ABSTRACT Character locomotion analysis in physics‐based simulation remains a challenging problem in the field of computer animation. Locomotion is a fundamental skill, yet generating robust and natural motion is challenging. This has led to the development of various locomotion controllers. Robustness, defined as responsiveness to external perturbations and environmental changes, is a fundamental requirement in locomotion control. However, a lack of objective and systematic comparisons is evident in existing controllers. In this article, we propose a benchmark framework for evaluating and comparing the robustness of locomotion controllers using multiple criteria. In order to assess the responsiveness of the controller, external perturbations are applied to a simulated biped character at different gait phases. This analysis offers insights into the behavior of controllers, highlights the limitations of existing approaches, and motivates a novel controller called MMIPM‐RL.

Generating realistic and robust motion for virtual characters under complex physical conditions, such as irregular terrain, real-time control scenarios, and external disturbances, remains a key challenge in computer graphics. While deep reinforcement learning has enabled high-fidelity physics-based character animation, such methods often suffer from limited generalizability, as learned controllers tend to overfit to the environments they were trained in. In contrast, simplified models, such as single rigid bodies, offer better adaptability, but traditionally require hand-crafted heuristics and can only handle short motion segments. In this paper, we present a general learning framework that trains a single-rigid-body (SRB) character controller from long and unstructured datasets, without the reliance on human-crafted rules. Our method enables zero-shot adaptation to diverse environments and unseen motion styles. The resulting controller generates expressive and physically plausible motions in real time and seamlessly integrates with high-level kinematic motion planners without retraining, enabling a wide range of downstream tasks.

In the field of computer graphics, physics-based simulations have been actively researched for decades to represent visually realistic motions of soft-body objects. To deform stiff objects, methods such as the finite element method (FEM) and position-based dynamics (PBD) have been traditionally used for physical simulations. However, there are many situations in which it is difficult to perform interactive simulations at high speeds on relatively low-performance devices. In this paper, we propose an approach by which to undertake rapid simulations of soft-body deformation of a 3D mesh model. Assuming an input object with high damping coefficients, we approximated the simulation process using connected rigid objects. To do this, we extracted a skeletal structure from an input mesh and generated collision meshes by clustering and decomposing the skeletal voxels into convex groups. Distributing each contact force to the rigid objects properly, our approach shows that various types of object models can deform convincingly as if they were soft-body objects. Our approach is fully automated for general users and able to simulate both rigid and soft-body objects in the current animation pipeline. • Realize rapid simulations of soft-body deformation of a 3D mesh model. • Simulate both rigid and soft-body objects in the current animation pipeline. • Automate the rigging process eliminating the laborious efforts and artistic skills.

Locomotion is one of the fundamental skills that is challenging to simulate in a manner that generalizes across a wide range of speeds and turning capabilities. In this paper, our goal is to develop a versatile locomotion controller applicable to various multi-legged character models (monopod, biped, and quadruped), enabling them to perform a range of tasks such as speed control, steering, moving to target locations, and slope walking. Our method is capable of generating diverse multi-legged locomotions without the need for reference motions, even when faced with the inherent challenge of coordinating multiple legs simultaneously. Based on deep reinforcement learning, we train our policy network to produce desired feet locations and orientations, enhancing sample efficiency and robustness compared to the commonly used joint angles. Utilizing end-effector configurations allows for intuitive adaptation to various locomotion gaits. Additionally, we design a style reward function that is applicable to different types of multi-legged models. The locomotion controller, trained with this reward, effectively performs given tasks in a physically simulated environment while maintaining the naturalness of locomotion. • Our multi-legged locomotion controller can create natural walking motions that perform the task entered by a user in real-time for various multi-legged models without using the reference motions. • Unlike the existing DRL-based method that generates a delta for each joint angle or uses torque values applied directly to the joint, this study proposes a more efficient learning process by treating action space based on the end effector. • We utilize an analytic IK solver with damping parameterization and stable PD-servo to improve the performance of the result. • The style reward function model is applicable for various multi-legged models to maintain the naturalness of the motion while achieving various tasks.

