Identification of motion parameters is an important issue in image restoration of a linear motion blur. Based on the human visual-motion sensing properties, an integrated approach with some known image processing techniques is proposed to the estimation of the direction and extent of motion on a linear motion blurred image. Experimental results confirm the feasibility of our approach.

To achieve more accurate measurements of the mobile station (MS) location, it is possible to integrate many kinds of measurements. In this paper we proposed several hybrid methods that utilized time of arrival (TOA) at seven base stations (BSs) and the angle of arrival (AOA) information at the serving BS to give location estimation of the MS in non-line-of-sight (NLOS) environments. Rather than applying the nonlinear circular lines of position (LOP), the proposed methods are easier by using linear LOP to determine the MS. In addition, the proposed methods can mitigate the NLOS effect, simply by applying the weighted sum of the intersections between different linear LOP and the AOA line, without requiring priori knowledge of NLOS error statistics. Simulation results show that the proposed methods can always yield superior performance in comparison with Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) and the previous proposed methods employing circular LOP.

Recently, Zhu et al. proposed an password-based authenticated key exchange protocol based on RSA such that it is efficient enough to be implemented on most of the target low-power devices such as smart cards and low-power Personal Digital Assistants in wireless networks. They claimed that the proposed scheme is secure against dictionary attacks. In this paper, we show that the scheme proposed by Zhu et al. is insecure against undetectable on-line password guessing attacks. Furthermore, we examine Zhu et al.'s protocol and find that Zhu et al.'s protocol does not achieve explicit key authentication. An improved version is then proposed to defeat the undetectable on-line password guessing attacks and also provide explicit key authentication.

Unmanned aerial vehicle mounted base stations (UAV-BSs) can provide wireless cellular service to ground users in a variety of scenarios. The efficient deployment of such UAV-BSs while optimizing the coverage area is one of the key challenges. We investigate the deployment of UAV-BS to maximize the coverage of ground users, and further analyzes the impact of the deployment of UAV-BS on the fairness of ground users. In this paper, we first calculated the location of the UAV-BS according to the QoS requirements of the ground users, and then the fairness of ground users is taken into account by calculating three different fairness indexes. The performance of two genetic algorithms, namely Standard Genetic Algorithm (SGA) and Multi-Population Genetic Algorithm (MPGA) are compared to solve the optimization problem of UAV-BS deployment. The simulations are presented showing that the performance of the two algorithms, and the fairness performance of the ground users is also given.

This paper proposes a write resistance tracking circuit (WRTC) to improve the memory window of HfOx-based resistive memory. With a 50-ns single voltage pulse, the minimal resistance of the high resistance state in the 1-kb array of resistive switching elements can increase from 25 kΩ to 65 kΩ by using the proposed verify circuit. The WRTC uses the transition current detection method based on the feedback of the memory cell to control the write driver. The WRTC achieves distinct bistable resistance states, avoids the occurrence of over-RESET, and enhances the memory window of the RRAM cell.

A mobile node in ad hoc networks may move arbitrarily and act as a router and a host simultaneously. Such a characteristic makes nodes in MANET vulnerable to potential attacks. The black hole problem, in which some malicious nodes pretend to be intermediate nodes of a route to some given destinations and drop packets that pass through it, is one of the major types of attack. In this paper, we propose a distributed and cooperative mechanism to tackle the black hole problem. The mechanism is distributed so that it suits the ad hoc nature of network, and nodes in the protocol cooperate so that they can analyze, detect, and eliminate possible multiple black-hole nodes in a more reliable fashion. Simulation results show that our method achieves a high black hole detection rate and good packet delivery rate, while the overhead is comparatively lower as the network traffic increases.

