When the random variable has a completely monotone density function, we call it bursty (BRST) random variable. At first, we prove that the entropy of inter-arrival time is smaller than or equal to the entropy of inter-departure time in an infinite-server system GI/GI/∞ having general renewal arrivals. On the basis of that result, we prove that a BRST/GI/∞ having bursty arrivals and the associated loss system BRST/GI/c/c have the following paradoxical behavior: In the BRST/GI/∞, the stationary number of customers as well as the inter-departure time become stochastically less variable, as the service time becomes stochastically more variable. Also for the loss system BRST/GI/c/c, the blocking probability decreases and the inter-departure time becomes stochastically less variable, as the service time becomes stochastically more variable.

Field Programmable Gate Array (FPGA) based robust model fitting enjoys immense popularity in image processing because of its high efficiency. This paper focuses on the tradeoff analysis of real-time FPGA implementation of robust circle and ellipse estimations based on the random sample consensus (RANSAC) algorithm, which estimates parameters of a statistical model from a data set of feature points which contains outliers. In particular, this paper mainly highlights implementation alternatives for solvers of simultaneous equations and compares Gauss-Jordan elimination and Cramer's rule by changing matrix size and arithmetic processes. Experimental evaluation shows a Cramer's rule approach coupled with long integer arithmetic can reduce most hardware resources without unacceptable degradation of estimation accuracy compared to floating point versions.

In this letter, we propose two robust and distributed game-based algorithms, which are the modifications of two algorithms proposed in [1], to solve the joint base station selection and resource allocation problem with imperfect information in heterogeneous cellular networks (HCNs). In particular, we repeatedly sample the received payoffs in the exploitation stage of each algorithm to guarantee the convergence when the payoffs of some users (UEs) in [1] cannot accurately be acquired for some reasons. Then, we derive the rational sampling number and prove the convergence of the modified algorithms. Finally, simulation results demonstrate that two modified algorithms achieve good convergence performances and robustness in the incomplete information scheme.

The present paper proposes a dynamic spectrum access policy for multi-hop cognitive radio networks (CRNs), where the transmission in each hop suffers a delay waiting for the communication channel to become available. Recognizing the energy constraints, we assume that each secondary user (SU) in the network is powered by a battery with finite initial energy. We develop an energy-efficient policy for CRNs using the Markov decision process, which searches for spectrum opportunities without a common communication channel and assigns each sensor's decision to every time slot. We first consider a single-sensor scenario. Due to the intermittent activation of the sensor, achieving the optimal sensing schedule requires excessive complexity and is computationally intractable, owing to the fact that the state space of the Markov decision process evolves exponentially with time variance. In order to overcome this difficulty, we propose a state-reduced suboptimal policy by relaxing the constrained state space, i.e., assuming that the electrical energy of a node is infinite, because this state-reduced suboptimal approach can substantially reduce the complexity of decision-making for CRNs. We then analyze the performance of the proposed policy and compare it with the optimal solution. Furthermore, we verify the performance of this spectrum access policy under real conditions in which the electrical energy of a node is finite. The proposed spectrum access policy uses the dynamic information of each channel. We prove that this schedule is a good approximation for the true optimal schedule, which is impractical to obtain. According to our theoretical analysis, the proposed policy has less complexity but comparable performance. It is proved that when the operating time of the CRN is sufficiently long, the data reception rate on the sink node side will converge to the optimal rate with probability 1. Based on the results for the single-sensor scenario, the proposed schedule is extended to a multi-hop CRN. The proposed schedule can achieve synchronization between transmitter and receiver without relying on a common control channel, and also has near-optimal performance. The performance of the proposed spectrum access policy is confirmed through simulation.

This paper presents an ontology-based driving decision making system, which can promptly make safety decisions in real-world driving. Analyzing sensor data for improving autonomous driving safety has become one of the most promising issues in the autonomous vehicles research field. However, representing the sensor data in a machine understandable format for further knowledge processing still remains a challenging problem. In this paper, we introduce ontologies designed for autonomous vehicles and ontology-based knowledge base, which are used for representing knowledge of maps, driving paths, and perceived driving environments. Advanced Driver Assistance Systems (ADAS) are developed to improve safety of autonomous vehicles by accessing to the ontology-based knowledge base. The ontologies can be reused and extended for constructing knowledge base for autonomous vehicles as well as for implementing different types of ADAS such as decision making system.

