MOTIVATION: For network-assisted analysis, which has become a popular method of data mining, network construction is a crucial task. Network construction relies on the accurate quantification of direct associations among variables. The existence of multiscale associations among variables presents several quantification challenges, especially when quantifying nonlinear direct interactions. RESULTS: In this study, the multiscale part mutual information (MPMI), based on part mutual information (PMI) and nonlinear partial association (NPA), was developed for effectively quantifying nonlinear direct associations among variables in networks with multiscale associations. First, we defined the MPMI in theory and derived its five important properties. Second, an experiment in a three-node network was carried out to numerically estimate its quantification ability under two cases of strong associations. Third, experiments of the MPMI and comparisons with the PMI, NPA and conditional mutual information were performed on simulated datasets and on datasets from DREAM challenge project. Finally, the MPMI was applied to real datasets of glioblastoma and lung adenocarcinoma to validate its effectiveness. Results showed that the MPMI is an effective alternative measure for quantifying nonlinear direct associations in networks, especially those with multiscale associations. AVAILABILITY AND IMPLEMENTATION: The source code of MPMI is available online at https://github.com/CDMB-lab/MPMI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A fully automated knee magnetic resonance imaging (MRI) segmentation method to study osteoarthritis (OA) was developed using a novel hierarchical set of random forests (RF) classifiers to learn the appearance of cartilage regions and their boundaries. A neighborhood approximation forest is used first to provide contextual feature to the second-level RF classifier that also considers local features and produces location-specific costs for the layered optimal graph image segmentation of multiple objects and surfaces (LOGISMOS) framework. Double-echo steady state MRIs used in this paper originated from the OA Initiative study. Trained on 34 MRIs with varying degrees of OA, the performance of the learning-based method tested on 108 MRIs showed significant reduction in segmentation errors ( ) compared with the conventional gradient-based and single-stage RF-learned costs. The 3-D LOGISMOS was extended to longitudinal-3-D (4-D) to simultaneously segment multiple follow-up visits of the same patient. As such, data from all time-points of the temporal sequence contribute information to a single optimal solution that utilizes both spatial 3-D and temporal contexts. 4-D LOGISMOS validation on 108 MRIs from baseline, and 12 month follow-up scans of 54 patients showed significant reduction in segmentation errors ( ) compared with 3-D. Finally, the potential of 4-D LOGISMOS was further explored on the same 54 patients using five annual follow-up scans demonstrating a significant improvement of measuring cartilage thickness ( ) compared with the sequential 3-D approach.

In this paper, we propose a sequential spectrum sensing algorithm for cognitive radio systems, which we term the sequential shifted chi-square test (SSCT). SSCT has the following attractive features for practical implementations. First, SSCT employs a simple test statistic and thus has a low implementation complexity. Secondly, SSCT is a sequential detection algorithm and is capable of achieving performance comparable to fixed sample size detection algorithms such as energy detection but with much reduced sensing time. Thirdly, SSCT is essentially a non-coherent detection algorithm in the sense that it does not require any deterministic knowledge of the primary signals. Lastly, SSCT is able to strike a desirable trade-off between sensing performance and sensing time particularly in the signal-to-noise ratio mismatched case. To evaluate sensing performance, we derive the exact false-alarm probability for SSCT, and develop numerical integration algorithms to compute misdetection probability and the average sample number. We further demonstrate the performance of SSCT with several numerical examples.

In this paper, we consider resource allocation in the 3GPP Long Term Evolution (LTE) cellular uplink (UL), which will be the most widely deployed next generation cellular uplink. The key features of the 3GPP LTE uplink are that it is based on a modified form of the orthogonal frequency division multiplexing-based multiple access (OFDMA), which enables channel dependent frequency selective scheduling, and that it allows for multiuser (MU) scheduling wherein multiple users can be assigned the same time-frequency resource. In addition to the considerable spectral efficiency improvements that are possible by exploiting these two features, the LTE UL allows for transmit antenna selection together with the possibility to employ advanced receivers at the base-station, which promise further gains. However, several practical constraints that seek to maintain a low signaling overhead are also imposed. In this paper, we show that the resulting resource allocation problem is APX-hard and then propose a local ratio test (LRT)-based constant-factor polynomial-time approximation algorithm. We then propose two enhancements to this algorithm as well as a sequential LRT-based MU scheduling algorithm that offers a constant-factor approximation and is another useful choice in the complexity versus performance tradeoff. Further, user preselection, wherein a smaller pool of good users is preselected and a sophisticated scheduling algorithm is then employed on the selected pool, is also examined. We suggest several such user preselection algorithms, some of which are shown to offer constant-factor approximations to the preselection problem. Detailed evaluations reveal that the proposed algorithms and their enhancements offer significant gains.

