Time performance optimization and resource conflict resolution are two important challenges in multiple project management contexts. Compared with traditional project management, multi-project management usually suffers limited and insufficient resources, and a tight and urgent deadline to finish all concurrent projects. In this case, time performance optimization of the global project management is badly needed. To our best knowledge, existing work seldom pays attention to the formal modeling and analyzing of multi-project management in an effort to eliminate resource conflicts and optimizing the project execution time. This work proposes such a method based on PRT-Net, which is a Petri net-based formulism tailored for a kind of project constrained by resource and time. The detailed modeling approaches based on PRT-Net are first presented. Then, resource conflict detection method with corresponding algorithm is proposed. Next, the priority criteria including a key-activity priority strategy and a waiting-short priority strategy are presented to resolve resource conflicts. Finally, we show how to construct a conflict-free PRT-Net by designing resource conflict resolution controllers. By experiments, we prove that our proposed priority strategy can ensure the execution time of global multiple projects much shorter than those without using any strategies.

This paper presents a practical system which allows instructors to easily introduce 3D games utilizing smartphones in a classroom. The system consists of a PC server, a big screen and smartphone clients. The server provides 3D models, so no 3D authoring is needed when using this system. For an instructor, preparing slides of quiz-questions with the correct answers is all that is required when designing 3D games. According to a quiz specified by an instructor, this system constructs a corresponding 3D game scene. The answers students provide on their smartphones will be used to play this game. Everyone in the classroom can see this 3D game in real time on a big screen. The game illustrates how every student has reacted to a quiz. This system also introduces specialized queues for mobile interactions; a queue for commands from an instructor and a queue for data from students. The command queue has higher priority than the data queue; so that an instructor can control this system by sending commands with clicks on a smartphone. Previous studies have mostly provided specially designed teaching materials to instructors, often treating them as passive consultants. However, by using slides, already familiar to instructors, this system enables instructors to combine their own teaching materials with 3D games in the classroom. Moreover, 3D games are expected to further motivate students to actively participate in classroom activities. This system is evaluated in this paper.

In many real-world classification problems, the class balance often changes between training and test datasets, due to sample selection bias or the non-stationarity of the environment. Naive classifier training under such changes of class balance systematically yields a biased solution. It is known that such a systematic bias can be corrected by weighted training according to the test class balance. However, the test class balance is often unknown in practice. In this paper, we consider a semi-supervised learning setup where labeled training samples and unlabeled test samples are available and propose a class balance estimator based on the energy distance. Through experiments, we demonstrate that the proposed method is computationally much more efficient than existing approaches, with comparable accuracy.

The Satellite/Terrestrial Integrated mobile Communication System (STICS), which allows terrestrial mobile phones to communicate directly through a satellite, has been studied [1]. Satellites are unaffected by the seismic activity that causes terrestrial damage, and therefore, the STICS can be expected to be a measure that ensures emergency call connection. This paper first describes the basic characteristics of call blocking rates of terrestrial mobile phone systems in areas where non-functional base stations are geographically clustered, as investigated through computer simulations that showed an increased call blocking rate as the number of non-functional base stations increased. Further simulations showed that restricting the use of the satellite system for emergency calls only ensures the STICS's capacity to transmit emergency communications; however, these simulations also revealed a weakness in the low channel utilization rate of the satellite system [2]. Therefore, in this paper, we propose increasing the channel utilization rate with a priority channel framework that divides the satellite channels between priority channels for emergency calls and non-priority channels that can be available for emergency or general use. Simulations of this priority channel framework showed that it increased the satellite system's channel utilization rate, while continuing to ensure emergency call connection [3]. These simulations showed that the STICS with a priority channel framework can provide efficient channel utilization and still be expected to provide a valuable secondary measure to ensure emergency communications in areas with clustered non-functional base stations during large-scale disasters.

We propose a reality-based model of a two-echelon repair system with “priority resupply” and present a method for analyzing the availability of the system operated in each base. The two echelon repair system considered in our model consists of one repair station, called depot, and several bases. In each base, n items which constitute a k-out-of-n: G system, called k/n system, are operated. Each item has two failure modes, failures repaired at a base (level 1) and failures repaired at the depot (level 2). When a level 2 failure occurs in a base, either a normal order or an emergency order of a spare item is issued depending on the number of operating items in the base. The spare item in the depot is sent preferentially to the base where the emergency order is placed. We propose two models, both including priority resupply. Firstly, we propose an approximation method for analyzing the basic model where a k/n system is operated in a base. Using a simulation method, we verify the accuracy of our approximation method. Secondly, we expand the basic model to a dual k/n system where the items of the system are interchangeable between two k/n systems in the case of an emergency, which is called “cannibalization”. Then, we show a numerical example and discuss the optimal timing for placing an emergency order.

