The rise of mixed reality (MR) as a key mobile application draws interest. Wireless headsets in femtocell networks complicate resource optimization. This study proposes a resource allocation for volumetric MR streaming in multi-BS settings. Neural networks predict viewing directions; based on predictions, confidence, and layers, the algorithm customizes vision scope and quality to boost utility. Simulations show superior performance.

The emergence of mixed reality (MR) as a significant application in mobile networks has garnered significant attention. Wireless headsets enable unrestricted user movement within femtocell networks comprising numerous small base stations, offering a promising solution for MR applications. However, the complexity of these systems poses challenges in optimizing resource allocation across base stations. This paper proposes a novel resource allocation method for volumetric MR streaming in multi-base-station environments. The method consists of two phases. Firstly, the method uses neural networks to model and forecast users’ viewing directions. Leveraging these predictions, their confidence levels, and layer characteristics, the algorithm adjusts video quality for each user and allocates transmission resources across base stations to optimize overall performance. Through comprehensive analysis, we prove that this novel problem is NP-hard and show that our approach achieves a performance within a bounded gap from the optimal solution. Simulation results reveal that our proposed algorithm outperforms existing techniques, enhancing aggregate performance across diverse scenarios.

Due to the growing need for bandwidth starving Multi-View Videos (MVV) in virtual reality, TV, and education, effectively allocating the resources of next-generation wireless technologies for MVV streams becomes increasingly crucial. To achieve high utility for MVV users, this article proposes a cross-layer resource allocation mechanism to leverage video synthesizing schemes (such as Depth-Image-Based Rendering (DIBR) for efficient MVV streaming with massive MIMO). First, we formulate a new problem,antenna allocation with video synthesis(AAVS), and prove its NP-hardness. Then, we design an approximation algorithm namedUtility-based Multi-View Synthesis(UMVS) with the analytical performance provided, and dynamic scenarios are addressed by augmenting UMVS with deep reinforcement learning. Data-driven simulation results show that UMVS outperforms existing antenna allocation schemes by at least 10%, and the DRL extension provides an additional 6% improvement in system utility under congested scenarios.

In a vehicle-to-everything (V2X) communication system involving multiple vehicle types, there is a more challenging and practical problem compared to a single-type scenario. Each vehicle type acts autonomously with distinct communication policies. While prior knowledge can establish behavior for each agent type, it may reduce the adaptability and versatility of the system. This paper proposes a role-oriented actor-critic (ROAC) approach, where vehicles of similar types share similar policies in a satellite-assisted V2X network for more precise and effective spectrum management. The vehicles are trained to optimize system utility by selecting transmission modes, power levels, and sub-channels. The social role properties enable each agent to make better decisions based on the environment and its type. The ROAC model provides 8-10% higher normalized system utility over other advanced methods, even with vehicle-role extension, in situations with heavier traffic.

As technology advancements are leading to the creation of 5G and next-generation base stations (BS) that offer improved performance and application integration, current solutions are mostly reliant on established technical standards. By incorporating intelligent wireless resource management technology, the current small cell system can be optimized and its transmission performance enhanced. The implementation of deep reinforcement learning was then added. By using indication reports as the state, the smart agent is able to dynamically select the optimal GF parameters to achieve high-efficiency transmission. In the context of ultra-reliable low latency communication (URLLC) applications, we have utilized 5G ns-3 simulation to simulate an IIoT factory scenario that diverges from traditional uplink methods. By implementing grant-free (GF) techniques, we can reduce delays while maintaining a suitable level of reliability. To dynamically select the most appropriate transmission mode under varying conditions, we have developed reinforcement learning (RL) methods. Our numerical results demonstrate a promising trend in the overall satisfaction rate.