Although wireless broadband technologies have evolved significantly over the past decade, they are still insufficient to support the fast-growing mobile traffic, especially due to the increasing popularity of mobile video applications. Wireless multicast, aiming to exploit the wireless broadcast advantage, is a viable approach to bridge the gap between the limited wireless capacity and the ever-increasing mobile video traffic demand. In this paper, we propose MuVi, a Multicast Video delivery scheme through joint optimal resource allocation and adaptive modulation and coding scheme in OFDMA-based 4G cellular networks. MuVi differentiates video frames based on their importance in reconstructing the video and incorporates an efficient radio resource allocation algorithm to optimize the overall video quality across all users in the multicast group. MuVi is a lightweight solution with most of the implementation in the gateway, slight modification in the base station, and no modification at the clients. We implement MuVi on a WiMAX testbed and compare its performance to a Naive wireless multicast scheme that employs the most robust Modulation and Coding Scheme (MCS), and an Adaptive scheme that employs the highest MCS supportable by all clients. Experimental results show that MuVi improves the average video peak signal-to-noise ratio (PSNR) by up to 13 and 7 dB compared to the Naive and the Adaptive schemes, respectively. MuVi does not require modification to the video encoding scheme or the air interface. Thus, it allows speedy deployment in existing systems.

Mobile operators are witnessing a dramatic increase in traffic spurred by a combination of popularity of smartphones, innovative applications and diverse services. As mobile traffic transitions from being voice dominated to video and data dominated, the revenue per byte for the mobile operators is declining at an unhealthy rate. To counter the traffic growth and build cost-effective networks, many operators are now forging alliances for RAN (Radio Access Network) sharing to improve coverage and capacity at reasonable investments and operational costs. This paper presents the design and implementation of NetShare, a network-wide radio resource management framework that provides effective RAN Sharing. NetShare introduces a novel two-level scheduler split between the mobile gateway and the cellular basestations to effectively manage and allocate the wireless resources of the radio access network composed of multiple basestations among multiple different entities (such as operators, content providers, etc.) that share the network. Firstly, NetShare provides performance isolation across entities with a minimum guaranteed resource allocation to each entity across the network. Secondly, NetShare optimally distributes the resources to each entity across the network proportional to the resource demand at each basestation. Through extensive LTE-based system simulations and prototype evaluations on a WiMAX testbed, we show the efficacy of NetShare in (a) providing isolation across entities and (b) efficiently distributing resources for each entity across the network thus achieving high utilization of resources for an entity.

We consider Multi-User MIMO (MU-MIMO) scheduling in the 3GPP LTE-Advanced (3GPP LTE-A) cellular uplink. The 3GPP LTE-A uplink allows for precoded multi-stream (precoded MIMO) transmission from each scheduled user and also allows flexible multi-user (MU) scheduling wherein multiple users can be assigned the same time-frequency resource. However, exploiting these features is made challenging by certain practical constraints that have been imposed in order to maintain a low signaling overhead. We show that while the scheduling problem in the 3GPP LTE-A cellular uplink is NP-hard, it can be formulated as the maximization of a submodular set function subject to one matroid and multiple knapsack constraints. We then propose constant-factor polynomial-time approximation algorithms and demonstrate their superior performance via simulations.

In LTE and LTE-Advanced uplink, the interference power in a cell depends on the user scheduling and power assignment in neighboring cells. As a result, it is hard to accurately estimate the signal-to-interference-plus-noise ratio (SINR) values and make the right MCS (Modulation and Coding Scheme) selection. To address this challenge, we propose open-loop and closed-loop power control schemes to reduce both the average and the variance of the interference power in order to improve the system performance. It is shown that compared to the existing fractional power control (FPC) in LTE, the proposed schemes improve the cell-average throughput by 7-8% while maintaining the same cell-edge throughput in networks with low penetration loss, and improve the cell-average throughput by 4-6% and the cell-edge throughput by 15-23% in networks with high penetration loss.

Scheduling plays a vital role in LTE downlink systems with Multiple Input and Multiple Output (MIMO) antennas. We consider the MIMO downlink scheduling problem at the base station (BS) in LTE networks under several practical constraints mandated by the 3GPP standards. We Define a new construct called transmission mode, which denotes a particular choice of MIMO operational mode, precoding matrix, transmission rank, as well as the modulation and coding schemes (MCSs) of up to two codewords and show that both LTE systems require that each scheduled user be served using only one transmission mode in every subframe. We prove that the resulting scheduling problems are NP-hard under both backlogged and finite queue traffic models, and then develop a unified low-complexity greedy algorithm that yields solutions guaranteed to be within 1/2 of the respective optima. Extensive performance evaluation in realistic settings reveals near-optimal performance of our proposed algorithm and that it significantly outperforms the state of the art, especially under the more practical, finite queue model.