Deploying low power nodes (LPNs), which reuse the spectrum licensed to a macrocell network, is considered to be a promising way to significantly boost network capacity. Due to the spectrum-sharing, the deployment of LPNs could trigger the severe problem of interference including intra-tier interference among dense LPNs and inter-tier interference between LPNs and the macro base station (MBS), which influences the system performance strongly. In this paper, we investigate a spectrum-sharing approach in the downlink for two-tier networks, which consists of small cells (SCs) with several LPNs and a macrocell with a MBS, aiming to mitigate the interference and improve the capacity of SCs. The spectrum-sharing approach is described as a multi-objective optimization problem. The problem is solved by the nondominated sorting genetic algorithm version II (NSGA-II), and the simulations show that the proposed spectrum-sharing approach is superior to the existing one.

In this paper, we propose a floorplan aware high-level synthesis algorithm with body biasing for delay variation compensation, which minimizes the average leakage energy of manufactured chips. In order to realize floorplan-aware high-level synthesis, we utilize huddle-based distributed register architecture (HDR architecture). HDR architecture divides the chip area into small partitions called a huddle and we can control a body bias voltage for every huddle. During high-level synthesis, we iteratively obtain expected leakage energy for every huddle when applying a body bias voltage. A huddle with smaller expected leakage energy contributes to reducing expected leakage energy of the entire circuit more but can increase the latency. We assign control-data flow graph (CDFG) nodes in non-critical paths to the huddles with larger expected leakage energy and those in critical paths to the huddles with smaller expected leakage energy. We expect to minimize the entire leakage energy in a manufactured chip without increasing its latency. Experimental results show that our algorithm reduces the average leakage energy by up to 39.7% without latency and yield degradation compared with typical-case design with body biasing.

To use millimeter wave bands in future cellular and outdoor wireless networks, understanding the multipath cluster characteristics such as delay and angular spread for different polarization is very important besides knowing the path loss and other large scale propagation parameters. This paper presents result from analysis of wide-band full polarimetric double directional channel measurement at the millimeter wave band in a typical urban pico-cell environment. Only limited number of multipath clusters with gains ranging from -8dB to -26.8dB below the free space path loss and mainly due to single reflection, double reflection and diffraction, under both line of sight (LOS) and obstructed LOS conditions are seen. The cluster gain and scattering intensity showed strong dependence on polarization. The scattering intensities for ϑ-ϑ polarization were seen to be stronger compared to ϕ-ϕ polarization and on average 6.1dB, 5.6dB and 4.5dB higher for clusters due to single reflection, double reflection and scattering respectively. In each cluster, the paths are highly concentrated in the delay domain with delay spread comparable to the delay resolution of 2.5ns irrespective of polarization. Unlike the scattering intensity, the angular spread of paths in each cluster did not show dependence on polarization. On the base station side, average angular spread in azimuth and in elevation were almost similar with ≤3.3° spread in azimuth and ≤3.2° spread in elevation for ϑ-ϑ polarization. These spreads were slightly smaller than those observed for ϕ-ϕ polarization. On the mobile station side the angular spread in azimuth was much higher compared to the base station side. On average, azimuth angular spread of ≤11.4° and elevation angular spread of ≤5° are observed for ϑ-ϑ polarization. These spreads were slightly larger than in ϕ-ϕ polarization. Knowing these characteristics will be vital for more accurate modeling of the channel, and in system and antenna design.

In this paper, a novel fusion center controlled media access control (MAC) protocol for physical wireless parameter conversion sensor networks (PHY-C SN), and a transmission power design for each sensor node are proposed. In PHY-C SN, the sensing information is converted to corresponding subcarrier number of orthogonal frequency division multiplexing (OFDM) signals, and all sensor nodes can send sensing information simultaneously. In most wireless sensor network standards, each sensor node detects the surrounding wireless signal through carrier sense. However, sensor nodes cannot send signals simultaneously if carrier sense is applied in PHY-C SN. Therefore, a protocol for PHY-C SN is devised. In the proposed protocol, the fusion center detects the surrounding wireless environment by carrier sense and requests sensing information transmission toward sensor nodes if no other wireless systems are detected. Once the sensor nodes receive the request signal, they transmit sensing information to the fusion center. Further, to avoid harmful interference with surrounding wireless systems, the transmission power of each sensor is designed to suit the considering communication range and avoid interference toward other wireless systems. The effectiveness of the proposed protocol is evaluated by computer simulation. The parameters for collection like the number of collecting sensor nodes and the radius of the collection area are also examined when determining the transmission power of sensor nodes. Results show that highly efficient information collection with reducing interference both from and towards surrounding wireless systems can be implemented with PHY-C SN.

