Orthogonal Frequency Division Multiple Access (OFDMA) is the technique for the next generation wireless networks, whose enhanced capacity is to serve a combination of traffic with diverse QoS requirements. To realize this, the resource allocation scheme has to be carefully designed so that the instantaneous channel condition, QoS provision, and the network utilization are integrated. In this paper, we propose the resource allocation scheme for downlink traffic of 2 classes; guaranteed and non-guaranteed, having different traffic contracts. We provide guaranteed throughput for the guaranteed class by considering the cost incurred from serving this class. Then, we formulate the assignment problem with the objective of minimizing this cost. For the non-guaranteed class, we aim to maximize network utilization and to maintain throughput fairness, by employing Proportional Fairness (PF) utility function and emphasizing on the portion of network resource that the user received and the individual user's queue length. We use a heuristic approach to schedule users' data into the downlink subframe by exploiting multi-user multi-channel diversity to utilize system's bandwidth efficiently. Intensive simulation shows that our scheme differentiates classes of traffic and provides satisfied throughput, lower packet drop rate, and lower queuing delay to the guaranteed class, comparing with those of the non-guaranteed class. Furthermore, the results also show that the scheme is fair to users in the same class in both throughput and service time.

This paper addresses the problem of joint transceiver design for Tomlinson-Harashima Precoding (THP) in the multiuser multiple-input-multiple-output (MIMO) downlink under both perfect and imperfect channel state information at the transmitter (CSIT). For the case of perfect CSIT, we differ from the previous work by performing stream-wise (both inter-user and intra-user) interference pre-cancelation at the transmitter. A minimum total mean square error (MT-MSE) criterion is used to formulate our optimization problem. By some convex analysis of the problem, we obtain the necessary conditions for the optimal solution. An iterative algorithm is proposed to handle this problem and its convergence is proved. Then we extend our designed algorithm to the robust version by minimizing the conditional expectation of the T-MSE under imperfect CSIT. Simulation results are given to verify the efficacy of our proposed schemes and to show their superiorities over existing MMSE-based THP schemes.

We investigate the symbol error rate (SER) of the cooperative transmission with the decode-and-forward relay protocol under Rayleigh fading channels. The technique of orthogonal space-time block coding (OSTBC) is applied at the links source-relay, source-destination and relay-destination. A closed-form SER expression is derived. Simulation results demonstrate the theoretical solutions.

Automated static timing analysis methods provide a safe but usually overestimated worst-case execution time (WCET) due to infeasible execution paths. In this paper, we propose a visual language, User Constraint Language (UCL), to obtain a tight WCET estimation. UCL provides intuitive visual notations with which users can easily specify various levels of flow information to characterize valid execution paths of a program. The user constraints specified in UCL are translated into finite automata. The combined automaton, constructed by a cross-production of the automata for program and user constraints, reflects the static structure and possible dynamic behavior of the program. It contains only the execution paths satisfying user constraints. A case study using part of a software program for satellite flight demonstrates the effectiveness of UCL and our approach.

Functional and formal verification are important methodologies for complex mixed-signal design validation. However the industry is still verifying such systems by pure simulation. This process lacks on error localization and formal verifications methods. This is the existing verification gap between the analog and digital blocks within a mixed-signal system. Our approach improves the verification process by creating temporal properties named mixed-signal assertions which are described by a combination of digital assertions and analog properties. The proposed method is a new assertion-based verification flow for designing mixed-signal circuits. The effectiveness of the approach is demonstrated on a Σ/Δ-converter.

Proofs written in algebraic specification languages are called proof scores. The proof score approach to design verification is attractive because it provides a flexible way to prove that designs for systems satisfy properties. Thus far, however, the approach has focused on safety properties. In this paper, we describe a way to verify that designs for systems satisfy liveness properties with the approach. A mutual exclusion protocol using a queue is used as an example. We describe the design verification and explain how it is verified that the protocol satisfies the lockout freedom property.

In peer-to-peer (P2P) content sharing, users can share their content by contributing their own resources to one another. However, since there is no incentive for contributing contents or resources to others, users may attempt to obtain content without any contribution. To motivate users to contribute their resources to the service, incentive-rewarding mechanisms have been proposed. On the other hand, emerging wireless technologies, such as IEEE 802.11 wireless local area networks, beyond third generation (B3G) cellular networks and mobile WiMAX, provide high-speed Internet access for wireless users. Using these high-speed wireless access, wireless users can use P2P services and share their content with other wireless users and with fixed users. However, this diversification of access networks makes it difficult to appropriately assign rewards to each user according to their contributions. This is because the cost necessary for contribution is different in different access networks. In this paper, we propose a novel incentive-rewarding mechanism called EMOTIVER that can assign rewards to users appropriately. The proposed mechanism uses an external evaluator and interactive learning agents. We also investigate a way of appropriately controlling rewards based on the system service's quality and managing policy.