As the quantity of mobile application traffic keeps increasing, operators are facing the scalability limits of VoIP protocols. Higher queuing delays at the Session Initiation Protocol (SIP) server create significantly more retransmissions in the network. When the message arrival rate including retransmissions exceeds the message serving capacity of a SIP server, the queue size increases and eventually the SIP server can crash. Our analysis demonstrates that server crash can be prevented if the buffer size of the SIP server is limited. However, having smaller buffer sizes yields side effects such as lower successful transaction ratio for bursty traffic. In this paper, we propose a new SIP server scheduling mechanism in which the original incoming SIP requests have strict priority over the retransmitted requests. The priority based scheduling mechanism provides network administrator with the ability to configure the buffer size of a SIP server to a moderately high value. We implement the proposed priority-based scheduling mechanism in the JAIN-SIP stack and confirm that the implementation requires minimal changes to the SIP standard. Numerical experiments show that the proposed scheduling mechanism provides significantly and consistently better scalability at high buffer sizes compared to the heavily used first-in-first-out scheduling, thus enabling us to avoid server overloads.

In this paper, an image prior based on soft-morphological filters and its application to image recovery are presented. In morphological image processing, a gray-scale image is represented as a subset in a three-dimensional space, which is spanned by spatial and intensity axes. Morphological opening and closing, which are basic operations in morphological image processing, respectively approximate the image subset and its complementary images as the unions of structuring elements that are translated in the three-dimensional space. In this study, the opening and closing filters are applied to an image prior to resolve the regularization problem of image recovery. When the proposed image prior is applied, the image is recovered as an image that has no noise component, which is eliminated by the opening and closing. However, the closing and opening filters are less able to eliminate Gaussian noise. In order to improve the robustness against Gaussian noise, the closing and opening filters are respectively approximated as soft-closing and soft-opening with relaxed max and min functions. In image recovery experiments, image denoising and deblurring using the proposed prior are demonstrated. Comparisons of the proposed prior with the existing priors that impose a penalty on the gradient of the intensity are also shown.

In this paper, we design an Unequal Error Protection (UEP) rateless code with special coding graph and apply it to propose a novel HTTP adaptive streaming based on UEP rateless code (HASUR). Our designed UEP rateless code provides high diversity on decoding probability and priority for data in different important level with overhead smaller than 0.27. By adopting this UEP rateless channel coding and scalable video source coding, our HASUR ensures symbols with basic quality to be decoded first to guarantee fluent playback experience. Besides, it also provides multiple layers to ensure the most suitable quality for fluctuant bandwidth and packet loss rate (PLR) without estimating them in advance. We evaluate our HASUR against the alternative solutions. Simulation results show that HASUR provides higher video quality and more adapts to bandwidth and PLR than other two commercial schemes under End-to-End transmission.

To enhance the throughput of wireless local area networks (WLANs) by efficiently utilizing the radio resource, a distributed coordination function-based (DCF-based) orthogonal frequency division multiple access (OFDMA) WLAN system has been proposed. In the system, since each OFDMA sub-channel is assigned to the associated station with the highest channel gain, the transmission rate of DATA frames can be enhanced thanks to multi-user diversity. However, the optimum allocation of OFDMA sub-channels requires the estimation of channel state information (CSI) of all associated stations, and this incurs excessive signaling overhead. As the number of associated stations increases, the signaling overhead severely degrades the throughput of DCF-based OFDMA WLAN. To reduce the signaling overhead while obtaining a sufficient diversity gain, this paper proposes a channel access scheme that performs multiple DCF-based channel access. The key idea of the proposed scheme is to introduce additional DCF-based prioritized access along with the traditional DCF-based random access. In the additional DCF-based prioritized access, by dynamically adjusting contention window size according to the CSI of each station, only the stations with better channel state inform their CSI to the access point (AP), and the signaling overhead can be reduced while maintaining high multi-user diversity gain. Numerical results confirm that the proposed channel access scheme enhances the throughput of OFDMA WLAN.

