts the densities behave in a manner which is similar to the simple boost law for energy-momentum four-vectors in special relativity. The developed formalism is used to obtain a collection of two-surface or boost invariants. With these invariants, one may ``build" several different mass definitions in general relativity, such as the Hawking expression. Also discussed in detail in this paper is the canonical action principle as applied to bounded spacetime regions with ``sharp corners."Comment: Revtex, 41 Pages, 4 figures added. Final version has been revised and improved quite a bit. To appear in Classical and Quantum Gravit

Decoherence and dephasing errors caused by D.C. Stark effect in rapid ion transport

We investigate the error due to D.C. Stark effect for quantum information processing for trapped ion quantum computers using the scalable architecture proposed in J. Res. Natl. Inst. Stan. 103, 259 (1998) and Nature 417, 709 (2002). As the operation speed increases, dephasing and decoherence due to the D.C. Stark effect becomes prominent as a large electric field is applied for transporting ions rapidly. We estimate the relative significance of the decoherence and dephasing effects and find that the latter is dominant. We find that the minimum possible of dephasing is quadratic in the time of flight, and an inverse cubic in the operational time scale. From these relations, we obtain the operational speed-range at which the shifts caused by D.C. Stark effect, no matter follow which trajectory the ion is transported, are no longer negligible. Without phase correction, the maximum speed a qubit can be transferred across a 100 micron-long trap, without excessive error, in about 10 ns for Calcium ion and 50 ps for Beryllium ion. In practice, the accumulated error is difficult to be tracked and calculated, our work gives an estimation to the range of speed limit imposed by D.C. Stark effect.Comment: 7 pages, 1 figure. v2: Title is changed in this version to make our argument more focused. Introduction is rewritten. A new section IV is added to make our point more prominent. v3: Title is changed to make our argument more specific. Abstract, introduction, and summary are revise

Lightcone reference for total gravitational energy

We give an explicit expression for gravitational energy, written solely in terms of physical spacetime geometry, which in suitable limits agrees with the total Arnowitt-Deser-Misner and Trautman-Bondi-Sachs energies for asymptotically flat spacetimes and with the Abbot-Deser energy for asymptotically anti-de Sitter spacetimes. Our expression is a boundary value of the standard gravitational Hamiltonian. Moreover, although it stands alone as such, we derive the expression by picking the zero-point of energy via a ``lightcone reference.''Comment: latex, 7 pages, no figures. Uses an amstex symbo

The effects of abdominal compartment hypertension after open and endovascular repair of a ruptured abdominal aortic aneurysm

ObjectiveThis study assessed if emergency endovascular repair (eEVR) reduces the increase in intra-abdominal compartment pressure and host inflammatory response in patients with ruptured abdominal aortic aneurysm (AAA).MethodsThirty patients with ruptured AAA were prospectively recruited. Patients were offered eEVR or emergency conventional open repair (eOR) depending on anatomic suitability. Intra-abdominal pressure was measured postoperatively, at 2 and 6 hours, and then daily for 5 days. Organ dysfunction was assessed preoperatively by calculating the Hardman score. Multiple organ dysfunction syndrome, systemic inflammatory response syndrome, and lung injury scores were calculated regularly postoperatively. Hematologic analyses included serum urea and electrolytes, liver function indices, and C-reactive protein. Urine was analyzed for the albumin-creatinine ratio.ResultsFourteen patients (12 men; mean age, 72.2 ± 6.2 years) underwent eEVR, and 16 (14 men; mean age, 71.4 ± 7.0 years) had eOR. Intra-abdominal pressure was significantly higher in the eOR cohort compared with the eEVR group. The eEVR patients had significantly less blood loss (P < .001) and transfused (P < .001) and total intraoperative intravenous fluid infusion (P = .001). The eOR group demonstrated a greater risk of organ dysfunction, with a higher systemic inflammatory response syndrome score at day 5 (P = .005) and higher lung injury scores at days 1 and 3 (P = .02 and P = .02) compared with eEVR. A significant correlation was observed between intra-abdominal pressure and the volume of blood lost and transfused, amount of fluid given, systemic inflammatory response syndrome score, multiple organ dysfunction score, lung injury score, and the length of stay in the intensive care unit and hospital.ConclusionThese results suggest that eEVR of ruptured AAA is less stressful and is associated with less intra-abdominal hypertension and host inflammatory response compared with eOR