The task of 3D physical design is to map a circuit from a netlist (structural) representation into a geometric (physical) representation according to a specific 3D IC technology with multiple active device layers. This paper discusses the recent progress made on the major steps in 3D physical design, including 3D floorplanning, 3D placement, 3D routing and thermal through-silicon via (TS via) planning, and outlines the challenges ahead.
Due to the lack of widely applicable fault models, testing for analog, mixed-signal (AMS), and radio frequency (RF) circuits has been, and will continue to be, primarily based on checking their conformance to the specifications. However, with the higher level of integration and increased diversity of specifications for measurement, specification-based testing is becoming increasingly difficult and costly. As a result, design for testability (DfT), combined with automatic test stimuli generation, has gradually become a necessity to ensure test quality at an affordable cost. This paper provides an overview of cost-effective test techniques that either enhance circuit testability, or enable built-in self-test (BIST) for integrated AMS/RF frontends. In addition, we introduce several low-cost testing paradigms including the loopback testing, alternate testing, and digitally-assisted testing that offer the promise of significant test cost reduction with little or even no compromise in test quality. Moving forward, in addition to screening the defective parts, testing will play an increasingly important role in supporting other post-silicon quality assurance functions such as post-silicon validation, tuning, and in-field reliability of system chips.
We have developed an “XC core” processor that achieves low cost, high performance, and low power consumption through the use of a highly parallel SIMD architecture (the SIMD mode), as well as achieves high flexibility by morphing into a MIMD architecture (MIMD mode). In this paper, we evaluate the effectiveness of the MIMD mode by using a white line detection algorithm for open roads. Our evaluation shows that the algorithm can be processed in real time (less than 33ms) by using the MIMD mode to execute verification of white line segments, which is a part of the algorithm not suitable to be executed by the SIMD mode. We also show that the verification can be executed five times faster by using region of interest (ROI) transfer instructions to efficiently transfer the ROI of an image. Furthermore, we also measured the execution time in the MIMD mode with changing the number of processing units (PUs) used, from 2 to 4, 8, 16 and 32. The measured results show that the performance improvement rate slows down when using more than 16 PUs in the MIMD mode, mainly due to insufficient parallelism in the verification process. Overall, a 10.68 times speedup was achieved by using 32 PUs in the MIMD mode, compared with only using the SIMD mode.
This paper presents a novel framework to generating efficient custom instructions for common configurable processors with limited numbers of I/O ports in the register files and fixed-length instruction formats, such as RISCs. Unlike previous approaches which generate a single custom instruction from each subgraph, our approach generates a sequence of multiple custom instructions from each subgraph by applying high-level synthesis techniques such as scheduling and binding to the subgraphs. Because of this feature, our approach can provide both of the following two advantages simultaneously: (1) generation of effective custom instructions from Multiple Inputs Multiple Outputs (MIMO) subgraphs without any change in the configurable processor hardware and the instruction format, and (2) resource sharing among custom instructions. We performed synthesis, placement and routing of the automatically generated Custom Functional Units (CFUs) on an FPGA. Experimental results showed that our approach could generate custom instructions with significant speedups of 28% on average compared to a state-of-the-art framework of custom instruction generation for configurable processors with limited numbers of I/O ports in the register file and fixed-length instruction formats.
Multi-FPGA prototyping systems are widely used to verify logic circuit designs. To implement a large circuit using such a system, the circuit is partitioned into multiple FPGAs. Subsequently, sub-circuits assigned to FPGAs are connected using interconnection resources among the FPGAs. Because of limited resources, time-multiplexed I/Os are used to accommodate all signals in exchange for system speed. In this study, we propose an optimization method of inter-FPGA connections for multi-FPGA systems with time-multiplexed I/Os to shorten the verification time by accelerating the systems. Our method decides whether each inter-FPGA signal is transferred by a normal I/O or a time-multiplexed I/O, which is slower than a normal I/O but can transfer multiple signals. Our method optimizes inter-FPGA connections not only between a single FPGA pair, but among all the FPGAs. Experiments showed that for four-way partitioned circuits, our method obtains an average system clock period 16.0% shorter than that of a conventional method.