Collaborative filtering with only implicit feedbacks has become a quite common scenario (e.g. purchase history, click-through log, and page visitation). This kind of feedback data only has a small portion of positive instances reflecting the user's interaction. Such characteristics pose great challenges to dealing with implicit recommendation problems. In this letter, we take full advantage of matrix factorization and relative preference to make the recommendation model more scalable and flexible. In addition, we propose to take into consideration the concept of covisitation which captures the underlying relationships between items or users. To this end, we propose the algorithm Integrated Collaborative Filtering for Implicit Feedback incorporating Covisitation (ICFIF-C) to integrate matrix factorization and collaborative ranking incorporating the covisitation of users and items simultaneously to model recommendation with implicit feedback. The experimental results show that the proposed model outperforms state-of-the-art algorithms on three standard datasets.

Replacement of highly stressed logic gates with internal node control (INC) logics is known to be an effective way to alleviate timing degradation due to NBTI. We propose a path clustering approach to accelerate finding effective replacement gates. Upon the observation that there exist paths that always become timing critical after aging, critical path candidates are clustered to select representative path in each cluster. With efficient data structure to further reduce timing calculation, INC logic optimization has first became tractable in practical time. Through the experiments using a processor, 171x speedup has been demonstrated while retaining almost the same level of mitigation gain.

The rapid prototyping of a mixed-signal system-on-chip (SoC) has been enabled by reusing predesigned intellectual properties (IPs) and by integrating newly designed IP into the top design of SoC. The IPs have been designed on various hardware description levels, which leads to challenges in simulations that evaluate the prototyping. One traditional solution is to convert these heterogeneous IP models into equivalent models, that are described in a single description language. This conversion approach often requires manual rewriting of existing IPs, and this results in description loss during the model projection due to the absence of automatic conversion tools. The other solutions are co-simulation/emulation approaches that are based on the coupling of multiple simulators/emulators through connection modules. The conventional methods do not have formal theoretical backgrounds and an explicit interface for integrating the simulator into their solutions. In this paper, we propose a general co-simulation approach based on the high-level architecture (HLA) and a newly-defined programming language interface for interoperation (PLI-I) between heterogeneous IPs as a formal simulator interface. Based on the proposed PLI-I and HLA, we introduce formal procedures of integration and interoperation. To reduce integration costs, we split these procedures into two parts: a reusable common library and an additional model-dependent signal-to-event (SE) converter to handle differently abstracted in/out signals between the coupled IPs. During the interoperation, to resolve the different time-advance mechanisms and increase computation concurrency between digital and analog simulators, the proposed co-simulation approach performs an advanced HLA-based synchronization using the pre-simulation concepts. The case study shows the validation of interoperation behaviors between the heterogeneous IPs in mixed-signal SoC design, the reduced design effort in integrating, and the synchronization speedup using the proposed approach.

This paper investigates the effect of noises added to hidden units of AutoEncoders linked to multilayer perceptrons. It is shown that internal representation of learned features emerges and sparsity of hidden units increases when independent Gaussian noises are added to inputs of hidden units during the deep network training. It is also shown that the weights that connect the contaminated hidden units with the next layer have smaller values and outputs of hidden units tend to be more definite (0 or 1). This is expected to improve the generalization ability of the network through this automatic structuration by adding the noises. This network structuration was confirmed by experiments for MNIST digits classification via a deep neural network model.

This letter presents a test suite CF3 designed to find bugs in arithmetic optimizers of C compilers. It consists of 13,720 test programs containing all the expression patterns covering all the permutations of 3 operators from 14 operators. CF3 detected more than 70 errors in GCC 4.2-4.5 within 2 hours.

Software-based error detection techniques, which includes error detection mechanism (EDM) transformation, are used for error localization in post-silicon validation. This paper evaluates the performance of EDM for timing error localization with a noise-aware logic simulator and 65-nm test chips assuming the following two EDM usage scenarios; (1) localizing a timing error occurred in the original program, and (2) localizing as many potential timing errors as possible. Simulation results show that the EDM transformation customized for quick error detection cannot locate electrical timing errors in the original program in the first scenario, but it detects 86% of non-masked errors potential bugs in the second scenario, which mean the EDM performance of detecting electrical timing errors affecting execution results is high. Hardware measurement results show that the EDM detects 25% of original timing errors and 56% of non-masked errors. Here, these hardware measurement results are not consistent with the simulation results. To investigate the reason, we focus on the following two differences between hardware and simulation; (1) design of power distribution network, and (2) definition of timing error occurrence frequency. We update the simulation setup for filling the difference and re-execute the simulation. We confirm that the simulation and the chip measurement results are consistent.