Although wireless broadband technologies have evolved significantly over the past decade, they are still insufficient to support the fast-growing mobile traffic, especially due to the increasing popularity of mobile video applications. Wireless multicast, aiming to exploit the wireless broadcast advantage, is a viable approach to bridge the gap between the limited wireless networking capacity and the ever-increasing mobile video traffic demand. In this work, we propose MuVi, a Multicast Video delivery scheme in OFDMA-based 4G wireless networks, to optimize multicast video traffic. MuVi differentiates video frames based on their importance in reconstructing the video and incorporates an efficient radio resource allocation algorithm to optimize the overall video quality across all users in the multicast group. MuVi is a lightweight solution with most of the implementation in the gateway, slight modification in the base-station, and no modification at the clients. We implement MuVi on a WiMAX testbed and compare its performance to a Naive wireless multicast scheme that employs the most robust MCS (Modulation and Coding Scheme), and an Adaptive scheme that employs the highest MCS supportable by all clients. Experimental results show that MuVi improves the average video PSNR (Peak Signal-to-Noise Ratio) by up to 13 and 7 dB compared to the Naive and the Adaptive schemes, respectively. MuVi does not require modification to the video encoding scheme or the air interface. Thus it allows speedy deployment in existing systems.

This paper describes the design and implementation of a network virtualization substrate (NVS) for effective virtualization of wireless resources in cellular networks. Virtualization fosters the realization of several interesting deployment scenarios such as customized virtual networks, virtual services, and wide-area corporate networks, with diverse performance objectives. In virtualizing a base station's uplink and downlink resources into slices, \ssrNVSmeets three key requirements-isolation, customization, and efficient resource utilization-using two novel features: 1)NVSintroduces a provably optimal slice scheduler that allows existence of slices with bandwidth-based and resource-based reservations simultaneously; and 2)NVSincludes a generic framework for efficiently enabling customized flow scheduling within the base station on a per-slice basis. Through a prototype implementation and detailed evaluation on a WiMAX testbed, we demonstrate the efficacy of \ssrNVS. For instance, we show for both downlink and uplink directions that \ssrNVScan run different flow schedulers in different slices, run different slices simultaneously with different types of reservations, and perform slice-specific application optimizations for providing customized services.

Future mobile broadband networks are characterized with high data rate and improved coverage, which will enable real-time video multicast and broadcast services. Scalable video coding (SVC), combined with adaptive modulation and coding schemes (MCS) and wireless multicast, provides an excellent solution for streaming video to heterogeneous wireless devices. By choosing different MCSs for different video layers, SVC can provide good video quality to users in good channel conditions while maintaining basic video quality for users in bad channel conditions. A key issue to apply SVC to wireless multicast streaming is to choose appropriate MCS for each video layer and to determine the optimal resource allocation among multiple video sessions. We formulate this problem as total utility maximization, subject to the constraint of available radio resources. We prove that the formulated problem is NP-hard and propose an optimal, two-step dynamic programming solution with pseudo-polynomial time complexity. Simulation results show that our algorithm offers significant improvement on the video quality over a naive algorithm and an adapted greedy algorithm, especially in the scenarios with multiple real video sequences and limited radio resources.

Using beamforming antennas to improve wireless multicast transmissions has received considerable attention recently. A recent work proposes to partition all single-lobe beams into groups and to form composite multilobe beam patterns to transmit multicast traffic. Depending on how the power is split among the individual beams constituting a composite beam pattern, two power models are considered: 1) equal power split (EQP), and 2) asymmetric power split (ASP). This paper revisits the key challenge-beam partitioning in the beamforming-multicast problem-and makes significant progress in both algorithmic and analytic aspects of the problem. Under EQP, we propose a low-complexity optimal algorithm based on dynamic programming. Under ASP, we prove that it is NP-hard to have (3/2-ϵ)-approximation algorithm for any ϵ >; 0. For discrete rate functions under ASP, we develop an Asymptotic Polynomial-Time Approximation Scheme (APTAS), an asymptotic (3/2+ β)-approximation solution (where β ≥ 0 depends on the wireless technology), and an asymptotic 2-approximation solution to the problem by relating the problem to a generalized version of the bin-packing problem. In retrospect, we also obtain an asymptotic 2-approximation solution for the generalized bin-packing problem, which is of independent interest. For continuous rate functions under ASP, we develop sufficient conditions under which the optimal number of composite beams is 1,K, and arbitrary, respectively, whereKis the total number of single-lobe beams. Both experimental results and simulations based on real-world channel measurements corroborate our analytical results by showing significant improvement compared to state-of-the-art algorithms.

In this paper, we consider resource allocation in the the fourth generation multi antenna (4G MIMO) cellular uplink. In particular, we consider the two 4G standards, IEEE 802.16m and 3GPP LTE-A, that have recently been approved. We show that the uplink resource allocation problems in both 802.16m and LTE-A networks are NP-hard. We then propose constant-factor polynomial-time approximation algorithms for both these problems. We also provide linear programming (LP) based upper bounds to benchmark the performance of our proposed approximation algorithms. Simulations reveal that the proposed algorithms have excellent performance, much superior to their worst-case guarantees.

