Advances in intelligent vehicle systems have led to modern automobiles being able to aid drivers with tasks such as lane following and automatic braking. Such automated driving tasks increasingly require reliable ego-localization. Although there is a large number of sensors that can be employed for this purpose, the use of a single camera still remains one of the most appealing, but also one of the most challenging. GPS localization in urban environments may not be reliable enough for automated driving systems, and various combinations of range sensors and inertial navigation systems are often too complex and expensive for a consumer setup. Therefore accurate localization with a single camera is a desirable goal. In this paper we propose a method for vehicle localization using images captured from a single vehicle-mounted camera and a pre-constructed database. Image feature points are extracted, but the calculation of camera poses is not required — instead we make use of the feature points' scale. For image feature-based localization methods, matching of many features against candidate database images is time consuming, and database sizes can become large. Therefore, here we propose a method that constructs a database with pre-matched features of known good scale stability. This limits the number of unused and incorrectly matched features, and allows recording of the database scales into “tracklets”. These “Feature scale tracklets” are used for fast image match voting based on scale comparison with corresponding query image features. This process reduces the number of image-to-image matching iterations that need to be performed while improving the localization stability. We also present an analysis of the system performance using a dataset with high accuracy ground truth. We demonstrate robust vehicle positioning even in challenging lane change and real traffic situations.

We have succeeded in developing three techniques, a precise lens-alignment technique, low-loss built-in Spatial Multiplexing optics and a well-matched electrical connection for high-frequency signals, which are indispensable for realizing compact high-performance TOSAs and ROSAs employing hybrid integration technology. The lens position was controlled to within ±0.3 µm by high-power laser irradiation. All components comprising the multiplexing optics are bonded to a prism, enabling the insertion loss to be held down to 0.8 dB due to the dimensional accuracy of the prism. The addition of an FPC layer reduced the impedance mismatch at the junction between the FPC and PCB. We demonstrated a compact integrated four-lane 25 Gb/s TOSA (15.1 mm × 6.5 mm × 5.6 mm) and ROSA (17.0 mm × 12.0 mm × 7.0 mm) using the built-in spatial Mux/Demux optics with good transmission performance for 100 Gb/s Ethernet. These are respectively suitable for the QSFP28 and CFP2 form factors.

In this paper, we study the achievable degrees of freedom (DoF) of a multiple-input multiple-output (MIMO) multi-way relay channel with asymmetric message set that models the scenario of the two-way communication between a base station and multiple users through a relay. Under the assumption of delayed channel state information at transmitters (CSIT), we propose an amplify-and-forward relaying scheme based on the scheme proposed by Maddah-Ali and Tse to support signal space alignment, so that the available dimensions of the signal spaces at the relay and the users can be efficiently utilized. The proposed scheme outperforms the traditional one-way scheme from the perspective of DoF, and is useful to relieve the communication bottleneck caused by the asymmetric traffic load inherent in cellular networks.

Spectrum sharing effectively improves the spectrum usage by allowing secondary users (SUs) to dynamically and opportunistically share the licensed bands with primary users (PUs). The concept of cooperative spectrum sharing allows SUs to use portions of the PUs' radio resource for their own data transmission, under the condition that SUs help the PUs' transmission. The key issue with designing such a scheme is how to deal with the resource splitting of the network. In this paper we propose a relay-based cooperative spectrum sharing scheme in which the network consists of one PU and multiple SUs. The PU asks the SUs to relay its data in order to improve its energy efficiency, in return it rewards the SUs with a portion of its authorized spectrum. However each SU is only allowed to transmit its data via the rewarded channel at a power level proportional to the contribution it makes to the PU. Since energy cost is considered, the SUs must carefully determine their power level. This scheme forms a non-cooperative Stackelberg resource allocation game where the strategy of PU is the bandwidth it rewards and the strategy of each SU is power level of relay transmission. We first investigate the second stage of the sub-game which is addressed as power allocation game. We prove there exists an equilibrium in the power allocation game and provide a sufficient condition for the uniqueness of the equilibrium. We further prove a unique Stackelberg equilibrium exists in the resource allocation game. Distributed algorithms are proposed to help the users with incomplete information achieve the equilibrium point. Simulation results validate our analysis and show that our proposed scheme introduces significant utility improvement for both PU and SUs.

