SWOT Analysis with Novel NimbusTech Cloud Computing based Research on Regional Tourism Poverty Alleviation Development Model

Cloud computing has the potential to revolutionize microfinance by providing access to affordable and scalable computing resources. Microfinance institutions (MFIs) can use cloud computing to streamline their operations, reduce costs, and improve their services to clients. Cloud computing can also help MFIs reach new clients by providing a cost-effective platform for deploying and managing new services. Regional tourism can have a significant impact on the local economy, creating jobs and generating income for local businesses. It can also provide opportunities for cultural exchange and contribute to the preservation of natural and cultural heritage sites. A poverty alleviation development model is a framework or approach that is used to reduce poverty in a specific region or community. Effective poverty alleviation development models require collaboration between the government, non-governmental organizations, and local communities. They should also be based on an understanding of the specific needs and priorities of the target population, as well as the broader social, economic, and political context in which they operate. This paper presents a SWOT analysis of a novel cloud computing environment, called NimbusTech, through the lens of a microfinance model for poverty alleviation with a focus on regional tourism. The SWOT analysis examines the strengths, weaknesses, opportunities, and threats of using NimbusTech to support microfinance initiatives aimed at reducing poverty levels in regions that rely on tourism. The analysis highlights that NimbusTech's strengths include its scalability, flexibility, and cost-effectiveness, which make it an ideal platform for implementing microfinance programs. On the other hand, its weaknesses include data security and privacy concerns, which could undermine trust in the platform. The opportunities for NimbusTech include the potential for leveraging big data analytics to improve the effectiveness of microfinance programs, while the threats include competition from other cloud computing platforms and potential regulatory barriers. Overall, the analysis shows that NimbusTech has the potential to support microfinance initiatives and promote regional tourism as a means of reducing poverty levels, but careful attention must be paid to its weaknesses and threats to ensure its success.

Photoacoustic imaging with a multi-view Fabry-Perot scanner

Planar Fabry-Pérot (FP) ultrasound sensor arrays have been used to produce in-vivo photoacoustic images of high quality due to their broad detection bandwidth, small element size, and dense spatial sampling. However like all planar arrays, FP sensors suffer from the limited view problem. Here, a multi-angle FP sensor system is described that mitigates the partial view effects of a planar FP sensor while retaining its detection advantages. The possibility of improving data acquisition speed through the use of sub-sampling techniques is also explored. The capabilities of the system are demonstrated with 3D images of pre-clinical targets

Orthogonal Fabry-Perot sensors for photoacoustic tomography

Photoacoustic images of exquisite quality have previously been obtained using planar Fabry-Pérot ultrasound sensors, as they can synthesize detection arrays with small, highly sensitive, elements. However, their planarity prevents reconstruction of structures perpendicular to the sensor plane, which gives rise to limited-view artifacts. Here, a novel FP sensor array configuration is described that incorporates two orthogonal planar arrays in order to overcome this limitation. Three dimensional photoacoustic images of suitably structured phantoms, obtained using a time reversal reconstruction algorithm, are used to demonstrate the significant improvement in the reconstructed images

Implicit 3D Orientation Learning for 6D Object Detection from RGB Images

We propose a real-time RGB-based pipeline for object detection and 6D pose estimation. Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views of a 3D model using Domain Randomization. This so-called Augmented Autoencoder has several advantages over existing methods: It does not require real, pose-annotated training data, generalizes to various test sensors and inherently handles object and view symmetries. Instead of learning an explicit mapping from input images to object poses, it provides an implicit representation of object orientations defined by samples in a latent space. Our pipeline achieves state-of-the-art performance on the T-LESS dataset both in the RGB and RGB-D domain. We also evaluate on the LineMOD dataset where we can compete with other synthetically trained approaches. We further increase performance by correcting 3D orientation estimates to account for perspective errors when the object deviates from the image center and show extended results.Comment: Code available at: https://github.com/DLR-RM/AugmentedAutoencode

Coupling Infusion and Gyration for the Nanoscale Assembly of Functional Polymer Nanofibers Integrated with Genetically Engineered Proteins

