This paper deals with the constrained DAG shortest path problem (CDSP), which finds the shortest path on a given directed acyclic graph (DAG) under any logical constraints posed on taken edges. There exists a previous work that uses binary decision diagrams (BDDs) to represent the logical constraints, and traverses the input DAG and the BDD simultaneously. The time and space complexity of this BDD-based method is derived from BDD size, and tends to be fast only when BDDs are small. However, since it does not prioritize the search order, there is considerable room for improvement, particularly for large BDDs. We combine the well-known A* search with the BDD-based method synergistically, and implement several novel heuristic functions. The key insight here is that the ‘shortest path’ in the BDD is a solution of a relaxed problem, just as the shortest path in the DAG is. Experiments, particularly practical machine learning applications, show that the proposed method decreases search time by up to 2 orders of magnitude, with the specific result that it is 2,000 times faster than a commercial solver. Moreover, the proposed method can reduce the peak memory usage up to 40 times less than the conventional method.

We propose a method for embedding the awareness state and response state in an image-based avatar to smoothly and automatically start an interaction with a user. When both states are not embedded, the image-based avatar can become non-responsive or slow to respond. To consider the beginning of an interaction, we observed the behaviors between a user and receptionist in an information center. Our method replayed the behaviors of the receptionist at appropriate times in each state of the image-based avatar. Experimental results demonstrate that, at the beginning of the interaction, our method for embedding the awareness state and response state increased subjective scores more than not embedding the states.

One of the problems associated with voice conversion from a nonparallel corpus is how to find the best match or alignment between the source and the target vector sequences without linguistic information. In a previous study, alignment was achieved by minimizing the distance between the source vector and the transformed vector. This method, however, yielded a sequence of feature vectors that were not well matched with the underlying speaker model. In this letter, the vectors were selected from the candidates by maximizing the overall likelihood of the selected vectors with respect to the target model in the HMM context. Both objective and subjective evaluations were carried out using the CMU ARCTIC database to verify the effectiveness of the proposed method.

Hardware implementation of neural networks usually have high computational complexity that increase exponentially with the size of a circuit, leading to more uncertain and unreliable circuit performance. This letter presents a novel Radial Basis Function (RBF) neural network based on parallel fault tolerant stochastic computing, in which number is converted from deterministic domain to probabilistic domain. The Gaussian RBF for middle layer neuron is implemented using stochastic structure that reduce the hardware resources significantly. Our experimental results from two pattern recognition tests (the Thomas gestures and the MIT faces) show that the stochastic design is capable to maintain equivalent performance when the stream length set to 10Kbits. The stochastic hidden neuron uses only 1.2% hardware resource compared with the CORDIC algorithm. Furthermore, the proposed algorithm is very flexible in design tradeoff between computing accuracy, power consumption and chip area.

This paper proposes a novel multiple access method that enables concurrent sensor data streaming from multiple batteryless, wireless sensor tags. The access method is a pseudo-FDMA scheme based on the subcarrier backscatter communication principle, which is widely employed in passive RFID and radar systems. Concurrency is realized by assigning a dedicated subcarrier to each sensor tag and letting all sensor tags backscatter simultaneously. Because of the nature of the subcarrier, which is produced by constant rate switching of antenna impedance without any channel filter in the sensor tag, the tag-to-reader link always exhibits harmonics. Thus, it is important to reject harmonics when concurrent data streaming is required. This paper proposes a harmonics rejecting receiver to allow simultaneous multiple subcarrier usage. This paper particularly focuses on analog sensor data streaming which minimizes the functional requirements on the sensor tag and frequency bandwidth. The harmonics rejection receiver is realized by carefully handling group delay and phase delay of the subcarrier envelope and the carrier signal to accurately produce replica of the harmonics by introducing Hilbert and inverse Hilbert transformations. A numerical simulator with Simulink and a hardware implementation with USRP and LabVIEW have been developed. Simulations and experiments reveal that even if the CIR before harmonics rejection is 0dB, the proposed receiver recovers the original sensor data with over 0.98 cross-correlation.