This paper chronicles the leaky-mode leakages in a corner-fed square patch. First, the measured peak RPA (relative power absorbed, 1-|S11|2-|S21|2) values of the two-port corner-fed square patch (two-port test circuit) are reported. These are 21.1%, 52.3%, 89.8% and 81.3% at 5.68 GHz, 11.76 GHz, 16.68 GHz and 22.29 GHz, respectively. Such periodicity of frequencies and the phenomenon of increasing losses at higher frequencies enable us to link these peak frequencies to leaky-mode excitations. The modal spectra of the higher-order leaky modes with a strip width of 630 mil, equal to the side length of the square patch, are obtained by the well-known space-domain integral equation method. The maximum available power gain (GA,max), obtained by simultaneously complex conjugate matched impedance at the referenced two ports of the test circuit, depicts that (1) nearly loss-free transmission parameter outside the leaky-mode regions and (2) substantial losses inside the leaky-mode regions. This result suggests that the leaky modes are the main sources causing losses for the two-port test circuit. Furthermore, the valley points of the measured and theoretical GA,max are about -7.5 dB, -9.7 dB and -12.0 dB at 10.13 GHz, 16.68 GHz and 22.29 GHz, respectively, and all are in the leaky-mode regions of the modal spectra. The one-port properties of the two-port patch with the second port opened are then investigated. The degenerated (0,N) and (N,0) modes (N=1, 2, 3 and 4), calculated by the cavity model method, fall into the strong leakage regions from the first to the fourth higher-order leaky modes. The well-known leaky line's frequency-scanning characteristics also appear in the one-port test circuit, with the angle of the main beam moving from θ=30 to θ=40 as the operating frequency is increased from 22.50 GHz to 23.75 GHz. Lastly, a two-dimensional (2-D) transmission-line model of the one-port test circuit is proposed. This model uses two orthogonal modal currents as excitations to stimulate the corner-fed square patch. At 22.60 GHz, in the fourth higher-order leaky mode (EH4) region, the current distributions obtained by the 2-D transmission-line model closely agree with those of the full-wave simulation. This consistency shows that the damped-oscillation current distributions of the corner-fed square patch at 22.60 GHz are caused significantly by the multiple reflections of the leaky mode. Furthermore, at the resonant frequencies of the patch, the tangled bound-mode resonance of the EH0 mode can enhance the leaky-mode leakages.

Considering the real existent fact of the ideal edge and the learning style of image analogy without reference parameters, a blind image recovery algorithm using a self-adaptive learning method is proposed in this paper. We show that a specific local image patch with degradation characteristic can be utilized for restoring the whole image. In the training process, a clear counterpart of the local image patch is constructed based on the ideal edge assumption so that identification of the Point Spread Function is no longer needed. Experiments demonstrate the effectiveness of the proposed method on remote sensing images.

In this paper, we provide several methods to construct nonlinear resilient functions with multiple good cryptographic properties, including high nonlinearity, high algebraic degree, and non-existence of linear structures. Firstly, we present an improvement on a known construction of resilient S-boxes such that the nonlinearity and the algebraic degree will become higher in some cases. Then a construction of highly nonlinear t-resilient Boolean functions without linear structures is given, whose algebraic degree achieves n-t-1, which is optimal for n-variable t-resilient Boolean functions. Furthermore, we construct a class of resilient S-boxes without linear structures, which possesses the highest nonlinearity and algebraic degree among all currently known constructions.

The heterogeneous network (HetNet), which deploys small cells such as picocells, femotcells, and relay nodes within macrocell, is regarded as a cost-efficient and energy-efficient approach to resolve increasing demand for data bandwidth and thus has received a lot of attention from research and industry. Since small cells share the same licensed spectrum with macrocells, concurrent transmission induces severe interference, which causes performance degradation, particularly when coordination among small cell base stations (BSs) is infeasible. Given the dense, massive, and unplanned deployment of small cells, mitigating interference in a distributed manner is a challenge and has been explored in recent papers. An efficient and innovative approach is to apply cognitive radio (CR) into HetNet, which enables small cells to sense and to adapt to their surrounding environments. Consequently, stations in each small cell are able to acquire additional information from surrounding environments and opportunistically operate in the spectrum hole, constrained by minimal inducing interference. This paper summarizes and highlights the CR-based interference mitigation approaches in orthogonal frequency division multiple access (OFDMA)-based HetNet networks. With special discussing the role of sensed information at small cells for the interference mitigation, this paper presents the potential cross-layer facilitation of the CR-enable HetNet.