Abstract Despite the growing demand for capturing diverse motions, collecting climbing motion data remains challenging due to difficulties in tracking obscured markers and scanning climbing structures. Additionally, preparing varied routes further adds to the complexities of the data collection process. To address these challenges, this paper introduces a physics‐based climbing controller for synthesizing climbing motions. The proposed method consists of two learning stages. In the first stage, a hanging policy is trained to naturally grasp holds. This policy is then used to generate a dataset containing hold positions, postures, and grip states, forming favourable initial poses. In the second stage, a climbing policy is trained using this dataset to perform actual climbing movements. The episode begins in a state close to the reference climbing motion, enabling the exploration of more natural climbing style states. This policy enables the character to reach the target position while utilizing its limbs more evenly. The experiments demonstrate that the proposed method effectively identifies good climbing postures and enhances limb coordination across environments with varying slopes and hold patterns.

We propose an accelerated inverse-kinematics (IK) solving method aimed at reconstructing the pose of a 3D model based on the positions of surface markers or feature points. The model encompasses a skeletal structure of joints and a triangular mesh constituting its external surface. A mesh-based IK solving method optimizes the joint configurations to achieve the desired surface pose, assuming that surface markers are attached to the joints using linear-blended skinning, and that the target positions for these surface markers are provided. In the conventional IK solving method, the final position of a given joint is determined by iteratively computing error gradients based on the target marker positions, typically implemented using a 3-nested loop structure. In this paper, we streamline the standard IK computation process by precomputing all redundant terms for future use, leading to a significant reduction in asymptotic time complexity. We experimentally show that our accelerated IK solving method exhibits increasingly superior performance gains as the number of markers increases. Our pose reconstruction tests show performance improvements ranging between 34% and three times compared to a highly optimized implementation of the conventional method. • Algorithm development for reconstructing character poses based on surface positions. • Reliably applicable to sparse target marker positions or feature points. • Acceleration of the conventional IK solving process by precomputing duplicate terms. • Enabling LBS models to be naturally deformed as desired at high speed.

Since the introduction of DeepMimic [Peng et al. 2018a], subsequent research has focused on expanding the repertoire of simulated motions across various scenarios. In this study, we propose an alternative approach for this goal, a deep reinforcement learning method based on the simulation of a single-rigid-body character. Using the centroidal dynamics model (CDM) to express the full-body character as a single rigid body (SRB) and training a policy to track a reference motion, we can obtain a policy that is capable of adapting to various unobserved environmental changes and controller transitions without requiring any additional learning. Due to the reduced dimension of state and action space, the learning process is sample-efficient. The final full-body motion is kinematically generated in a physically plausible way, based on the state of the simulated SRB character. The SRB simulation is formulated as a quadratic programming (QP) problem, and the policy outputs an action that allows the SRB character to follow the reference motion. We demonstrate that our policy, efficiently trained within 30 minutes on an ultraportable laptop, has the ability to cope with environments that have not been experienced during learning, such as running on uneven terrain or pushing a box, and transitions between learned policies, without any additional learning.

Abstract In this study, we focus on developing a motion synthesis framework that generates a natural transition motion between two different behaviours to interact with a moving object. Specifically, the proposed framework generates the transition motion, bridging from a locomotive behaviour to an object interaction behaviour. And, the transition motion should adapt to the spatio‐temporal variation of the target object in an online manner, so as to naturally connect the behaviours. To solve this issue, we propose a framework that combines a regression model and a transition motion planner. The neural network‐based regression model estimates the reference transition strategy to guide the reference pattern of the transitioning, adapted to the varying situation. The transition motion planner reconstructs the transition motion based on the reference pattern while considering dynamic constraints that avoid the footskate and interaction constraints. The proposed framework is validated to synthesize various transition motions while adapting to the spatio‐temporal variation of the object by using object grasping motion, and athletic motions in soccer.

Abstract We developed a new framework to generate hand and finger grasping motions. The proposed framework provides online adaptation to the position and orientation of objects and can generate grasping motions even when the object shape differs from that used during motion capture. This is achieved by using a mesh model, which we call primitive object grasping (POG), to represent the object grasping motion. The POG model uses a mesh deformation algorithm that keeps the original shape of the mesh while adapting to varying constraints. These characteristics are beneficial for finger grasping motion synthesis that satisfies constraints for mimicking the motion capture sequence and the grasping points reflecting the shape of the object. We verify the adaptability of the proposed motion synthesizer according to its position/orientation and shape variations of different objects by using motion capture sequences for grasping primitive objects, namely, a sphere, a cylinder, and a box. In addition, a different grasp strategy called a three‐finger grasp is synthesized to validate the generality of the POG‐based synthesis framework.

