Coscheduling has been gained a resurgence of interest as an effective technique to enhance the performance of parallel applications in multi-programmed clusters. However, existing coscheduling schemes do not adequately handle priority boost conflicts, leading to significantly degraded performance. To address this problem, in our previous study, we devised a novel algorithm that reorders the scheduling sequence of conflicting processes based on the rescheduling latency of their correspondents in remote nodes. In this paper, we exhaustively explore the design issues and implementation details of our contention-aware coscheduling scheme over Myrinet-based cluster system. We also practically analyze the impact of various system parameters and job characteristics on the performance of all considered schemes on a heterogeneous Linux cluster using a generic coscheduling framework. The results show that our approach outperforms existing schemes (by up to 36.6% in avg. job response time), reducing both boost conflict ratio and overall message delay.

Bayesian methods are often applied for estimating the event rate from a series of event occurrences. However, the Bayesian posterior distribution requires the computation of the marginal likelihood which generally involves an analytically intractable integration. As an event rate is defined in a very high dimensional space, it is computationally demanding to obtain the Bayesian posterior distribution for the rate. We estimate the rate underlying a sequence of event counts by deriving an approximate Bayesian inference algorithm for the time-varying binomial process. This enables us to calculate the posterior distribution analytically. We also provide a method for estimating the prior hyperparameter, which determines the smoothness of the estimated event rate. Moreover, we provide an efficient method to compute the upper and lower bounds of the marginal likelihood, which evaluate the approximation accuracy. Numerical experiments demonstrate the effectiveness of the proposed method in terms of the estimation accuracy.

In this paper, a preemptive priority queueing model is developed to derive the system dwelling time of secondary calls in a cognitive radio system in which a primary call's reoccupation of the channel is modeled as a preemptive event that forces a secondary call to attempt a spectrum handover. The suspension of secondary call service which may happen when the immediate spectrum handover fails, is included in our computation of the system dwelling time. The results are helpful in evaluating cognitive radio systems in terms of service delay and in determining system design parameters such as required buffer size and system capacity.

This paper introduces an interactive expression editing system that allows users to design facial expressions easily. Currently, popular example-based methods construct face models based on the examples of target face. The shortcoming of these methods is that they cannot create expressions for novel faces: target faces not previously recorded in the database. We propose a solution to overcome this limitation. We present an interactive facial-geometric-feature animation system for generating expressions of novel faces. Our system is easy to use. By click-dragging control points on the target face, on the computer screen display, unique expressions are generated automatically. To guarantee natural animation results, our animation model employs prior knowledge based on various individuals' expressions. One model prior is learned from motion vector fields to guarantee effective facial motions. Another, different, model prior is learned from facial shape space to ensure the result has a real facial shape. Interactive animation problem is formulated in a maximum a posterior (MAP) framework to search for optimal results by combining the priors with user-defined constraints. We give an extension of the Motion Propagation (MP) algorithm to infer facial motions for novel target faces from a subset of the control points. Experimental results on different facial animations demonstrate the effectiveness of the proposed method. Moreover, one application of our system is exhibited in this paper, where users create expressions for facial sketches interactively.

In this paper, we introduce a new sequential pattern, the Interactive User Sequence Pattern (IUSP). This pattern is useful for grouping highly interrelated users in one-way communications such as e-mail, SMS, etc., especially when the communications include many spam users. Also, we propose an efficient algorithm for discovering IUSPs from massive one-way communication logs containing only the following information: senders, receivers, and dates and times. Even though there is a difficulty in that our new sequential pattern violates the Apriori property, the proposed algorithm shows excellent processing performance and low storage cost in experiments on a real dataset.

Delay Tolerant Networks (DTNs) are a class of emerging networks that experience frequent and long-duration partitions. Delay is inevitable in DTNs, so ensuring the validity and reliability of the message transmission and making better use of buffer space are more important than concentrating on how to decrease the delay. In this paper, we present a novel routing protocol named Location and Direction Aware Priority Routing (LDPR) for DTNs, which utilizes the location and moving direction of nodes to deliver a message from source to destination. A node can get its location and moving direction information by receiving beacon packets periodically from anchor nodes and referring to received signal strength indicator (RSSI) for the beacon. LDPR contains two schemes named transmission scheme and drop scheme, which take advantage of the nodes' information of the location and moving direction to transmit the message and store the message into buffer space, respectively. Each message, in addition, is branded a certain priority according to the message's attributes (e.g. importance, validity, security and so on). The message priority decides the transmission order when delivering the message and the dropping sequence when the buffer is full. Simulation results show that the proposed LDPR protocol outperforms epidemic routing (EPI) protocol, prioritized epidemic routing (PREP) protocol, and DTN hierarchical routing (DHR) protocol in terms of packet delivery ratio, normalized routing overhead and average end-to-end delay. It is worth noting that LDPR doesn't need infinite buffer size to ensure the packet delivery ratio as in EPI. In particular, even though the buffer size is only 50, the packet delivery ratio of LDPR can still reach 93.9%, which can satisfy general communication demand. We expect LDPR to be of greater value than other existing solutions in highly disconnected and mobile networks.