In this paper we investigate the issue of exploiting switched beamforming antennas to improve multicast transmission in wireless networks. Under the practical assumptions that different beams may have arbitrary overlapping patterns and that each user can receive data at different rates under different beams, we formulate the problem as minimizing the total multicast delay subject to the constraint that all users need to be covered. We prove that the problem is NP-hard and propose a greedy algorithm that achieves log(J) approximation guarantee where J is the number of clients. Measurements in both indoor and outdoor experiments validate our assumptions on the beamforming model. Trace-driven simulations based on these measurements show significant improvement over previous schemes and corroborate our analytical results.

Wireless multicast based video delivery is constrained by the user with the lowest channel quality within a multicast group. Beamforming increases the SINR at the clients thereby increasing the minimum channel quality among the users that form the multicast group. Thus it can be utilized to enhance the wireless multicast video transmission. In this paper, we investigate how to exploit switched beamforming antennas to improve wireless multicast based video transmission in indoor environments. The fundamental problem to be solved is the selection and scheduling of beams to be used to cover different subsets of users within the multicast group. We consider both multi-resolution videos and multi-layered videos and formulate the problem as maximizing the total utility of all multicast clients subject to a total delay constraint, where the utility is a general measure of video quality or user-satisfaction. We prove that it is NP-hard to have a (1-1/e+ε)-approximation solution to this problem for any ε >; 0, under both multi-resolution and multi-layered video models. For the multi-resolution video model, we develop a unified approximation algorithm with a parameter k that controls both the algorithm complexity and the approximation factor. For k = 0, 1, 2, 3, we prove that the proposed algorithm achieves an approximation guarantee close to 0.31, 0.38, 0.55, and 0.63, respectively. For the multi-layered video model, we propose a similar heuristic solution. The proposed algorithms are evaluated with both hypothetical video sequences and real video sequences using channel data collected in an indoor wireless testbed. Evaluation results show that the proposed algorithms have much better performance than naive multicast algorithms for scheduling the beams.

Using beamforming antennas to improve wireless multicast transmissions has received considerable attention recently. The work in proposes to partition all single-lobe beams into groups and to form composite multi-lobe beam patterns to transmit multicast traffic. The key challenge left is, how to partition the beams into multiple groups in order to minimize the total multicast delay. This work makes significant progress in both algorithmic and analytic aspects of the problem. We prove that, under the asymmetric power split (ASP) model, it is NP-hard to have (3/2 - ε)-approximation algorithm for any ε >; 0. We then develop an APTAS, an asymptotic (3/2 + β)-approximation solution (where β ≥ 0 depends on the wireless technology), and an asymptotic 2-approximation solution to the problem by relating the problem to a generalized version of the bin-packing problem. Extensive trace-driven simulations based on real-world channel measurements corroborate our analytical results by showing significant improvement compared to state of the art algorithms.

This paper is motivated by two observations: (1) cellular network operators are actively exploring advertisement delivery as a new means of revenue generation, and (2) cellular basestations perceive intermittent overloads at different times of day. Bringing the two observations together, we design and implement Opal, a novel system for opportunistically aligning advertisement delivery with basestation overload. Such alignment improves the overall perception of network availability for users. To achieve the alignment systematically, Opal builds on an analytical framework for tunable unavailability of network service to users during overload. At the same time, if the network is not overloaded enough during a certain period, Opal schedules enough advertisements to satisfy the advertisement delivery contracts. Opal minimizes the amount of state to be maintained to play advertisements to users uniformly and also maximizes the number of viewers for each advertisement. We implement a prototype of Opal on a Picochip based WiMAX testbed, and demonstrate its efficacy using simulations, analysis and prototype evaluation.

We demonstrate OPAL [2]- a system for opportunistically aligning advertisement delivery with basestation overload periods, which reduces the total number of times network service is unavailable to users. This system is motivated by two trends: Firstly, as networks get dominated by non-elastic traffic such as video, overload management using admission control will be imminent, thereby exposing network unavailability to users. Secondly, as mobile advertising is rapidly growing, mobile network operators are increasingly interested in leveraging its huge revenue potential, and are also better equipped than any other entity in reaching the users.

Inter-cell interference mitigation is a key challenge in the next generation wireless networks which are expected to use an aggressive frequency reuse factor and a high-density base station deployment to improve coverage and spectral efficiency. In this work, we consider the problem of maximizing the weighted sum-rate of a wireless cellular network via coordinated scheduling and discrete power control. We present two distributed iterative algorithms which require limited information exchange and data processing at each base station. Both algorithms provably converge to a solution where no base station can unilaterally modify its status (i.e., transmit power and user selection) to improve the weighted sum-rate of the network. Numerical studies are carried out to assess the performance of the proposed schemes in a realistic system based on the IEEE 802.16m specifications. Simulation results show that the proposed algorithms achieve a significant rate gain over uncoordinated transmission strategies for both cell-edge and inner users.