Abstract A motion retargeting process is necessary as the body size and proportion of the actors are generally different from those of the target characters. However, the original spatial relationship between the multiple characters and the environment is easily broken when using previous motion retargeting methods, which are generally performed for each character independently. Therefore, time‐consuming manual adjustments by animators are usually required to obtain satisfactory results. To address these issues, we present a novel multicharacter motion retargeting method that preserves various types of spatial relationships between characters and environments. We establish a unified deformation‐based framework for the motion retargeting of multiple characters (more than two) or nonhuman characters with complex interactions. Also, an interactive motion editing interface with immediate feedback to the user is provided. We experimentally show that our method achieves a speedup when compared with previous motion retargeting methods.

Abstract An interactive interface for character posing is important in the field of computer graphics, games, and virtual reality. The inverse kinematics (IK) solver is the most popular approach that satisfies both the kinematic equations and the user‐defined position and orientation constraints on the end‐effectors. In this article, we present a novel interactive IK framework that efficiently handles various types of collisions and the spatial relationship constraints during the character posing process. Based on our method, a desired human pose can be easily obtained while resolving a collision with the environment and maintaining the spatial relationship of body parts. In addition, the character pose is smoothly updated throughout the user manipulation.

We present a flexible and efficient approach for generating multilegged locomotion. Our model-predictive control (MPC) system efficiently generates terrain-adaptive motions, as computed using a three-level planning approach. This leverages two commonly-used simplified dynamics models, an inverted pendulum on a cart model (IPC) and a centroidal dynamics model (CDM). Taken together, these ensure efficient computation and physical fidelity of the resulting motion. The final full-body motion is generated using a novel momentum-mapped inverse kinematics solver and is responsive to external pushes by using CDM forward dynamics. For additional efficiency and robustness, we then learn a predictive model that then replaces two of the intermediate steps. We demonstrate the rich capabilities of the method by applying it to monopeds, bipeds, and quadrupeds, and showing that it can generate a very broad range of motions at interactive rates, including banked variable-terrain walking and running, hurdles, jumps, leaps, stepping stones, monkey bars, implicit quadruped gait transitions, moon gravity, push-responses, and more.

In this paper, we propose a method to model social interaction between a human and a virtual avatar. To this end, two human performers fist perform social interactions according to the Learning from Demonstration paradigm. Then, the relative relevance of all joints of both performers should be reasonably modeled based on human demonstrations. However, among all possible combinations of relative joints, it is necessary to select only some of the combinations that play key roles in social interaction. We select such significant features based on the joint motion significance, which is a metric to measure the significance degree by calculating both temporal entropy and spatial entropy of all human joints from a Gaussian mixture model. To evaluate our proposed method, we performed experiments on five social interactions: hand shaking, hand slapping, shoulder holding, object passing, and target kicking. In addition, we compared our method to existing modeling methods using different metrics, such as principal component analysis and information gain.

Abstract In this paper, we propose a novel motion controller for the online generation of natural character locomotion that adapts to new situations such as changing user control or applying external forces. This controller continuously estimates the next footstep while walking and running, and automatically switches the stepping strategy based on situational changes. To develop the controller, we devise a new physical model called an inverted‐pendulum‐based abstract model (IPAM). The proposed abstract model represents high‐dimensional character motions, inheriting the naturalness of captured motions by estimating the appropriate footstep location, speed and switching time at every frame. The estimation is achieved by a deep learning based regressor that extracts important features in captured motions. To validate the proposed controller, we train the model using captured motions of a human stopping, walking, and running in a limited space. Then, the motion controller generates human‐like locomotion with continuously varying speeds, transitions between walking and running, and collision response strategies in a cluttered space in real time.

In this study, we propose a motion generation technique which generates natural motions based on a double inverted pendulum model (DIPM) and motion capture data (Mocap). While generating the motions, the proposed controller keeps the balance of the character. A DIPM uses a hip strategy to maintain the character's stability so that the zero moment point (ZMP) stays inside the support area, composed by the feet. The naturalness of the generated motion is inherited from mocap data by aligning the motion capture sequence with the DIPM. We match the DIPM with the motion capture data in order to satisfy both the character's stability and the naturalness. To validate the proposed motion generation technique, we use two kinds of motion capture data: a balancing motion under external forces and a grasping motion with a box.

