This paper reviews methods for computer assisted medical intervention using statistical models and machine learning technologies, which would be particularly useful for representing prior information of anatomical shape, motion, and deformation to extrapolate intraoperative sparse data as well as surgeons' expertise and pathology to optimize interventions. Firstly, we present a review of methods for recovery of static anatomical structures by only using intraoperative data without any preoperative patient-specific information. Then, methods for recovery of intraoperative motion and deformation are reviewed by combining intraoperative sparse data with preoperative patient-specific stationary data, which is followed by a survey of articles which incorporated biomechanics. Furthermore, the articles are reviewed which addressed the used of statistical models for optimization of interventions. Finally, we conclude the survey by describing the future perspective.

We propose a practical local and global path-planning algorithm for an autonomous vehicle or a car-like robot in an unknown semi-structured (or unstructured) environment, where obstacles are detected online by the vehicle's sensors. The algorithm utilizes a probabilistic method based on particle filters to estimate the dynamic obstacles' locations, a support vector machine to provide the critical points and Bezier curves to smooth the generated path. The generated path safely travels through various static and moving obstacles and satisfies the vehicle's movement constraints. The algorithm is implemented and verified on simulation software. Simulation results demonstrate the effectiveness of the proposed method in complicated scenarios that posit the existence of multi moving objects.

Recently much attention is being devoted to a femtocell's potential for improving indoor cellular coverage with the provision of high data rate services in a wireless environment. Femtocells are usually deployed in homes and buildings and overlay existing macrocells, or microcells which cover wider service areas. In such an overlaid network structure, one of the important issues in network planning is the analysis of system capacity achievable by femtocells, which could be significantly affected by indoor radio propagation properties. This paper addresses a typical environmental scenario where a detailed indoor radio propagation model can be adopted. Moreover, a performance evaluation of embedded femteocell networks reflecting various environmental scenarios and factors is provided by the metrics of outage probability, dynamic range of spectral efficiency, and required separation distances for various wall structures, distance, and the number of walls between the home femtocell and the user. Our computer simulation and numerical analysis indicate an outage probability of 1%∼58%, dynamic range of spectral varies from around 2.2 to 7, while the required separation from the macrocell station is 25 m ∼ 327 m. This information could be useful for femtocell network planning.

Heterogeneous resources such as configurable logic blocks (CLBs), multiplier blocks (MULs) and RAM blocks (RAMs) where millions of logic gates are included have been added to field programmable gate arrays (FPGAs). The fixed-outline floorplanning used by the existing methods always has a big penalty item in the objective function to ensure all the modules are placed in the specified chip region, which maybe greatly degrade the wirelength. This paper presents a three-phase floorplanning method for heterogeneous FPGAs. First, a non-slicing free-outline floorplanning method is used to optimize the wirelength, however, in this phase, the satisfaction of resource requirements from functional modules might fail. Second, a min-cost-max-flow algorithm is used to tune the assignment of CLBs to functional modules, and assign contiguous regions to each module so that all the functional modules satisfy CLB requirements. Finally, the MULs and RAMs are allocated to modules by a network flow model. CLBs hold the maximum quantity among all the resources. Therefore, making a high utilization of them means an enhancement of the FPGA densities. The proposed method can improve the utilization of CLBs, hence, much larger circuits could be mapped to the same FPGA chip. The results show that about 7–85% wirelength reduction is obtained, and CLB utilization is improved by about 25%.

In this paper, we propose a synthesis method for asynchronous circuits with bundled-data implementation. The proposed method iteratively applies behavioral synthesis and floorplanning to obtain a near optimum circuit in the term of latency under given design constraints. To improve latency, behavioral synthesis and floorplanning are carried out so that the delay of the control circuit is minimized and the addition of delay elements to satisfy timing constraints is minimized. We evaluate the effectiveness of the proposed method in terms of latency, area, and the number of timing violations while synthesizing several benchmarks. Experimental results show that the proposed method synthesizes faster circuits compared to the circuit synthesized without the proposed method. Also, the proposed method is effective to reduce the number of timing violations.

Telecommunications networks have become an important social infrastructure, and their robustness is considered to be a matter of social significance. Conventional network planning methods are generally based on the maximum volume of ordinary traffic and only assume explicitly specified failure scenarios. Therefore, present networks have marginal survivability against multiple failures induced by an extraordinarily high volume of traffic generated during times of natural disasters or popular social events. This paper proposes a telecommunications network planning method based on probabilistic risk assessment. In this method, risk criterion reflecting the degree of risk due to extraordinarily large traffic loads is predefined and estimated using probabilistic risk assessment. The probabilistic risk assessment can efficiently calculate the small but non-negligible probability that a series of multiple failures will occur in the considered network. Detailed procedures for the proposed planning method are explained using a district mobile network in terms of the extraordinarily large traffic volume resulting from earthquakes. As an application example of the proposed method, capacity dimensioning for the local session servers within the district mobile network is executed to reduce the risk criterion most effectively. Moreover, the optimum traffic-rerouting scheme that minimizes the estimated risk criterion is ascertained simultaneously. From the application example, the proposed planning method is verified to realize a telecommunications network with sufficient robustness against the extraordinarily high volume of traffic caused by the earthquakes.