In sensor networks, many studies have been proposed to process in-network aggregation efficiently. Unlike general aggregation queries, skyline query processing compares multi-dimensional data for the result. Therefore, it is very difficult to process the skyline queries in sensor networks. It is important to filter unnecessary data for energy-efficient skyline query processing. Existing approaches get rid of unnecessary data transmission by deploying filters to whole sensors. However, network lifetime is reduced due to energy consumption for transmitting filters. In this paper, we propose a lazy filtering-based in-network skyline query processing algorithm to reduce energy consumption by transmitting filters. Our algorithm creates the skyline filter table (SFT) in the data gathering process which sends data from sensor nodes to the base station and filters out unnecessary data transmissions using it. The experimental results show that our algorithm reduces false positive by 53% and improves network lifetime by 44% on average over the existing method.

Although the probabilistic model checking tool called PRISM has been applied in many communication systems, such as wireless local area network, Bluetooth, and ZigBee, the technique is not used in a controller area network (CAN). In this paper, we use PRISM to model the mechanism of priority messages for CAN because the mechanism has allowed CAN to become the leader in serial communication for automobile and industry control. Through modeling CAN, it is easy to analyze the characteristic of CAN for further improving the security and efficiency of automobiles. The Markov chain model helps us to model the behaviour of priority messages.

We investigate delay analysis of multi-hop wireless mesh network (WMN) where nodes have multi-channel and multiple transceivers to increase the network capacity. The functionality of the multi-channel and multiple transceivers allows the whole WMN to be decomposed into disjoint zones in such a way that i) nodes in a zone are within one-hop distance, and relay node and end nodes with different CWmins contend to access the channel based on IEEE 802.11e EDCA, ii) different channels are assigned to neighbor zones to prevent the hidden node problem, iii) relay nodes can transmit and receive the packets simultaneously by multi-channel and multiple transceivers. With this decomposition of the network, we focus on the delay at a single zone and then the end-to-end delay can be obtained as the sum of zone-delays. In order to have the location-independent end-to-end delay to the gateway regardless of source nodes' locations, we propose two packet management schemes, called the differentiated CW policy and the strict priority policy, at each relay node where relay packets with longer hop count are buffered in higher priority queues according to their experienced hop count. For the differentiated CW policy, a relay node adopts the functionality of IEEE 802.11e EDCA where a higher priority queue has a shorter minimum contention window. We model a typical zone as a one-hop IEEE 802.11e EDCA network under non-saturation condition where priority queues have different packet arrival rates and different minimum contention window sizes. First, we find the PGF (probability generating function) of the HoL-delay of packets at priority queues in a zone. Second, by modeling each queue as M/G/1 queue with the HoL-delay as a service time, we obtain the packet delay (the sum of the queueing delay and the HoL-delay) of each priority queue in a zone. Third, the average end-to-end delay of packet generated at end node in each zone is obtained by summing up the packet delays at each zone. For the strict priority policy, we regard a relay node as a single queueing system with multiple priority queues where relay packets in priority queues are served in the order of strict priority. Relay node has smaller CWmin than end node has and relay node competes with end nodes in a zone. Using the PGF of HoL-delay of packet at relay node and end node, we obtain the packet delay in a zone. The average end-to-end delay to the gateway generated at end node in each zone is obtained. Finally, for both the differentiated CW policy and strict priority policy, by equating all end-to-end delays to be approximately equal, we find the minimum contention window sizes of each priority queue numerically by trial and error method so that end-to-end delays of packets are almost equal regardless of their source's location, respectively. Numerical results show that proposed two methods obtain almost same end-to-end delay of packets regardless of their generated locations and our analytical results are shown to be well matched with the simulation results.