We present a technique for controlling physically simulated characters using user inputs from an off-the-shelf depth camera. Our controller takes a real-time stream of user poses as input, and simulates a stream of target poses of a biped based on it. The simulated biped mimics the user's actions while moving forward at a modest speed and maintaining balance. The controller is parameterized over a set of modulated reference motions that aims to cover the range of possible user actions. For real-time simulation, the best set of control parameters for the current input pose is chosen from the parameterized sets of pre-computed control parameters via a regression method. By applying the chosen parameters at each moment, the simulated biped can imitate a range of user actions while walking in various interactive scenarios.

Designing a unified framework for simulating a broad variety of human behaviors has proven to be challenging. In this article, we present an approach for control system design that can generate animations of a diverse set of behaviors including walking, running, and a variety of gymnastic behaviors. We achieve this generalization with a balancing strategy that relies on a new form of inverted pendulum model (IPM), which we call the momentum-mapped IPM (MMIPM). We analyze reference motion capture data in a pre-processing step to extract the motion of the MMIPM. To compute a new motion, the controller plans a desired motion, frame by frame, based on the current pendulum state and a predicted pendulum trajectory. By tracking this time-varying trajectory, the controller creates a character that dynamically balances, changes speed, makes turns, jumps, and performs gymnastic maneuvers.

We present a real-time framework for planning natural and smooth grasping motions in an online manner based on the interaction with human. The proposed framework is able to change its grasp strategies agilely according to the interaction with human. Given human demonstrations, we develop a motion field graph consisting of nodes and edges where the nodes contains reference finger poses and their time derivatives, and the edges indicates the similarity between a pair of nodes. Based on the graph, a new grasping motion can be planned that adapts to the changes of the environment and the interaction. The motion field guarantees smooth motions by integrating the velocities obtained from the demonstrations. To validate the framework, we build a demo system where a human can hand a cup over to a tele-operated robot or a virtual humanoid avatar which are controlled by an another person at a remote location. Also, the virtual avatar can grasp and manipulate a cola can.

While the deployment of base stations (BSs) becomes increasingly dense in order to accommodate the growth in traffic demand, these BSs may be under-utilized during most hours except peak hours. The deactivation of these under-utilized BSs is regarded as the key to reducing network power consumption; however, the remaining active BSs should increase their transmit power in order to fill network coverage holes that result from BS switching off. This paper investigates the optimal balance between such beneficial and harmful effects of BS switching off in terms of minimizing the network power consumption, through comprehensively considering the spatial BS distribution, BS transmit power, BS power consumption behaviors, radio propagation environments, and frequency reuse. The suboptimal and approximated design problems are formulated as geometric programming and the numerical results demonstrate that the proposed suboptimal solutions for the spatial density, transmit power, and frequency reuse of remaining active BSs significantly contribute to reducing the power consumption of BSs.

Abstract This study aims to develop a controller for use in the online simulation of two interacting characters. This controller is capable of generalizing two sets of interaction motions of the two characters based on the relationships between the characters. The controller can exhibit similar motions to a captured human motion while reacting in a natural way to the opponent character in real time. To achieve this, we propose a new type of physical model called a coupled inverted pendulum on carts that comprises two inverted pendulum on a cart models, one for each individual, which are coupled by a relationship model. The proposed framework is divided into two steps: motion analysis and motion synthesis. Motion analysis is an offline preprocessing step, which optimizes the control parameters to move the proposed model along a motion capture trajectory of two interacting humans. The optimization procedure generates a coupled pendulum trajectory which represents the relationship between two characters for each frame, and is used as a reference in the synthesis step. In the motion synthesis step, a new coupled pendulum trajectory is planned reflecting the effects of the physical interaction, and the captured reference motions are edited based on the planned trajectory produced by the coupled pendulum trajectory generator. To validate the proposed framework, we used a motion capture data set showing two people performing kickboxing. The proposed controller is able to generalize the behaviors of two humans to different situations such as different speeds and turning speeds in a realistic way in real time.