In recent years, self-driving vehicles have gradually been appearing on the road, but society has also begun to worry about the possibility of accidents caused by the anomaly self-driving system. Many researchers have begun to study the anomaly detection of self-driving vehicles, and each has proposed different detection algorithms. However, since self-driving vehicles are not yet popular, how to collect data, simulate attacks, and verify and compare multiple algorithms is a major obstacle to research. In this regard, we built an Internet of Vehicles platform, VADtalk, that facilitate anomaly detection modeling and deployment for self-driving vehicles. VADtalk contains programs such as anomaly detection model training and vehicle connection. When developers complete model uploading and setting through the GUI, the platform will automatically collect self-driving data, train the model, and even verify the operation of the model using a self-driving simulator, and then provide the results to the developer. After the developer determines the model, VADtalk can connect the trained model with the self-driving vehicle to actually perform real-time anomaly detection on it.

The 5G heterogeneous network (Het-Net) comprises macro cells and small cells. The small cells with the ultra-dense deployment can offload mobile data traffic from macro cells and extend service area while consuming less energy. However, frequent handoffs between the two types of cells result in high signaling costs and interference. Thus, determining when to switch small cells between active and inactive modes is crucial to reducing operation cost. This paper proposes a Reinforcement Learning-based network management mechanism for 5G HetNet, and simulation experiments were conducted to evaluate its performance, in contrast to previous works that utilized 3GPP standardized Self-Organizing Network (SON) for network management mechanisms.

MOTIVATION: Taxonomic classification of 16S ribosomal RNA gene amplicon is an efficient and economic approach in microbiome analysis. 16S rRNA sequence databases like SILVA, RDP, EzBioCloud and HOMD used in downstream bioinformatic pipelines have limitations on either the sequence redundancy or the delay on new sequence recruitment. To improve the 16S rRNA gene-based taxonomic classification, we merged these widely used databases and a collection of novel sequences systemically into an integrated resource. RESULTS: MetaSquare version 1.0 is an integrated 16S rRNA sequence database. It is composed of more than 6 million sequences and improves taxonomic classification resolution on both long-read and short-read methods. AVAILABILITY AND IMPLEMENTATION: Accessible at https://hub.docker.com/r/lsbnb/metasquare_db and https://github.com/lsbnb/MetaSquare. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Due to the growing need for Multi-View Videos (MVV) in advertisement, TV, and education, effectively allocating the resources of next-generation wireless technologies to provide MVV streams is challenging. To achieve the high utility for MVV users, this paper proposes a cross-layer resource allocation mechanism to leverage video synthesizing schemes (such as Depth-Image-Based Rendering (DIBR)) for efficient MVV streaming with massive MIMO. We formulate a new problem Antenna Allocation with Video Synthesizing (AAVS) and prove its NP-hardness. Then, we design an algorithm, named Marginal Utility-Based Iteration (MUBI). The performance is evaluated by the data-driven simulations, and the results manifest that MUBI outperforms the baselines regarding the total utility of users.

Under the rapid development of the Internet of Things (IoT), vehicles can be recognized as mobile smart agents that communicating, cooperating, and competing for resources and information. The task between vehicles is to learn and make decisions depending on the policy to improve the effectiveness of the multi-agent system (MAS) that deals with the continually changing environment. The multi-agent reinforcement learning (MARL) is considered as one of the learning frameworks for finding reliable solutions in a highly dynamic vehicular MAS. In this paper, we provide a survey on research issues related to vehicular networks such as resource allocation, data offloading, cache placement, ultra-reliable low latency communication (URLLC), and high mobility. Furthermore, we show the potential applications of MARL that enables decentralized and scalable decision making in vehicle-to-everything (V2X) scenarios.

The evolution of cellular networks under Long Term Evolution (LTE) has paved the path for LTE-Advanced (LTE-A) Pro that proposes forward LTE enhancements for Machine Type Communications (MTC) and meets the stringent requirements for realization of the Internet-of-Things (IoT). This paper identifies possible improvements in LTE-A Pro's existing group paging scheme, which is more expedient for human-to-human communications and inadequate for IoT applications. We propose a novel energy efficient group paging scheme by considering diverse IoT characteristics including Quality of Service considerations. Simulation results reveal that our proposed approach can significantly reduce energy consumption of IoT devices over existing group paging schemes.