In ASR (Automatic Speech Recognition) applications, one of the most important issues in the real-time beamforming of microphone arrays is the inability to capture the whole acoustic dynamics via a finite-length of data and a finite number of array elements. For example, the reflected source signal impinging from the side-lobe direction presents a coherent interference, and the non-minimal phase channel dynamics may require an infinite amount of data in order to achieve perfect equalization (or inversion). All these factors appear as uncertainties or un-modeled dynamics in the receiving signals. Traditional adaptive algorithms such as NLMS that do not consider these errors will result in performance deterioration. In this paper, a time domain beamformer using H∞ filtering approach is proposed to adjust the beamforming parameters. Furthermore, this work also proposes a frequency domain approach called SPFDBB (Soft Penalty Frequency Domain Block Beamformer) using H∞ filtering approach that can reduce computational efforts and provide a purified data to the ASR application. Experimental results show that the adaptive H∞ filtering method is robust to the modeling errors and suppresses much more noise interference than that in the NLMS based method. Consequently, the correct rate of ASR is also enhanced.

We develop a novel construction method for n-dimensional Hilbert space-filling curves. The construction method includes four steps: block allocation, Gray permutation, coordinate transformation and recursive construction. We use the tensor product theory to formulate the method. An n-dimensional Hilbert space-filling curve of 2r elements on each dimension is specified as a permutation which rearranges 2rn data elements stored in the row major order as in C language or the column major order as in FORTRAN language to the order of traversing an n-dimensional Hilbert space-filling curve. The tensor product formulation of n-dimensional Hilbert space-filling curves uses stride permutation, reverse permutation, and Gray permutation. We present both recursive and iterative tensor product formulas of n-dimensional Hilbert space-filling curves. The tensor product formulas are directly translated into computer programs which can be used in various applications. The process of program generation is explained in the paper.

Rendering realistic trees is quite important for simulating a 3D natural scene. Separating the trunk from its background is the first step toward the 3D model construction of the tree. In this paper, a three-phase algorithm is developed to extract the trunk structure of the tree and hence segment the trunk from the image. Some experiments were conducted and results confirmed the feasibility of proposed algorithm.

The location-based routing protocol has proven to be scalable and efficient in large wireless sensor networks with mobile sinks. A great challenge in location-based routing protocols is the design of scalable distributed location service that tracks the current locations of mobile sinks. Although various location services have been proposed in the literature, hierarchical-based location services have the significant advantage of high scalability. However, most of them depend on a global hierarchy of grids. A major disadvantage of this design is that high control overhead occurs when mobile sinks cross the boundaries of the top level grids. In this paper, we introduce Hierarchical Ring Location Service (HRLS) protocol, a practical distributed location service that provides sink location information in a scalable and distributed manner. In contrast to existing hierarchical-based location services, each sink in HRLS constructs its own hierarchy of grid rings distributively. To reduce the communication overhead of location update, sinks utilize the lazy update mechanism with their indirect location. Once a sensor node detects a target, it queries the location of a sink by sending request packets in eight directions. HRLS is evaluated through mathematical analysis and simulations. Compared with a well-known hierarchical-based location service, our results show that HRLS provides a more scalable and efficient distributed location service in scenarios with various network size, sink mobility and increasing number of source nodes.