This paper presents a high-level synthesizer to map a complete program efficiently on a dynamically reconfigurable processor (DRP). Initially, we introduce our DRP architecture, which is suitable for control-intensive programs since it has a stand-alone finite state machine that switches “contexts” consisting of many processing elements (PEs). Then, we propose three new techniques optimized for our DRP. Firstly, we explain how synthesized control steps are mapped onto the contexts. Several control steps are combined as a context to utilize PEs efficiently since each control step does not require the same amount of operational units. Secondly, we describe a modulo scheduling algorithm for loop pipelining, considering both spatial and time dimensions of our DRP. Lastly, we explain a scheduling technique to optimize clock frequency, which can take advantage of multiplexer, wire and routing switch delays. We have demonstrated a JPEG-based image decoder example to evaluate our methods. Experimental results show that high area efficiency is achieved by balancing the number of PEs between contexts. Despite an overall increase in performance on pipelining of 3.6 times that without pipelining, the number of operational units increased by a factor of 2.2. The clock frequency is maximized with accurate delay estimation.
This paper proposes programmable architectures and design methods for numeric function generators (NFGs) of two-variable functions. To realize a two-variable function in hardware, we partition a given domain of the function into segments, and approximate the function by a polynomial in each segment. This paper introduces two planar segmentation algorithms that efficiently partition a domain of a two-variable function. This paper also introduces a design method for symmetric two-variable functions (i.e. f(X, Y)=f(Y, X)). This method can reduce the memory size needed for symmetric functions by nearly half with small speed penalty. The proposed architectures allow a systematic design of various two-variable functions. We compare our approach with one based on a one-variable NFG. FPGA implementation results show that, for a complicated function, our NFG achieves 57% of memory size and 60% of delay time of a circuit designed based on a one-variable NFG.
As the minimum feature size shrinks down far below sub-wavelength, Design for Manufacturability or layout regularity plays an important role for maintaining pattern fidelity in photolithography. However, it also incurs overheads in circuit performances due to parasitic capacitance. In this paper, we examine the effect of layout regularity on printability and circuit performance by lithography simulation and transistor-level simulation. It is shown that regularity-enhanced cells provide better Critical Dimension (CD) stability under defocus and lead to delay increase. Then we evaluate the effect of layout regularity by a real chip measurement in 90nm, 65nm and 45nm processes. For example, in a 65nm process, inverter Ring Oscillators (ROs) that have the smallest poly pitch with dummy-poly insertion exhibits 19% reduction of WID and D2D variation with delay overhead of 2.5%, compared to the ROs without dummy-poly insertion. However, we have observed that the effect of layout regularity varies depending on fabrication processes and circuit structures. It is therefore important to obtain the best trade-off among performance overhead and variability reduction for each process technology.
In these years, 3D-LSIs which consist of several silicon layers have been developed and attracted attention. For floorplaning of 3D-LSIs, a rectangular solid dissection, which is a dissection of a rectangular solid into smaller rectangular solids by planes, also has attracted attention and been studied much. However, not so many properties have been clarified about a rectangular solid dissection. This paper presents the relation between the number of rooms and that of walls in a rectangular solid dissection.
The prevalence of multi-tuners, high-definition digital video recorder systems and home networking is increasing the number of simultaneous streams that must be processed by recorder storage devices. Whereas recent hard-disk drives provide enough performance to theoretically handle such workloads, general purpose file systems and I/O schedulers used by operating systems such as Linux do not satisfy the quality of service (QoS) requirements necessary for efficient processing of real-time video streams. In this paper, we introduce the Audio/Video File System (AVFS) composed of a file system and a disk I/O scheduler optimized for handling simultaneous high bit-rate real-time streams. Using a precise QoS measurement method, evaluation results of a Linux implementation of AVFS show that, compared to traditional file systems such as ext3 and JFS, AVFS provides QoS guarantees for real-time streams and more stable performance.
In this paper, we study the problem of mining frequent diamond episodes efficientlyfrom an input event sequence with sliding a window. Here, a diamond episode is of the form a → E → b, which means that every event of E follows an event a and is followed by an event b. Then, we design a polynomial-delay and polynomial-space algorithm PolyFreqDmd that finds all of the frequent diamond episodes without duplicates from an event sequence in O(|Σ|2l) time per an episode and in O(|Σ|+l) space, where Σ and l are an alphabet and the length of the event sequence, respectively. Finally, we give experimental results on artificial and real-world event sequences with varying several mining parameters to evaluate the efficiency of the algorithm.