Scalable video coding (SVC) and Adaptive Modulation and Coding (AMC) are two key techniques for video multicast in broadband wireless networks. They can improve the system performance by enhancing the wireless resource allocation schemes and assigning appropriate modulation and coding schemes (MCS) to different layers of scalable video sequences. We formulate the problem as maximizing the total system utility subject to the total resource constraint, where the utility of each user is a generic non-negative, non-decreasing function of received rate. We then propose a fast intra-session MCS assignment algorithm based on dynamic programming. This algorithm can be integrated with an existing inter-session resource allocation algorithm and applied to multi-session scenarios. Simulation results show that our algorithm achieves significant improvement on the video quality over a naive algorithm and an adapted greedy algorithm, and greatly reduces the running time in both single-session and multi-session scenarios compared to a former optimal algorithm.

Scalable video coding (SVC), together with adaptive modulation and coding (AMC), can improve wireless multicast streaming video by jointly performing radio resource allocation and modulation and coding scheme (MCS) selection. However, the existing schemes in the literature allocate radio resources for different video layers separately, which leads to a waste of radio resources. In this work, we introduce the notion of joint layer resource allocation which allows to jointly allocate resources to multiple video layers that are assigned the same MCS. We formulate this problem and prove it to be NP-hard. Then we develop a pseudo-polynomial algorithm that finds the optimal total system utility. Our algorithm assumes a very generic utility function and flexible video layer rates. To reduce the complexity of the algorithm, we also propose Fully Polynomial Time Approximation Schemes (FPTAS) for the same problem. Simulation results show that our optimal algorithm offers significant improvement on system utility over a previous optimal algorithm and a greedy algorithm both of which do not support joint layer resource allocation. The proposed approximation algorithm provides controllable tradeoff between performance and computational complexity and, with appropriately chosen parameters, it outperforms the greedy algorithm with 40% less running time.

This paper describes the design and implementation of a network virtualization substrate NVS) for effective virtualization of wireless resources in WiMAX networks. Virtualization fosters the realization of several interesting deployment scenarios such as customized virtual networks, virtual services and wide-area corporate networks, with diverse performance objectives. In virtualizing a basestation's uplink and downlink resources into slices, NVS meets three key requirements - isolation, customization, and efficient resource utilization - using two novel features: (1) NVS introduces a provably-optimal slice scheduler that allows existence of slices with bandwidth-based and resource-based reservations simultaneously, and (2) NVS includes a generic framework for efficiently enabling customized flow scheduling within the basestation on a per-slice basis. Through a prototype implementation and detailed evaluation on a WiMAX testbed, we demonstrate the efficacy of NVS. For instance, we show for both downlink and uplink directions that NVS can run different flow schedulers in different slices, run different slices simultaneously with different types of reservations, and perform slice-specific application optimizations for providing customized services.

We present a cross-layer design of transmitting scalable video streams from a base station to multiple clients over a shared fading wireless network by jointly considering the application layer information and the wireless channel conditions. We first design a long-term resource allocation algorithm that determines the optimal wireless scheduling policy in order to maximize the weighted sum of average video quality of all streams. We prove that our algorithm achieves the global optimum even though the problem is not concave in the parameter space. We then devise two on-line scheduling algorithms that utilize the results obtained by the long-term resource allocation algorithm for user and packet scheduling as well as video frame dropping strategy. We compare our schemes with existing video scheduling and buffer management schemes in the literature and simulation results show our proposed schemes significantly outperform existing ones.

In this study, a combination of active shape model (ASM) and active appearance model (AAM) was used to segment the left and right ventricles of normal and Tetralogy of Fallot (TOF) hearts on 4-D (3-D+time) MR images. For each ventricle, a 4-D model was first used to achieve robust preliminary segmentation on all cardiac phases simultaneously and a 3-D model was then applied to each phase to improve local accuracy while maintaining the overall robustness of the 4-D segmentation. On 25 normal and 25 TOF hearts, in comparison to the expert traced independent standard, our comprehensive performance assessment showed subvoxel segmentation accuracy, high overlap ratios, good ventricular volume correlations, and small percent volume differences. Following 4-D segmentation, novel quantitative shape and motion features were extracted using shape information, volume-time and dV/dt curves, analyzed and used for disease status classification. Automated discrimination between normal/TOF subjects achieved 90%-100% sensitivity and specificity. The features obtained from TOF hearts show higher variability compared to normal subjects, suggesting their potential use as disease progression indicators. The abnormal shape and motion variations of the TOF hearts were accurately captured by both the segmentation and feature characterization.

