OBJECTIVE: This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or "digital-twins (DT)" using patient medical images. The framework is applied to assess radiation dose to radiosensitive organs in CT imaging of individual patients. METHOD: Given a volume of patient CT images, iPhantom segments selected anchor organs and structures (e.g., liver, bones, pancreas) using a learning-based model developed for multi-organ CT segmentation. Organs which are challenging to segment (e.g., intestines) are incorporated from a matched phantom template, using a diffeomorphic registration model developed for multi-organ phantom-voxels. The resulting digital-twin phantoms are used to assess organ doses during routine CT exams. RESULT: iPhantom was validated on both with a set of XCAT digital phantoms (n = 50) and an independent clinical dataset (n = 10) with similar accuracy. iPhantom precisely predicted all organ locations yielding Dice Similarity Coefficients (DSC) 0.6 - 1 for anchor organs and DSC of 0.3-0.9 for all other organs. iPhantom showed <10% errors in estimated radiation dose for the majority of organs, which was notably superior to the state-of-the-art baseline method (20-35% dose errors). CONCLUSION: iPhantom enables automated and accurate creation of patient-specific phantoms and, for the first time, provides sufficient and automated patient-specific dose estimates for CT dosimetry. SIGNIFICANCE: The new framework brings the creation and application of CHPs (computational human phantoms) to the level of individual CHPs through automation, achieving wide and precise organ localization, paving the way for clinical monitoring, personalized optimization, and large-scale research.

This paper proposes a novel online motion modification method in motion reproduction system. Motion reproduction is used to reproduce human motion saved through bilateral control. Bilateral control is a teleoperation which can transmit both position and force. Human judgement is needed for compensating the saved motion when the environment is different. However, conventional motion reproduction system only relies on saved motion or environment in slave side. Therefore operators in master side cannot modify the motion after motion has been saved. Instead of just reproducing the motion data, the proposed method adds master system at motion reproduction phase so that an operator can modify the motion. If the master system is simply added to motion reproduction system, the operator's motion and saved motion will interfere each other. Therefore, we propose to use weightings to suppress the interference. Simulated results validates the effectiveness of the proposal.

Master-slave surgical robots have been studied and developed to support surgeons. Robotic functions such as motion scaling and hand shaking correction can extend the ability of surgeons. However, some problems are remaining in robotic surgery. Operation time which can become the burden to surgeons is still long. Surgeons must assess the situation, decide the next task correctly, and achieve tasks precisely in long operation. By conducting a part of surgical tasks by automatic motion of surgical robots, the burden to surgeons can be reduced. However, surgical tasks require skill of surgeons. Therefore, human skill must be incorporated in robotic motion. In this paper, a teleoperation system to conduct a ligation task by both the teleoperation and the automatic motion is presented. The teleoperation system is composed of haptic forceps robots implementing the motion reproduction system which is a method to preserve and reproduce human motion by a master-slave system. A ligation task is achieved by the bilateral teleoperation and the reproduced motion in the experiment. The saved motion and the reproduced motion are evaluated to validate the usefulness of the teleoperation system implementing the motion reproduction system.

In this paper, motion-reproduction system for picking by multiple-DoFs manipulator with a stereo camera is proposed. The conventional motion-reproduction is able to pick different sized objects because of considering force in the control system. However, the conventional system has difficulty in picking the positional changed objects in motion-reproduction. The proposed method compensates the positional fluctuation of picking objects by image processing information and reproduce saved motion successfully. The superiority of proposal is confirmed through experiments. In the experiment tomato and a toy strawberry was used for picking by multiple-DoFs manipulator. The manipulator with proposed method picked the object successfully.

In this work, we provide energy-efficient architectural support for floating point accuracy. For each floating point addition performed, we "recycle" that operation's rounding error. We make this error architecturally visible such that it can be used, whenever desired, by software. We also design a compiler pass that allows software to automatically use this feature. Experimental results on physical hardware show that software that exploits architecturally recycled error bits can (a) achieve accuracy comparable to a 64-bit FPU with performance and energy that are comparable to a 32-bit FPU, and (b) achieve accuracy comparable to an all-software scheme for 128-bit accuracy with far better performance and energy usage.

We present a novel method for aligning images in an HDR (high-dynamic-range) image stack to produce a new exposure stack where all the images are aligned and appear as if they were taken simultaneously, even in the case of highly dynamic scenes. Our method produces plausible results even where the image used as a reference is either too dark or bright to allow for an accurate registration.

Elementary functions are extensively used in computer graphics, signal and image processing, and communication systems. This paper presents a special-purpose compiler that automatically generates customized look-up tables and implementations for elementary functions under user given constraints. The generated implementations include a C/C++ code that can be used directly by applications running on multicores, as well as a MATLAB-like code that can be translated directly to a hardware module on FPGA platforms. The experimental results show that our solutions for function evaluation bring significant performance improvements to applications on multicores as well as significant resource savings to designs on FPGAs.