We have developed an alignment tool for comparing protein local surfaces (AltPS). This program enables efficient exhaustive searches of the entire protein surfaces, using a feature vector for a surface atom with 6 to 18 elements to describe the geometrical and physicochemical properties in the local environment, without referring sequence or fold homology. AltPS runs on a personal computer with the input of a pair of PDB coordinates and outputs similarity scores between identified similar surfaces, alignments of the surface atoms, and corresponding superposed coordinates, based on cluster analysis of similar surface regions. In this report, we present some results on the application of AltPS to several protein pairs with similar functions to identify similar functional sites. AltPS can be downloaded from http://d-search.atnifty.com/research.html
In this paper, we describe Topolo Surface, a 2D fiducial tracking system we developed. Topolo Surface is a prototype system that implements a novel fiducial tracking method based on the combination of topological region adjacency and angle information. Existing systems based only on topological region adjacency information, such as D-Touch and ReacTIVision, have several desirable features including fast processing speed and robustness against false positive detection. Yet, the method used in these systems also has several deficits. The unique ID range in existing topology-based methods is very narrow and the cost to generate the set of such unique fiducial markers can be computationally very expensive, especially when compared to existing matrix-based systems. Also, several useful techniques to improve robustness, such as CRC or hamming distance, cannot be applied to existing topology-based systems. Our novel fiducial tracking method utilizes the combination of topological region adjacency and angle information. By using topological information together with geometrical information, our prototype system achieved much larger unique ID range at very cheap computational cost to generate its fiducial markers. This is achieved while maintaining the desirable features of fast processing speed and robustness against false positives in a topology-based method. Also, CRC or hamming distance can be applied to our method to improve the robustness, if necessary.
To identify protein-protein interaction pairs with high accuracy, we propose a method for predicting these interactions based on characteristics obtained from protein-protein docking evaluations. Previous studies assumed that the required protein affinity strength for an interaction was not dependent on protein functions. However, the protein affinity strength appears to differ with different docking schemes, such as rigid-body or flexible docking, and these schemes may be related to protein functions. Thus, we propose a new scoring system that is based on statistical analysis of affinity score distributions sampled by their protein functions. As a result, of all possible protein pair combinations, a newly developed method improved prediction accuracy of F-measures. In particular, for bound antibody-antigen pairs, we obtained 50.0% recall (=sensitivity) with higher F-measures compared with previous studies. In addition, by combining two proposed scoring systems, Receptor-Focused Z-scoring and Ligand-Focused Z-scoring, further improvement was achieved. This result suggested that the proposed prediction method improved the prediction accuracy (i.e., F-measure), with few false positives, by taking biological functions of protein pairs into consideration.
A Monte Carlo based algorithm is proposed to predict gene regulatory network structure of mouse nuclear receptor superfamily, about which little is known although those genes are believed to be related with several difficult diseases. The gene expression data is regarded as sample vector trajectories from a stochastic dynamical system on a graph. The problem is formulated within a Bayesian framework where the graph prior distribution is assumed to follow a Zipf distribution. Appropriateness of a graph is evaluated by the graph posterior mean. The algorithm is implemented with the Exchange Monte Carlo method. After validation against synthesized data, an attempt is made to use the algorithm for predicting network structure of the target, the mouse nuclear receptor superfamily. Several remarks are made on the feasibility of the predicted network from a biological viewpoint.
Superinstruction is well-known techniques of improving performance of interpreters. Superinstructions eliminate jumps between VM operations (interpreter dispatch) and enable more optimizations in merged code. In past, processors with simple BTB-based branch predictors had high misprediction rate when executing interpreted code, resulting in high overhead of interpreter dispatch, so superinstructions were used to reduce it. However, this assumption is incorrect for Ruby on current hardware. Accordingly, using superinstructions for eliminating jump instructions only marginally improves performance. In this paper, we consider applying superinstructions differently to improve performance of floating point computation. We note that high percentage of objects allocated during numeric computation are boxed floating point values, meanwhile garbage collection takes significant part of the execution time. Using superinstructions composed from pairs of arithmetic operations we were able to reduce allocation of boxed floats by up to 36%, and obtain improvement in performance of up to 22%.
GridRPC is known as an effective programming model to develop Grid applications. However, it is still difficult for non-expert users to apply it efficiently. For example, a GridRPC application user needs to select computational resources, monitor the resources and estimate the application performance on the resources. In this paper, we propose InterS, an interactive scheduling system for GridRPC applications. First, the automatic scheduling mechanism provides resource allocation plans, from which the user can choose the most suitable one. Second, the execution advice mechanism helps the user to improve the performance of the application at run time while overload or failure on the resource(s) is(are) detected. Third, the scheduling policy mechanism provides the user with an interface in ClassAd format to define the scheduling policy applied in InterS. This paper also presents experimental results to show the advantage of interactive scheduling and how they can be performed at run time.
