Automatic recognition of finger gestures can be used for promotion of life quality. For example, a senior citizen can control the home appliance, call for help in emergency, or even communicate with others through simple finger gestures. Here, we focus on one-stroke finger gesture, which are intuitive to be remembered and performed. In this paper, we proposed and evaluated an accelerometer-based method for detecting the predefined one-stroke finger gestures from the data collected using a MEMS 3D accelerometer worn on the index finger. As alternative to the optoelectronic, sonic and ultrasonic approaches, the accelerometer-based method is featured as self-contained, cost-effective, and can be used in noisy or private space. A compact wireless sensing mote integrated with the accelerometer, called MagicRing, is developed to be worn on the finger for real data collection. A general definition on one-stroke gesture is given out, and 12 kinds of one-stroke finger gestures are selected from human daily activities. A set of features is extracted among the candidate feature set including both traditional features like standard deviation, energy, entropy, and frequency of acceleration and a new type of feature called relative feature. Both subject-independent and subject-dependent experiment methods were evaluated on three kinds of representative classifiers. In the subject-independent experiment among 20 subjects, the decision tree classifier shows the best performance recognizing the finger gestures with an average accuracy rate for 86.92 %. In the subject-dependent experiment, the nearest neighbor classifier got the highest accuracy rate for 97.55 %.

In this paper, we address adaptive link switching over heterogeneous wireless access networks including IEEE 802.11. When an IEEE 802.11 link is congested, the transmission link of a terminal with multi-RATs (radio access technologies) is switched to another radio access systems. To this end, we propose link-level metrics of LC (link cost) and AC (access cost) for quantifying TCP congestion over IEEE 802.11 networks. The proposed metric can be easily measured at a local wireless terminal, and that enables each multi-RAT terminal to work in a distributed way. Through various indoor and outdoor experiments using a test-bed system, we verify that the proposed link level metrics are good indicators of TCP traffic congestion. Experimental results show that the proposed metrics can detect congestion occurrence quickly, and avoid the TCP throughput degradation of other neighboring terminals, when they are used for transmission link switching.

This paper proposes an unsupervised word segmentation algorithm that identifies word boundaries in continuous source language text in order to improve the translation quality of statistical machine translation (SMT) approaches. The method can be applied to any language pair in which the source language is unsegmented and the target language segmentation is known. In the first step, an iterative bootstrap method is applied to learn multiple segmentation schemes that are consistent with the phrasal segmentations of an SMT system trained on the resegmented bitext. In the second step, multiple segmentation schemes are integrated into a single SMT system by characterizing the source language side and merging identical translation pairs of differently segmented SMT models. Experimental results translating five Asian languages into English revealed that the proposed method of integrating multiple segmentation schemes outperforms SMT models trained on any of the learned word segmentations and performs comparably to available monolingually built segmentation tools.

Object Constraint Language (OCL) is frequently applied in software development for stipulating formal constraints on software models. Its platform-independent characteristic allows for wide usage during the design phase. However, application in platform-specific processes, such as coding, is less obvious because it requires usage of bespoke tools for that platform. In this paper we propose an approach to generate assertion code for OCL constraints for multiple platform specific languages, using a unified framework based on structural similarities of programming languages. We have succeeded in automating the process of assertion code generation for four different languages using our tool. To show effectiveness of our approach in terms of development effort, an experiment was carried out and summarised.

This paper presents a robust framework of human-computer interaction from the hand gesture vision in the presence of realistic and challenging scenarios. To this end, several novel components are proposed. A hybrid approach is first proposed to automatically infer the beginning position of hand gestures of interest via jointly optimizing the regions given by an offline skin model trained from Gaussian mixture models and a specific hand gesture classifier trained from the Adaboost technique. To consistently track the hand in the context of using kernel based tracking, a semi-supervised feature selection strategy is further presented to choose the feature subspaces which appropriately represent the properties of offline hand skin cues and online foreground-background-classification cues. Taking the histogram of oriented gradients as the descriptor to represent hand gestures, a soft-decision approach is finally proposed for recognizing static hand gestures at the locations where severe ambiguity occurs and hidden Markov model based dynamic gestures are employed for interaction. Experiments on various real video sequences show the superior performance of the proposed components. In addition, the whole framework is applicable to real-time applications on general computing platforms.