Adaptive transmit power control in 802.11 wireless LANs (WLANs) on a per-link basis helps increase network capacity and improves battery life of WiFi-enabled mobile devices. However, it faces the following challenges: 1) it can exacerbate receiver-side interference and asymmetric channel access; 2) it can incorrectly lead to lowering the data rate of a link; 3) mobility-induced channel variations at short timescales make detecting and avoiding these problems more complex. Despite substantial prior research, state-of-the-art solutions lack comprehensive techniques to address the above problems. In this paper, we design and implement Symphony, a synchronous two-phase rate and power control system whose agility in adaptation enables us to systematically address the three problems while maximizing the benefits of power control on a per-link basis. We implement Symphony in the Linux MadWifi driver and show that it can be realized on hardware that supports transmit power control with no modifications to the 802.11 MAC, thereby fostering immediate deployability. Our extensive experimental evaluation on a real testbed in an office environment demonstrates that Symphony : 1) enables up to 80% of the clients in three different cells to settle at 50%-94% lower transmit power than a per-cell power control solution; 2) increases network throughput by up to 50% across four realistic deployment scenarios; 3) improves the throughput of asymmetry-affected links by 300%; and 4) opportunistically reduces the transmit power of mobile clients running VOIP calls by up to 97% while only causing a negligible degradation of voice quality.

Cognitive radio that supports a secondary and opportunistic access to licensed spectrum shows great potential to dramatically improve spectrum utilization. Spectrum sensing performed by secondary users to detect unoccupied spectrum bands, is a key enabling technique for cognitive radio. This paper proposes a truncated sequential spectrum sensing scheme, namely the sequential shifted chi-square test (SSCT). The SSCT has a simple test statistic and does not rely on any deterministic knowledge about primary signals. As figures of merit, the exact false-alarm probability is derived, and the miss-detection probability as well as the average sample number (ASN) are evaluated by using a numerical integration algorithm. Corroborating numerical examples show that, in comparison with fixed-sample size detection schemes such as energy detection, the SSCT delivers considerable reduction on the ASN while maintaining a comparable detection performance.

Future 4G cellular networks are featured with high data rate and improved coverage, which will enable realtime video multicast and broadcast services. Scalable video coding is very appropriate for wireless multicast service because it allows to choose different modulation and coding schemes (MCSs) for different video layers and can provide good video quality to users in good channel conditions while still maintaining reasonable video quality for other users. In order to apply scalable video coding to wireless multicast streaming, it is important to choose appropriate MCS for each video layer and to determine the right resource allocation among multiple video sessions. In this paper we propose a two-step dynamic programming algorithm that finds the optimal total system utility of all users where the system utility can be a generic non-negative, non-decreasing function of received rate. Our algorithm supports variable layer size and thus allows flexibility during the video encoding process. Simulation results show that our algorithm offers significant improvement on the video quality over a naive algorithm and an adapted greedy algorithm, especially in the scenarios with multiple real video sequences and limited radio resources.

We consider the transmission of multiple scalable video streams from a server to multiple users over a fading wireless channel. We first present a long-term resource allocation algorithm that determines the scheduling policy and the parameters used by the scheduling policy in order to maximize the weighted sum of PSNR of all video streams. We then present an online scheduling algorithm that utilizes the results obtained by the long-term resource allocation algorithm for user and packet scheduling as well as video frame dropping strategy. Simulation results show that our proposed scheduling scheme significantly improves the video quality compared to the best-known scheduling algorithms in the literature.

We consider the problem of maximizing the weighted sum data rate in multi-cell and multi-carrier wireless data systems in the presence of interference. We present a scheme that jointly considers load balancing, user scheduling, and interference mitigation to improve the system performance. Our proposed scheme iteratively applies two processes. The first process solves the sub-problem of load balancing and user scheduling while fixing the power allocation of each BS (and thus fixing the interference). We prove that this sub-problem is NP-hard, and devise a 1/2-approximation algorithm to solve the problem. We also consider an extended model capturing finite queue size and propose a 1/2-approximation algorithm under this model. The second process solves the problem of interference mitigation assuming fixed load balancing and user scheduling. We develop a local-improvement based algorithm to solve this problem. Via simulations, we demonstrate that our joint scheme improves both average system throughput and fairness significantly. Compared to the scheme with fixed user-BS association and 1/3 frequency reuse, the lowest 10% cell-edge users obtain more than 60% performance improvement and 90%of users enjoy more than 30%throughput improvement.

We consider the packet scheduling of video frames in the wireless last hop. At the time of network congestion and bad channel conditions, it is necessary to drop some (less important) frames in order to maintain the continuity of the video streaming. The decision on whether to drop and how many frames need to be dropped has to be based on the deadline, size, and importance/utility (in terms of the contribution to the video quality) of each frame. In this paper, we propose an adaptive scheduling algorithm that dynamically decides which frames need to be transmitted and which ones need to be dropped at any transmission opportunity based on the current channel conditions and on the characteristics of each video frame. Simulation results show our scheduling algorithm achieves near optimal performance compared to an optimal offline algorithm that knows the outcome of each transmission opportunity in the future.