In text-independent online writer identification, the Gaussian Mixture Model(GMM) writer model trained with the GMM-Universal Background Model(GMM-UBM) framework has acquired excellent performance. However, the system assumes the items in the observation sequence are independent, which neglects the dynamic information between observations. This work shows that although in the text-independent application, the dynamic information between observations is still important for writer identification. In order to extend the GMM-UBM system to use the dynamic information, the hidden Markov model(HMM) with Gaussian observation model is used to model each writer's handwriting in this paper and a new training schematic is proposed. In particular, the observation model parameters of the writer specific HMM are set with the Gaussian component parameters of the GMM writer model trained with the GMM-UBM framework and the state transition matrix parameters are learned from the writer specific data. Experiments show that incorporating the dynamic information is capable of improving the performance of the GMM-based system and the proposed training method is effective for learning the HMM writer model.

Periodic interference frequently affects the measurement of small signals and causes problems in clinical diagnostics. Adaptive filters can be used as potential tools for cancelling such interference. However, when the interference has a frequency fluctuation, the ideal adaptive-filter coefficients for cancelling the interference also fluctuate. When the adaptation property of the algorithm is slow compared with the frequency fluctuation, the interference-cancelling performance is degraded. However, if the adaptation is too quick, the performance is degraded owing to the target signal. To overcome this problem, we propose an adaptive filter that suppresses the fluctuation of the ideal coefficients by utilizing a $rac{pi}{2}$ phase-delay device. This method assumes a frequency response that characterizes the transmission path from the interference source to the main input signal to be sufficiently smooth. In the numerical examples, the proposed method exhibits good performance in the presence of a frequency fluctuation when the forgetting factor is large. Moreover, we show that the proposed method reduces the calculation cost.

Due to heavy rendering load and unstable frame rate when rendering large terrain, this paper proposes a geometry clipmaps based algorithm. Triangle meshes are generated by few tessellation control points in GPU tessellation shader. ‘Cracks’ caused by different resolution between adjacent levels are eliminated by modifying outer tessellation level factor of shared edges between levels. Experimental results show the algorithm is able to improve rendering efficiency and frame rate stability in terrain navigation.

Recently, the Static Heterogeneous Particle Swarm Optimization (SHPSO) has been studied by more and more researchers. In SHPSO, the different search behaviours assigned to particles during initialization do not change during the search process. As a consequence of this, the inappropriate population size of exploratory particles could leave the SHPSO with great difficulties of escaping local optima. This motivated our attempt to improve the performance of SHPSO by introducing the dynamic heterogeneity. The self-adaptive heterogeneity is able to alter its heterogeneous structure according to some events caused by the behaviour of the swarm. The proposed triggering events are confirmed by keeping track of the frequency of the unchanged global best position (pg) for a number of iterations. This information is then used to select a new heterogeneous structure when pg is considered stagnant. According to the different types of heterogeneity, DHPSO-d and DHPSO-p are proposed in this paper. In, particles dynamically use different rules for updating their position when the triggering events are confirmed. In DHPSO-p, a global gbest model and a pairwise connection model are automatically selected by the triggering configuration. In order to investigate the scalability of and DHPSO-p, a series of experiments with four state-of-the-art algorithms are performed on ten well-known optimization problems. The scalability analysis of and DHPSO-p reveals that the dynamic self-adaptive heterogeneous structure is able to address the exploration-exploitation trade-off problem in PSO, and provide the excellent optimal solution of a problem simultaneously.