Nanofibers featuring functional nanoassemblies show great promise as enabling constituents for a diverse range of applications in areas such as tissue engineering, sensing, optoelectronics, and nanophotonics due to their controlled organization and architecture. An infusion gyration method is reported that enables the production of nanofibers with inherent biological functions by simply adjusting the flow rate of a polymer solution. Sufficient polymer chain entanglement is obtained at Berry number > 1.6 to make bead-free fibers integrated with gold nanoparticles and proteins, in the diameter range of 117-216 nm. Integration of gold nanoparticles into the nanofiber assembly is followed using a gold-binding peptide tag genetically conjugated to red fluorescence protein (DsRed). Fluorescence microscopy analysis corroborated with Fourier transform infrared spectroscopy (FTIR) data confirms the integration of the engineered red fluorescence protein with the nanofibers. The gold nanoparticle decorated nanofibers having red fluorescence protein as an integral part keep their biological functionality including copper-induced fluorescence quenching of the DsRed protein due to its selective Cu(+2) binding. Thus, coupling the infusion gyration method in this way offers a simple nanoscale assembly approach to integrate a diverse repertoire of protein functionalities into nanofibers to generate biohybrid materials for imaging, sensing, and biomaterial applications

Single-pixel camera photoacoustic tomography

Since it was first demonstrated more than a decade ago, the single-pixel camera concept has been used in numerous applications in which it is necessary or advantageous to reduce the channel count, cost, or data volume. Here, three-dimensional (3-D), compressed-sensing photoacoustic tomography (PAT) is demonstrated experimentally using a single-pixel camera. A large area collimated laser beam is reflected from a planar Fabry-Pérot ultrasound sensor onto a digital micromirror device, which patterns the light using a scrambled Hadamard basis before it is collected into a single photodetector. In this way, inner products of the Hadamard patterns and the distribution of thickness changes of the FP sensor-induced by the photoacoustic waves-are recorded. The initial distribution of acoustic pressure giving rise to those photoacoustic waves is recovered directly from the measured signals using an accelerated proximal gradient-type algorithm to solve a model-based minimization with total variation regularization. Using this approach, it is shown that 3-D PAT of imaging phantoms can be obtained with compression rates as low as 10%. Compressed sensing approaches to photoacoustic imaging, such as this, have the potential to reduce the data acquisition time as well as the volume of data it is necessary to acquire, both of which are becoming increasingly important in the drive for faster imaging systems giving higher resolution images with larger fields of view

Automatic Inference of Cross-modal Connection Topologies for X-CNNs

This paper introduces a way to learn cross-modal convolutional neural network (X-CNN) architectures from a base convolutional network (CNN) and the training data to reduce the design cost and enable applying cross-modal networks in sparse data environments. Two approaches for building X-CNNs are presented. The base approach learns the topology in a data-driven manner, by using measurements performed on the base CNN and supplied data. The iterative approach performs further optimisation of the topology through a combined learning procedure, simultaneously learning the topology and training the network. The approaches were evaluated agains examples of hand-designed X-CNNs and their base variants, showing superior performance and, in some cases, gaining an additional 9% of accuracy. From further considerations, we conclude that the presented methodology takes less time than any manual approach would, whilst also significantly reducing the design complexity. The application of the methods is fully automated and implemented in Xsertion library.Comment: 10 pages, 3 figures, 2 tables, to appear in ISNN 201

Photoacoustic Reconstruction Using Sparsity in Curvelet Frame: Image Versus Data Domain

Curvelet frame is of special significance for photoacoustic tomography (PAT) due to its sparsifying and microlocalisation properties. We derive a one-to-one map between wavefront directions in image and data spaces in PAT which suggests near equivalence between the recovery of the initial pressure and PAT data from compressed/subsampled measurements when assuming sparsity in Curvelet frame. As the latter is computationally more tractable, investigation to which extent this equivalence holds conducted in this paper is of immediate practical significance. To this end we formulate and compare DR , a two step approach based on the recovery of the complete volume of the photoacoustic data from the subsampled data followed by the acoustic inversion, and p0R , a one step approach where the photoacoustic image (the initial pressure, p0 ) is directly recovered from the subsampled data. Effective representation of the photoacoustic data requires basis defined on the range of the photoacoustic forward operator. To this end we propose a novel wedge-restriction of Curvelet transform which enables us to construct such basis. Both recovery problems are formulated in a variational framework. As the Curvelet frame is heavily overdetermined, we use reweighted ℓ1 norm penalties to enhance the sparsity of the solution. The data reconstruction problem DR is a standard compressed sensing recovery problem, which we solve using an ADMM-type algorithm, SALSA. Subsequently, the initial pressure is recovered using time reversal as implemented in the k-Wave Toolbox. The p0 reconstruction problem, p0R , aims to recover the photoacoustic image directly via FISTA, or ADMM when in addition including a non-negativity constraint. We compare and discuss the relative merits of the two approaches and illustrate them on 2D simulated and 3D real data in a fair and rigorous manner