Pre-trained CNNs on ImageNet have been widely used in object tracking for feature extraction. However, due to the domain mismatch between image classification and object tracking, the submergence of the target-specific features by noise largely decreases the expression ability of the convolutional features, resulting in an inefficient tracking. In this paper, we propose a robust tracking algorithm with low-dimensional target-specific feature extraction. First, a novel cascaded PCA module is proposed to have an explicit extraction of the low-dimensional target-specific features, which makes the new appearance model more effective and efficient. Next, a fast particle filter process is raised to further accelerate the whole tracking pipeline by sharing convolutional computation with a ROI-Align layer. Moreover, a classification-score guided scheme is used to update the appearance model for adapting to target variations while at the same time avoiding the model drift that caused by the object occlusion. Experimental results on OTB100 and Temple Color128 show that, the proposed algorithm has achieved a superior performance among real-time trackers. Besides, our algorithm is competitive with the state-of-the-art trackers in precision while runs at a real-time speed.

To reconstruct low-resolution facial photographs which are in focus and without motion blur, a novel algorithm based on local similarity preserving is proposed. It is based on the theories of local manifold learning. The innovations of the new method include mixing point-based entropy and Euclidian distance to search for the nearest points, adding point-to-patch degradation model to restrict the linear weights and compensating the fusing patch to keep energy coherence. The compensation reduces the algorithm dependence on training sets and keeps the luminance of reconstruction constant. Experiments show that our method can effectively reconstruct 1612 images with the magnification of 88 and the 3224 facial photographs in focus and without motion blur.

This investigation proposed two array beamformers SPFDBB (Soft Penalty Frequency Domain Block Beamformer) and FDABB (Frequency Domain Adjustable Block Beamformer). Compared with the conventional beamformers, these frequency-domain methods can significantly reduce the computation power requirement in ASR (Automatic Speech Recognition) based applications. Like other reference signal based techniques, SPFDBB and FDABB minimize microphone's mismatch, desired signal cancellation caused by reflection effects and resolution due to the array's position. Additionally, these proposed methods are suitable for both near-field and far-field environments. Generally, the convolution relation between channel and speech source in time domain cannot be modeled accurately as a multiplication in the frequency domain with a finite window size, especially in ASR applications. SPFDBB and FDABB can approximate this multiplication by treating several frames as a block to achieve a better beamforming result. Moreover, FDABB adjusts the number of frames on-line to cope with the variation of characteristics in both speech and interference signals. A better performance was found to be achievable by combining these methods with an ASR mechanism.

A novel divide-by-3 injection-locked frequency divider (ILFD) is proposed. The ILFD circuit is realized with a cross-coupled n-core MOS LC-tank oscillator embedded with a push-push signal generator and two injection MOSFETs for coupling the injection signal into the resonator. The ILFD uses the linear mixer to extend the locking range and has been implemented in a standard 0.18 µm CMOS process. The core power consumption of the ILFD core is 3.12 mW. The divider's free-running frequency is tunable from 4.26 GHz to 4.9 GHz by tuning the varactor's control bias, and at the incident power of 0 dBm the locking range of the ILFD used as a divide-by-3 divider is 1.5 GHz, from 12.5 GHz to 14.0 GHz.

Selective attention mechanism, plays an important role in human visual perception, can be investigated by developing an approach to perceiving the multi-meaningful-dotted-pattern in a color blindness plate (CBP). In this Letter, a perception model driven by a simple active vision mechanism is presented for the image segmentation and understanding of a CBP. Experiments show that to understand one meaningful pattern in an image containing multi-meaningful patterns, the active visual search (i.e., pattern attention) is a very useful function.

In this paper we present hybrid positioning schemes that combine time of arrival (TOA) and angle of arrival (AOA) measurements from only two base stations (BSs) to locate the mobile station (MS) in non-line-of-sight (NLOS) environments. The proposed methods utilize two TOA circles and two AOA lines to find all the possible intersections to locate the MS without requiring a priori information about the NLOS error. The commonly known Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) have convergence problems, and the relative positioning between the MS and the BSs greatly affects the location accuracy. The resulting geometry creates a situation where small measurement errors can lead to significant errors in the estimated MS location. Simulation results show that the proposed methods always perform better than TSA and HLOP for different levels of NLOS errors, particularly when the MS/BSs have an undesirable geometric layout.