There have been many studies on understanding data visualizations regarding general users. However, we have a limited understanding of how people with ADHD comprehend data visualizations and how it might be different from the general users. To understand accessible data visualization for people with ADHD, we conducted a crowd-sourced survey involving 70 participants with ADHD and 77 participants without ADHD. Specifically, we tested the chart components of color, text amount, and use of visual embellishments/pictographs, finding that some of these components and ADHD affected participants’ response times and accuracy. We outlined the neurological traits of ADHD and discussed specific findings on accessible data visualizations for people with ADHD. We found that various chart embellishment types affected accuracy and response times for those with ADHD differently depending on the types of questions. Based on these results, we suggest visual design recommendations to make accessible data visualizations for people with ADHD.

In this work, we propose a semi-autonomous scheme to synergistically share the complicated task of manipulation and cutting of an unknown deformable tissue (U-DT) between a remote surgeon and a surgical robot. Particularly, utilizing the da Vinci Research Kit (dVRK) platform, we have designed and successfully demonstrated a fully functional shared control scheme for an autonomous tensioning and tele-cutting of a U-DT. We have shown the system’s ability to cooperate with a remote surgeon by leveraging an online data-driven learning and adaptive control method coupled with a reduced-order trajectory planning module that depends on just two parameters. By performing 25 experiments on custom-designed silicon phantoms and defining a set of success/failure metrics, we have put forward findings that establish a causal relationship between these two important parameters and the success or failure of the performed experiments.

Rising demand for last-mile deliveries in the logistics sector has prompted the adoption of Autonomous Delivery Robots (ADRs) and electric trucks (eTrucks) for their efficiency and cost-effectiveness. This paper proposes an optimization model for an integrated eTruck-and-ADR system. The model employs a range of information sources to optimize vehicle routing and robot allocation, emphasizing energy efficiency and operating cost. This includes incorporating Geographic Information System (GIS) to estimate customer demand based on demographics and utilizing a battery aging/degradation model to account for hardware depreciation. A metaheuristic Genetic Algorithm is employed to solve optimal vehicle routing and customer node clustering. In a simulated case study conducted with real GIS and geographic data, the proposed model demonstrates efficacy in determining the optimal number of ADRs for specific census tracts, with a cost breakdown highlighting the dominance of human labor costs.

In this paper, we present the design, fabrication, and evaluation of a novel flexible, yet structurally strong, Concentric Tube Steerable Drilling Robot (CT-SDR) to improve minimally invasive treatment of spinal tumors. Inspired by concentric tube robots, the proposed two degree-of-freedom (DoF) CT-SDR, for the first time, not only allows a surgeon to intuitively and quickly drill smooth planar and out-of-plane J- and U- shape curved trajectories, but it also, enables drilling cavities through a hard tissue in a minimally invasive fashion. We successfully evaluated the performance and efficacy of the proposed CT-SDR in drilling various planar and out-of-plane J-shape branch, U-shape, and cavity drilling scenarios on simulated bone materials.

We present a visual analytics framework, CMed, for exploring medical image data annotations acquired from crowdsourcing. CMed can be used to visualize, classify, and filter crowdsourced clinical data based on a number of different metrics such as detection rate, logged events, and clustering of the annotations. CMed provides several interactive linked visualization components to analyze the crowd annotation results for a particular video and the associated workers. Additionally, all results of an individual worker can be inspected using multiple linked views in our CMed framework. We allow a crowdsourcing application analyst to observe patterns and gather insights into the crowdsourced medical data, helping him/her design future crowdsourcing applications for optimal output from the workers. We demonstrate the efficacy of our framework with two medical crowdsourcing studies: polyp detection in virtual colonoscopy videos and lung nodule detection in CT thin-slab maximum intensity projection videos. We also provide experts' feedback to show the effectiveness of our framework. Lastly, we share the lessons we learned from our framework with suggestions for integrating our framework into a clinical workflow.

Graph convolution is a generalization of the convolution operation from structured grid data to unstructured graph data. Because any type of data can be represented on a feature graph, graph convolution has been a powerful tool for modeling various types of data. However, such flexibility comes with a price: expensive time and space complexities. Even with state-of-the-art scalable graph convolution algorithms, it remains challenging to scale graph convolution for practical applications. Hence, we propose using Diverse Power Iteration Embeddings (DPIE) to construct scalable graph convolution neural networks. DPIE is an approximated spectral embedding with orders of magnitude faster speed that does not incur additional space complexity, resulting in efficient and effective graph convolution approximation. DPIE-based graph convolution avoids expensive convolution operation in the form of matrix-vector multiplication using the embedding of a lower dimension. At the same time, DPIE generates graphs implicitly, which dramatically reduces space cost when building graphs from unstructured data. The method is tested on various types of data. We also extend the graph convolution to extreme-scale data never-before studied in the graph convolution field. Experiment results show the scalability and effectiveness of DPIE-based graph convolution.

We introduce a transfer-function-guided three-dimensional (3-D) block-based saliency-aware compression scheme for volumetric data that is both content and spatially scalable. Salient 3-D volumetric blocks are identified and weighted with the help of a transfer function which is used to render the data. We describe our method in the form of a framework for processing, progressive transmission, and visualization of volumetric data on a target device, such as a mobile device with limited computational resources. In particular, we address the transmission bottleneck incurred when transferring 3-D volumetric data. Identified salient regions are progressively transmitted to the target device. The received data are rendered progressively in the respective order with a predefined or user-defined transfer function. Our method is developed with medical applications in mind, where preservation of all information is essential for clinical diagnosis. Because our method is integrated into a resolution scalable coding scheme with an integer wavelet transform of the image, it allows the rendering of each significant region at a different resolution up to fully lossless reconstruction. We perform a thorough qualitative and quantitative evaluation of the saliency detection method and the resulting saliency-aware compression schemes. Our results show reduced error in representation of the volumetric data with our method.

This paper presents an H.264/AVC baseline profile decoder based on a SoC platform design methodology. The overall decoding throughput is increased by optimized software and a dedicated hardware accelerator. We minimize the number of bus accesses and use macroblock (MB) level pipeline processing techniques to achieve a real time operation. We implemented and verified a prototype on a SoC platform with a 32-bit RISC CPU core and FPGA module. Our design can process up to 20 frames/sec with QCIF (176/spl times/144). The proposed architecture can be easily applied to many mobile video application areas such as a digital camera and a DMB (digital multimedia broadcasting) phone.

This paper presents an H.264/AVC baseline profile decoder based on an SOC platform design methodology. The overall decoding throughput is increased by optimized software and a dedicated hardware accelerator. We minimize the number of bus accesses and use macroblock level pipeline processing techniques to achieve a real time operation. We implemented and verified a prototype on an SOC platform with a 32-bit RISC CPU core and FPGA module. Our design can process up to 30 frames/sec with CIF_(352times288). The proposed architecture can be easily applied to many mobile video application areas such as a digital camera and a DMB (digital multimedia broadcasting) phone

This paper proposes a flexible architecture of the transform processor for multi-CODECs (JPEG, MPEG-2, 4 and H.264). Also the memory control scheme to efficiently store intermediate data is presented. In the proposed architecture, four arrays block process at the same time with 4 parallel process elements and pipelined structure for improving the processing time. For verification, FPGA platform with ARM-9 core is used. The results show that the proposed architecture satisfies the requirements of each CODECS such as JPEG, MPEG-2, 4 and H.264 standard