A non-deterministic finite automaton (NFA)-based parallel string matching scheme is proposed. To parallelize the operations of NFAs, a graphic processing unit (GPU) is adopted. Considering the resource occupancy of threads and size of the shared memory, the optimized resource allocation is performed in the proposed string matching scheme. Therefore, the performance is enhanced significantly in all evaluations.

In this paper, we propose a new paradigm of deterministic PSO, named piecewise-linear particle swarm optimizer (PPSO). In PPSO, each particle has two search dynamics, a convergence mode and a divergence mode. The trajectory of each particle is switched between the two dynamics and is controlled by parameters. We analyze convergence condition of each particle and investigate parameter conditions to allow particles to converge to an equilibrium point through numerical experiments. We further compare solving performances of PPSO. As a result, we report here that the solving performances of PPSO are substantially the same as or superior to those of PSO.

When debugging bugs, programmers often prepare test cases to reproduce buggy behaviours. However, for concurrent programs, test cases alone are typically insufficient to reproduce buggy behaviours, due to the nondeterminism of multi-threaded executions. In literature, various approaches have been proposed to reproduce buggy behaviours for concurrency bugs deterministically, but to the best of our knowledge, they are still limited. In particular, we have recognized three debugging scenarios from programming practice, but existing approaches can handle only one of the scenarios. In this paper, we propose a novel approach, called SPDebugger, that provides finer-grained thread controlling over test cases, programs under test, and even third party library code, to reproduce the predesigned thread execution schedule. The evaluation shows that SPDebugger handles more debugging scenarios than the state-of-the-art tool, called IMUnit, with similar human effort.

We consider a decentralized similarity control problem for composite nondeterministic discrete event systems, where each subsystem has its own local specification and the entire specification is described as the synchronous composition of local specifications. We present necessary and sufficient conditions for the existence of a complete decentralized supervisor that solves a similarity control problem under the assumption that any locally uncontrollable event is not shared by other subsystems. We also show that the system controlled by the complete decentralized supervisor that consists of maximally permissive local supervisors is bisimilar to the one controlled by the maximally permissive monolithic supervisor under the same assumption.

In ad hoc networks, broadcast forwarding protocols called OR (opportunistic routing) have been proposed to gain path diversity for higher packet delivery rates and shorter end-to-end delays. In general backoff-based OR protocols, each receiver autonomously makes a forwarding decision by using certain metrics to determine if a random backoff time is to be applied. However, each forwarder candidate must wait for the expiration of the backoff timer before forwarding a packet. Moreover, they cannot gain path diversity if the forwarding path includes local sparse areas, and this degrades performance as it strongly depends on the terminal density. In this paper, we propose a novel OR protocol called PRIOR (prioritized forwarding for opportunistic routing). In PRIOR, a terminal, called a prioritized forwarder and which forwards packets without using a backoff time, is selected from among the neighbours. In addition, PRIOR uses lightweight hop-by-hop retransmission control to mitigate the effect of terminal density. Moreover, we introduce an enhancement to PRIOR to reduce unnecessary forwarding by using an explicit acknowledgement. We evaluate PRIOR in comparison with conventional protocols in computer simulations.

The self-organizing nature of ad hoc networks is a good aspect in that terminals are not dependent on any infrastructure, that is, networks can be formed with decentralized and autonomous manner according to communication demand. However, this characteristic might affect the performance in terms of stability, reliability and so forth. Moreover, ad hoc networks face a scalability problem, which arise when the number of terminals in a network increases or a physical network domain expands, due to the network capacity limitation caused by the decentralized and the autonomous manner. Regarding this problem, some hierarchical and cluster-based routings have been proposed to effectively manage the networks. In this paper, we apply the concept of hierarchical routing and clustering to opportunistic routing, which can forward packets without using any pre-established path to achieve a path diversity gain with greater reachability. The simulation results show that the proposed method can achieve 11% higher reliability with a reasonable end-to-end delay in dense environments and 30% higher in large-scale networks.

