In the Robot Operating System (ROS), a major middleware for robots, the Transform Library (TF) is a mandatory package that manages transformation information between coordinate systems by using a directed forest data structure and providing methods for registering and computing the information. However, the structure has two fundamental problems. The first is its poor scalability: since it accepts only a single thread at a time due to using a single giant lock for mutual exclusion, the access to the tree is sequential. Second, there is a lack of data freshness: it retrieves non-latest synthetic data when computing coordinate transformations because it prioritizes temporal consistency over data freshness. In this paper, we propose methods based on transactional techniques. This will allow us to avoid anomalies, achieve high performance, and obtain fresh data. These transactional methods show a throughput of up to 429 times higher than the conventional method on a read-only workload and a freshness of up to 1276 times higher than the conventional one on a read-write combined workload.

Copper (Cu) electroless plating was conducted on planar and microstructured polydimethylsiloxane (PDMS) substrates. In this study, organic thin films terminated with nitrogen (N)-containing groups, e.g. poly (dimethylaminoethyl methacrylate) brush (PDMAEMA), aminopropyl trimethoxysilane monolayer (APTES), and polydopamine (PDA) were used to anchor palladium (Pd) catalyst. While electroless plating was successfully promoted on all sample surfaces, PDMAEMA was found to achieve the best adhesion strength to the PDMS surfaces, compared to APTES- and PDA-covered PDMS substrates, due to covalent bonding, anchoring effects of polymer chains as well as high affinity of N atoms to Pd species. Our process was also successfully applied to the electroless plating of microstructured PDMS substrates.

Explicit evaluation of the rate-distortion function has rarely been achieved when it is strictly greater than its Shannon lower bound since it requires to identify the support of the optimal reconstruction distribution. In this paper, we consider the rate-distortion function for the distortion measure defined by an ε-insensitive loss function. We first present the Shannon lower bound applicable to any source distribution with finite differential entropy. Then, focusing on the Laplacian and Gaussian sources, we prove that the rate-distortion functions of these sources are strictly greater than their Shannon lower bounds and obtain upper bounds for the rate-distortion functions. Small distortion limit and numerical evaluation of the bounds suggest that the Shannon lower bound provides a good approximation to the rate-distortion function for the ε-insensitive distortion measure. By using the derived bounds, we examine the performance of a scalar quantizer. Furthermore, we discuss variants and extensions of the ε-insensitive distortion measure and obtain lower and upper bounds for the rate-distortion function.

Many successful methods for recognizing human action are spatio-temporal interest point (STIP) based methods. Given a test video sequence, for a matching-based method using a voting mechanism, each test STIP casts a vote for each action class based on its mutual information with respect to the respective class, which is measured in terms of class likelihood probability. Therefore, two issues should be addressed to improve the accuracy of action recognition. First, effective STIPs in the training set must be selected as references for accurately estimating probability. Second, discriminative STIPs in the test set must be selected for voting. This work uses ε-nearest neighbors as effective STIPs for estimating the class probability and uses a variance filter for selecting discriminative STIPs. Experimental results verify that the proposed method is more accurate than existing action recognition methods.

This paper presents an inertial estimator learning automata scheme by which both the short-term and long-term perspectives of the environment can be incorporated in the stochastic estimator – the long term information crystallized in terms of the running reward-probability estimates, and the short term information used by considering whether the most recent response was a reward or a penalty. Thus, when the short-term perspective is considered, the stochastic estimator becomes pertinent in the context of the estimator algorithms. The proposed automata employ an inertial weight estimator as the short-term perspective to achieve a rapid and accurate convergence when operating in stationary random environments. According to the proposed inertial estimator scheme, the estimates of the reward probabilities of actions are affected by the last response from environment. In this way, actions that have gotten the positive response from environment in the short time, have the opportunity to be estimated as “optimal”, to increase their choice probability and consequently, to be selected. The estimates become more reliable and consequently, the automaton rapidly and accurately converges to the optimal action. The asymptotic behavior of the proposed scheme is analyzed and it is proved to be ε-optimal in every stationary random environment. Extensive simulation results indicate that the proposed algorithm converges faster than the traditional stochastic-estimator-based S ERI scheme, and the deterministic-estimator-based DGPA and DPRI schemes when operating in stationary random environments.