Cache placement is a critical issue in fog networks. It is essential to simultaneously consider the quality of network connection, the demand of contents, and the users' activities. This paper proposes an efficient cache placement by placing the files based on popularity categories within a fog node cluster. Requested files are categorized into three popularity levels and strategically cached in fog nodes of various activity levels This work aims to reduce energy consumption by reducing the number of cells to serve the users based on content popularity. Another contribution of this paper is the clustering method for the fog nodes to select the node to host for the cache contents. The effectiveness of the algorithm is tested using the simulation regarding energy efficiency.

The NarrowBand Internet of Things (NB-IoT) is one of the most promising technologies that fits the requirements of the low-power wide area networks (LPWAN). NB-IoT targets on supporting communications for small amount of data during a relatively long period of time (i.e., delay tolerant), which is one of the key features of IoT applications. Several studies have been conducted by some standard working groups to define the standards for NB-IoT. For example, the working group 3GPP aims to provide communication protocols with low energy consumption, good coverage penetration, and so on. Compared with the existing wireless communication protocols, less spectrum is allocated for NB-IoT. Thus, how to more efficiently use the resource/spectrum (i.e., resource allocation and scheduling) is one of the key issues. In this paper, we first describe the design of the physical layer for the downlink communication, especially the scheduling process. Secondly, we discuss the issues for resource allocation in NB-IoT while the delay requirement in the physical layer is satisfied. Thirdly, we discuss the open questions and possible solutions.

Internet of Things (IoT) technology is the key enabler of the future with a massive number of connected devices. With the evolution of Machine Type Communications (MTC), the number of MTC devices in mobile networks is increasing rapidly to support IoT services. The massive number of short and bursty sessions introduced by MTC devices may result in congestion and system overload impacting human to human (H2H) communications in mobile networks. To study the overall network performance for the future scenarios to come, a flexible traffic model based on practical data is necessary. Unfortunately, scalable and realistic MTC traffic data is inaccessible in most cases. In this work, we propose a hybrid traffic framework by integrating open big data and MTC traffic models. The generated MTC traffic is based on geo-referenced H2H activities and can be used to help assessing mobile network performance.

With evolution toward the fifth generation (5G) cellular technologies, forecasting and understanding of mobile Internet traffic based on big data is the foundation to enable intelligent management features. To take full advantage of machine learning, a more comprehensive investigation on a mobile traffic dataset with the latest deep learning models is desired. Therefore, a multitask learning architecture using deep learning networks for mobile traffic forecasting is presented in this work. State-of-the-art deep learning models are studied, including 1) recurrent neural network (RNN), 2) three-dimensional convolutional neural network (3D cNn), and 3) combination of CNN and RNN (CNN-RNN). The experiments reveal that CNN and RNN can extract geographical and temporal traffic features respectively. Comparing with either deep or non-deep learning approaches, CNN-RNN is a reliable model leading in all tasks with 70 to 80% forecasting accuracy.

With development of wireless communication technologies, mobile devices are commonly equipped with multiple network interfaces and ready to adopt emerging transport layer protocols such as multipath TCP (MPTCP). The protocol is specifically useful for Internet of Things streaming applications with critical latency and bandwidth demands. To achieve full potential of MPTCP, major challenges on congestion control, fairness, and path scheduling are identified and draw considerable research attention. In this paper, we propose a joint congestion control and scheduling algorithm allowing out-of-order transmission as an overall solution. It is achieved by adaptive window coupling, congestion discrimination, and delay-aware packet ordering. The algorithm is implemented in the Linux kernel for real-world experiments. Favorable results are obtained in both shared or distinct bottleneck scenarios.

We investigate the multicast of layered multimedia content in single hop cognitive radio (CR) networks and ways of improving the subscriber utility. The proposed unified opportunistic scheduling (UOS) aggressively utilizes diversity potential present in CR by exploiting two types of multiuser diversity (MUD), one between multicast groups and another between data stream subscribers in a group. As a result, the resource usage efficiency is significantly increased, and better utility can be achieved. According to derived effective average subscriber throughput under unified MUD, a scheduling algorithm is designed to approach the theoretical throughput improvement. We specifically introduce controlled competition between multicast groups to balance the increasing resource efficiency and the risk of overstretching the limited available spectrum. The algorithm combines prioritization, rateless coding, and data fragmentation to inherently cope with dynamic spectrum availability in CR. The presented simulation results show improved performance in various situations in comparison to other schemes not taking full advantage of the present MUD potential.