In this letter, we propose an improved single image haze removal algorithm using image segmentation. It can effectively resolve two common problems of conventional algorithms which are based on dark channel prior: halo artifact and wrong estimation of atmospheric light. The process flow of our algorithm is as follows. First, the input hazy image is over-segmented. Then, the segmentation results are used for improving the conventional dark channel computation which uses fixed local patches. Also, the segmentation results are used for accurately estimating the atmospheric light. Finally, from the improved dark channel and atmospheric light, an accurate transmission map is computed allowing us to recover a high quality haze-free image.

We consider the problem of learning a classifier using only positive and unlabeled samples. In this setting, it is known that a classifier can be successfully learned if the class prior is available. However, in practice, the class prior is unknown and thus must be estimated from data. In this paper, we propose a new method to estimate the class prior by partially matching the class-conditional density of the positive class to the input density. By performing this partial matching in terms of the Pearson divergence, which we estimate directly without density estimation via lower-bound maximization, we can obtain an analytical estimator of the class prior. We further show that an existing class prior estimation method can also be interpreted as performing partial matching under the Pearson divergence, but in an indirect manner. The superiority of our direct class prior estimation method is illustrated on several benchmark datasets.

Combinatorial interaction testing has been well studied in recent years, and has been widely applied in practice. It generally aims at generating an effective test suite (an interaction test suite) in order to identify faults that are caused by parameter interactions. Due to some constraints in practical applications (e.g. limited testing resources), for example in combinatorial interaction regression testing, prioritized interaction test suites (called interaction test sequences) are often employed. Consequently, many strategies have been proposed to guide the interaction test suite prioritization. It is, therefore, important to be able to evaluate the different interaction test sequences that have been created by different strategies. A well-known metric is the Average Percentage of Combinatorial Coverage (shortly APCCλ), which assesses the rate of interaction coverage of a strength λ (level of interaction among parameters) covered by a given interaction test sequence S. However, APCCλ has two drawbacks: firstly, it has two requirements (that all test cases in S be executed, and that all possible λ-wise parameter value combinations be covered by S); and secondly, it can only use a single strength λ (rather than multiple strengths) to evaluate the interaction test sequence - which means that it is not a comprehensive evaluation. To overcome the first drawback, we propose an enhanced metric Normalized APCCλ (NAPCC) to replace the APCCλ Additionally, to overcome the second drawback, we propose three new metrics: the Average Percentage of Strengths Satisfied (APSS); the Average Percentage of Weighted Multiple Interaction Coverage (APWMIC); and the Normalized APWMIC (NAPWMIC). These metrics comprehensively assess a given interaction test sequence by considering different interaction coverage at different strengths. Empirical studies show that the proposed metrics can be used to distinguish different interaction test sequences, and hence can be used to compare different test prioritization strategies.

Sparse representation has been studied within the field of signal processing as a means of providing a compact form of signal representation. This paper introduces a sparse representation based framework named Sparse Probabilistic Linear Discriminant Analysis in speaker recognition. In this latent variable model, probabilistic linear discriminant analysis is modified to obtain an algorithm for learning overcomplete sparse representations by replacing the Gaussian prior on the factors with Laplace prior that encourages sparseness. For a given speaker signal, the dictionary obtained from this model has good representational power while supporting optimal discrimination of the classes. An expectation-maximization algorithm is derived to train the model with a variational approximation to a range of heavy-tailed distributions whose limit is the Laplace. The variational approximation is also used to compute the likelihood ratio score of all trials of speakers. This approach performed well on the core-extended conditions of the NIST 2010 Speaker Recognition Evaluation, and is competitive compared to the Gaussian Probabilistic Linear Discriminant Analysis, in terms of normalized Decision Cost Function and Equal Error Rate.

As a network evolves following initial deployment, its service functions remain diversified through the openness of the network functions. This indicates that appropriate simplification of the service functions is essential if the evolving network is to achieve the required scalability of service processing and service management. While the screening of service functions is basically performed by network users and the market, several service functions will be automatically simplified based on the growth of the evolving network. This paper verifies the simplification of service functions resulting from the evolution of the network itself. First, the principles that serve as the basis for simplifying the service functions are explained using several practical examples. Next, a simulation model is proposed to verify the simplification of service functions in terms of the priority control function for path routing and load balancing among multiple paths. From the results of the simulation, this study clarifies that the anticipated simplification of service functions is actually realizable and the service performance requirements can be reduced as the network evolves after deployment. When the simplification of service functions can improve network quality, it accelerates the evolution of the network and increases the operator's revenue.

