This paper presents a method to realize index generation functions using multiple Index Generation Units (IGUs). The architecture implements index generation functions more efficiently than a single IGU when the number of registered vectors is very large. This paper proves that independent linear transformations are necessary in IGUs for efficient realization. Experimental results confirm this statement. Finally, it shows a fast update method to IGUs.

Many factors, such as noise level in the original image and the noise-removal methods that clean the image prior to performing a vectorization, may play an important role in affecting the line detection of raster-to-vector conversion methods. In this paper, we propose an empirical performance evaluation methodology that is coupled with a robust statistical analysis method to study many factors that may affect the quality of line detection. Three factors are studied: noise level, noise-removal method, and the raster-to-vector conversion method. Eleven mechanical engineering drawings, three salt-and-pepper noise levels, six noise-removal methods, and three commercial vectorization methods were used in the experiment. The Vector Recovery Index (VRI) of the detected vectors was the criterion used for the quality of line detection. A repeated measure ANOVA analyzed the VRI scores. The statistical analysis shows that all the studied factors affected the quality of line detection. It also shows that two-way interactions between the studied factors affected line detection.

We propose a multiparty protocol for comparator networks which are used to compute various functions in statistical analysis, such as the maximum, minimum, median, and quartiles, for example, through sorting and searching. In the protocol, all values which are inputted to a comparator network and all intermediate outputs are kept secret assuming the presence of an honest majority. We also introduce an application of the protocol for a secure (M+1)-st price auction.

Random variations in Id-Vg characteristics of MOS transistors in an LSI chip are shown to be concisely characterized by using only 3 transistor parameters (Vth, β0, vSAT) in the MOS level 3 SPICE model. Statistical analyses of the transistor parameters show that not only the threshold voltage variation, ΔVth, but also the current factor variation, Δβ0, independently induces Id-variation, and that Δβ0 is negatively correlated with the saturation velocity variation, ΔvSAT. Using these results, we have proposed a simple method that effectively takes the correlation between parameters into consideration when creating statistical model parameters for designing a circuit. Furthermore, we have proposed a sensitivity analysis methodology for estimating the process window of SRAM cell operation taking transistor variability into account. By applying the concise statistical model parameters to the sensitivity analysis, we are able to obtain valid process windows without the large volume of data-processing and long turnaround time associated with the Monte Carlo simulation. The process window was limited not only by ΔVth, but also by Δβ0 which enhanced the failure region in the process window by 20%.

This paper describes a novel discrimination method of pulmonary nodules based on statistical analysis of thoracic computed tomography (CT) scans. Our previous Computer-Aided Diagnosis (CAD) system can detect pulmonary nodules from CT scans, but, at the same time, yields many false positives. In order to reduce the false positives, the method proposed in the present paper uses a relationship between pulmonary nodules, false positives and image features in CT scans. The trend of variation of the relationships is acquired through statistical analysis of a set of CT scans prepared for training. In testing, by use of the trend, the method predicts the appearances of pulmonary nodules and false positives in a CT scan, and improves the accuracy of the previous CAD system by modifying the system's output based on the prediction. The method is applied to 218 actual thoracic CT scans with 386 actual pulmonary nodules. The receiver operating characteristic (ROC) analysis is used to evaluate the results. The area under the ROC curve (Az) is statistically significantly improved from 0.918 to 0.931.

In this paper, we propose an effective asynchronous datapath synthesis system to optimize statistical performance of asynchronous systems. The proposed algorithm is a heuristic method which simultaneously performs scheduling and resource binding. During the design process, decisions will be made based on the statistical schedule length analysis. It is demonstrated that asynchronous datapaths with the reduced mean total computation time are successfully synthesized for some datapath synthesis benchmarks.

Distributed denial-of-service attacks on public servers have recently become more serious. More are SYN Flood attacks, since the malicious attackers can easily exploit the TCP specification to generate traffic making public servers unavailable. To assure that network services will not be interrupted, we need faster and more accurate defense mechanisms against malicious traffic, especially SYN Floods. One of the problems in detecting SYN Flood traffic is that server nodes or firewalls cannot distinguish the SYN packets of normal TCP connections from those of SYN Flood attack. Moreover, since the rate of normal network traffic may vary, we cannot use an explicit threshold of SYN arrival rates to detect SYN Flood traffic. In this paper we introduce a mechanism for detecting SYN Flood traffic more accurately by taking into consideration the time variation of arrival traffic. We first investigate the statistics of the arrival rates of both normal TCP SYN packets and SYN Flood attack packets. We then describe our new detection mechanism based on the statistics of SYN arrival rates. Our analytical results show that the arrival rate of normal TCP SYN packets can be modeled by a normal distribution and that our proposed mechanism can detect SYN Flood traffic quickly and accurately regardless of time variance of the traffic.