Abstract With the growth of multimedia traffic over mobile networks, multicast becomes one of more effective transmission approaches to be widely considered. The Multimedia Broadcast multicast service Single Frequency Network (MBSFN) has been introduced for long term evolution to improve signal‐to‐interference‐plus‐noise ratio at the cell edges, while selecting efficient modulation and coding schemes (MCS) for transmission is still an open issue. Unlike existing solutions in literature, the opportunistic multicasting concept is adopted in this work to aggressively enhance the spectrum efficiency (SE) of single frequency networks. Based on the derived model of effective SE in extended MCS adaptation intervals, we propose a statistical feedback‐based approach to achieve opportunistic multicasting in MBSFN. The algorithm carefully selects the MCS complying with the adaptation need for channel quality changes and enabling high bottleneck user SE under reduced feedbacks. Comparing with other schemes for a single stream setup, the simulations show up to 340% higher SE over non‐opportunistic method and 25% over the second best method maximizing the average SE. The number of successfully transmitted streams in a multi‐stream scenario increased by 250% over the non‐opportunistic approach and 40% over the average SE maximizing approach. Copyright © 2016 John Wiley & Sons, Ltd.

Machine-to-Machine (M2M) communication is the emerging technology for automation applications without human intervention. The 3GPP organization defines Machine Type Communications (MTC) to enhance support of M2M devices. Due to a potentially massive number of coexisting MTC devices, achieving efficient device identifier (ID) management and signaling is challenging. In this paper, a scheduled device grouping scheme is proposed to minimize the number of required device IDs and signaling overhead. The grouped devices share one ID while sequentially attaching to the network to execute application specific tasks. Applications with various goals are fulfilled considering expiration time and task dependency. Beyond formulating an integer programming problem, the issue is mapped into a variation of bin packing problem. The solution reaches 30% to 50% ID reduction by adopting a classic fitting algorithm.

OBJECTIVE: The aim of this study is to analyze and visualize the polymorbidity associated with chronic kidney disease (CKD). The study shows diseases associated with CKD before and after CKD diagnosis in a time-evolutionary type visualization. MATERIALS AND METHODS: Our sample data came from a population of one million individuals randomly selected from the Taiwan National Health Insurance Database, 1998 to 2011. From this group, those patients diagnosed with CKD were included in the analysis. We selected 11 of the most common diseases associated with CKD before its diagnosis and followed them until their death or up to 2011. We used a Sankey-style diagram, which quantifies and visualizes the transition between pre- and post-CKD states with various lines and widths. The line represents groups and the width of a line represents the number of patients transferred from one state to another. RESULTS: The patients were grouped according to their states: that is, diagnoses, hemodialysis/transplantation procedures, and events such as death. A Sankey diagram with basic zooming and planning functions was developed that temporally and qualitatively depicts they had amid change of comorbidities occurred in pre- and post-CKD states. DISCUSSION: This represents a novel visualization approach for temporal patterns of polymorbidities associated with any complex disease and its outcomes. The Sankey diagram is a promising method for visualizing complex diseases and exploring the effect of comorbidities on outcomes in a time-evolution style. CONCLUSIONS: This type of visualization may help clinicians foresee possible outcomes of complex diseases by considering comorbidities that the patients have developed.

The ability to analyze and assimilate Electronic Medical Records (EMR) has great value to physicians, clinical researchers, and medical policy makers. Current EMR systems do not provide adequate support for fully exploiting the data. The growing size, complexity, and accessibility of EMRs demand a new set of tools for extracting knowledge of interest from the data. This paper presents an interactive visual mining solution for cohort study of EMRs. The basis of our design is multidimensional, visual aggregation of the EMRs. The resulting visualizations can help uncover hidden structures in the data, compare different patient groups, determine critical factors to a particular disease, and help direct further analyses. We introduce and demonstrate our design with case studies using EMRs of 14,567 Chronic Kidney Disease (CKD) patients.