Editing large-scale crowd animation is a daunting task due to the lack of an efficient manipulation method. This paper presents a novel cage-based editing method for large-scale crowd animation. The cage encloses animated characters and supports convenient space/time manipulation methods that were unachievable with previous approaches. The proposed method is based on a combination of cage-based deformation and as-rigid-as-possible deformation with a set of constraints integrated into the system to produce desired results. Our system allows animators to edit existing crowd animations intuitively with real-time performance while maintaining complex interactions between individual characters. Our examples demonstrate how our cage-based user interfaces mitigate the time and effort for the user to manipulate large crowd animation.

We present a biped locomotion controller for humanoid models actuated by more than a hundred Hill-type muscles. The key component of the controller is our novel algorithm that can cope with step-based biped locomotion balancing and the coordination of many nonlinear Hill-type muscles simultaneously. Minimum effort muscle activations are calculated based on muscle contraction dynamics and online quadratic programming. Our controller can faithfully reproduce a variety of realistic biped gaits (e.g., normal walk, quick steps, and fast run) and adapt the gaits to varying conditions (e.g., muscle weakness, tightness, joint dislocation, and external pushes) and goals (e.g., pain reduction and efficiency maximization). We demonstrate the robustness and versatility of our controller with examples that can only be achieved using highly-detailed musculoskeletal models with many muscles.

In wireless peer-to-peer networks that serve various proximity-based applications, peer discovery is the key to identifying other peers with which a peer can communicate and an understanding of its performance is fundamental to the design of an efficient discovery operation. This paper analyzes the performance of wireless peer discovery through comprehensively considering the wireless channel, spatial distribution of peers, and discovery operation parameters. The average numbers of successfully discovered peers are expressed in closed forms for two widely used channel models, i.e, the interference limited Nakagami-m fading model and the Rayleigh fading model with nonzero noise, when peers are spatially distributed according to a homogeneous Poisson point process. These insightful expressions lead to the design principles for the key operation parameters including the transmission probability, required amount of wireless resources, level of modulation and coding scheme (MCS), and transmit power. Furthermore, the impact of shadowing on the spatial performance and suggested design principles is evaluated using mathematical analysis and simulations.

Recently, wireless communication industries have begun to extend their services to machine-type communication devices as well as to user equipments. Such machine-type communication devices as meters and sensors need intermittent uplink resources to report measured or sensed data to their serving data collector. It is however hard to dedicate limited uplink resources to each of them. Thus, efficient service of a tremendous number of devices with low activities may consider simple random access as a solution. The data collectors receiving the measured data from many sensors simultaneously can successfully decode only signals with signal-to-interference-plus-noise-ratio (SINR) above a certain value. The main design issues for this environment become how many data collectors are needed, how much power sensor nodes transmit with, and how wireless channels affect the performance. This paper provides answers to those questions through a stochastic analysis based on a spatial point process and on simulations.

The mitigation of intercell interference is an importance issue for current and next-generation wireless cellular networks where frequencies are aggressively reused and hierarchical cellular structures may heavily overlap. The paper examines the benefit of coordinating transmission strategies and resource allocation schemes across multiple base-stations for interference mitigation. Two different wireless cellular architectures are studied: a multicell network where base-stations coordinate in their transmission strategies, and a mixed macrocell and femtocell/picocell deployment with coordination among macro and femto/pico base-stations. For both scenarios, this paper proposes a heuristic joint proportionally fair scheduling, spatial multiplexing, and power spectrum adaptation algorithm that coordinates multiple base-stations with an objective of optimizing the overall network utility. The proposed scheme optimizes the user schedule, transmit and receive beamforming vectors, and transmit power spectra jointly, while taking into consideration both the intercell and intracell interference and the fairness among the users. System-level simulation results show that coordination at the transmission strategy and resource allocation level can already significantly improve the overall network throughput as compared to a conventional network design with fixed transmit power and per-cell zero-forcing beamforming.

In cognitive radio (CR) systems, harmful interference from the secondary system degrades the data rate of the primary system. However, this interference may be beneficial to the primary system in terms of the secrecy rate, when unauthorized users eavesdrop on the primary link. This paper explores multiple-input single-output (MISO) CR systems where the secondary system secures the primary communication in return for permission to use the spectrum. In this context, the optimal transmission strategy has to be found which provides the best tradeoff between the useful and harmful effects of interference on the secrecy rate of the primary system. Considering the cases of perfect and imperfect channel state information of the eavesdroppers we formulate optimization problems for maximizing the primary secrecy rate under secondary data rate requirements. The resulting non-convex optimization problems are solved through a sequence of convex semidefinite programs. The simulation results reveal that the proposed schemes improve the secrecy level of the primary system while meeting the data rate requirements of the secondary system.