This paper describes a nonlinear filter that can extract the image feature from noise corrupted image labeled self-quotient ε-filter (SQEF). SQEF is an improved self-quotient filter (SQF) to extract the image feature from noise corrupted image. Although SQF is a simple approach for feature extraction from the images, it is difficult to extract the feature when the image includes noise. On the other hand, SQEF can extract the image feature not only from clear images but also from noise corrupted images with uniform noise, Gaussian noise and impulse noise. We show the algorithm of SQEF and describe its feature when it is applied to uniform noise corrupted image, Gaussian noise corrupted image and impulse noise corrupted image. Experimental results are also shown to confirm the effectiveness of the proposed method.

This paper establishes a new unified method for fixed-length source coding problems of general sources. Specifically, we introduce an alternative definition of the smooth Renyi entropy of order zero, and show a unified approach to present the fixed-length coding rate in terms of this information quantity. Our definition of the smooth Renyi entropy has a clear operational meaning, and hence is easy to calculate for finite block lengths. Further, we represent various ε-source coding rate and the strong converse property for general sources in terms of the smooth Renyi entropy, and compare them with the results obtained by Han and Renner et al.

In this letter, we propose a new tuning method of ε value, which is a parameter in the ε-filter, using a metric between signal distributions, i.e., Hellinger distance. The difference between the input and output signals is evaluated using Hellinger distance and used for the parameter tuning in the proposed method.

A band-pass bilateral filter is an improved variant of a bilateral filter that does not have low-pass characteristics but has band-pass characteristics. Unfortunately, its computation time is relatively large since all pixels are subjected to Gaussian calculation. To solve this problem, we pay attention to a nonlinear filter called ε-filter and propose an advanced ε-filter labeled band-pass ε-filter. As ε-filter has low-pass characteristics due to spatial filtering, it does not enhance the image contrast. On the other hand, band-pass ε-filter does not have low-pass characteristics but has band-pass characteristics to enhance the image contrast around edges unlike ε-filter. The filter works not only as a noise reduction filter but also as an edge detection filter depending on the filter setting. Due to its simple design, the calculation cost is relatively small compared to the band-pass bilateral filter. To show the effectiveness of the proposed method, we report the results of some comparison experiments on the filter characteristics and computational cost.

ε-filter is a nonlinear filter for reducing noise and is applicable not only to speech signals but also to image signals. The filter design is simple and it can effectively reduce noise with an adequate filter parameter. This paper presents a method for estimating the optimal filter parameter of ε-filter based on signal-noise decorrelation and shows that it yields the optimal filter parameter concerning a wide range of noise levels. The proposed method is applicable where the noise to be removed is uncorrelated with signal, and it does not require any other knowledge such as noise variance and training data.

In this paper, we propose an enlargement method for images with Gaussian noise based on the Laplacian pyramid (LP) representation. Unlike lowpass pre-processing approaches to the LP enlargement method, an embedded approach is used in this paper. Since the amplitude of Gaussian noise signals is smaller than the amplitude of image edge signals in the predicted LP stage, we adopt a modified ε-filter in the proposed LP enlargement algorithm to reduce the Gaussian noise. Experimental results show that the proposed method can obtain high accuracy denoise enlarged images.

