This paper discusses the decidability of determinacy and subsumption of tree transducers. For two tree transducers T1 and T2, T1 determines T2 if the output of T2 can be identified by the output of T1, that is, there is a partial function f such that [[T2]]=f∘[[T1]] where [[T1]] and [[T2]] are tree transformation relations induced by T1 and T2, respectively. Also, T1 subsumes T2 if T1 determines T2 and the partial function f such that [[T2]]=f∘[[T1]] can be defined by a transducer in a designated class that T2 belongs to. In this paper, we show that determinacy is in coNEXPTIME for single-valued linear extended bottom-up tree transducers as the determiner class and single-valued bottom-up tree transducers as the determinee class. We also show that subsumption is in coNEXPITME for these classes, and a bottom-up tree transducer T3 such that [[T2]]=[[T3]]∘[[T1]] can be constructed if T1 subsumes T2.

The usage of peer-to-peer (P2P) networks that provide sharing of real-time environmental data by internet users is becoming more and more popular. As a result, it's necessary to identify the problems during P2P communication and to develop proper solutions. One of the major problems of P2P communication is that it's not possible to reach the clients behind devices that create private networks like network address translation (NAT) and firewalls from the public network. Among the solutions proposed for this problem, Interactive Connectivity Establishment (ICE) and Real Time Media Flow Protocol (RTMFP) are the methods most preferred in the literature. These methods seem more attractive than other NAT traversal mechanisms since they are independent from internet infrastructure and are also appropriate for dynamic structures. However, they do have some disadvantages. In this study, a new state-based end-to-end communication technique (SBN) for NAT traversal was designed and realized. The performance of the designed method was evaluated against three criteria, connectivity check delay, connection packet count and bandwidth, and compared with the ICE method. The results revealed that the suggested SBN method proved an average of 78% success in connectivity check delay, 69% in the number of packets used and 66% in the consumption of bandwidth over the ICE method.

In this paper, we consider the problem of generating uniformly random mosaic floorplans. We propose a polynomial-time algorithm that generates such floorplans with f faces. Two modified algorithms are created to meet additional criteria.

Suppose that we are given two vertex covers C0 and Ct of a graph G, together with an integer threshold k ≥ max{|C0|, |Ct|}. Then, the vertex cover reconfiguration problem is to determine whether there exists a sequence of vertex covers of G which transforms C0 into Ct such that each vertex cover in the sequence is of cardinality at most k and is obtained from the previous one by either adding or deleting exactly one vertex. This problem is PSPACE-complete even for planar graphs. In this paper, we first give a linear-time algorithm to solve the problem for even-hole-free graphs, which include several well-known graphs, such as trees, interval graphs and chordal graphs. We then give an upper bound on k for which any pair of vertex covers in a graph G has a desired sequence. Our upper bound is best possible in some sense.

Software peer review is regarded as one of the most important approaches to preserving software quality. Due to the distributed collaborations in Open Source Software (OSS) development, the review techniques and processes conducted in OSS environment differ from the traditional review method that based on formal face-to-face meetings. Unlike other related works, this study investigates peer review processes of OSS projects from the social perspective: communication and interaction in peer review by using social network analysis (SNA). Moreover, the relationship between peer review contributors and their activities is studied. We propose an approach to evaluating contributors' activeness and social relationship using SNA named Peer Review Social Network (PeRSoN). We evaluate our approach by empirical case study, 326,286 review comments and 1,745 contributors from three representative industrial OSS projects have been extracted and analyzed. The results indicate that the social network structure influences the realistic activeness of contributors significantly. Based on the results, we suggest our approach can support project leaders in assigning review tasks, appointing reviewers and other activities to improve current software processes.