Efficient use of information technology (IT) is considered a major determinant of an end-user's business performance and an enterprise's competitiveness. A 16-item tool that can efficiently measure end-user information competency is presented with the measures. The validity and reliability of the tool is confirmed, and the tool's theoretical and practical applications are discussed.

This paper studies the impact of dummy fill for chemical mechanical polishing (CMP)-induced capacitance variation on buffer insertion based on a virtual CMP fill estimation model. Compared with existing methods, our algorithm is more feasible by performing buffer insertion not in post-process but during early physical design. Our contributions are threefold. First, we introduce an improved fast dummy fill amount estimation algorithm based on [4], and use some speedup techniques (tile merging, fill factor and amount assigning) for early estimation. Second, based on some reasonable assumptions, we present an optimum virtual dummy fill method to estimate dummy position and the effect on the interconnect capacitance. Then the dummy fill estimation model was verified by our experiments. Third, we use this model in early buffer insertion after layer assignment considering the effects of dummy fill. Experimental results verified the necessity of early dummy fill estimation and the validity of our algorithm. Buffer insertion considering dummy fill during early physical design is necessary and our algorithm is promising.

This paper proposes a novel algorithm for image feature extraction-the dual two-dimensional fuzzy class preserving projections ((2D)2FCPP). The main advantages of (2D)2FCPP over two-dimensional locality preserving projections (2DLPP) are: (1) utilizing the fuzzy assignation mechanisms to construct the weight matrix, which can improve the classification results; (2) incorporating 2DLPP and alternative 2DLPP to get a more efficient dimensionality reduction method-(2D)2LPP.

Transmit beamforming can exploit the spatial diversity afforded by multiple-input multiple-output (MIMO) systems with low complexity. To apply this technique in more practical systems with the constraint of limited feedback, codebook based beamforming and vector quantization technique have been considered in various papers. On the other hand, multi-user scheduling is able to achieve another form of diversity arising from the independence of fading for different users, however, has not been fully taken into account in existing codebook based beamforming schemes. In this letter, a multi-codebook based beamforming and scheduling scheme is proposed, which exploits both spatial diversity and multi-user diversity by switching the codebook for different resource blocks. Meanwhile, the multi-codebook design issue is addressed, the corresponding theoretical analysis is provided, and the performance gain of proposed scheme is simulated. Furthermore, the impacts of related parameters on the performance gain are also investigated.

In this paper, we propose a continuous range query processing method over moving objects. To efficiently process continuous range queries, we design a main-memory-based query index that uses smaller storage and significantly reduces the query processing time. We show through performance evaluation that the proposed method outperforms the existing methods.

In human-machine systems, a user gets abstracted information of a machine via an interface and operates it referring to a manual. If a manual has an erroneous description leading to automation surprises, the user may be lost in his/her operations so that he/she may make a serious human error. In this paper, we propose an algorithm for generating a manual by which automation surprises never occur. We model the machine and the interface as a discrete event system and a mapping from machine's state to a display of the interface, respectively. First, we represent a manual as a finite language and model behavior of the system operated by the user with the manual as a tree called an operational tree. Next, we characterize three automation surprises using the tree. Finally, we propose an algorithm for generating an operational tree by which the machine reaches a target state.

To guarantee the timely provisioning of QoS to real-time oriented multiparty and distributed computing applications (e.g., video conferencing and grid computing) that require the utilization of time and quantity-related resources, the resource should be reserved in advance. However, this new type of reservation (i.e., advance reservation) may collide with legacy (i.e., immediate) reservations that do not specify their session duration. In this paper, to suggest a balanced solution between sharing pools of resources and the managing of collisions between reservation calls, a revenue-based resource sharing scheme (focusing on the bandwidth) is proposed. The proposed scheme attempts to avoid possible collisions by employing a virtual profile of resources reserved in advance in an effort to achieve enhanced resource utilization. It is shown through NS-2 [22] -based network simulations that the proposed scheme can achieve balanced performance when compared with other schemes, including static resource partitioning and complete resource sharing.