A TM010 cavity power combiner is presented, which achieves direct interface to microstrip lines via magnetic field coupling. A prototype is fabricated and its S-matrix measured. From the S-parameters we calculate that it shows less than 0.85 dB insertion loss over 250 MHz bandwidth at X-band. The return power to the input ports is less than -15 dB over this bandwidth. We verify the insertion loss estimation using S-matrix, by measuring transmission S-parameter of a concatenated 2-port divider-combiner network. Similarly analyzed is the case of performance of power combiner when one of the input fails. We find that we can achieve graceful degradation provided we ensure some particular reflection phase at the degraded port.

Knowledge graphs have been shown to be useful to many tasks in artificial intelligence. Triples of knowledge graphs are traditionally structured by human editors or extracted from semi-structured information; however, editing is expensive, and semi-structured information is not common. On the other hand, most such information is stored as text. Hence, it is necessary to develop a method that can extract knowledge from texts and then construct or populate a knowledge graph; this has been attempted in various ways. Currently, there are two approaches to constructing a knowledge graph. One is open information extraction (Open IE), and the other is knowledge graph embedding; however, neither is without problems. Stanford Open IE, the current best such system, requires labeled sentences as training data, and knowledge graph embedding systems require numerous triples. Recently, distributed representations of words have become a hot topic in the field of natural language processing, since this approach does not require labeled data for training. These require only plain text, but Mikolov showed that it can perform well with the word analogy task, answering questions such as, “a is to b as c is to __?.” This can be considered as a knowledge extraction task from a text for finding the missing entity of a triple. However, the accuracy is not sufficiently high when applied in a straightforward manner to relations in knowledge graphs, since the method uses only one triple as a positive example. In this paper, we analyze why distributed representations perform such tasks well; we also propose a new method for extracting knowledge from texts that requires much less annotated data. Experiments show that the proposed method achieves considerable improvement compared with the baseline; in particular, the improvement in HITS@10 was more than doubled for some relations.

A circuit-aging simulation that efficiently calculates temporal change of rare circuit-failure probability is proposed. While conventional methods required a long computational time due to the necessity of conducting separate calculations of failure probability at each device age, the proposed Monte Carlo based method requires to run only a single set of simulation. By applying the augmented reliability and subset simulation framework, the change of failure probability along the lifetime of the device can be evaluated through the analysis of the Monte Carlo samples. Combined with the two-step sample generation technique, the proposed method reduces the computational time to about 1/6 of that of the conventional method while maintaining a sufficient estimation accuracy.

Small delay caused by a resistive open is difficult to test since circuit delay varies depending on various factors such as process variations and crosstalk even in fault-free circuits. We consider the problem of discriminating a resistive open by anomaly detection using delay distributions obtained by the effect of various input signals provided to adjacent lines. We examined the circuit delay in a fault-free circuit and a faulty circuit by applying electromagnetic simulator and circuit simulator for a line structure with adjacent lines under consideration of process variations. The effectiveness of the method that discriminates a resistive open is shown for the results obtained by the simulation.

A Tikhonov regularized RLS algorithm with an exponential weighting factor, i.e., a leaky RLS (LRLS) algorithm was proposed by the author. A quadratic version of the LRLS algorithm also exists in the literature of adaptive filters. In this letter, a cubic version of the LRLS filter which is computationally efficient is proposed when the length of the adaptive filter is short. The proposed LRLS filter includes only a divide per iteration although its multiplications and additions increase in number. Simulation results show that the proposed LRLS filter is faster for its short length than the existing quadratic version of the LRLS filter.