This paper discusses clock skew due to manufacturing variability and environmental change. In clock tree design, transition time constraint is an important design parameter that controls clock skew and power dissipation. In this paper, we evaluate clock skew under several variability models, and demonstrate relationship among clock skew, transition time constraint and power dissipation. Experimental results show that constraint of small transition time reduces clock skew under manufacturing and supply voltage variabilities, whereas there is an optimum constraint value for temperature gradient. Our experiments in a 0.18 µm technology indicate that clock skew is minimized when clock buffer is sized such that the ratio of output and input capacitance is four.

The χ2-attack was originally proposed by Knudsen and Meier. This attack is one of the most effective attacks for RC6. The χ2-attack can be used for both distinguishing attacks and for key recovery attacks. Although, up to the present, theoretical analysis of χ2-attacks, especially the relation between a distinguishing attack and a key recovery attack, has not been discussed, the security against key recovery attacks has been often discussed by the results of distinguishing attacks. In this paper, we investigate the theoretical relation between the distinguishing attack and the key recovery attack, and prove one theorem to evaluate the exact security against the key recovery attacks by using the results of the distinguishing attack. Furthermore we propose two key recovery attacks against RC5-64 and implement them. Our best key recovery attack can analyze RC5-64 with 16 rounds by using 2125.23 plaintexts with a success probability of 30%. This result works faster than exhaustive key search. As far as the authors know, this is the best result of known plaintext attacks to RC5-64. We also apply our theory on our key recovery attacks and demonstrate the validity.

In this paper we present a case study of a hierarchical statistical analysis. The method which we use here bridges the statistical information between process-level and system-level, and enables us to know the effect of the process variation on the system performance. We use two modeling techniques--intermediate model and response surface model--in order to link the statistical information between adjacent design levels. We show an experiment of the hierarchical statistical analysis applied to a Phase Locked Loop (PLL) circuit, and indicate that the hierarchical statistical analysis is practical with respect to both accuracy and simulation cost. Following three applications are also presented in order to show advantage of this linking method; these are Monte Carlo analysis, worst-case analysis, and sensitive analysis. The results of the Monte Carlo and the worst-case analysis indicate that this method is realistic statistical one. The result of the sensitive analysis enables us to evaluate the effect of process variation at the system level. Also, we can derive constraints on the process variation from a performance requirement.

This paper treats the systematic design of chaos generators which are capable of generating continuous-time signals with prescribed probability density function and power density spectra. For a specific signal model a statistical analysis is performed such that the inverse problem, i. e. the calculation of the model parameters from prescribed signal characteristics, can be solved. Finally from the obtained model parameters and the model structure the signal generating system is constructed. The approach is illustrated by several examples.

The performance of a constrained (that is, minimal order) Yule-Walker (CYW) single tone frequency estimator is studied. A closed form expression for the asymptotic error variance is derived. It is shown that CYW does not satisfactorily utilize the informaiton in data, and estimators with improved performance are proposed. Simulation results which lend support to the theoretical findings are included.

Scientific database systems for the analysis of genes and proteins are becoming very important these days. We have developed a deductive database system PACADE for analyzing the three dimensional and secondary structures of proteins. In this paper, we describe the statistical data classification component of PACADE. We implemented the component for cluster analysis and discrimination analysis. In addition, we enhanced the aggregation function in order to calculate the characteristic values which are useful for data classification. By using the cluster analysis function, the proteins are thereby classified into different types of structural characteristics. The results of these structural analysis experiments are also described in this paper.

In this work, a statistical analysis is performed for a simple constrained high-order Yule-Walker (YW) tone frequency estimator obtained from the first equation of the constrained high-order YW equations. Explicit expressions for its estimation bias and variance are efficiently derived by virtue of a Taylor series expansion technique. Especially, being explicit in terms of frequency, data length and Signal-to-Noise Ratio (SNR) value, the resulting bias expression can not be obtained by using the asymptotic analyses used for the parameter estimation methods. The obtained expressions are compared with their counterparts of the Pisarenko tone frequency estimator. Simulations are performed to support the theoretical results.

A ner trial of statistical evaluation for a nonstationary traffic flow and its traffic noise is proposed as a prediction method of its probability distribution function by considering the temporal change of distribution parameters especially from a structural viewpoint. First, a headway distribution of the nonstationary traffic flow passing through within a road segment is proposed on the basis of an Erlang distribution by reflecting a temporal change of its distribution parameters. Then, an initial phase density concerning with asynchronous counting method and the probability of counting n cars over a long time interval are derived from the above nonstationary expression of headway distribution. Thus, the statistics of noise intensity at an observation point has been predicted by combining the above probabilistic factors and deterministic factors related to noise propagation environment with use of a compound stochastic process model. Finally, te effectivenss of the proposed theory has been confirmed experimentally by applying it to the actual traffic flow on a highway.