Control Area Network (CAN) development began in 1983 and continues today. The forecast for annual world production in 2008 is approximately 65-67 million vehicles with 10-15 CAN nodes per vehicle on average . Although the CAN network is successful in automobile and industry control because the network provides low cost, high reliability, and priority messages, a starvation problem exists in the network because the network is designed to use a fixed priority mechanism. This paper presents a priority inversion scheme, belonging to a dynamic priority mechanism to prevent the starvation problem. The proposed scheme uses one bit to separate all messages into two categories with/without inverted priority. An analysis model is also constructed in this paper. From the model, a message with inverted priority has a higher priority to be processed than messages without inverted priority so its mean waiting time is shorter than the others. Two cases with and without inversion are implemented in our experiments using a probabilistic model checking tool based on an automatic formal verification technique. Numerical results demonstrate that low-priority messages with priority inversion have better expression in the probability in a full queue state than others without inversion. However, our scheme is very simple and efficient and can be easily implemented at the chip level.

We propose a high-speed protection scheme for multiple-priority-class traffic transmission in WDM ring networks. This scheme achieves high-speed protection by quickly suspending transmission of low-priority traffic when a failure is detected. Each node suspends transmission of the low-priority traffic being sent over the backup path corresponding to the impaired primary path after receiving a failure notification and checking a table that includes the primary paths passing through the node, the backup paths corresponding to the primary paths, and low-priority traffic transmitted by the node. If a node detects a failure, it sends a single failure notification for each ring to the source node of the primary path, and the nodes on the route sequentially suspend low-priority traffic. Simulation shows that the proposed scheme reduces the failure-recovery time by up to 60% compared with the conventional scheme.

The aim of this work is to develop an efficient medical image segmentation technique by fitting a nonlinear shape model with pre-segmented images. In this technique, the kernel principle component analysis (KPCA) is used to capture the shape variations and to build the nonlinear shape model. The pre-segmentation is carried out by classifying the image pixels according to the high level texture features extracted using the over-complete wavelet packet decomposition. Additionally, the model fitting is completed using the particle swarm optimization technique (PSO) to adapt the model parameters. The proposed technique is fully automated, is talented to deal with complex shape variations, can efficiently optimize the model to fit the new cases, and is robust to noise and occlusion. In this paper, we demonstrate the proposed technique by implementing it to the liver segmentation from computed tomography (CT) scans and the obtained results are very hopeful.

This paper investigates dynamic spectrum access based on MAC-Layer spectrum sensing and prior channel pre-allocation strategy. We first combine channel utilization with channel state transition probability from idle to busy to reflect the channel opportunity quality in cognitive radio systems. Then a MAC-Layer spectrum sensing algorithm based on Channel Opportunity Quality Descending Order (COQDO) is proposed for the single secondary user scenario, so that the single secondary user can be provided with dynamic spectrum access. For the multi-secondary users scenario, in order to solve the channel collision problem among secondary users in dynamic spectrum access, a joint MAC-Layer spectrum sensing and prior channel pre-allocation algorithm is proposed and analyzed. Channel collision problem occurs when more than one secondary users detect the channel as idle and access it at the same time. Furthermore, the prior channel pre-allocation is optimized by using the conventional Color Sensitive Graph Coloring (CSGC) algorithm. Extensive simulation results are presented to compare our proposed algorithms with existing algorithms in terms of idle channel search delay and accumulated channel handoff delay.

This paper proposes an enhanced method for estimating the a priori Signal-to-Disturbance Ratio (SDR) to be employed in the Acoustic Echo and Noise Suppression (AENS) system for full-duplex hands-free communications. The proposed a priori SDR estimation technique is modified based upon the Two-Step Noise Reduction (TSNR) algorithm to suppress the background noise while preserving speech spectral components. In addition, a practical approach to determine accurately the Echo Spectrum Variance (ESV) is presented based upon the linear relationship assumption between the power spectrum of far-end speech and acoustic echo signals. The ESV estimation technique is then employed to alleviate the acoustic echo problem. The performance of the AENS system that employs these two proposed estimation techniques is evaluated through the Echo Attenuation (EA), Noise Attenuation (NA), and two speech distortion measures. Simulation results based upon real speech signals guarantee that our improved AENS system is able to mitigate efficiently the problem of acoustic echo and background noise, while preserving the speech quality and speech intelligibility.