Let $mathbb{F}_q$ be a finite field of q elements, $R=mathbb{F}_q+umathbb{F}_q$ (u2=0) and D2n= be a dihedral group of order n. Left ideals of the group ring R[D2n] are called left dihedral codes over R of length 2n, and abbreviated as left D2n-codes over R. Let n be a positive factor of qe+1 for some positive integer e. In this paper, any left D2n-code over R is uniquely decomposed into a direct sum of concatenated codes with inner codes Ai and outer codes Ci, where Ai is a cyclic code over R of length n and Ci is a linear code of length 2 over a Galois extension ring of R. More precisely, a generator matrix for each outer code Ci is given. Moreover, a formula to count the number of these codes is obtained, the dual code for each left D2n-code is determined and all self-dual left D2n-codes over R are presented, respectively.

Intelligent transportation systems (ITS) are a set of technological solutions used to improve the performance and safety of road transportation. Since one of the most important information sources on ITS are sensors, the integration and sharing the sensor data become a big challenging problem in the application of sensor networks to these systems. In order to make full use of the sensor data, is crucial to convert the sensor data into semantic data, which can be understood by computers. In this work, we propose to use the SSN ontology to manage the sensor information in an intelligent transportation architecture. The system was tested in a traffic light settings application, allowing to predict and avoid traffic accidents, and also for the routing optimization.

An improved beamformer, which uses joint estimation of the reconstructed interference-plus-noise (IPN) covariance matrix and array steering vector (ASV), is proposed. It can mitigate the problem of performance degradation in situations where the desired signal exists in the sample covariance matrix and the steering vector pointing has large errors. In the proposed method, the covariance matrix is reconstructed by weighted sum of the exterior products of the interferences' ASV and their individual power to reject the desired signal component, the coefficients of which can be accurately estimated by the compressed sensing (CS) and total least squares (TLS) techniques. Moreover, according to the theorem of sequential vector space projection, the actual ASV is estimated from an intersection of two subspaces by applying the alternating projection algorithm. Simulation results are provided to demonstrate the performance of the proposed beamformer, which is clearly better than the existing robust adaptive beamformers.

This paper considers a design method of a discrete-time adaptive output feedback control system with a feedforward input based on almost strict positive realness (ASPR-ness). The proposed scheme utilizes the property of ASPR of the controlled plant, and the reference signal is used as feedforward input. The parallel feedforward compensator (PFC) which renders an ASPR augmented controlled plant is also investigated. Besides, it is shown that the output of original plant can track reference signal perfectly without any steady state error. The effectiveness of the proposed scheme is confirmed through a pilot-scale temperature control system.

Wireless sensor networks usually deploy sensor nodes with limited energy resources in unattended environments so that people have difficulty in replacing or recharging the depleted devices. In order to balance the energy dissipation and prolong the network lifetime, this paper proposes a routing spanning tree-based clustering algorithm (RSTCA) which uses routing spanning tree to analyze clustering. In this study, the proposed scheme consists of three phases: setup phase, cluster head (CH) selection phase and steady phase. In the setup phase, several clusters are formed by adopting the K-means algorithm to balance network load on the basis of geographic location, which solves the randomness problem in traditional distributed clustering algorithm. Meanwhile, a conditional inter-cluster data traffic routing strategy is created to simplify the networks into subsystems. For the CH selection phase, a novel CH selection method, where CH is selected by a probability based on the residual energy of each node and its estimated next-time energy consumption as a function of distance, is formulated for optimizing the energy dissipation among the nodes in the same cluster. In the steady phase, an effective modification that counters the boundary node problem by adjusting the data traffic routing is designed. Additionally, by the simulation, the construction procedure of routing spanning tree (RST) and the effect of the three phases are presented. Finally, a comparison is made between the RSTCA and the current distributed clustering protocols such as LEACH and LEACH-DT. The results show that RSTCA outperforms other protocols in terms of network lifetime, energy dissipation and coverage ratio.

Scalable high efficiency video coding (SHVC) can provide variable video quality according to terminal devices. However, the computational complexity of SHVC is increased by introducing new techniques based on high efficiency video coding (HEVC). In this paper, a hardware oriented low complexity algorithm is proposed. The hardware oriented proposals have two key points. Firstly, the coding unit depth is determined by analyzing the boundary correlation between coding units before encoding process starts. Secondly, the redundant calculation of R-D optimization is reduced by adaptively using the information of the neighboring coding units and the co-located units in the base layer. The simulation results show that the proposed algorithm can achieve over 62% computation complexity reduction compared to the original SHM11.0. Compared with other related work, over 11% time saving have been achieved without PSNR loss. Furthermore, the proposed algorithm is hardware friendly which can be implemented in a small area.