The mitigation of intercell interference is a central issue for future-generation wireless cellular networks where frequencies are reused aggressively and where hierarchical cellular structures may heavily overlap. The paper examines the benefit of coordinating transmission strategies and resource allocation schemes across multiple cells for interference mitigation. For a multicell network serving multiple users per cell sectors and where both the base-stations and the remote users are equipped with multiple antennas, this paper proposes a joint proportionally fair scheduling, spatial multiplexing, and power spectrum adaptation method that coordinates multiple base-stations with an objective of optimizing the overall network utility. The proposed scheme optimizes the user schedule, transmit and receive beamforming vectors, and transmit power spectra jointly, while taking into consideration both the intercell and intracell interference and the fairness among the users. The proposed system is shown to significantly improve the overall network throughput while maintaining fairness as compared to a conventional network with per-cell zero-forcing beamforming and with fixed transmit power spectrum. The proposed system goes toward the vision of a fully coordinated multicell network, whereby transmission strategies and resource allocation schemes (rather than transmit signals) are coordinated across the base-stations as a first step.

Abstract Given a pair of keyframe formations for a group consisting of multiple individuals, we present a spectral‐based approach to smoothly transforming a source group formation into a target formation while respecting the clusters of the involved individuals. The proposed method provides an effective means for controlling the macroscopic spatiotemporal arrangement of individuals for applications such as expressive formations in mass performances and tactical formations in team sports. Our main idea is to formulate this problem as rotation interpolation of the eigenbases for the Laplacian matrices, each of which represents how the individuals are clustered in a given keyframe formation. A stream of time‐varying formations is controlled by editing the underlying adjacency relationships among individuals as well as their spatial positions at each keyframe, and interpolating the keyframe formations while producing plausible collective behaviors over a period of time. An interactive system of editing existing group behaviors in a hierarchical fashion has been implemented to provide flexible formation control of large crowds.

Animating a crowd of characters is an important problem in computer graphics. The latest techniques enable highly realistic group motions to be produced in feature animation films and video games. However, interactive methods have not emerged yet for editing the existing group motion of multiple characters. We present an approach to editing group motion as a whole while maintaining its neighborhood formation and individual moving trajectories in the original animation as much as possible. The user can deform a group motion by pinning or dragging individuals. Multiple group motions can be stitched or merged to form a longer or larger group motion while avoiding collisions. These editing operations rely on a novel graph structure, in which vertices represent positions of individuals at specific frames and edges encode neighborhood formations and moving trajectories. We employ a shape-manipulation technique to minimize the distortion of relative arrangements among adjacent vertices while editing the graph structure. The usefulness and flexibility of our approach is demonstrated through examples in which the user creates and edits complex crowd animations interactively using a collection of group motion clips.

In this paper, we deal with the problem of synthesizing novel motions of standing-up martial arts such as Kickboxing, Karate, and Taekwondo performed by a pair of human-like characters while reflecting their interactions. Adopting an example-based paradigm, we address three non-trivial issues embedded in this problem: motion modeling, interaction modeling, and motion synthesis. For the first issue, we present a semi-automatic motion labeling scheme based on force-based motion segmentation and learning-based action classification. We also construct a pair of motion transition graphs each of which represents an individual motion stream. For the second issue, we propose a scheme for capturing the interactions between two players. A dynamic Bayesian network is adopted to build a motion transition model on top of the coupled motion transition graph that is constructed from an example motion stream. For the last issue, we provide a scheme for synthesizing a novel sequence of coupled motions, guided by the motion transition model. Although the focus of the present work is on martial arts, we believe that the framework of the proposed approach can be conveyed to other two-player motions as well.

Abstract For applications such as video games and virtual walk‐throughs, on‐line locomotion control is an important issue. In general, the user prescribes a sequence of motions one by one while providing an input trajectory. Since the input trajectory lacks in human characteristics, one may not synthesize quality motions by blindly following it. In this paper, we present a novel data‐driven scheme for transforming a user‐prescribed trajectory to a human trajectory in an on‐line manner. As preprocessing, we analyze example motion data to extract human steering behavior. At run‐time, the input trajectory is refined to reflect the steering behavior. Together with an existing on‐line motion synthesis system, our scheme forms a feedback loop, in which the user effectively specifies an intended human trajectory. Copyright © 2007 John Wiley & Sons, Ltd.