This paper introduces parallelization strategies for the Non-Uniform FFT (NUFFT) data translation on multicore architectures. The NUFFT enables the use of the cele-brated FFT with un-equally spaced data in numerous situ-ations in signal and image processing as well as in scientific computing. The critical extension lies at the translation of non-equally spaced or non-uniformly sampled data onto an equally spaced Cartesian grid or vice versa. The data trans-lation can be made sufficiently accurate, with the arithmetic complexity linearly proportional to the size of the data en-semble. For large NUFFTs, however, the data translation is found substantially dominant in computation time on mod-ern computers while it is expected to be dominated by the FFT. In order to match the FFT performance achieved by FFTW, data locality and parallelism in the data translation must be explored and exploited as well. We are concerned with two fundamental issues. First, the data translation can be described as a matrix-vector multiplication with a matrix of irregular sparsity. This is beyond the effective scope of the conventional tiling and parallelization schemes applied by a compiler for performance improvement on computa-tion with dense matrices. Secondly, multicore processors exist and emerge in many different configurations, and are expected to evolve further in architectural variety. This may mean the end of performance tuning on a single type of ar-chitecture. In this paper, we introduce an automation tool that takes two specifications as input, one on an application-specific data translation algorithm, the other on a target multicore processor architecture. The tool generates a par-allel code that explores the data locality and parallelism by utilizing both geometric structures in data translation and the processor-memory configurations in the target architec-

This paper describes TANOR, an automated framework for designing hardware accelerators for numerical computation on reconfigurable platforms. Applications utilizing numerical algorithms on large-size data sets require high-throughput computation platforms. The focus is on N-body interaction problems which have a wide range of applications spanning from astrophysics to molecular dynamics. The TANOR design flow starts with a MATLAB description of a particular interaction function, its parameters, and certain architectural constraints specified through a graphical user interface. Subsequently, TANOR automatically generates a configuration bitstream for a target FPGA along with associated drivers and control software necessary to direct the application from a host PC. Architectural exploration is facilitated through support for fully custom fixed-point and floating-point representations in addition to standard number representations such as single-precision floating point. Moreover, TANOR enables joint exploration of algorithmic and architectural variations in realizing efficient hardware accelerators. TANOR's capabilities have been demonstrated for three different N-body interaction applications: the calculation of gravitational potential in astrophysics, the diffusion or convolution with Gaussian kernel common in image processing applications, and the force calculation with vector-valued kernel function in molecular dynamics simulation. Experimental results show that TANOR-generated hardware accelerators achieve lower resource utilization without compromising numerical accuracy, in comparison to other existing custom accelerators.

Algorithm-architecture co-exploration is hindered by the lack of efficient tools. As a consequence, designers are currently able to explore only a limited set of points in the whole design space. Therefore, a tool that can allow fast exploration of algorithmic and architectural tradeoffs in an automated manner is highly desired. In this paper, we describe TANOR an automated tool targeted for designing hardware accelerators for the class of N-body interaction problems. The design flow, starting from a high level (MATLAB) description, configures the entire system automatically. We describe the design of TANOR and demonstrate the effectiveness and adaptability of our tool using three different target applications, namely, the gravitational kernel used in astrophysics, the gaussian kernel common in image processing applications, and a force calculation kernel applied in molecular dynamics. Our results demonstrate that TANOR generates hardware accelerator that are competitive with existing custom accelerator.

We present a hierarchical model for the analysis of proactive fault management in the presence of system resource leaks. At the low level of the model hierarchy is a degradation model in which we use a nonhomogeneous Markov chain to establish an explicit connection between resource leaks, and the failure rate. With the degradation model, we prove that the failure rate is asymptotically constant in the absence of resource leaks, and it is increasing as leaks occur & accumulate, which confirms the resource leaks as an aging source. The proactive fault management (PFM) is modeled at the higher level as a semi-Markov process. The PFM model takes as input the degradation analysis from the low-level model, and allows us to determine optimal rejuvenation schedules with respect to various system measures.

We present a framework of adaptive estimation and rejuvenation of software system performance in the presence of aging sources. The framework specifies that a degradation model not only describe an aging process but also enable the adaptation of model-based performance estimates to on-line measurements of data pertaining to the aging process. The adaptive estimation uses model-based a priori estimation and obtains a posteriori estimation based on the data measurements. With the adaptive estimation, the rejuvenation policy determines the time epochs for data collection and rejuvenation according to system dynamics. In the specific context of resource leaks previously assumed to lead to aging, we present a non-homogeneous Markov model to explicitly establish a connection between resource leaks and the failure rate. We demonstrate an increasing failure rate in the presence of resource leaks. 1. Introduction: the Adaptation Problem We present a framework for adaptive estimation and rejuvenation of software system performance in the presence of aging sources. We are concerned with system resource loss, in particular, memory leakage. Memory is indispensable in computer and communication systems. Memory leakage is a typical aging source for server systems due to software bugs in the client applications that use server resources. Our framework for adaptive estimation and rejuvenation consists of three integral components: a degradation model, an adaptive estimation scheme, and an adaptive rejuvenation scheduling policy. The degradation model allows adaptation of model-based performance estimates to on-line measurements of data pertaining to the aging process. The adaptive estimation scheme uses the model-based a priori estimation and obtains a posteriori estimation based on the measurements. With the adaptive estimation, the rejuvenation scheduling policy determines time epochs for data

We develop an algorithmic framework for reducing the bandwidth of symmetric matrices via orthogonal similarity transformations. This framework includes the reduction of full matrices to banded or tridiagonal form and the reduction of banded matrices to narrower banded or tridiagonal form, possibly in multiple steps. Our framework leads to algorithms that require fewer floating-point operations than do standard algorithms, if only the eigenvalues are required. In addition, it allows for space-time tradeoffs and enables or increases the use of blocked transformations.

We present a software toolbox for symmetric band reduction via orthogonal transformations, together with a testing and timing program. The toolbox contains drivers and computational routines for the reduction of full symmetric matrices to banded form and the reduction of banded matrices to narrower banded or tridiagonal form, with optional accumulation of the orthogonal transformations, as well as repacking routines for storage rearrangement. The functionality and the calling sequences of the routines are described, with a detailed discussion of the “control” parameters that allow adaptation of the codes to particular machine and matrix characteristics. We also briefly describe the testing and timing program included in the toolbox.