This study formulates and solves the wire planning problem with electro-migration and interference using an effective integer linear programming (ILP)-based approach. For circuits without obstacles, the proposed approach obtains a wire planning with the minimum wiring area. An effective approach for estimating the length of feasible routing wire is proposed to handle circuits with obstacles. In addition, the space reservation technique, which allocates the ring of the free silicon space around obstacles, is presented to improve interference among routing wires and on-obstacle wires. For circuits with obstacles, the proposed method minimizes total wiring area and reduces interference. Experimental results show that the integer linear-programming-based approach effectively and efficiently minimizes wiring area of routing wires.

This paper introduces a future and past search engine, ChronoSeeker, which can help users to develop long-term strategies for their organizations. To provide on-demand searches, we tackled two technical issues: (1) organizing efficient event searches and (2) filtering out noises from search results. Our system employed query expansion with typical expressions related to event information such as year expressions, temporal modifiers, and context terms for efficient event searches. We utilized a machine-learning technique of filtering noise to classify candidates into information or non-event information, using heuristic features and lexical patterns derived from a text-mining approach. Our experiment revealed that filtering achieved an 85% F-measure, and that query expansion could collect dozens more events than those without expansion.

Fractal structures emerge from statistical and hierarchical processes in urban development or network evolution. In a class of efficient and robust geographical networks, we derive the size distribution of layered areas, and estimate the fractal dimension by using the distribution without huge computations. This method can be applied to self-similar tilings based on a stochastic process.

The wireless robotic sensor network (WRSN) is a combination of a mobile robot and wireless sensor networks. In WRSN, robots perform high-level missions such as human rescue, exploration in dangerous areas, and maintenance and repair of unmanned networks in cooperation with surrounding sensor nodes. In such a network, robots should move to the accident site as soon as possible. This paper proposes a distance-aware robot routing (DAR) algorithm, which focuses on how to pick the shortest path for the mobile robot by considering characteristics different from packet routing. Simulations are performed to demonstrate the benefits of using the proposed algorithm.

ICI (Inter-Cell Interference) mitigation schemes at the cell border are frequently dealt with as a special issue in 3GPP LTE (Long Term Evolution). However, few papers have analyzed the outage performance for the ICI mitigation schemes. In this paper, we propose a generalized cell planning scheme termed QBPA (Quality of Service based Band Power Allocation). Utilizing the QBPA scheme, we measure how much increase in channel capacity can be obtained through the flexible control of bandwidth and power in multi-cell forward-link environments. In addition, the feasible performance of the conventional schemes can be evaluated as long as those schemes are specific forms of the QBPA.

With the increasing demand for services provided by communication networks, quality and reliability of such services as well as confidentiality of data transfer are becoming ones of the highest concerns. At the same time, because of growing hacker's activities, quality of provided content and reliability of its continuous delivery strongly depend on integrity of data transmission and availability of communication infrastructure, thus on information security of a given IT landscape. But, the amount of resources allocated to provide information security (like security staff, technical countermeasures and etc.) must be reasonable from the economic point of view. This fact, in turn, leads to the need to employ a forecasting technique in order to make planning of IT budget and short-term planning of potential bottlenecks. In this paper we present an approach to make such a forecasting for a wide class of information security related incidents (ISRI) -- unambiguously detectable ISRI. This approach is based on different auto regression models which are widely used in financial time series analysis but can not be directly applied to ISRI time series due to specifics related to information security. We investigate and address this specifics by proposing rules (special conditions) of collection and storage of ISRI time series, adherence to which improves forecasting in this subject field. We present an application of our approach to one type of unambiguously detectable ISRI -- amount of spam messages which, if not mitigated properly, could create additional load on communication infrastructure and consume significant amounts of network capacity. Finally we evaluate our approach by simulation and actual measurement.