Adaptive transmit power control in 802.11 Wireless LANs (WLANs) on a per-link basis helps increase network capacity and improves battery life of Wifi-enabled mobile devices. However, it faces the following challenges: (1) it can exacerbate receiver-side interference and asymmetric channel access, (2) it can incorrectly lead to lowering the data rate of a link, (3) mobility-induced channel variations at short timescales make detecting and avoiding these problems more complex. Despite significant research in rate and power control, state of the art solutions lack comprehensive techniques to address the above problems.In this paper, we design and implement Symphony - a Synchronous Two-phase Rate and Power control system, whose agility in adaptation enables us to systematically address the three problems, while maximizing the benefits of power control on a per-link basis. We implement Symphony in the Linux Madwifi driver, and show that it can be realized on hardware that supports transmit power control with no modifications to the 802.11 MAC, thereby fostering immediate deployability. Our extensive experimental evaluation on a real testbed in an office environment demonstrates that Symphony (1) enables up to 80% of the clients in 3 different cells to settle at 50% to 94% lower transmit power than a per-cell power control solution, (2) increases network throughput by up to 50% across realistic deployment scenarios, (3) improves the throughput of asymmetry-affected links by 300%, and (4) opportunistically reduces the transmit power of mobile clients running VOIP calls by up to 97%, while causing minimum impact on voice quality.

Automated and accurate segmentation of the aorta in 4D (3D+time) cardiovascular magnetic resonance (MR) image data is important for early detection of congenital aortic disease leading to aortic aneurysms and dissections. An automated 4D segmentation method is reported in this study. Our automated segmentation method combines level-set and optimal surface segmentation algorithms so that the final aortic surfaces in n cardiac phases are determined in a single optimization process. 4D MR image data sets acquired from 21 normal and connective tissue disorder subjects were used to evaluate the performance of our method. The automated 4D segmentation results produced accurate aortic surfaces in 16 cardiac phases, covering the aorta from the left-ventricular outflow tract to the diaphragm, yielding subvoxel accuracy with signed surface positioning errors of-0.07±1.16 voxel (-0.10±2.05 mm). 1

Abstract — Before a sensor network is deployed, it is important to determine how many sensors are required to achieve a certain coverage degree. The number of sensor required for maintaining k-coverage depends on the area of the monitored region, the probability that a node fails or powers off (to save energy), and the deployment strategy. In this paper, we derive the density required to maintain k-coverage under three deployment strategies: (i) nodes are deployed as a Poisson point process, (ii) nodes are uniformly randomly distributed, (iii) nodes are deployed on regular grids. Our results show that under most circumstances, grid deployment renders asymptotically lower node density than random deployment. These results override a previous conclusion that grid deployment may render higher node density than random node distributions. I.

We introduce a new type of data for classification of regional segments of myocardium. We have analyzed strain measurements taken throughout the cardiac cycle from the echocardiograms of pigs. Classifications by both principal component analysis (PCA) and by neural network (NN) are combined for a data mining operation. Differences in strain waveforms between normal and diseased myocardium may further elucidate the corresponding changes in physiology. Altered functioning of the heart muscle is reflected by strain, and objective computer analysis should aid in the diagnosis of ischemia. We hypothesize that the entire strain waveform over one heart cycle can be classified to functionally determine whether or not a myocardial region is perfused.

In this paper, we study how the achievable throughput scales in a wireless network with randomly located nodes as the number of nodes increases, under a communication model where (i) each node has a maximum transmission power W/sub O/ and is capable of utilizing B Hz of bandwidth and (ii) each link can obtain a channel throughput according to the Shannon capacity. Under the limit case that B tends to infinity, we show that each node can obtain a throughput of /spl theta/(n/sup (/spl alpha/-1)/2/) where n is the density of the nodes and /spl alpha/ > 1 is the path loss exponent. Both the upper bound and lower bound are derived through percolation theory. In order to derive the capacity bounds, we have also derived an important result on random geometric graphs: if the distance between two points in a Poisson point process with density n is non-diminishing, the minimum power route requires a power rate at least /spl Omega/(n/sup (1-/spl alpha/)/2/). Our results show that the most promising approach to improving the capacity bounds in wireless ad hoc networks is to employ unlimited bandwidth resources, such as the ultra wide band (UWB).