In this paper, we theoretically analyze a certain extension of a finite automaton, called a linear separation automaton (LSA). An LSA accepts a sequence of real vectors, and has a weight function and a threshold sequence at every state, which determine the transition from some state to another at each step. Transitions of LSAs are just corresponding to the behavior of perceptrons. We develop the theory of minimizing LSAs by using Myhill-Nerode theorem for LSAs. Its proof is performed as in the proof of the theorem for finite automata. Therefore we find that the extension to an LSA from the original finite automaton is theoretically natural.
This paper presents a novel labeling scheme for dynamic XML trees. The scheme employs history-offset encoding method for multidimensional datasets and takes advantage of this method by embedding an XML tree into a multidimensional extendible array. Even if structural updates are made on the XML tree, no relabeling of nodes is required under the support of extra data structure for preserving the document order. The most significant advantage of our scheme over other existing labeling schemes is that the storage cost for generated labels is very small irrespective of the order and the position of node insertions; in most of our competing schemes, the generated label size would become very large if the insertions occur around the same position. After describing our labeling scheme, label size, total label storage cost and node access performance are examined compared with other sophisticated schemes, such as ORDPATH, QED, DLN and Prime Numbering, and proves that our scheme outperforms these schemes in some criteria.
This paper presents an evolutionary synthesis of feature extraction programs for object recognition. The evolutionary synthesis method employed is based on linear genetic programming which is combined with redundancy-removed recombination. The evolutionary synthesis can automatically construct feature extraction programs for a given object recognition problem, without any domain-specific knowledge. Experiments were done on a lawn weed detection problem with both a low-level performance measure, i.e., segmentation accuracy, and an application-level performance measure, i.e., simulated weed control performance. Compared with four human-designed lawn weed detection methods, the results show that the performance of synthesized feature extraction programs is significantly better than three human-designed methods when evaluated with the low-level measure, and is better than two human-designed methods according to the application-level measure.
We propose an automatic and precise moving-object extraction method for use in video streams that can also be used for 3-D system applications. The method generates a statistical model for each pixel using several frames, and then uses it to generate trimap images. After manually initializing a frame, unknown regions are automatically determined either background or foreground for the rest of frames. The key technology proposed is an adaptive training scheme, which estimates detection thresholds locally through the algorithm, followed by matting approaches using an iterative process and weighted statistical distance minimization. Experiments demonstrate outperformance of our method for both indoor and outdoor video streams, and also for 3-D modeling and representation.
This paper presents a user-friendly texture mapping engine for semi-automatically texturing 3D models from real-world images. The engine implements a novel approach to simplify the user's interactions when registering images to geometry. Our approach has three benefits. First, the number of interactions required by the user is significantly reduced. Second, the system works well even with low-precision corrections by the user. Third, these interactions are simple because they consist only of dragging operations controlled via real-time feedback. The key idea is to take advantage of a three-dimensional orientation sensor attached to the camera so as to simplify the object pose estimation. Geometric computation is introduced to implement this idea. To robustly refine the pose, we implemented an existing tracking method by extensively taking advantage of Lie group formalism. A set of experiments demonstrating the efficiency and practicability of this approach was conducted.
A two-dimensional continuous dynamic programming (2DCDP) method is proposed for two-dimensional (2D) spotting recognition of images. Spotting recognition is the simultaneous segmentation and recognition of an image by optimal pixel matching between a reference image and an input image. The proposed method performs optimal pixel-wise image matching and 2D pixel alignment, which are not available in conventional algorithms. Experimental results show that 2DCDP precisely matches the pixels of nonlinearly deformed images.
In this paper, the authors attempt to develop a technique for the analysis of the motions of dances having no stylized motion structure, focusing on joint motions. The variance-covariance matrix given by the statistical analysis of the time-series data of joint motions is selected for the evaluation index characterizing dance motions. The application of the derived evaluation index to the representation of dissimilarity between dances is shown to be effective when the whole commonness appearing in both the dances compared should be considered. It is also confirmed that the application of multidimensional scaling (MDS) with the orthogonal rotation of coordinate axes is effective to extract the distribution feature of a database of dances. The evaluation items characterizing all the dances belonging to the database are automatically extracted by the analysis of correlation between the coordinate axes given by MDS and the elements of the variance-covariance matrix.