Patch clamp measurement technique is one of the most important techniques in the field of electrophysiology. The elucidation of the channels, nerve cells, and brain activities as well as contribution of the treatment of neurological disorders is expected from the measurement of ion current. A current-to-voltage converter, which is the front end circuit of the patch clamp measurement system is fabricated using 0.18µm CMOS technology. The current-to-voltage converter requires a resistance as high as 50MΩ as a feedback resistor in order to ensure a high signal-to-noise ratio for very small signals. However, the circuit becomes unstable due to the large parasitic capacitance between the poly layer and the substrate of the on-chip feedback resistor and the instability causes the peaking at lower frequency. The instability of a current-to-voltage converter with a high-resistance as a feedback resistor is analyzed theoretically. A compensation circuit to stabilize the amplifier by driving the N-well under poly resistor to suppress the effect of parasitic capacitance using buffer circuits is proposed. The performance of the proposed circuit is confirmed by both simulation and measurement of fabricated chip. The peaking in frequency characteristic is suppressed properly by the proposed method. Furthermore, the bandwidth of the amplifier is expanded up to 11.3kHz, which is desirable for a patch clamp measurement. In addition, the input referred rms noise with the range of 10Hz ∼ 10kHz is 2.09 Arms and is sufficiently reach the requirement for measure of both whole-cell and a part of single-channel recordings.

In this paper, we propose a novel architecture for a deep learning system, named k-degree layer-wise network, to realize efficient geo-distributed computing between Cloud and Internet of Things (IoT). The geo-distributed computing extends Cloud to the geographical verge of the network in the neighbor of IoT. The basic ideas of the proposal include a k-degree constraint and a layer-wise constraint. The k-degree constraint is defined such that the degree of each vertex on the h-th layer is exactly k(h) to extend the existing deep belief networks and control the communication cost. The layer-wise constraint is defined such that the layer-wise degrees are monotonically decreasing in positive direction to gradually reduce the dimension of data. We prove the k-degree layer-wise network is sparse, while a typical deep neural network is dense. In an evaluation on the M-distributed MNIST database, the proposal is superior to a state-of-the-art model in terms of communication cost and learning time with scalability.

In this paper, we formulate an optimal stabilization problem of quantitative discrete event systems (DESs) under partial observation. A DES under partial observation is a system where its behaviors cannot be completely observed by a supervisor. In our framework, the supervisor observes not only masked events but also masked states. Our problem is then to synthesize a supervisor that drives the DES to a given target state with the minimum cost based on the detected sequences of masked events and states. We propose an algorithm for deciding the existence of an optimal stabilizing supervisor, and compute it if it exists.

Workflow nets (WF-nets for short) are a mathematical model of real world workflows. A WF-net is often updated in accordance with the change of real world. This may cause places that are redundant from the viewpoint of the behavior. Such places are called implicit. We first proposed a necessary and sufficient condition to find implicit places. Then we proved that removing of implicit places is a reduction operation which forms branching bisimilarity. We also constructed an algorithm for the reduction. Next, we applied the proposed reduction operation to WF-net refactoring. Then we showed the usefulness of the proposed refactoring with two examples.

Traffic is a key aspect of everyday life. Its study, as it happens with other complex phenomena, has found in simulation a basic tool. However, the use of simulations faces important limitations. Building them requires considering different aspects of traffic (e.g. urbanism, car features, and individual drivers) with their specific theories, that must be integrated to provide a coherent model. There is also a variety of simulation platforms with different requirements. Many of these problems demand multi-disciplinary teams, where the different backgrounds can hinder the communication and validation of simulations. The Model-Driven Engineering (MDE) of simulations has been proposed in other fields to address these issues. Such approaches develop graphical Modelling Languages (MLs) that researchers use to model their problems, and then semi-automatically generate simulations from those models. Working in this way promotes communication, platform independence, incremental development, and reutilisation. This paper presents the first steps for a MDE framework for traffic simulations. It introduces a tailored extensible ML for domain experts. The ML is focused on human actions, so it adopts an Agent-Based Modelling perspective. Regarding traffic aspects, it includes concepts commonly found in related literature following the Driver-Vehicle-Environment model. The language is also suitable to accommodate additional theories using its extension mechanisms. The approach is supported by an infrastructure developed using Eclipse MDE projects: the ML is specified with Ecore, and a model editor and a code generator tools are provided. A case study illustrates how to develop a simulation based on a driver's behaviour theory for a specific target platform using these elements.