This paper proposes a Bayesian approach to image recognition based on separable lattice hidden Markov models (SL-HMMs). The geometric variations of the object to be recognized, e.g., size, location, and rotation, are an essential problem in image recognition. SL-HMMs, which have been proposed to reduce the effect of geometric variations, can perform elastic matching both horizontally and vertically. This makes it possible to model not only invariances to the size and location of the object but also nonlinear warping in both dimensions. The maximum likelihood (ML) method has been used in training SL-HMMs. However, in some image recognition tasks, it is difficult to acquire sufficient training data, and the ML method suffers from the over-fitting problem when there is insufficient training data. This study aims to accurately estimate SL-HMMs using the maximum a posteriori (MAP) and variational Bayesian (VB) methods. The MAP and VB methods can utilize prior distributions representing useful prior information, and the VB method is expected to obtain high generalization ability by marginalization of model parameters. Furthermore, to overcome the local maximum problem in the MAP and VB methods, the deterministic annealing expectation maximization algorithm is applied for training SL-HMMs. Face recognition experiments performed on the XM2VTS database indicated that the proposed method offers significantly improved image recognition performance. Additionally, comparative experiment results showed that the proposed method was more robust to geometric variations than convolutional neural networks.

In this paper, we propose and analyze the opportunistic amplify-and-forward (AF) relaying scheme using antenna selection in conjunction with different adaptive transmission techniques over Rayleigh fading channels. In this scheme, the best antenna of a source and the best relay are selected for communication between the source and destination. Closed-form expressions for the outage probability and average symbol error rate (SER) are derived to confirm that increasing the number of antennas is the best option as compared with increasing the number of relays. We also obtain closed-form expressions for the average channel capacity under three different adaptive transmission techniques: 1) optimal power and rate adaptation; 2) constant power with optimal rate adaptation; and 3) channel inversion with a fixed rate. The channel capacity performance of the considered adaptive transmission techniques is evaluated and compared with a different number of relays and various antennas configurations for each adaptive technique. Our derived analytical results are verified through extensive Monte Carlo simulations.

The introduction of deep neural networks (DNNs) leads to a significant improvement of the automatic speech recognition (ASR) performance. However, the whole ASR system remains sophisticated due to the dependent on the hidden Markov model (HMM). Recently, a new end-to-end ASR framework, which utilizes recurrent neural networks (RNNs) to directly model context-independent targets with connectionist temporal classification (CTC) objective function, is proposed and achieves comparable results with the hybrid HMM/DNN system. In this paper, we investigate per-dimensional learning rate methods, ADAGRAD and ADADELTA included, to improve the recognition of the end-to-end system, based on the fact that the blank symbol used in CTC technique dominates the output and these methods give frequent features small learning rates. Experiment results show that more than 4% relative reduction of word error rate (WER) as well as 5% absolute improvement of label accuracy on the training set are achieved when using ADADELTA, and fewer epochs of training are needed.

In the conventional cellular macrocell implementation strategy, the main base station transmits the radio signals in the omnidirectional manner in order to provide a wide range of cellular transmission to the users. In reality, however, the users move from one place to another depending on their activities, hence, sometimes this creates areas where no user exists inside the macrocell. Nevertheless, the base station continues to transmit the radio signals to all the coverage areas due to its involuntary manner, thus causing waste of energy. In our previous work, an energy efficient LTE macrocell base station scheme based on hourly user location distribution, which utilized opportunistic beamforming, was proposed in order to provide the cellular transmission only to the area where the user density is high. The drawback of this scheme was that there were many users who cannot receive the cellular transmission because of the limitation of the beamforming shape. In this paper, to overcome this difficulty, a new energy efficient macrocell strategy will be proposed. Here, additional low power consumption femtocell access points are deployed inside the macrocell to support the energy efficient opportunistic beamforming based on the hourly user location distribution. Concretely, the femtocell access points are woken up only when the active calling users exist inside its range. The proposed new strategy will be evaluated in terms of the hourly successful calling user ratio, the total power consumption and the hourly average downlink throughput compared with the previously proposed beamforming transmission strategy and the conventional omnidirectional transmission. The results will show the effectiveness of the proposed strategy in providing an energy efficient cellular macrocell system with high quality cellular services.