An ambient environment encompasses a wide variety of devices, various technologies and context of use to provide the users an environment that makes them feel comfortable and best suits to their needs. Although the Web-enabled mobile devices used in such an environment might vary in their capabilities, they would always be a good candidate for interaction with different applications in an ambient environment. We describe our framework for designing web applications based on context-of-device used where both the device capabilities and environmental changes are taken into consideration to design the user interface for specific devices. Our framework enables the web interface to be tailored on the devices based on their supporting capabilities and hence giving different presentation views to interact with the same application.
This work proposes space partitioning, a new approach to evolutionary many-objective optimization. The proposed approach instantaneously partitions the objective space into subspaces and concurrently searches in each subspace. A partition strategy is used to define a schedule of subspace sampling, so that different subspaces can be emphasized at different generations. Space partitioning is implemented with adaptive ε-ranking, a procedure that re-ranks solutions in each subspace giving selective advantage to a subset of well distributed solutions chosen from the set of solutions initially assigned rank-1 in the high dimensional objective space. Adaptation works to keep the actual number of rank-1 solutions in each subspace close to a desired number. The effects on performance of space partitioning are verified on MNK-Landscapes. Also, a comparison with two substitute distance assignment methods recently proposed for many-objective optimization is included.
In this article we investigate ‘real-time’ watermarking of single-sensor digital camera images (often called ‘raw’ images) and blind watermark detection in demosaicked images. We describe the software-only implementation of simple additive spread-spectrum embedding in the firmware of a digital camera. For blind watermark detection, we develop a scheme which adaptively combines the polyphase components of the demosaicked image, taking advantage of the interpolated image structure. Experimental results show the benefits of the novel detection approach for several demosaicking techniques.
The purpose of the work reported in this paper is to detect humans from images. This paper proposes a method for extracting feature descriptors consisting of co-occurrence histograms of oriented gradients (CoHOG). Including co-occurrence with various positional offsets, the feature descriptors can express complex shapes of objects with local and global distributions of gradient orientations. Our method is evaluated with a simple linear classifier on two well-known human detection benchmark datasets: “DaimlerChrysler pedestrian classification benchmark dataset” and “INRIA person data set”. The results show that our method reduces the miss rate by half compared with HOG, and outperforms the state-of-the-art methods on both datasets. Furthermore, as an example of a practical application, we applied our method to a surveillance video eight hours in length. The result shows that our method reduces false positives by half compared with HOG. In addition, CoHOG can be calculated 40% faster than HOG.
In this paper, we propose a new wavelet denoising method with edge preservation for digital images. Traditionally, most denoising methods assume additive Gaussian white noise or statistical models; however, we do not make such an assumption here. Briefly, the proposed method consists of a combination of dyadic lifting schemes and edge-preserving wavelet thresholding. The dyadic lifting schemes have free parameters, enabling us to construct filters that preserve important image features. Our method involves learning such free parameters based on some training images with and without noise. The learnt wavelet filters preserve important features of the original training image while removing noise from noisy images. We describe how to determine these parameters and the edge-preserving denoising algorithm in detail. Numerical image denoising experiments demonstrate the high performance of our method.
A very compact algorithm is presented for fundamental matrix computation from point correspondences over two images. The computation is based on the maximum likelihood (ML) principle, minimizing the reprojection error. The rank constraint is incorporated by the EFNS procedure. Although our algorithm produces the same solution as all existing ML-based methods, it is probably the most practical of all, being small and simple. By numerical experiments, we confirm that our algorithm behaves as expected.
In this paper, we present a projector-camera system for virtually altering the surface reflectance of a real object by projecting images onto it using projectors. The surface of the object is assumed to have an arbitrary shape and have a diffuse reflectance whose quantitative information is unknown. The system consists of multiple projectors and a camera. The proposed method first estimates the object surface along with the internal and external parameters of the projectors and the camera, based on the projection of structured patterns. It then improves the accuracy of surface normals by using the method of photometric stereo, where the same projectors are used as point sources of illumination. Owing to the combination of triangulation based on structured light projection and the method of photometric stereo, the surface normals of the object along with its surface shape can be accurately measured, which enables high-quality synthesis of virtual appearance. Our experimental system succeeded in giving a number of viewers a visual experience in which several plaster objects appeared as if their surfaces were made of different materials such as metals.