Although a lot of works for location management in wireless networks have been reported in the literature, most of the works have been focused on designing per-user-based strategies. This means that they can achieve the performance enhancement only for a certain class of mobile users with a specific range of CMR (call-to-mobility ratio). However, these per-user-based strategies can actually degrade the performance if a user's CMR changes significantly. Therefore, an efficient uniform location management strategy, which can be commonly applied to all mobile users regardless of their CMR, is proposed and analyzed in this paper. The motivation behind the proposed strategy is to exploit the concepts of the two well-known existing strategies: the location caching strategy and the local anchor strategy. That is, the location caching strategy exploits locality in a user's calling pattern, whereas the local anchor strategy exploits locality in a user's mobility pattern. By exploiting these characteristics of both strategies together with the profile management at the HLR (home location register), the proposed strategy can reduce the frequent access to the HLR, and thus effectively results in significant reduction in terms of the total location management cost. The analytical results also demonstrate that the proposed strategy can be uniformly applied to all mobile users, while always maintaining the performance gain over the IS-41 standard regardless of a user's CMR and the network traffic conditions.

This paper proposes a novel approach of constructing statistical preference models for context-aware personalized applications such as recommender systems. In constructing context-aware statistical preference models, one of the most important but difficult problems is acquiring a large amount of training data in various contexts/situations. In particular, some situations require a heavy workload to set them up or to collect subjects capable of answering the inquiries under those situations. Because of this difficulty, it is usually done to simply collect a small amount of data in a real situation, or to collect a large amount of data in a supposed situation, i.e., a situation that the subject pretends that he is in the specific situation to answer inquiries. However, both approaches have problems. As for the former approach, the performance of the constructed preference model is likely to be poor because the amount of data is small. For the latter approach, the data acquired in the supposed situation may differ from that acquired in the real situation. Nevertheless, the difference has not been taken seriously in existing researches. In this paper we propose methods of obtaining a better preference model by integrating a small amount of real situation data with a large amount of supposed situation data. The methods are evaluated using data regarding food preferences. The experimental results show that the precision of the preference model can be improved significantly.

Various types of data from environment are used to provide user-adaptive services. Among them, data of a user's past moving paths are useful to predict a moving user's next location and provide related services. This paper proposes a method to predict a moving user's location through analyzing his/her past moving paths. This method analyzes the user's moving path by using three elements of distance, time and direction of moving paths and Dynamic Time Warping (DTW), and selects the representative path, which is the one most similar to the current moving path in the past paths. The selected path can be used to provide service like space and time estimation.

In real-time embedded systems, there is requirement for adapting both energy consumption and Quality of Services (QoS) of tasks according to their importance. This paper proposes an adaptive power-aware resource allocation method to resolve a trade-off between the energy consumption and QoS levels according to their importance with guaranteeing fairness. The proposed resource allocator consists of two components: the total resource optimizer to search for the optimal total resource and QoS-fairness-based allocator to allocate resource to tasks guaranteeing the fairness. These components adaptively achieve the optimal resource allocation formulated by a nonlinear optimization problem with the time complexity O(n) for the number of tasks n even if tasks' characteristics cannot be identified precisely. The simulation result shows that the rapidness of the convergence of the resource allocation to the optimal one is suitable for real-time systems with large number of tasks.

This paper proposes a DCT-based steganographic method named StegErmelc in the JPEG domain. Three strategies are proposed, namely (i) edge-like block selection, (ii) recursive matrix encoding, and (iii) largest coefficient serving, to form a novel steganographic method for achieving scalable carrier capacity, low detectability by universal blind steganalyzer, and high image quality, simultaneously. For a given message length, StegErmelc flexibly scales its carrier capacity to accommodate the message while trading off with stego detectability. At full capacity, StegErmelc has comparable carrier capacity relative to the existing methods. When embedding the same amount of information, StegErmelc remarkably reduces the stego detection rate to about 0.3-0.5 lower than that of the existing methods considered, and consequently StegErmelc can withstand blind steganalyzer when embedding up to 0.10 bpc. Under the same condition, StegErmelc produces stego image with quality higher than that of the existing methods considered. Graphical comparison with three additional evaluation metrics is also presented to show the relative performance of StegErmelc with respect to the existing methods considered.

A fast class-of-service oriented packet scheduling (FCOPS) has a service fairness problem since a credit pool for a service class is initialized at the beginning of a transmission cycle whose starting moment is fixed at a specific ONU. To remedy the service unfairness of FCOPS, we suggest an enhanced class-of-service oriented packet scheduling (ECOPS) that uses a new initialization cycle whose starting moment is fairly distributed to each ONU. Also, ECOPS generates a colorless grant to utilize the resource wastage, when traffic is light and the total sum of grants of an ONU is less than a minimum size. Using simulation, we validate ECOPS as superior to FCOPS in the mean delay and the service fairness.