A parallel Aho-Corasick (AC) approach, named PAC-k, is proposed for string matching in deep packet inspection (DPI). The proposed approach adopts graphic processing units (GPUs) to perform the string matching in parallel for high throughput. In parallel string matching, the boundary detection problem happens when a pattern is matched across chunks. The PAC-k approach solves the boundary detection problem because the number of characters to be scanned by a thread can reach the longest pattern length. An input string is divided into multiple sub-chunks with k characters. By adopting the new starting position in each sub-chunk for the failure transition, the required number of threads is reduced by a factor of k. Therefore, the overhead of terminating and reassigning threads is also decreased. In order to avoid the unnecessary overlapped scanning with multiple threads, a checking procedure is proposed that decides whether a new starting position is in the sub-chunk. In the experiments with target patterns from Snort and realistic input strings from DEFCON, throughputs are enhanced greatly compared to those of previous AC-based string matching approaches.

DTNs (Delay/Disruption-Tolerant Networks) composed of mobile nodes in low node-density environments have attracted considerable attention in recent years. In this paper, we propose a CD-BCAST (Contact Duration BroadCAST) mechanism that can reduce the number of message forwardings while maintaining short message delivery delays in DTNs composed of mobile nodes. The key idea behind CD-BCAST is to increase the probability of simultaneous forwarding by intentionally delaying message forwarding based on the contact duration distribution measured by each node. Through simulations, we show that CD-BCAST needs substantially less message forwardings than conventional mechanisms and it does not require parameter tuning under varieties of communication ranges and node densities.

Spatial compressive sensing (SCS) has recently been applied to direction-of-arrival (DOA) estimation, owing to its advantages over conventional versions. However the performance of compressive sensing (CS)-based estimation methods degrades when the true DOAs are not exactly on the discretized sampling grid. We solve the off-grid DOA estimation problem using the deterministic maximum likelihood (DML) estimation method. In this letter, on the basis of the convexity of the DML function, we propose a computationally efficient algorithm framework for off-grid DOA estimation. Numerical experiments demonstrate the superior performance of the proposed methods in terms of accuracy, robustness and speed.

Routing is a challenging issue in mobile social networks (MSNs) because of time-varying links and intermittent connectivity. In order to enable nodes to make right decisions while forwarding messages, exploiting social relationship has become an important method for designing efficient routing protocols in MSNs. In this paper, we first use the temporal evolution graph model to accurately capture the dynamic topology of the MSN. Based on the model, we introduce the social relationship metric for detecting the quality of human social relationship from contact history records. Utilizing this metric, we propose social relationship based betweenness centrality metric to identify influential nodes to ensure messages forwarded by the nodes with stronger social relationship and higher likelihood of contacting other nodes. Then, we present SRBet, a novel social-based forwarding algorithm, which utilizes the aforementioned metric to enhance routing performance. Simulations have been conducted on two real world data sets and results demonstrate that the proposed forwarding algorithm achieves better performances than the existing algorithms.

OSEK/VDX, a standard for an automobile OS, has been widely adopted by many manufacturers to design and develop a vehicle-mounted OS. With the increasing functionalities in vehicles, more and more complex applications are be developed based on the OSEK/VDX OS. However, how to ensure the reliability of developed applications is becoming a challenge for developers. To ensure the reliability of developed applications, model checking as an exhaustive technique can be applied to discover subtle errors in the development process. Many model checkers have been successfully applied to verify sequential software and general multi-threaded software. However, it is hard to directly use existing model checkers to precisely verify OSEK/VDX applications, since the execution characteristics of OSEK/VDX applications are different from the sequential software and general multi-threaded software. In this paper, we describe and develop an approach to translate OSEK/VDX applications into sequential programs in order to employ existing model checkers to precisely verify OSEK/VDX applications. The value of our approach is that it can be considered as a front-end translator for enabling existing model checkers to verify OSEK/VDX applications.