There are several scheduling algorithms for Voice over IP (VoIP) services in IEEE 802.16 systems, such as unsolicited grant service (UGS), real-time polling service (rtPS), UGS with Activity Detection (UGS-AD), and Lee's algorithm using Grant-Me bit of the generic MAC header. However, these algorithms have some problems of a waste of uplink resources, additional access delay, and MAC overhead for supporting VoIP services with variable data rates and silence suppression. To solve these problems, we propose a novel uplink scheduling algorithm (Extended-rtPS) for the VoIP services in IEEE 802.16 systems. Through the performance analysis and simulation results of resource utilization, VoIP capacity, total throughput, and packet transmission delay, we show that our proposed algorithm can solve the problems of the conventional algorithms, and has the best performance among these algorithms. In addition, with simulation results of packet transmission delay, we prove that our proposed algorithm can support more 74%, 24%, and 9% voice users compared with the UGS, rtPS, and UGS-AD (Lee's) algorithms, respectively.

This paper introduces a resource allocation strategy which focuses on minimizing power consumption in orthogonal frequency division multiplexing (OFDM) systems. In case of multicasting systems, it is appropriate to allocate resource with power saving strategy, since the effect of frequency selective channel is ignorable. By this strategy, the number of OFDM symbols MSs receive is minimized. A great amount of power can be saved, because the radio frequency and baseband processes are the dominant factors of power consumption. This paper also proposes a heuristic algorithm for finding the suboptimal solution of resource allocation with low complexity. By this algorithm, resource allocation process requires O(n3) computations with little performance degradation. The numerical analysis and simulation results show that the performance of this algorithm is close to the optimum.

At present, mobile multimedia services such as sports information and mobile TV services have been delivered over point-to-point connections. But it is clear that the point-to-multipoint connections can support these mobile multimedia services more efficiently because they can simultaneously transmit data packets from a single source to multiple destinations in a multicast group. Recently, the simulcast scheme for multimedia broadcast and multicast service (MBMS) in which the same signal is transmitted from all the cells within the system, has been proposed, but, in order to adopt this technology, the same channel bands or time slots for only broadcast/multicast service should be reserved at all cells. The reservation of these resources may not be suitable for services in a local region, and some operators may also not be easy to reserve these resources. So, in this paper, we propose multicast strategies which make multicast resources easily multiplex with unicast resources and enhance cell-capacity by improving the performance of cell-boundary users. In addition, we mathematically analyze the link-level performance and cell throughput of conventional and proposed schemes, and discuss their numerical results

There are several scheduling algorithms for voice over IP (VoIP) services in IEEE 802.16e systems, such as unsolicited grant service (UGS), real-time polling service (rtPS), and extended real-time polling service (ertPS). The ertPS is a new scheduling algorithm for VoIP services with variable data rates and silence suppression, and this algorithm is recently proposed and accepted in the IEEE 802.16e standard. In this paper, we analyze and discuss the performance of the scheduling algorithms recommended in IEEE 802.16e systems including the ertPS algorithm. Through the analysis of resource utilization efficiency and VoIP capacity, we show that the UGS and rtPS algorithms have some problems, which are the waste of uplink resources in the UGS algorithm, and additional access delay and MAC overhead due to bandwidth request process in the rtPS algorithm, to support the VoIP services. In addition, for analysis of VoIP capacity, we utilize OPNET simulation, and show that the ertPS algorithm can support more 21% and 35% voice users compared with the UGS and rtPS algorithms, respectively

One of main issues in 1/spl times/EV-DV systems is how to support and satisfy various QoS requirements of data services and voice service simultaneously. Another main issue in 1/spl times/EV-DV systems is how to increase throughput of data services without sacrificing voice service QoS requirements. The main motivation for our proposed architecture, MQC (multiple quality control), is to use per-stream buffers in the physical layer and to fully utilize air resources. The problem of existing architectures is that PDUs from only one stream can be transmitted and those PDUs may not fill out the allocated time slots. In contrast to existing architectures, MQC can multiplex several streams into one PLP (physical layer packet) because MQC utilizes per-service buffers in the physical layer. We also introduce a multiplexing scheme that minimizes queueing delays of real-time traffic and maximizes cell throughputs.