Recently, notable improvements in voice activity detection (VAD) problem have been achieved by adopting several machine learning techniques. Among them, the deep neural network (DNN) which learns the mapping between the noisy speech features and the corresponding voice activity status with its deep hidden structure has been one of the most popular techniques. In this letter, we propose a novel approach which enhances the robustness of DNN in mismatched noise conditions with multi-task learning (MTL) framework. In the proposed algorithm, a feature enhancement task for speech features is jointly trained with the conventional VAD task. The experimental results show that the DNN with the proposed framework outperforms the conventional DNN-based VAD algorithm.

This letter presents a new time-digital single-slope ADC (TDSS) architecture for CMOS image sensors. In the proposed ADC, a conventional single-slope ADC is used in coarse phase and a time to digital convertor is employed in fine phase. Through second comparison of the two different slope voltages (discharge input voltage and ramp voltage), the proposed ADC achieves low bit precision compensation. Compared with multiple-ramp single-slope (MRSS) ADC, the proposed ADC not only has a simple digital judgment circuit, but also increases conversion speed without complicated structure of ramp generator. A 10-bit TDSS ADC consisting of 7-bit conventional single-slope ADC and 3-bit time to digital converter was realized in a 0.13µm CIS process. Simulations demonstrate that the conversion speed of a TDSS ADC is almost 3.5 times faster than that of a single-slope ADC.

We consider a bisimilarity control problem for transition systems. For this control problem, a necessary and sufficient condition for its solvability and a method for synthesizing a state feedback controller have been presented in the literature. However, the state of the system to be controlled is not necessarily observable. In this paper, we synthesize an observer-based output feedback controller for the bisimilarity control problem under a certain condition, and show that this output feedback controller is a solution to the control problem.

Disassembly, as a principal reverse-engineering tool, is the process of recovering the equivalent assembly instructions of a program's machine code from its binary representation. However, when disassembling a firmware file, the disassembly process cannot be performed well if the image base is unknown. In this paper, we propose an innovative method to determine the image base of a firmware file with ARM/Thumb instruction set. First, based on the characteristics of the function entry table (FET) for an ARM processor, an algorithm called FIND-FET is proposed to identify the function entry tables. Second, by using the most common instructions of function prologue and FETs, the FIND-BASE algorithm is proposed to determine the candidate image base by counting the matched functions and then choose the one with maximal matched FETs as the final result. The algorithms are applied on some firmwares collected from the Internet, and results indicate that they can effectively find out the image base for the majority of example firmware files.

High structural perfection, wafer uniformity, and reproducibility are key parameters for high-volume, low cost manufacture of resonant tunnelling diode (RTD) terahertz (THz) devices. Low-cost, rapid, and non-destructive techniques are required for the development of such devices. In this paper, we report photoluminescence (PL) spectroscopy as a non-destructive characterisation technique for high current densityInGaAs/AlAs/InP RTD structures grown by metal-organic vapour phase epitaxy (MOVPE) for THz applications. By using a PL line scanning technique across the edge of the sample, we identify characteristic luminescence from the quantum well (QW) and the undoped/n+ InGaAs layers. By using the Moss-Burstein effect, we are able to measure the free-electron concentration of the emitter/collector and contact layers in the RTD structure. Whilst the n+ InGaAs luminescence provides information on the doping concentration, information on the alloy composition and compositional variation is extracted from the InGaAs buffer layer. The QW luminescence provides information on the average well width and provides a monitor of the structural perfection with regard to interface and alloy disorder.