This paper proposes a low-complexity concatenated (LCC) soft-in soft-out (SISO) detector for spreading OFDM systems. The LCC SISO detector uses the turbo principle to compute the extrinsic information of the optimal maximum a priori probability (MAP) SISO detector with extremely low complexity. To develop the LCC SISO detector, we first partition the spreading matrix into some concatenated sparse matrices separated by interleavers. Then, we use the turbo principle to concatenate some SISO detectors, which are separated by de-interleavers or interleavers. Each SISO detector computes the soft information for each sparse matrix. By exchanging the soft information between the SISO detectors, we find the extrinsic information of the MAP SISO detector with extremely low complexity. Simulation results show that using the LCC SISO detector produces a near-optimal performance for both uncoded and coded spreading OFDM systems. In addition, by using the LCC SISO detector, the spreading OFDM system significantly improves the BER of the conventional OFDM system.

This paper proposes the test case prioritization in regression testing. The large size of a test suite to be executed in regression testing often causes large amount of testing cost. It is important to reduce the size of test cases according to prioritized test sequence. In this paper, we apply the Markov chain Monte Carlo random testing (MCMC-RT) scheme, which is a promising approach to effectively generate test cases in the framework of random testing. To apply MCMC-RT to the test case prioritization, we consider the coverage-based distance and develop the algorithm of the MCMC-RT test case prioritization using the coverage-based distance. Furthermore, the MCMC-RT test case prioritization technique is consistently comparable to coverage-based adaptive random testing (ART) prioritization techniques and involves much less time cost.

The time efficiency of aerial video stitching is still an open problem due to the huge amount of input frames, which usually results in prohibitive complexities in both image registration and blending. In this paper, we propose an efficient framework aiming to stitch aerial videos in real time. Reasonable distortions are allowed as a tradeoff for acceleration. Instead of searching for globally optimized solutions, we directly refine frame positions with sensor data to compensate for the accumulative error in alignment. A priority scan method is proposed to select pixels within overlapping area into the final panorama for blending, which avoids complicated operations like weighting or averaging on pixels. Experiments show that our method can generate satisfying results at very competitive speed.

Allowing WLANs to exploit opportunistic spectrum access (OSA) is a promising approach to alleviate spectrum congestion problems in overcrowded unlicensed ISM bands, especially in highly dense WLAN deployments. In this context, novel channel assignment mechanisms jointly considering available channels in both unlicensed ISM and OSA-enabled licensed bands are needed. Unlike classical schemes proposed for legacy WLANs, channel assignment mechanisms for OSA-enabled WLAN should face two distinguishing issues: channel prioritization and spectrum heterogeneity. The first refers to the fact that additional prioritization criteria other than interference conditions should be considered when choosing between ISM or licensed band channels. The second refers to the fact that channel availability might not be the same for all WLAN Access Points because of primary users' activity in the OSA-enabled bands. This paper firstly formulates the channel assignment problem for OSA-enabled WLANs as a Binary Linear Programming (BLP) problem. The resulting BLP problem is optimally solved by means of branch and bound algorithms and used as a benchmark to develop more computationally efficient heuristics. Upon such a basis, a novel channel assignment algorithm based on weighted graph coloring heuristics and able to exploit both channel prioritization and spectrum heterogeneity is proposed. The algorithm is evaluated under different conditions of AP density and primary band availability.

We provide a theoretical analysis of the capacity achievable by an open/closed-access cognitive radio system, where the system uses spectrum resources primarily allocated to a macro cellular system. For spectrum sharing, we consider two methods based on listen-before-talk and adaptive transmit power control principles. Moreover, outdoor and indoor installations of CRS stations are investigated. We have also taken the effect of antenna heights into consideration. Numerical results reveal the capacities possible from CRS base stations installed within the coverage area of the macro cell system. We show numerical examples that compare the capacities achievable by open-access and closed access cognitive radio systems.

In this paper, we present a maximum a posteriori probability (MAP) approach to the problem of blind estimation of single-input, multiple-output (SIMO), finite impulse response (FIR) channels. A number of methods have been developed to date for this blind estimation problem. Some of those utilize prior knowledge on input signal statistics. However, there are very few that utilize channel statistics too. In this paper, the unknown channel to be estimated is assumed as the frequency-selective Rayleigh fading channel, and we incorporate the channel prior distributions (and hyperprior distributions) into our model in two different ways. Then for each case an iterative MAP estimator is derived approximately. Performance comparisons over existing methods are conducted via numerical simulation on randomly generated channel coefficients according to the Rayleigh fading channel model. It is shown that improved estimation performance can be achieved through the MAP approaches, especially for such channel realizations that have resulted in large estimation error with existing methods.