As machine-to-machine (M2M) communication is growing and number of connected devices is increasing, the electromagnetic pollution and interference become critical issues due to limited spectrum resources. The M2M deployment facing challenges that need to be solved in a way with optimal spectrum utilization in mind. This paper focuses on how cognitive M2M (CM2M) can improve the efficiency of M2M communication systems by dealing with the spectrum scarcity and improving the large scale M2M applications. The survey highlights spectrum sharing and service layering models of CM2M, and then provides insights on key technologies such as spectrum access and game theoretical approaches.

Downsampling video coding, whereby downsampled frames are encoded, provides improved perceptual quality in rate-constrained situations. This method shows considerable advantages over other approaches, particularly in wide-spreading high-definition video formats. This paper provides a comprehensive analysis of downsampling video coding. The study proposes a spatially scalable rate-distortion (RD) model, comprising quantization-distortion and quantization-rate models, and develops an optimal encoding frame size determination framework. The proposed method achieves a gain up to 2.3 dB peak signal-to-noise ratio (PSNR) at 1 Mb/s when compared with conventional full frame size coding. The RD performance is close to the optimal scenario, in which the ideal frame size is obtained by heuristically performing downsampling coding in various allowable sizes.

In wireless multicast services, a base station (BS) requires channel quality indicator (CQI) feedback from subscribers in order to determine a proper modulation and coding scheme (MCS). However, as the group sizes increase, the required feedbacks also increase, which will cause more signaling overheads in uplink. This paper proposes an efficient CQI feedback resource allocation scheme for wireless video multicast applications. More specifically, when a BS is serving several multicast groups simultaneously, our algorithm determines the proper sizes of feedback sets for each multicast group so that the system performance, in terms of average number of multicast service supported subscribers, is maximized. Simulation results not only well demonstrate the advantages of our proposed scheme, but also match with theoretical analyses.

The concept of cognitive radio (CR) technology is emerging as a new paradigm for designing next generation wireless networks with dynamic spectrum sharing. Game theory is one of key mathematical tools to model and analyze the spectrum sharing process. In this paper, an environment where multiple licensed service providers compete to sell spectrum to infrastructure based CR networks is investigated. We formulate the situation involving few sellers and a buyer as an oligopoly market. In contrast to commonly applied pricing games, we contribute a Cournot quantity game for sellers which has more efficient Nash equilibrium. As a result, the quantity game converges to higher profit with no collusion required between spectrum sellers. In practice, reducing the need of collusion can lead to a lightweight and distributed CR deployment. We demonstrate the advantage of enabling quantity games in both static and dynamic fashion.

Cognitive radio (CR) represents an exciting new paradigm on spectrum utilization and potentially more bandwidth for exploding multimedia traffic. We focus on the layer encoded video multicast problem over CR and contribute 1) an opportunistic multicasting framework, based on rateless forward error correction (FEC) codes, 2) a mechanism for adaptation of data fragment size to improve transmission efficiency, 3) a joint secondary channel (SC) and video data selection algorithm. By adapting fragment size, tracking video group receiving rate, and adapting transmission parameters, we are able to realize the opportunistic multicasting advantage in a challenging CR environment. The proposed overall SC and video data selection algorithm finds the best rateless FEC code length, physical layer modulation and coding schemes (MCS), fragment size and SC combination to increase the effective throughput and heuristically reach maximum system utility. Favorable results comparing to other algorithms showcase the improved performance.

