Variable Stiffness Devices Using Fiber Jamming for Application in Soft Robotics and Wearable Haptics.

Variable stiffness actuation has applications in a wide range of fields, including wearable haptics, soft robots, and minimally invasive surgical devices. There have been numerous design approaches to control and tune stiffness and rigidity; however, most have relatively low specific load-carrying capacities (especially for flexural loads) in the most rigid state that restricts their use in small or slender devices. In this article, we present an approach to the design of slender, high flexural stiffness modules based on the principle of fiber jamming. The proposed fiber jamming modules (FJMs) consist of axially packed fibers in an airtight envelope that transition from a flexible to a rigid beam when a vacuum is created inside the envelope. This FJM can provide the flexural stiffness of up to eight times that of a particle jamming module in the rigid state. Unlike layer jamming modules, the design of FJMs further allows them to control stiffness while bending in space. We present an analytical model to guide the parameter choices for the design of fiber jamming devices. Finally, we demonstrate applications of FJMs, including as a versatile tool, as part of a kinesthetic force feedback haptic glove and as a programmable structure.

Deep learning improves contrast in low-fluence photoacoustic imaging.

Low fluence illumination sources can facilitate clinical transition of photoacoustic imaging because they are rugged, portable, affordable, and safe. However, these sources also decrease image quality due to their low fluence. Here, we propose a denoising method using a multi-level wavelet-convolutional neural network to map low fluence illumination source images to its corresponding high fluence excitation map. Quantitative and qualitative results show a significant potential to remove the background noise and preserve the structures of target. Substantial improvements up to 2.20, 2.25, and 4.3-fold for PSNR, SSIM, and CNR metrics were observed, respectively. We also observed enhanced contrast (up to 1.76-fold) in an in vivo application using our proposed methods. We suggest that this tool can improve the value of such sources in photoacoustic imaging.

Visualization of three-dimensional ultra-high resolution OCT in virtual reality.

Three-dimensional reconstruction of optical coherence tomography (OCT) images is a modern technique that helps interpret the images and understand the underlying disease. However, the 3D reconstruction displayed on commercial devices is of limited quality: images are shown on 2D screens and it is difficult or impossible to adjust the view point and capture the data set from a meaningful perspective. We did a preliminary study to evaluate the applicability of a novel, 3D TV-based virtual reality system with interactive volume rendering software to clinical diagnostics and present a workflow, which can incorporate virtual reality technology at various levels of immersion into the daily medical practice, from interactive VR systems to printed media.

Multi-Channel Transfer Function with Dimensionality Reduction.

The design of transfer functions for volume rendering is a difficult task. This is particularly true for multi-channel data sets, where multiple data values exist for each voxel. In this paper, we propose a new method for transfer function design. Our new method provides a framework to combine multiple approaches and pushes the boundary of gradient-based transfer functions to multiple channels, while still keeping the dimensionality of transfer functions to a manageable level, i.e., a maximum of three dimensions, which can be displayed visually in a straightforward way. Our approach utilizes channel intensity, gradient, curvature and texture properties of each voxel. The high-dimensional data of the domain is reduced by applying recently developed nonlinear dimensionality reduction algorithms. In this paper, we used Isomap as well as a traditional algorithm, Principle Component Analysis (PCA). Our results show that these dimensionality reduction algorithms significantly improve the transfer function design process without compromising visualization accuracy. In this publication we report on the impact of the dimensionality reduction algorithms on transfer function design for confocal microscopy data.

Dimensionality Reduction on Multi-Dimensional Transfer Functions for Multi-Channel Volume Data Sets.

The design of transfer functions for volume rendering is a non-trivial task. This is particularly true for multi-channel data sets, where multiple data values exist for each voxel, which requires multi-dimensional transfer functions. In this paper, we propose a new method for multi-dimensional transfer function design. Our new method provides a framework to combine multiple computational approaches and pushes the boundary of gradient-based multi-dimensional transfer functions to multiple channels, while keeping the dimensionality of transfer functions at a manageable level, i.e., a maximum of three dimensions, which can be displayed visually in a straightforward way. Our approach utilizes channel intensity, gradient, curvature and texture properties of each voxel. Applying recently developed nonlinear dimensionality reduction algorithms reduces the high-dimensional data of the domain. In this paper, we use Isomap and Locally Linear Embedding as well as a traditional algorithm, Principle Component Analysis. Our results show that these dimensionality reduction algorithms significantly improve the transfer function design process without compromising visualization accuracy. We demonstrate the effectiveness of our new dimensionality reduction algorithms with two volumetric confocal microscopy data sets.

High-precision radiocarbon dating and historical biblical archaeology in southern Jordan.

Recent excavations and high-precision radiocarbon dating from the largest Iron Age (IA, ca. 1200-500 BCE) copper production center in the southern Levant demonstrate major smelting activities in the region of biblical Edom (southern Jordan) during the 10th and 9th centuries BCE. Stratified radiocarbon samples and artifacts were recorded with precise digital surveying tools linked to a geographic information system developed to control on-site spatial analyses of archaeological finds and model data with innovative visualization tools. The new radiocarbon dates push back by 2 centuries the accepted IA chronology of Edom. Data from Khirbat en-Nahas, and the nearby site of Rujm Hamra Ifdan, demonstrate the centrality of industrial-scale metal production during those centuries traditionally linked closely to political events in Edom's 10th century BCE neighbor ancient Israel. Consequently, the rise of IA Edom is linked to the power vacuum created by the collapse of Late Bronze Age (LB, ca. 1300 BCE) civilizations and the disintegration of the LB Cypriot copper monopoly that dominated the eastern Mediterranean. The methodologies applied to the historical IA archaeology of the Levant have implications for other parts of the world where sacred and historical texts interface with the material record.