Object detection is an essential and expensive process in many computer vision systems. Standard off-the-shelf embedded processors are hard to achieve performance-power balance for implementation of object detection applications. In this work, we explore an Application Specific Instruction set Processor (ASIP) for object detection using Histogram of Oriented Gradients (HOG) feature. Algorithm simplifications are adopted to reduce memory bandwidth requirements and mathematical complexity without losing reliability. Also, parallel histogram generation and on-the-fly Support Vector Machine (SVM) calculation architecture are employed to reduce the necessary cycle counts. The HOG algorithm on the proposed ASIP was accelerated by a factor of 63x compared to the pure software implementation. The ASIP was synthesized for a standard 90nm CMOS library, with a silicon area of 1.31mm2 and 47.8mW power consumption at a 200MHz frequency. Our object detection processor can achieve 42 frames-per-second (fps) on VGA video. The evaluation and implementation results show that the proposed ASIP is both area-efficient and power-efficient while being competitive with commercial CPUs/DSPs. Furthermore, our ASIP exhibits comparable performance even with hard-wire designs.

In this letter, a new rapid and accurate synchronization scheme based on PMF-FFT for high dynamic GPS receiver is proposed, with a fine Doppler frequency estimation inserted between the acquisition and tracking modules. Fine Doppler estimation is firstly achieved through a simple interpolation of the PMF-FFT outputs in terms of LSE criterion. Then a high dynamic tracking loop based on UKF is designed to verify the synchronization speed and accuracy. Numerical results show that the fine frequency estimation can closely approach the CRB, and the high dynamic receiver can obtain fine synchronization rapidly just through a very narrow bandwidth. The simplicity and low complexity give the proposed scheme a strong and practical-oriented ability, even for weak GPS signals.

Interconnection network is one of the inevitable components in parallel computers, since it is responsible to communication capabilities of the systems. It affects the system-level performance as well as the physical and logical structure of the systems. Although many studies are reported to enhance the interconnection network technology, we have to discuss many issues remaining. One of the most important issues is congestion management. In an interconnection network, many packets are transferred simultaneously and the packets interfere to each other in the network. Congestion arises as a result of the interferences. Its fast spreading speed seriously degrades communication performance and it continues for long time. Thus, we should appropriately control the network to suppress the congested situation for maintaining the maximum performance. Many studies address the problem and present effective methods, however, the maximal performance in an ideal situation is not sufficiently clarified. Solving the ideal performance is, in general, an NP-hard problem. This paper introduces particle swarm optimization (PSO) methodology to overcome the problem. In this paper, we first formalize the optimization problem suitable for the PSO method and present a simple PSO application as naive models. Then, we discuss reduction of the size of search space and introduce three practical variations of the PSO computation models as repetitive model, expansion model, and coding model. We furthermore introduce some non-PSO methods for comparison. Our evaluation results reveal high potentials of the PSO method. The repetitive and expansion models achieve significant acceleration of collective communication performance at most 1.72 times faster than that in the bursty communication condition.

An efficient reciprocity and passivity preserving balanced truncation for RLC networks is presented in this paper. Reciprocity and passivity are fundamental principles of linear passive networks. Hence, reduction with preservation of reciprocity and passivity is necessary to simulate behavior of the circuits including the RLC networks accurately and stably. Moreover, the proposed method is more efficient than the previous balanced truncation methods, because sparsity patterns of the coefficient matrices for the circuit equations of the RLC networks are fully available. In the illustrative examples, we will show that the proposed method is compatible with PRIMA, which is known as a general reduction method of RLC networks, in efficiency and used memory, and is more accurate at high frequencies than PRIMA.