This paper summarizes Wi-SUN communication systems and their physical (PHY) layer and media access control (MAC) specifications. Firstly, the Wi-SUN communication systems are categorized into three. The key PHY and MAC standards, IEEE 802.15.4g and .4e, that configure the systems are explained, and fundamental transmission performances of the systems in the PHY layer and MAC layer are evaluated by computer simulations. Then, the Wi-SUN alliance and the Wi-SUN profiles that include IEEE 802.15.4g and .4e are explained. Finally, to understand the transmission performance of actual IEEE 802.15.4g Wi-SUN radio devices, PER performances under AWGN and multipath fading environments are measured by using IEEE 802.15.4g compliant and Wi-SUN alliance certified radio modules. This paper is an instruction paper for the beginners of the Wi-SUN based communications systems.

Bayesian networks are a powerful approach for representation and reasoning under conditions of uncertainty. Of the many good algorithms for learning Bayesian networks from data, the bio-inspired search algorithm is one of the most effective. In this paper, we propose a hybrid mutual information-modified binary particle swarm optimization (MI-MBPSO) algorithm. This technique first constructs a network based on MI to improve the quality of the initial population, and then uses the decomposability of the scoring function to modify the BPSO algorithm. Experimental results show that, the proposed hybrid algorithm outperforms various other state-of-the-art structure learning algorithms.

During the development of a human embryo, the position of eyes moves medially and caudally in the viscerocranium. A statistical model of this process can play an important role in embryology by facilitating qualitative analyses of change. This paper proposes an algorithm to construct a spatiotemporal statistical model for the eyeballs of a human embryo. The proposed modeling algorithm builds a statistical model of the spatial coordinates of the eyeballs independently for each Carnegie stage (CS) by using principal component analysis (PCA). In the process, a q-Gaussian distribution with a model selection scheme based on the Aaike information criterion is used to handle a non-Gaussian distribution with a small sample size. Subsequently, it seamlessly interpolates the statistical models of neighboring CSs, and we present 10 interpolation methods. We also propose an estimation algorithm for the CS using our spatiotemporal statistical model. A set of images of eyeballs in human embryos from the Kyoto Collection was used to train the model and assess its performance. The modeling results suggested that information geometry-based interpolation under the assumption of a q-Gaussian distribution is the best modeling method. The average error in CS estimation was 0.409. We proposed an algorithm to construct a spatiotemporal statistical model of the eyeballs of a human embryo and tested its performance using the Kyoto Collection.

Knowledge graph (KG) embedding aims at learning the latent semantic representations for entities and relations. However, most existing approaches can only be applied to KG completion, so cannot identify relations including unseen entities (or Out-of-KG entities). In this paper, motivated by the zero-shot learning, we propose a novel model, namely JointE, jointly learning KG and entity descriptions embedding, to extend KG by adding new relations with Out-of-KG entities. The JointE model is evaluated on entity prediction for zero-shot embedding. Empirical comparisons on benchmark datasets show that the proposed JointE model outperforms state-of-the-art approaches. The source code of JointE is available at https://github.com/yzur/JointE.

In this paper, we propose a redundant task allocation method for multi-core systems based on the Duplication with Temporary Triple-Modular Redundancy and Reconfiguration (DTTR) scheme. The proposed method determines task allocation of a given task graph to a given multi-core system model from task scheduling in given fault patterns. Fault patterns defined in this paper consist of a set of faulty cores and a set of surviving cores. To optimize the average failure rate of the system, task scheduling minimizes the execution time of the task graph preserving the property of the DTTR scheme. In addition, we propose a selection method of fault patterns to be scheduled to reduce the task allocation time. In the experiments, at first, we evaluate the proposed selection method of fault patterns in terms of the task allocation time. Then, we compare the average failure rate among the proposed method, a task allocation method which packs tasks into particular cores as much as possible, a task allocation method based on Simulated Annealing (SA), a task allocation method based on Integer Linear Programming (ILP), and a task allocation method based on task scheduling without considering the property of the DTTR scheme. The experimental results show that task allocation by the proposed method results in nearly the same average failure rate by the SA based method with shorter task allocation time.