We propose Opportunistic Layered Multicasting (OLM), a joint user scheduling and resource allocation algorithm that provides enhanced quality and efficiency for layered video multicast over Mobile WiMAX. This work is a lead off and complete synergy of layered video multicasting with opportunistic concept. The target application is characterized by groups of users acquiring popular video programs over a fading channel. To accommodate various bandwidth requirements and device capability, video streams are coded into base and enhancement layers using scalable video coding technology. Correspondingly, the optimization problems, which select the best subset of users to receive a specific video layer and assign the most appropriate modulation and coding scheme for this video layer, are specifically formulated for both video layer types. We also design fast and effective algorithms to bridge the gap between theoretical throughput capacity and implementation concerns. Thus, the basic video quality can be efficiently guaranteed to all subscribers while creating most utility out of limited resources on enhancement information. To overcome the inevitable packet loss in a multicast session, an FEC rate adaptation scheme to approach theoretical performance is also presented. Favorable performance of the proposed algorithms is demonstrated by simulations utilizing realistic Mobile WiMAX parameters.

Cognitive radio (CR) represents an exciting new paradigm on spectrum utilization and potentially more bandwidth for exploding multimedia traffic. We focus on the layer encoded video multicast problem over CR and contribute 1) a quality based ranking in opportunistic spectrum access (OSA) for sub-channel selection, and 2) opportunistic layered multicasting (OLM) inspired scheduling designed particularly for CR networks. The 2-step ranking in OSA takes outcomes of periodic sensing and prediction to expand the system-wide throughput while keeping collision rates acceptable. By tracking group receiving rate across CR sub-channels and data expiration time, we are able to realize the OLM advantage under much more challenging CR environments. The overall joint opportunistic spectrum access and scheduling (OSAS) algorithm finds precise transmission parameters to heuristically reach maximum system utility. Favorable results comparing OSAS with not fully opportunistic methods demonstrate OSAS to be the best performing one.

Internet Protocol Television (IPTV) is an emerging and killer application for WiMAX networks, which takes advantage of multicasting mechanisms to efficiently deliver the video data to groups of subscribers. In this paper, we propose a Reception-rate-tracking Opportunistic Multicast Scheduling (ROMS) to enhance the performance of scalable IPTV layered multicasting over WiMAX networks. The proposed ROMS jointly considers the subscriber scheduling and resource allocation to achieve resource efficient multicasting. We validate our solution through theoretical analyses and empirical simulations. Our study indicates that the proposed solution outperforms the Slot-throughput-tracking Opportunistic Multicast Scheduling (SOMS) in terms of resource efficiency for 19.6%-33% in different subscriber group sizes.

Ethernet passive optical network (EPON) and worldwide interoperability for microwave access (WiMax) are two promising broadband access technologies for next-generation wired and wireless access network. The integrated architecture of complementary EPON and WiMax technologies combine the benefits of optical fiber capacity and wireless network mobility. In this paper, we propose a dynamic bandwidth allocation (DBA) and scheduling scheme for the integrated architecture in the access network to support quality of service (QoS). Combining the medium access control (MAC) of both IEEE 802.3ah and IEEE 802.16, the proposed QoS-based dynamic bandwidth allocation (QDBA) for EPON can offer efficient operation and minimize the packet delay. Furthermore, the proposed queue-based scheduling scheme for base station can provide call admission control to serve classified traffic. The simulation results demonstrate that the proposed scheme enhances performance in terms of drop probability, average queue length and average end-to-end packet delay, respectively.

We propose a resource allocation algorithm that provides enhanced QoS and efficiency for layered video multicast over mobile WiMAX. The application is characterized by groups of users acquiring popular video programs over a fading channel. To accommodate various bandwidth requirements and adaptive perceptual quality to different users, video streams are coded into base and enhancement layers using scalable video coding technology. Correspondingly, the optimization problems, which assign the most appropriate modulation schemes to provide best video quality to as many users, can be formulated using opportunistic multicasting concept. We maximize the minimum effective throughput across all users for mandatory (base) layer delivery through adapting modulation and coding schemes. At the same time, optional (enhancement) layers are allocated to maximize total utility. Thus the basic video quality can be efficiently guaranteed to all subscribers while making most out of limited resources on enhancement information. We further present an FEC rate adaptation scheme to approach theoretical performance. Favorable performance of the proposed algorithms is demonstrated by simulations utilizing realistic Mobile WiMAX parameters.