Per-flow unfairness of TCP throughput in the IEEE 802.11 wireless LAN (WLAN) environment has been reported in past literature. A number of researchers have proposed various methods for alleviating the unfairness; most require modification of MAC protocols or queue management mechanisms in access points. However, the MAC protocols of access points are generally implemented at hardware level, so changing these protocols is costly. As the first contribution of this paper, we propose a transport-layer solution for alleviating unfairness among TCP flows, requiring a small modification to TCP congestion control mechanisms only on WLAN stations. In the past literature on fairness issues in the Internet flows, the performance of the proposed solutions for alleviating the unfairness has been evaluated separately from the network bandwidth utilization, meaning that they did not consider the trade-off relationships between fairness and bandwidth utilization. Therefore, as the second contribution of this paper, we introduce a novel performance metric for evaluating trade-off relationships between per-flow fairness and bandwidth utilization at the network bottleneck. We confirm the fundamental characteristics of the proposed method through simulation experiments and evaluate the performance of the proposed method through experiments in real WLAN environments. We show that the proposed method can achieve better a trade-off between fairness and bandwidth utilization, regardless of vendor implementations of wireless access points and wireless interface cards.

Iris recognition is an important biometric method for personal identification. The accuracy of an iris recognition system highly depends on the success of an iris segmentation step. In this paper, a robust and accurate iris segmentation algorithm for closed-up NIR eye images is developed. The proposed method addressed problems of different characteristics of iris databases using local image properties. A precise pupil boundary is located with an adaptive thresholding combined with a gradient-based refinement approach. A new criteria, called a local signal-to-noise ratio (LSNR) of an edge map of an eye image is proposed for localization of the iris's outer boundary. The boundary is modeled with a weighted circular integral of LSNR optimization technique. The proposed method is experimented with multiple iris databases. The obtained results demonstrated that the proposed iris segmentation method is robust and desirable. The proposed method accurately segments iris region, excluding eyelids, eyelashes and light reflections against multiple iris databases without parameter tunings. The proposed iris segmentation method reduced false negative rate of the iris recognition system by half, compared to results obtained using Masek's method.

Various recent intelligent transport system projects are promoting vehicle safety and efficient vehicular traffic control all over the world. One of ITS applications is a system that solves road traffic problems by using vehicular communications technology. Inter-vehicle communication (IVC) is the communications technology for vehicles to exchange moving vehicle information by wireless networks without any base stations. The Vehicular Ad-hoc Network (VANET) is expected to provide new applications for passengers of vehicles by enabling vehicles to communicate with each other via IVC as well as with roadside base stations via roadside-to-vehicle communications. However, when each vehicle transmits its own information to neighboring vehicles, the amount of information being transmitted increases significantly. To solve this problem, we present a novel real-time recognition method for vehicular traffic congestion via VANET with IVC. Vehicles collect the original GPS information of other vehicles by communicating with each other, and they create content that may be useful for drivers by analyzing that original information. The proposed method can reduce the information amount and deliver the analyzed contents to other vehicles efficiently. Computer simulation results show that the proposed method provides real-time information of vehicular accidents and traffic congestion to distant vehicles accurately.

Quantity-based event reliability protocols have been proposed for reliable event detection in wireless sensor networks. They support the event reliability by achieving the desired number of data packets successfully transmitted from sensor nodes sensing an event to a sink by controlling the transport process. However, since many data collisions and buffer overflows frequently happen due to data congestions on limited data delivery paths from an event to a sink, the quantity-based event reliability protocols are hard to achieve the desired number due to lost data packets. Thus, this letter proposes a Quality-based Event Reliability Protocol (QERP) utilizing a property that the data packets from sensor nodes have different Contribution Degree (CD) values for event detection according to their environmental conditions. QERP selects sensor nodes to forward their data packets according to CD, and differentially transports the data packets by CD-based buffer management and load balancing.