In this paper, the performance of a vehicle information sharing (VIS) system for an intersection collision warning system (ICWS) is analyzed. The on-board unit (OBU) of the ICWS sharing obstacle detection sensor information (ICWS-ODSI) is mounted on a vehicle, and it obtains information about the surrounding vehicles, such as their position and velocity, by its in-vehicle obstacle detection sensors. These information are shared with other vehicles via an intervehicle communication network. In this analysis, a T-junction is assumed as the road environment for the theoretical analysis of the VIS performance in terms of the mean of entire vehicle information acquiring probability (MEVIAP). The MEVIAP on OBU penetration rate indicated that the ICWS-ODSI is superior to the conventional VIS system that only shares its own individual driving information via an intervehicle communication network. Furthermore, the MEVIAP on the sensing range of the ICWS-ODSI is analyzed, and it was found that the ISO15623 sensor used for the forward vehicle collision warning system becomes a candidate for the in-vehicle detection sensor of ICWS-ODSI.

Active oxygen species (AOS), e.g., excited singlet oxygen atom [O(1D)], excited singlet oxygen molecules (1O2), ground-state oxygen atom [O(3P)] and hydroxyl radical (OH), generated under two wavelengths (185 and 254 nm) of ultraviolet (UV) light were exposed to polyethylene (PE), polypropylene (PP) and polystyrene (PS) sheets. We investigated effects of the AOS exposure on the surface modification of these polymer sheets. Nonwoven sheet was used for the surface modification to eliminate an effect of the UV light irradiation. Although hydrophobicity of the PE and PP surfaces was maintained, the PS was changed into the hydrophilic surface.

In this letter, we propose a novel speech separation method based on perceptual weighted deep recurrent neural network (DRNN) which incorporate the masking properties of the human auditory system. In supervised training stage, we firstly utilize the clean label speech of two different speakers to calculate two perceptual weighting matrices. Then, the obtained different perceptual weighting matrices are utilized to adjust the mean squared error between the network outputs and the reference features of both the two clean speech so that the two different speech can mask each other. Experimental results on TSP speech corpus demonstrate that the proposed speech separation approach can achieve significant improvements over the state-of-the-art methods when tested with different mixing cases.

We prepared a bR thin film by the wire-bar coating technique, and investigated the transient photo-current characteristics of the bR photocell. The transient photo-current signal of bR photocells prepared by the wire-bar coating technique and the dip coating technique was compared. An almost identical transient photo-current signal intensity was obtained both for the wire-bar coating technique and dip coating technique, while the thickness of bR thin film prepared by the wire-bar coating technique is slightly thinner than that prepared by the dip-coating technique. Transparent conductive oxide dependence of the transient photo-current signal is almost the same dependence for the bR photocells with a bR thin film prepared by both techniques. Application of the wire-bar coating technique is significant from the viewpoints of the bR's sample consumption as well as simplicity of sample preparation.

In this paper, we present an FPGA hardware implementation for a phylogenetic tree reconstruction with a maximum parsimony algorithm. We base our approach on a particular stochastic local search algorithm that uses the Progressive Neighborhood and the Indirect Calculation of Tree Lengths method. This method is widely used for the acceleration of the phylogenetic tree reconstruction algorithm in software. In our implementation, we define a tree structure and accelerate the search by parallel and pipeline processing. We show results for eight real-world biological datasets. We compare execution times against our previous hardware approach, and TNT, the fastest available parsimony program, which is also accelerated by the Indirect Calculation of Tree Lengths method. Acceleration rates between 34 to 45 per rearrangement, and 2 to 6 for the whole search, are obtained against our previous hardware approach. Acceleration rates between 2 to 36 per rearrangement, and 18 to 112 for the whole search, are obtained against TNT.

Event-triggered control is a control method that the measured signal is sent to the controller only when a certain triggering condition on the measured signal is satisfied. In this paper, we propose a linear quadratic regulator (LQR) with decentralized triggering conditions. First, a suboptimal solution to the design problem of LQRs with decentralized triggering conditions is derived. A state-feedback gain can be obtained by solving a convex optimization problem with LMI (linear matrix inequality) constraints. Next, the relation between centralized and decentralized triggering conditions is discussed. It is shown that control performance of an LQR with decentralized event-triggering is better than that with centralized event-triggering. Finally, a numerical example is illustrated.

In this paper, we propose a method to find similar sentences based on language resources for building a parallel corpus between English and Korean from Wikipedia. We use a Wiki-dictionary consisted of document titles from the Wikipedia and bilingual example sentence pairs from Web dictionary instead of traditional machine readable dictionary. In this way, we perform similarity calculation between sentences using sequential matching of the language resources, and evaluate the extracted parallel sentences. In the experiments, the proposed parallel sentences extraction method finally shows 65.4% of F1-score.