In this paper we describe a new technique for live video segmentation of human regions from dynamic backgrounds. Correct segmentations are produced in real-time even in severe background changes caused by camera movement and illumination changes. There are three key contributions. The first contribution is the employing of the thermal cue which proves to be very effective when fused with color. Second, we propose a new speed-up GraphCut algorithm by combining with the Bayesian estimation. The third contribution is a novel online learning method using cumulative histograms. The segmentation accuracy and speed are quite capable of the live video segmentation purpose.
Recently, various navigation systems have found a place in many aspects of our daily life. However, it is difficult for people to effectively use such a system because the ability to understand the maps generated by and the guidance given by the system differs from person to person. We think that the fact that the system gives the same guidance to everyone is a problem for some people. Therefore, it is necessary for us to investigate a person's ability to understand maps and the related guidance, that is, the human map-reading ability. Moreover, we consider that it is important that a navigation system analyzes the map-reading ability of its user and highlights the user's weaknesses in this aspect. In this paper, we have developed a system to simulate the way people find a particular place in real space. First, we performed outdoor experiments for studying how pedestrians find their way to a certain place by using the proposed system. Next, we proposed three major indicators of pedestrian behavior and finally measured the human map-reading ability using the proposed system. Consequently, we think that our approach will open up a new vista for personal guidance services.
This essay concerns the problems surrounding the use of the term “concept” in current ontology and terminology research. It is based on the constructive dialogue between realist ontology on the one hand and the world of formal standardization of health informatics on the other, but its conclusions are not restricted to the domain of medicine. The term “concept” is one of the most misused even in literature and technical standards which attempt to bring clarity. In this paper we propose to use the term “concept” in the context of producing defined professional terminologies with one specific and consistent meaning which we propose for adoption as the agreed meaning of the term in future terminological research, and specifically in the development of formal terminologies to be used in computer systems. We also discuss and propose new definitions of a set of cognate terms. We describe the relations governing the realm of concepts, and compare these to the richer and more complex set of relations obtaining between entities in the real world. On this basis we also summarize an associated terminology for ontologies as representations of the real world and a partial mapping between the world of concepts and the world of reality.
Structured prediction has become very important in recent years. A simple but notable class of structured prediction is one for sequences, so-called sequential labeling. For sequential labeling, it is often required to take a summation over all the possible output sequences, for instance when estimating the parameters of a probabilistic model. We cannot directly calculate such a summation from its definition in practice. Although the ordinary forward-backward algorithm provides an efficient way to do it, it is applicable to limited types of summations. In this paper, we propose a generalization of the forward-backward algorithm, by which we can calculate much broader types of summations than the conventional forward-backward algorithm. We show that this generalization subsumes some existing calculations required in past studies, and we also discuss further possibilities of this generalization.
Traditional video services on the Internet are of a broadcasting service nature such as streaming and video-on-demand (VoD). Recent services incorporate more of the interactive nature of network applications such as easy video sharing and those with a chat function. Meanwhile, we have been conducting experimental Internet broadcasting in practice and found it difficult for non-professional broadcasters to provide audiences with satisfactory contents since they do not have a large budget or the technical knowledge to produce broadcasting contents compared to the professional ones. In this paper, we propose an audience-driven broadcast service model in which audiences can send their wish to a broadcaster such that they would like to see some specific objects while broadcasting; the broadcaster can reply back to the request as well. We implemented a prototype system for audience-driven live broadcasting and studied its effects and problems based on the results from the experimental broadcast at our university graduation ceremony and our campus festival. This paper reports our experiments and findings of the audience-driven live broadcasting.
Recently, ubiquitous Internet-access services have been provided by Internet service providers (ISPs) by deploying wireless local area networks (LANs) in public spaces including stations, hotels, and coffee shops. The IEEE802.1X protocol is usually used for user authentications to allow only authorized users to access services. Then, although user personal information of access locations, services, and operations can be easily collected by ISPs and thus, their strict management has been demanded, it becomes very difficult when multiple ISPs provide roaming services by their corporations. In this paper, we present an anonymous IEEE802.1X authentication system using a group signature scheme to allow user authentication without revealing their identities. Without user identities, ISPs cannot collect personal information. As an efficient revocable group signature scheme, we adopt the verifier-local revocation (VLR) type with some modifications for use of the fast pairing computation. We show the implementation of our proposal and evaluation results where the practicality of our system is confirmed for up to 1, 000 revoked users.