The safety applications for cooperative driving in VANETs, typically require the dissemination of safety-related information to all vehicles with high reliability and a strict timeline. However, due to the high vehicle mobility, dynamic traffic density, and a self-organized network, Safety message dissemination has a special challenge to efficiently use the limited network resources to satisfy its requirements. With this motivation, we propose a novel broadcasting protocol referred to as congestion awareness multi-hop broadcasting (CAMB) based loosely on a TDMA-like transmission scheduling scheme. The proposed protocol was evaluated using different traffic scenarios within both a realistic channel model and an 802.11p PHY/MAC model in our simulation. The simulation results showed that the performance of our CAMB protocol was better than those of the existing broadcasting protocols in terms of channel access delay, packet delivery ratio, end-to-end delay, and network overhead.

In this paper, we discuss the parallel degree of well-structured workflow nets, WF-nets, for short. First, we give the definition of parallel degree, PARAdeg, for WF-nets. Second, we show it is intractable to compute the value of PARAdeg for acyclic well-structured WF-nets. Next we construct two heuristic algorithms to compute the value. The first algorithm is focused on nest structure and the second one is focused on the longest path. Finally, we perform an experiment to compare the two algorithms and the result is that the accuracy of the first algorithm based on nest structure was higher than that of the second one based on the longest path for most well-structured WF-nets and the accuracy of the second one is better than that of first one only when the well-structured workflow nets are mainly composed by the parallel structures.

TCP's performance significantly degrades in multi-hop wireless networks because TCP's retransmission timeouts (RTOs) are frequently triggered regardless of congestion due to sudden delay and wireless transmission errors. Such RTOs non-related to congestions lead to TCP's unnecessary behaviors such as retransmitting all the outstanding packets which might be located in the bottleneck queue or reducing sharply its sending rate and increasing exponentially its back-off value even when the network is not congested. Since traditional TCP has no ability to identify if a RTO is triggered by congestion or not, it is unavoidable for TCP to underutilize available bandwidth by blindly reducing its sending rate for all the RTOs. In this paper, we propose an algorithm to detect the RTOs non-related to congestion in order to let TCP respond to the RTOs differently according to the cause. When a RTO is triggered, our algorithm estimates the queue usage in the network path during the go-back-N retransmissions, and decides if the RTO is triggered by congestion or not when the retransmissions end. If any RTO non-related to congestion is detected, our algorithm prevents TCP from increasing unnecessarily its back-off value as well as reducing needlessly its sending rate. Throughout the extensive simulation scenarios, we observed how frequently RTOs are triggered regardless of congestion, and evaluated our algorithm in terms of accuracy and goodput. The experiment results show that our algorithm has the highest accuracy among the previous works and the performance enhancement reaches up to 70% when our algorithm is applied to TCP.

This paper presents an autonomous traffic engineering framework, named ATE, a highly efficient data dissemination mechanism for multipath data forwarding in Wireless Sensor Networks (WSNs). The proposed ATE has several salient features. First, ATE utilizes three coordinating schemes: an incipient congestion inference scheme, an accurate link quality estimation scheme and a dynamic traffic diversion scheme. It significantly minimizes packet drops due to congestion by dynamically and adaptively controlling the data traffic over congested nodes and/or poorer quality links, and by opportunistically exploiting under-utilized nodes for traffic diversion, while minimizing the estimation and measurement overhead. Second, ATE can provide with high application fidelity of the network even for increasing values of bit error rates and node failures. The proposed link quality estimation and congestion inference schemes are light weight and distributed, improving the energy efficiency of the network. Autonomous Traffic Engineering has been evaluated extensively via NS-2 simulations, and the results have shown that ATE provides a better performance with minimum overhead than those of existing approaches.