This paper proposes a method based on modulation transfer function (MTF) to restore the power envelope of noisy reverberant speech by using a Kalman filter with linear prediction (LP). Its advantage is that it can simultaneously suppress the effects of noise and reverberation by restoring the smeared MTF without measuring room impulse responses. This scheme has two processes: power envelope subtraction and power envelope inverse filtering. In the subtraction process, the statistical properties of observation noise and driving noise for power envelope are investigated for the criteria of the Kalman filter which requires noise to be white and Gaussian. Furthermore, LP coefficients drastically affect the Kalman filter performance, and a method is developed for deriving LP coefficients from noisy reverberant speech. In the dereverberation process, an inverse filtering method is applied to remove the effects of reverberation. Objective experiments were conducted under various noisy reverberant conditions to evaluate how well the proposed Kalman filtering method based on MTF improves the signal-to-error ratio (SER) and correlation between restored power envelopes compared with conventional methods. Results showed that the proposed Kalman filtering method based on MTF can improve SER and correlation more than conventional methods.

Recent development in network technology can realize the control of a remote plant by a digital controller. However, there is a delay caused by data transmission of control inputs and outputs. The delay degrades the control performance without taking it into consideration. In general, it is a difficult problem to identify the delay beforehand. We also assume that the plant's parameters have uncertainty. To solve the problem, we use reinforcement learning to achieve optimal digital control. First, we consider state feedback control. Next, we consider the case where the plant's outputs are observed, and apply reinforcement learning to output feedback control. Finally, we demonstrate by simulation that the proposed control method can search for the optimal gain and that it can adapt to the change of the delay.

This paper proposes a novel image integral equation of the first type (IIE-1) for a TE plane wave scattering from periodic rough surfaces with perfect conductivity by means of the method of image Green's function. Since such an IIE-1 is valid for any incident wavenumbers including the critical wavenumbers, the analytical properties of the scattered wavefield can be generally and rigorously discussed. This paper firstly points out that the branch point singularity of the bare propagator inevitably appears on the incident wavenumber characteristics of the scattered wavefield and its related quantities just at the critical wavenumbers. By applying a quadrature method, the IIE-1 becomes a matrix equation to be numerically solved. For a periodic rough surface, several properties of the scattering are shown in figures as functions of the incident wavenumbers. It is then confirmed that the branch point singularity clearly appears in the numerical solution. Moreover, it is shown that the proposed IIE-1 gives a numerical solution satisfying sufficiently the optical theorem even for the critical wavenumbers.

In this paper, we propose a novel censor-based cooperative spectrum sensing strategy, called adaptive energy-efficient sensing (AES), in which both sequential sensing and censoring report mechanism are employed, aiming to reduce the sensing energy consumption of secondary user relays (SRs). In AES, an anchor secondary user (SU) requires cooperative sensing only when it does not detect the presence of PU by itself, and the cooperative SR adopts decision censoring report only if the sensing result differs from its previous one. We derive the generalized-form expressions false alarm and detection probabilities over Rayleigh fading channels for AES. The sensing energy consumption is also analyzed. Then, we study sensing energy overhead minimization problem and show that the sensing time allocation can be optimized to minimize the miss detection probability and sensing energy overhead. Finally, numerical results show that the proposed strategy can remarkably reduce the sensing energy consumption while only slightly degrading the detection performance compared with traditional scheme.

Accidental falling among elderly people has become a public health concern. Thus, there is a need for systems that detect a fall when it happens. This paper presents a portable real-time remote health monitoring system that can remotely monitor patients' movements. The system is designed and implemented using ZigBee wireless technologies, and the data is analysed using Matlab. The purpose of this research is to determine the acceleration thresholds for fall detection, using tri-axial accelerometer readings at the head, waist, and knee. Seven voluntary subjects performed purposeful falls and Activities of Daily Living (ADL). The results indicated that measurements from the waist and head can accurately detect falls; the sensitivity and reliability measurements of fall detection ranged between 80% and 90%. In contrast, the measurements showed that the knee is not a useful position for the fall detection.