It is known that the schedulability of a non-preemptive task set with fixed priority can be determined in pseudo-polynomial time. However, since Rate Monotonic scheduling is not optimal for non-preemptive scheduling, the applicability of existing polynomial time tests that provide sufficient schedulability conditions, such as Liu and Layland's bound, is limited. This letter proposes a new sufficient condition for non-preemptive fixed priority scheduling that can be used for any fixed priority assignment scheme. It is also shown that the proposed schedulability test has a tighter utilization bound than existing test methods.

Scheduling algorithms are crucial in radio resource management especially for multimedia networks. Many scheduling algorithms are based on the assumption of error-free connections, which is not suitable for wireless networks. Therefore, a scheduling algorithm based on the modification of Static Priority (SP) algorithm and Earliest-Due-Date (EDD) algorithm is proposed for wireless multimedia networks with link adaptation in this paper. In the proposed algorithm, various quality of service requirements, such as delay, throughput, and packet loss ratio, are considered. Particularly, the influence of error tolerance of voice communications, which is usually ignored in most scheduling algorithms, is taken into account. Simulation results show that the proposed algorithm, compared with SP, EDD, and other scheduling algorithms, succeeds in meeting the delay and packet loss ratio (PLR) requirements at much heavier traffic load.

A spectrum sharing method is proposed for systems that share the same frequency band or adjacent bands with services that have different priorities. The proposed method adaptively controls transmission power according to information provided by the high-priority system receivers. We give the theoretical capacities achieved by low-priority systems when the proposed method and a conventional method (constant transmit power) are applied. Numerical results confirm that the proposed method attains 1.5-2 times larger capacity than the conventional method.

In this paper, we focus on the adaptive resource allocation issue for uplink OFDMA systems. The resources are allocated according to a proportional fairness criterion, which can strike an alterable balance between fairness and efficiency. Optimization theory is used to analyze the multi-constraint resource allocation problem and some heuristic characteristics about the optimal solution are obtained. To deal with the cohesiveness of the necessary conditions, we resort to bargaining theory that has been deeply investigated in game theory. Firstly, we summarize some assumptions about bargaining theory and show their similarities with the resource allocation process. Then we propose a priority-ranked bargaining model, whose primary contribution is applying the economic thought to the resource allocation process. A priority-ranked bargaining algorithm (PRBA) is subsequently proposed to permit the base station to auction the subcarriers one by one according to the users' current priority. By adjusting the predefined rate ratio flexibly, PRBA can achieve different degrees of fairness among the users' capacity. Simulation results show that PRBA can achieve similar performance of the max-min scheme and the NBS scheme in the case of appropriate predefined rate ratio.

In the latest video coding frameworks, efficiency of motion vector (MV) coding is becoming increasingly important because of the growing bit rate portion of motion information. However, neither the conventional median predictor, nor the newer schemes such as the minimum bit rate prediction scheme and the hybrid scheme, can effectively eliminate the local redundancy of motion vectors. In this paper, we present the prioritized reference decision scheme for efficient motion vector coding, based on the H.264/AVC framework. This scheme makes use of a boolean indicator to specify whether the median predictor is to be used for the current MV or not. If not, the median prediction is considered not suitable for the current MV, and this information is used for refining the possible space of a group of reference MVs including 4 neighboring MVs and the zero MV. This group of MVs is organized to be a prioritized list so that the reference MV with highest priority is to be selected as the prediction value. Furthermore, the boolean indicators are coded into the modified code words of mb_type and sub_mb_type, so as to reduce the overhead. By applying the proposed scheme, the structure and the applicability problems with the state-of-the-art MBP scheme have been overcome. Experimental result shows that the proposed scheme achieves a considerable reduction of bits for MVDs, compared with the conventional median prediction algorithm. It also achieves a better and much stabler performance than MBP-based MV coding.

The spike timings of neurons are irregular and are considered to be a one-dimensional point process. The Bayesian approach is generally used to estimate the time-dependent firing rate function from sequences of spike timings. It can also be used to estimate the firing rate from only a single sequence of spikes. However, the rate function has too many degrees of freedom in general, so approximation techniques are often used to carry out the Bayesian estimation. We applied the transfer matrix method, which efficiently computes the exact marginal distribution, to the estimation of the firing rate and developed an algorithm that enables the exact results to be obtained for the Bayesian framework. Using this estimation method, we investigated how the mismatch of the prior hyperparameter value affects the marginal distribution and the firing rate estimation.