Robot path-planning is one of the important issues in robotic navigation. This paper presents a novel robot path-planning approach based on the associative memory using Self-Organizing Incremental Neural Networks (SOINN). By the proposed method, an environment is first autonomously divided into a set of path-fragments by junctions. Each fragment is represented by a sequence of preliminarily generated common patterns (CPs). In an online manner, a robot regards the current path as the associative path-fragments, each connected by junctions. The reasoning technique is additionally proposed for decision making at each junction to speed up the exploration time. Distinct from other methods, our method does not ignore the important information about the regions between junctions (path-fragments). The resultant number of path-fragments is also less than other method. Evaluation is done via Webots physical 3D-simulated and real robot experiments, where only distance sensors are available. Results show that our method can represent the environment effectively; it enables the robot to solve the goal-oriented navigation problem in only one episode, which is actually less than that necessary for most of the Reinforcement Learning (RL) based methods. The running time is proved finite and scales well with the environment. The resultant number of path-fragments matches well to the environment.

Since traffic in IP access networks is less aggregated than in backbone networks, its variance could be significant and its distribution may be long-tailed rather than Gaussian in nature. Such characteristics make it difficult to forecast traffic volume in IP access networks for appropriate capacity planning. This paper proposes a traffic forecasting method that includes a function to control residual error distribution in IP access networks. The objective of the proposed method is to grasp the statistical characteristics of peak traffic variations, while conventional methods focus on average rather than peak values. In the proposed method, a neural network model is built recursively while weighting residual errors around the peaks. This enables network operators to control the trade-off between underestimation and overestimation errors according to their planning policy. Evaluation with a total of 136 daily traffic volume data sequences measured in actual IP access networks demonstrates the performance of the proposed method.

In 3D IC design, thermal issue is a critical challenge. To eliminate hotspots, physical layouts are always adjusted by some incremental changes, such as shifting or duplicating hot blocks. In this paper, we distinguish the thermal-aware incremental changes in three different categories: migrating computation, growing unit and moving hotspot blocks. However, these modifications may degrade the packing area as well as interconnect distribution greatly. In this paper, mixed integer linear programming (MILP) models are devised according to these different incremental changes so that multiple objectives can be optimized simultaneously. Furthermore, to avoid random incremental modification, which may be inefficient and need long runtime to converge, here potential gain is modeled for each candidate incremental change. Based on the potential gain, a novel thermal optimization flow to intelligently choose the best incremental operation is presented. Experimental results show that migrating computation, growing unit and moving hotspot can reduce max on-chip temperature by 7%, 13% and 15% respectively on MCNC/GSRC benchmarks. Still, experimental results also show that the thermal optimization flow can reduce max on-chip temperature by 14% to the initial packings generated by an existing 3D floorplanning tool CBA, and achieve better area and total wirelength improvement than individual operations do. The results with the initial packings from CBA_T (Thermal-aware CBA floorplanner) show that 13.5% temperature reduction can be obtained by our incremental optimization flow.

This paper presents a motion planning method for a bimanual robot for executing assembly tasks. The method employs an adaptive modeling which can automatically generate an assembly model and modify the model during actual assembly. Bimanual robotic assembly is modeled at the task-level using contact states of workpieces and their transitions. The lower-level velocity commands of the workpieces are automatically derived by solving optimization problem formulated with assembly constraints, position of the workpieces, and kinematics of manipulators. Motion requirements of the workpieces are transformed to motion commands of the bimanual robot. The proposed approach is evaluated with experiments on peg-in-hole assembly with an L-shaped peg.

An optimization model with maximum ratio combining (MRC) diversity soft handover is proposed for WCDMA base station location planning with heuristic algorithm, which can calculate the influence of MRC diversity soft handover directly in the process of base station location planning. Experimental results show that the proposed model can get better capacity and coverage performance in the planning results than the traditional optimization model without MRC diversity.

A novel mobile assignment method based on transmit power and cell load is proposed for WCDMA base station location planning. Experimental results show that, compared with the currently widely used mobile assignment method based on link attenuation, the proposed mobile assignment method is more reasonable and unnecessary base stations are reduced in the planning results.

In the floorplan design of System-on-Chip (SOC), Buffer Site Approach (BSA) has been used to relax the buffer congestion problem. However, for a floorplan with dominant wide bus, BSA may instead worsen the congestion. Our proposed Enhanced Buffer Site Approach (EBSA) extends existing BSA in a way that buffers of dominant wide bus can be distributed more evenly while reserving the same fast operation speed as BSA does. Experiments have been performed to integrate our model into an iterative floorplanning algorithm, and the results reveal that buffer congestion in a floorplan with dominant wide bus can be much abated.

This paper considers a motion planning method for humanoid robots. First, we review a modular state net which is a state net representing behavior of a part of the humanoid robots. Each whole body motion of the humanoid robots is represented by a combination of modular state nets for those parts. In order to obtain a feasible path of the whole body, a timed Petri net is used as an abstracted model of a set of all modular state nets. Next, we show an algorithm for constructing nonlinear dynamics which describes a periodic motion. Finally, we extend the state net in order to represent primitive periodic motions and their transition relation so that we can generate a sequence of primitive periodic motions satisfying a specified task.