Most existing deterministic multithreading systems are costly on pipeline parallel programs due to load imbalance. In this letter, we propose a Load-Balanced Deterministic Runtime (LBDR) for pipeline parallelism. LBDR deterministically takes some tokens from non-synchronization-intensive threads to synchronization-intensive threads. Experimental results show that LBDR outperforms the state-of-the-art design by an average of 22.5%.

Novel deterministic digital calibration of pipelined ADC has been proposed and analyzed theoretically. Each MDAC is dithered exploiting its inherent redundancy during the calibration. The dither enables fast accurate convergence of calibration without requiring any accurate reference signal and hence with minimum area and power overhead. The proposed calibration can be applied to both the 1.5-bit/stage MDAC and the multi-bit/stage MDAC. Due to its simple structure and algorithm, it can be modified to the background calibration easily. The effectiveness of the proposed calibration has been confirmed by both the extensive simulations and the measurement of the prototype 0.13-µm-CMOS 50-MS/s pipelined ADC using the op-amps with only 37-dB gain. As expected, SNDR and SFDR have improved from 35.5dB to 58.1dB and from 37.4dB to 70.4dB, respectively by the proposed calibration.

The mobile cloud computing (MCC) paradigm is aimed at integrating mobile devices with cloud computing. In the client-server architecture of MCC, mobile devices offload tasks to the cloud to utilize the computation and storage resources of data centers. However, due to the rapid increase in the traffic demand and complexity of mobile applications, service providers have to continuously upgrade their infrastructures at great expense. At the same time, modern mobile devices have greater resources (communication, computation, and sensing), and these resources are not always fully utilized by device users. Therefore, mobile devices, from time to time, encounter other devices that could provide resources to them. Because the amount of such resources has increased with the number of mobile devices, researchers have begun to consider making use of these resources, located at the “edge” of mobile networks, to increase the scalability of future information networks. This has led to a cooperation based architecture of MCC. This paper reports the concept and design of an resource sharing mechanism that utilize resources in mobile devices through opportunistic contacts between them. Theoretical models and formal definitions of problems are presented. The efficiency of the proposed mechanism is validated through formal proofs and extensive simulation.

Researchers have developed several social-based routing protocols for delay tolerant networks (DTNs) over the past few years. Two main routing metrics to support social-based routing in DTNs are centrality and similarity metrics. These two metrics help packets decide how to travel through the network to achieve short delay or low drop rate. This study presents a new routing scheme called Community-Relevance based Opportunistic routing (CROP). CROP uses a different message forwarding approach in DTNs by combining community structure with a new centrality metric called community relevance. One fundamental change in this approach is that community relevance values do not represent the importance of communities themselves. Instead, they are computed for each community-community relationship individually, which means that the level of importance of one community depends on the packet's destination community. The study also compares CROP with other routing algorithms such as BubbleRap and SimBet. Simulation results show that CROP achieves an average delivery ratio improvement of at least 30% and can distribute packets more fairly within the network.

In this paper, we analyze the performance of a dual-hop multiuser amplify-and-forward (AF) relay network with the effect of the feedback delay, where the source and each of the K destinations are equipped with Nt and Nr antennas respectively, and the relay is equipped with a single antenna. In the relay network, multi-antenna and multiuser diversities are guaranteed via beamforming and opportunistic scheduling, respectively. To examine the impact of delayed feedback, the new exact analytical expressions for the outage probability (OP) and symbol error rate (SER) are derived in closed-form over Rayleigh fading channel, which are useful for a large number of modulation schemes. In addition, we present the asymptotic expressions for OP and SER in the high signal-to-noise ratio (SNR) regime, from which we gain an insight into the system performance with deriving the diversity order and array gain. Moreover, based on the asymptotic expressions, we determine power allocation among the network nodes such that the OP is minimized. The analytical expressions are validated by Monte-Carlo simulations.