Security software such as anti-virus software and personal firewall are usually installed in every host within an enterprise network. There are mainly two kinds of security software: signature-based software and anomaly-based software. Anomaly-based software generally has a “threshold” that discriminates between normal traffic and malware communications in network traffic observation. Such a threshold involves the number of packets used for behavior checking by the anomaly-based software. Also, it indicates the number of packets sent from an infected host before the infected host is contained. In this paper, we propose a mathematical model that uses discrete mathematics known as combinatorics, which is suitable for situations in which there are a small number of infected hosts. Our model can estimate the threshold at which the number of infected hosts can be suppressed to a small number. The result from our model fits very well with the result of computer simulation using typical existing scanning malware and a typical network.
Reprogramming sensor nodes is important for managing sensor networks. The latest reprogramming protocols use radio communication to distribute software data. In multi-base station sensor networks, the placement of the base stations affects several wireless reprogramming performance metrics. We developed a method for placing base stations, and we evaluated the features of software dissemination for multi-base station sensor networks. Simulations showed that the placement and number of base stations and the number of data segments were the key parameters in software dissemination.
This paper discusses a buffering strategy for a delay-tolerant multimedia sensor network (DTMSN), whose typical application is video surveillance. In DTMSN, a sensor node observes events around it and stores the data in its own buffer memory. All the data is collected to the sink. Sensor nodes have restrictions on buffer memory as well as battery capacity. The entire data size is much larger than a single node's memory size. Thus, developing a strategy for buffering satisfying these restrictions is a critical issue for DTMSN. In this paper, we propose a novel buffering scheme for DTMSN called cooperative buffering (CB). In the proposed CB, the sensor node which has a large amount of data cooperates with its neighbor nodes to buffer the data in a distributed manner. CB uses mobile sinks. The cooperatively buffered data are transmitted directly to the mobile sink when it arrives. After proposing CB, this paper discusses extension for easy collection of the sink, extension for multi source nodes, and some sink mobility strategies of sink mobility. It evaluates the power consumption performance of CB via theoretical formulation and computer simulation. As a result, we show from the results that the proposed CB can handle multimedia data while operating at low-power.
Many safety applications in Vehicular Ad hoc Networks (VANETs) are based on broadcast. Designing a broadcast protocol that satisfies VANET applications' requirements is very crucial. In this paper, we propose a reliable and efficient multi-hop broadcast routing protocol for VANETs. The proposed protocol provides the strict reliability in various traffic conditions. This protocol also performs low overhead by means of reducing rebroadcast redundancy in a high-density network environment. We also propose an enhanced multipoint relay (MPR) selection algorithm that considers vehicles' mobility and then use it for relay node selection. We show the performance analysis of the proposed protocol by simulation with ns-2 in different conditions, and give the simulation results demonstrating effectiveness of the proposed protocol compared with other VANET broadcast schemes.
Effective bandwidth utilization and scalability are vital issues for IP networking over a large-scale uni-directional link (UDL), such as a wide-area wireless broadcast over satellite or terrestrial digital broadcasting. On a large-scale UDL, the current network architecture is not scalable to cover an extraordinary number of receivers that communicate using a Link-layer Tunneling Mechanism (LLTM). This paper proposes a network architecture for a large-scale UDL that: (1) decreases the traffic load of LLTM at the upstream network of the UDL, (2) coordinates the data link layer and network layer of receivers without communications via UDL, and (3) enables neighbor discovery for direct communication between receivers via a bi-directional link that is used as a return path for LLTM. Simulation results showed that our approach reduces by more than 90% the control messages to be sent via UDL compared with IPv6 stateless address autoconfiguration on the existing network architecture. Our proposal improves the UDL bandwidth consumption from O(N) to O(1), so that the bulk of the bandwidth can be utilized for delivering services, not for network configuration of receivers.
Power conservation has become a serious concern during people's daily life. Ubiquitous computing technologies clearly provide a potential way to help us realize a more environment-friendly lifestyle. In this paper, we propose a ubiquitous power management system called Gynapse, which uses multi-modal sensors to predict the exact usage of each device, and then switches their power modes based on predicted usage to maximize the total energy saving under the constraint of user required response time. We build a three-level Hierarchical Hidden Markov Model (HHMM) to represent and learn the device level usage patterns from multi-modal sensors. Based on the learned HHMM, we develop our predictive mechanism in Dynamic Bayesian Network (DBN) scheme to precisely predict the usage of each device, with user required response time under consideration. Based on the predicted usage, we follow a four-step process to balance the total energy saving and response time of devices by switching their power modes accordingly. Preliminary results demonstrate that Gynapse has the capability to reduce power consumption while keeping the response time within user's requirement, and provides a complementary approach to previous power management systems.