Controlling synchrony as well as desynchrony in a network of neuronal oscillators has been one of the focus issues in nonlinear science and engineering. It has been well known that spike stimuli injected commonly to multiple neurons can synchronize them if the strength of the common spike stimuli is high enough. Our recent study showed that this common spike-induced synchrony could be suppressed by introducing heterogeneity to inhibitory connections, through which the common spikes are transmitted. The aim of the present study is apply this methodology to electronic neurons as a real physical hardware. Using an Axon-Hillock circuit that represents basic properties of the leaky integrate-and-fire (LIF) neuron, our experiment demonstrated that the method was quite effective for desynchronizing the neuron circuits. The experimental results are also in a good agreement with the linear response theory that describes the input-output relationship of LIF neurons. Our method of suppressing the neuronal synchrony should be of practical use for enhancement of neural information processing as well as for improvement of pathological state of the brain.

This paper presents human-centered video feature selection via mRMR-SCMMCCA (minimum Redundancy and Maximum Relevance-Specific Correlation Maximization Multiset Canonical Correlation Analysis) algorithm for preference extraction. The proposed method derives SCMMCCA, which simultaneously maximizes two kinds of correlations, correlation between video features and users' viewing behavior features and correlation between video features and their corresponding rating scores. By monitoring the derived correlations, the selection of the optimal video features that represent users' individual preference becomes feasible.

This letter presents a method for solving several linear equations in max-plus algebra. The essential part of these equations is reduced to constraint satisfaction problems compatible with mixed integer programming. This method is flexible, compared with optimization methods, and suitable for scheduling of certain discrete event systems.

To deal with the recent explosion of mobile data traffic, heterogeneous cellular networks, in which a large number of small cells are deployed in a macro-cell coverage area, are considered to be a promising approach. However, when a mobile terminal (MT) traveling at a high velocity moves through several small cells in a short period of time, the frequent handovers (HOs) that occur between small cells lead to a deterioration of user quality of experience. To avoid such HO problems, while improving the network capacity in the heterogeneous cellular network, it is effective to introduce an inter-layer HO control policy where MTs traveling at high velocities are connected to the macro-cell layer to reduce the number of HOs and MTs traveling at low velocities or which are stationary are connected to the small-cell layer for offloading traffic from the macro-cells to the small-cells. However, to realize such inter-layer HO control policy in the heterogeneous cellular network, it is crucial to estimate the velocity of each MT. Due to the technological constraints of MT velocity estimation based on the Global Positioning Systems (GPS), we focus on MT velocity estimation algorithms which do not require information provided by GPS. First, we discuss the issues of the existing MT velocity estimation algorithms and then focus on a MT velocity estimation algorithm based on a conventional Doppler spread detection using Fast Fourier Transform (FFT). Since few studies have evaluated Doppler spread based MT velocity estimation techniques for practical communication systems in actual radio propagation environments, we implement the MT velocity estimation algorithm to a Long Term Evolution (LTE) based experimental system, and perform its field experiments. Based on these experimental results we also evaluate the high or low velocity decision accuracy for the inter-layer HO control policy and show that good decision accuracy is achieved in both line-of-sight (LOS) and non-line-of-sight (NLOS) outdoor propagation environment. These results show its feasibility for practical mobile communication systems in actual radio propagation environments.

We present a novel receiver for reliable IoT communications. In this letter, it is assumed that IoT communications are based on ZigBee under frequency-selective indoor environments. The ZigBee includes IEEE 802.15.4 specification for low-power and low-cost communications. The presented receiver fully follows the specification. However, the specification exhibits extremely low performance under frequency-selective environments. Therefore, a channel estimation approach is proposed for reliable communications under frequency-selective fading indoor environments. The estimation method relies on FFT operations, which are usually embedded in cellular phones. We also suggest a correlation method for accurate recovery of original information. The simulation results show that the proposed receiver is very suitable for IoT communications under frequency-selective indoor environments.