In this paper, we investigate the minimum total power (termed as critical total power) required to ensure asymptotic k-connectivity in heterogeneous wireless networks where nodes may transmit using different levels of power. We show that under the assumption that wireless nodes form a homogeneous Poisson point process with density /spl lambda/ on a unit square region [0, 1]/sup 2/ and the Toroidal model [M.D. Penrose, 1997], the critical total power required for maintaining k-connectivity is /spl theta/((/spl Gamma/(e/2+k))/((k-1)l)/spl lambda//sup 1-e/2/) with probability approaching one as /spl lambda/ goes to infinity, where e is the path loss exponent. Compared with the results that all nodes use a common critical transmission power for maintaining k-connectivity [M.D. Penrose, 1999], [P.-J. Wan and C. Yi, 2004], we show that the critical total power can be reduced by an order of (log /spl lambda/)e/2 by allowing nodes to optimally choose different levels of transmission power. This result is not subject to any specific power/topology control algorithm, but rather a fundamental property in wireless networks.

In this article, we explore the fundamental limits of sensor network lifetime that all algorithms can possibly achieve. Specifically, under the assumptions that nodes are deployed as a Poisson point process with density λ in a square region with side length ℓ and each sensor can cover a unit-area disk, we first derive the necessary and sufficient condition of the node density in order to maintain complete k -coverage with probability approaching 1. With this result, we obtain that if λ = log ℓ 2 + ( k + 2)loglog ℓ 2 + c (ℓ), c (ℓ) → −∞, as ℓ → + ∞, the sensor network lifetime (for maintaining complete coverage) is upper bounded by kT with probability approaching 1 as ℓ → + ∞, where T is the lifetime of a single sensor. Second, we derive, given a fixed node density in a finite (but reasonably large) region, the upper bounds of lifetime when only α-portion of the region is covered at any time. We also carry out several sets of experiments to validate the derived theoretical results. Numerical results indicate that the derived upper bounds apply not only to networks of large sizes and homogeneous nodal distributions but also to small size networks with clustering nodal distributions.

In this paper, we explore the fundamental limits of sensor network lifetime that all algorithms can possibly achieve. Specifically, under the assumptions that nodes are deployed as a Poisson point process with density λ in a square region with side length l and each sensor can cover a unit-area disk, we first derive the necessary and sufficient condition of the node density in order to maintain complete k-coverage with probability approaching 1. With this result, we obtain that if #955; = log l2 + (k+2)log log l2 + c(l), c(l) → -∞, as l → +∞, the sensor network lifetime (for maintaining complete coverage) is upper bounded by kT with probability approaching 1 as l → +∞, where T is the lifetime of each sensor. Second, we derive, given a fixed node density in a finite (but reasonably large) region, the upper bounds of lifetime when only α-portion of the region is required to be covered at any time. We also carry out simulations to validate the derived results. Simulation results indicate that the derived upper bounds apply not only to networks of large areas but also to small-area networks.

Bluetooth is a promising new wireless technology that enables portable devices to form short-range wireless ad hoc networks. In this paper, we present a new, distributed Bluetooth scatternet formation algorithm, called loop scatternet formation, which forms scatternets with slave/slave bridges only. In addition to meeting the criteria of maintaining connectivity, minimizing the number of piconets and the maximum degree of devices, the proposed algorithm formalizes the notion of network diameter and node contention. The loop scatternet thus formed incurs a much smaller network diameter and the number of node pairs for which a device has to serve, as a relay node is significantly smaller than that in the other types of scatternets. To validate the design, we derive the bounds of the number of piconets, the network diameter, and the maximum node contention. We also conduct ns-2 simulation to evaluate the performance of loop scatternets. Both analytical and simulation results validate the desirable features of loop scatternets.

TCP responds to all losses by invoking congestion control and avoidance algorithms, resulting in degraded end-to-end performance in lossy environments. In recent years, two approaches have been taken to improve the performance of TCP in such networks. One is local retransmission and the other is end-to-end control. These schemes either impose a heavy computational burden on base station, or cannot achieve both efficient bandwidth usage and fairness between flows because they cannot distinguish the flows on low quality wireless links from flows on high quality links. In this paper, we present a new wireless TCP protocol-End-to-end Link State Aware TCP (TCP-ELSA)-that adjusts the sending rate of a TCP flow according to the wireless link quality, but still provides good congestion control when congestion-related losses occur. The aim of TCP-ELSA is to adapt the sending rate of TCP to the link quality so that the efficiency of bandwidth usage is increased. We present simulation results that demonstrate the effectiveness of TCP-ELSA in various scenarios.

Although arrays of SIMD PEs can be built with very high operating frequencies, problems exist in keeping the array busy. The inherent mismatch between host and array makes it difficult to maintain high array utilization: either the rate of instruction issue is very low or PE data locality is compromised, having the same effect. Our solution is based on an array control unit (ACU) design that expands macro instructions in two stages, first by data tile and then into microinstructions. The expansion itself solves the issue problem; decoupling the expansion modalities maintains data locality. Several issues involving host/ACU interaction need to be resolved to effect this solution.