In this paper, we reveal inherent robustness issues of XCP (eXplicit Control Protocol), and propose extensions to XCP for increasing its robustness. XCP has been proposed as an efficient transport-layer protocol for wide-area and high-speed network. XCP is a transport-layer protocol that performs congestion control based on explicit feedback from routers. In the literature, many performance studies of XCP have been performed. However, the effect of traffic dynamics on the XCP performance has not been fully investigated. In this paper, through simulation experiments, we first show that XCP has the following problems: (1) the bottleneck link utilization is lowered against XCP traffic dynamics, and (2) operation of XCP becomes unstable in a network with both XCP and non-XCP traffic. We then propose XCP-IR (XCP with Increased Robustness) that operates efficiently even for dynamic XCP and non-XCP traffic.

Let T be a tree with n nodes, in which each edge is associated with a length and a weight. The density-constrained longest (heaviest) path problem is to find a path of T with maximum path length (weight) whose path density is bounded by an upper bound and a lower bound. The path density is the path weight divided by the path length. We show that both problems can be solved in optimal O(n log n) time.

Ubiquitous technologies such as sensor network and RFID have enabled companies to realize more rapid and agile manufacturing and service systems. In this paper, we addresses how the huge amount of real-time events coming from these devices can be filtered and integrated to business process such as manufacturing, logistics, and supply chain process. In particular, we focus on complex event processing of sensor and RFID events in order to integrate them to business rules in business activities. We also illustrate a ubiquitous event processing system, named ueFilter, which helps to filter and aggregate sensor event, to detect event patterns from sensors and RFID by means of event pattern languages (EPL), and trigger event-condition-action (ECA) in logistics processes.

We present a session initiation protocol (SIP) network design for a voice-over-IP network to prevent congestion caused by people calling friends and family after a disaster. The design increases the capacity of SIP servers in a network by using all of the SIP servers equally. It takes advantage of the fact that equipment for voice data packets is different from equipment for signaling packets in SIP networks. Furthermore, the design achieves simple routing on the basis of telephone numbers. We evaluated the performance of our design in preventing congestion through simulation. We showed that the proposed design has roughly 20 times more capacity, which is 57 times the normal load, than the conventional design if a disaster were to occur in Niigata Prefecture struck by the Chuetsu earthquake in 2004.

In this letter, we propose three algorithms to reduce congestion for greedy forwarding, which is one of common principles in geographic routing. The new algorithms take geographic position information and network congestion metrics to balance traffic. When these algorithms are combined with well-known GPSR protocol [1], packet delivery ratio is enhanced by reducing number of lost packets in a buffer. In addition, end-to-end delay is reduced by bypassing congested nodes. These features are evaluated and analyzed through several simulation results.

This paper presents Probabilistic Bi-polar Radial Reach Correlation (PrBPRRC), a change detection method that is robust against illumination changes and background movements. Most of the traditional change detection methods are robust against either illumination changes or background movements; BPRRC is one of the illumination-robust change detection methods. We introduce a probabilistic background texture model into BPRRC and add the robustness against background movements including foreground invasions such as moving cars, walking people, swaying trees, and falling snow. We show the superiority of PrBPRRC in the environment with illumination changes and background movements by using three public datasets and one private dataset: ATON Highway data, Karlsruhe traffic sequence data, PETS 2007 data, and Walking-in-a-room data.

With the emergence of bandwidth-greedy application services, high-speed transport protocols are expected to effectively and aggressively use large amounts of bandwidth in current broadband and multimedia networks. However, when high-speed transport protocols compete with other standard TCP flows, they can occupy most of the available bandwidth leading to disruption of service. To deploy high-speed transport protocols on the Internet, such unfair situations must be improved. In this paper, therefore, we propose a method to improve fairness, called Kyushu-TCP (KTCP), which introduces a non-aggressive period in the congestion avoidance phase to give other standard TCP flows more chances of increasing their transmission rates. This method improves fairness in terms of the throughput by estimating the stably available bandwidth-delay product and adjusting its transmission rate based on this estimation. We show the effectiveness of the proposed method through simulations.