The notion of k-wise independent permutations has several applications. From the practical point of view, it often suffices to consider almost (i.e., ε-approximate) k-wise independent permutation families rather than k-wise independent permutation families, however, we know little about how to construct families of ε-approximate k-wise independent permutations of small size. For any n > 0, let Sn be the set of all permutations on {0,1,..., n - 1}. In this paper, we investigate the size of families of ε-approximate k-wise independent permutations and show that (1) for any constant ε 0, if a family Sn of permutations is ε-approximate k-wise independent, then || n(n - 1) (n - k + 1) if ε< 1; || {n(n - 1) (n - k + 1)}/(1 +ε) otherwise; (2) for any constant 0< ε 1, there exists a family Sn of ε-approximate k-wise independent permutations such that || = ; (3) for any constant ε> 0 and any n = pm - 1 with p prime, it is possible to construct a polynomial time samplable family Sn of ε-approximate pairwise independent permutations such that || = O(n(n - 1)/ε); (4) for any constant ε> 0 and any n = pm with p prime, it is possible to construct a polynomial time samplable family Sn of ε-approximate 3-wise independent permutations such that || = O(n(n - 1)(n - 2)/ε). Our results are derived by combinatorial arguments, i.e., probabilistic methods and linear algebra methods.

An amperometric thin-film glucose biosensor based on a plasma-polymerized film using hexamethyldisiloxane as the monomer is presented. The plasma-polymerized film, achieved in plasma in the vapor phase, offers a new alternative for use in the design of the electrode-enzyme interface of biosensors. The film shows promise of high sensor performance; namely, rapid sensor response, low noise, a wide dynamic range, reproducibility, and reduction in the effects of interfering materials such as ascorbic acid. In this study, we examined the usefulness of the hexamethyldisiloxane plasma-polymerized film and investigated how the thickness of the plasma-polymerized film on a platinum electrode affected sensor characteristics: the selectivity for hydrogen peroxide versus interfering agents, the sensor response due to enzymatic reaction, and oxygen depletion.

In this letter, we remark a well-known nonlinear filtering technique realize immediate effect to suppress the influence of the additive measurement noise in the input to a set theoretic linear blind deconvolution scheme. Numerical examples show ε-separating nonlinear pre-filtering techniques work suitably to this noisy blind deconvolution problem.

A nonlinear inverse filter is proposed for restoring signals degraded by a linear system and additive Gaussian noise. The proposed filter consists of combination of a linear high pass filter and an ε-filter, which is modified from the cascaded linear filter. The nonlinear property of the ε-filter is utilized to suppress pre-enhanced additive random noise and to restore sharp edges. It is demonstrated that the filter can be reduced to a multi-layered neural network model, and the optimal design is described by using the back propagation algorithm. The nonlinear function is approximated by a piecewise linear function, which results in simple and robust training algorithm. An application to image restoration is also presented, illustrating the effectiveness over the linear filter, especially when the amplitude of additive noise is small.

Epsilon serializability (ESR) was proposed to relax serializability constraints by allowing transactions to execute with a limited amount of inconsistency (ε-spec). Divergence control algorithms, viewed as extensions of concurrency control algorithms, enable read-only transactions to complete if their inconsistencies do not exceed ε-spec. This paper studies the performance of two-phase locking divergence control (2PLDC) and optimistic divergence control (ODC) algorithms. We develop a central part of the ESR transaction processing system that runs with 2PLDC and ODC. We applied a comprehensive centralized database simulation model to measure the performance. Evaluations are conducted with multi-class workloads where on-line update transactions and long-duration queries progress under various ε-spec. Our results demonstrate that significant performance enhancements are achieved with a non-zero tolerable inconsistency. With sufficient ε-spec and limited system resources, both algorithms result in comparable performance. However, with low resource contention, ODC performs significantly better than 2PLDC. Furthermore, in the range of small ε-spec, the queries committed by ODC have more accurate results than those committed by 2PLDC.

A glass precursor resist (GPR) is designed on the basis of an idea of conversion of organosilicon polymer to an inorganic substance by lithographic procedure. Developed chemical amplification resist system is composed of poly (di-t-butoxysiloxane) and a photoacid generator. It has a high sensitivity of 1.6 µC/cm2, a resolution of 0.2 µm and an extremely high O2-RIE durability compared with bottom resist. Exposed film changed into silicate glass, and it was confirmed by IR spectra.