This paper discusses the potential problems due to cultural differences, which foreign companies may face in Brazil concerning information security. Top 3 investing countries in Brazil, namely US, Netherlands, and Japan are examined. Potential problems concerning the management of people in information security are developed by using Geert Hofstede's framework and based upon the authors' experience in global business activities. To evaluate the magnitude of potential of problems, a recently proposed measure called Level of Potential (LoP) is adopted. A survey was conducted in Brazil to evaluate the severity of potential problems and the practicability of LoP. To examine the practicability of LoPs, the logical LoPs are compared with their surveyed severities. Our results show that LoP can predict problems to a certain extent in the Brazilian business environment. The results reveal that Japanese companies may face problems least, while the Dutch ones face the difficulties most. The problem of “Using previous company's confidential information” is a problem with the highest severity among the potential problems since “teaching others” is encouraged by employees' belief.
In order to improve the readability, we often segment a mail text into smaller paragraphs than necessary. However, this oversegmentation is a problem of mail text processing. It would negatively affect discourse analysis, information extraction, information retrieval, and so on. To solve this problem, we propose methods of estimating the connectivity between paragraphs in a mail. In this paper, we compare paragraph connectivity estimation based on machine learning methods (SVM and ME) with a rule-based method and show that the machine learning methods outperform the rule-based method.
In this paper, we propose a method for exploring the Japanese construction N1-Adj-N2, which often establishes a relationship between an object (N2), an attribute (N1), and an evaluation of that attribute (Adj). As this construction connects two nouns, our method involves constructing a graph of the noun relations, which can be considered as representing selectional restrictions for the argument of a target adjective. The exploration of N1-Adj-N2 constructions is useful for opinion mining, lexicographical analysis of adjectives, and writing aid, among others.
Word segmentation and POS tagging are two important problems included in many NLP tasks. They, however, have not drawn much attention of Vietnamese researchers all over the world. In this paper, we focus on the integration of advantages from several resourses to improve the accuracy of Vietnamese word segmentation as well as POS tagging task. For word segmentation, we propose a solution in which we try to utilize multiple knowledge resources including dictionary-based model, N-gram model, and named entity recognition model and then integrate them into a Maximum Entropy model. The result of experiments on a public corpus has shown its effectiveness in comparison with the best current models. We got 95.30% F1 measure. For POS tagging, motivated from Chinese research and Vietnamese characteristics, we present a new kind of features based on the idea of word composition. We call it morpheme-based features. Our experiments based on two POS-tagged corpora showed that morpheme-based features always give promising results. In the best case, we got 89.64% precision on a Vietnamese POS-tagged corpus when using Maximum Entropy model.
Comparing with the traditional way of manually developing grammar based on lin- guistic theory, corpus-oriented grammar development is more promising. To develop HPSG grammar through the corpus-oriented way, a treebank is an indispensable part. This paper first compares existing Chinese treebanks and chooses one of them as the basic resource for HPSG grammar development. Then it proposes a new design of part-of-speech tags based on the assumption that it is not only simple enough to re-duce ambiguity of morphological analysis as much as possible, but also rich enough for HPSG grammar development. Finally, it introduces some on-going work about utilizing a Chinese scientific paper treebank in HPSG grammar development.
This paper reports how to treat legal sentences including itemized expressions in three languages. Thus far, we have developed a system for translating legal sentences into logical formulae. Although our system basically converts words and phrases in a target sentence into predicates in a logical formula, it generates some useless predicates for itemized and referential expressions. In the previous study, focusing on Japanese Law, we have made a front end system which substitutes corresponding referent phrases for these expressions. In this paper, we examine our approach to the Vietnamese Law and the United States Code. Our linguistic analysis shows the difference in notation among languages or nations, and we extracted conventional expressions denoting itemization for each language. The experimental result shows high accuracy in terms of generating independent, plain sentences from the law articles including itemization. The proposed system generates a meaningful text with high readability, which can be input into our translation system.
Large amounts of data are essential for training statistical machine translation systems. In this paper we show how training data can be expanded by paraphrasing one side of a parallel corpus. The new data is made by parsing then generating using an open-source, precise HPSG-based grammar. This gives sentences with the same meaning, but with minor variations in lexical choice and word order. In experiments paraphrasing the English in the Tanaka Corpus, a freely-available Japanese-English parallel corpus, we show consistent, statistically-significant gains on training data sets ranging from 10,000 to 147,000 sentence pairs in size as evaluated by the BLEU and METEOR automatic evaluation metrics.