We propose a distributed cross-layer congestion control algorithm that provides enhanced quality of service QoS and reliable operation for real-time uplink video over WiFi applications. Such applications are characterized by many wireless devices transmitting video at various PHY rates over a relatively congested channel. Unfortunately, today's off-the-shelf 802.11 equipment can be easily demonstrated to suffer catastrophic failure when subject to these conditions-let alone provide acceptable perceptual quality to the user. We show that in order to remedy these issues, it is preferable to apply airtime fairness with a cross-layer approach. The idea is to use a fast frame-by-frame control loop in the carrier sense multiple access/collision avoidance (CSMA/CA)-based medium access control (MAC) layer while simultaneously exploiting the powerful control loop gain attainable by performing source-rate adaptation in the application layer. We support the proposed algorithm through both simulation and experimentation with various channel and PHY rate scenarios.

We propose a distributed cross-layer congestion control algorithm that provides enhanced QoS and reliable operation for real-time uplink video over WiFi applications. Such applications are characterized by many wireless devices transmitting video at various PHY rates over a relatively congested channel. Unfortunately, today's off-the-shelf 802.11 equipment can be easily demonstrated to suffer catastrophic failure when subject to these conditions - let alone provide acceptable perceptual quality to the user. We show that in order to remedy these issues, it is preferred to use a cross-layer approach rather than a single-layer approach. The idea is to use a fast frame-by-frame control loop in the MAC layer while simultaneously exploiting the powerful control-loop gain attainable by performing source-rate adaptation in the APP layer. We support the proposed algorithm through both simulation and experimentation.

Voice over IP (VoIP) has become the fastest growing wireless alternative to conventional telephony service by way of ongoing deployment of WLAN hotspots and even powerful WiMAX coverage. Resulting from the wired/wireless combined best-effort based heterogeneous IP networks which provide more fluctuation in available bandwidth and end-to-end delay, the performance of VoIP quality, especially using the handheld wireless devices, has been greatly degraded due to frequent packet loss and longer delays. This paper proposes a real-time embedded packet train probing scheme for estimating end-to-end available bandwidth so as to accomplish effective congestion control. By trading acceptable delays with adaptive packetization of voice bitstreams, as well as adaptive insertion of forward error correction (FEC) packets, an optimized system driven QoS approach for VoIP can thus be achieved.

A VideoGIS system combining geo-referenced video information with conventional geographic information (GI) is developed to provide a more comprehensive understanding over a spatial area. In our on-going project, the hypermedia can be transmitted to GPS-guided vehicles in a scalable (layered) fashion while providing highlighting and tracking of landmark objects on video upon drivers' request. Special efforts on GPS error calibration and target objects tracking are reported in this paper.

Since the problems in bioinformatics are related to massive computing and massive data. In recent years, due to distributed computing is gaining recognition. The task originally requiring high computing power does not only rely on supercomputer. Distributed computing used off-the-shelf PC with high speed network can offer low cost and high performance computing power to handle the task. Therefore, the purpose of this paper is to implement a complete distributed computing platform based on peer-to-peer file sharing technology. The platform integrated scheduling, load balancing, file sharing, maintenance of data integrity, and user-friendly interface etc. functions. Through the platform can assist bioinformaticists in massive computing and massive data problems. Besides, the platform is easier use, more reliable, and more helpful than others for researchers to conduct bioinformatics research.

We present our new results in news video story segmentation and classification in the context of the TRECVID video retrieval benchmarking event 2003. We applied and extended the maximum entropy statistical model to fuse diverse features effectively from multiple levels and modalities, including visual, audio, and text. We have included various features such as motion, face, music/speech types, prosody, and high-level text segmentation information. The statistical fusion model is used to discover automatically relevant features contributing to the detection of story boundaries. One novel aspect of our method is the use of a feature wrapper to address different types of features - asynchronous, discrete, continuous and delta ones. We also developed several novel features related to prosody. Using the large news video set from the TRECVID 2003 benchmark, we demonstrate satisfactory performance (F1 measure up to 0.76) and, more importantly, observe an interesting opportunity for further improvement.