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.

This paper is concerned with synthesizing VLSI array processors from iterative algorithms. Our primary objective is to obtain the highest processor efficiency but not the shortest completion time. Unlike most of the previous work that assumes the index space of the given iterative algorithm to be boundless, the proposed method takes into account the effects of the boundaries of the index space. Due to this consideration, the pseudo-dependence relations are excluded, and most of the independent computations can therefore be uniformly grouped. With the method described in this paper, the index space is partitioned into equal-size blocks and the corresponding computations are systematically and uniformly mapped into processing elements. The synthesized VLSI array processors possess the attractive feature of very high processor efficiency, which, in general, is superior to what is derived from the conventional linear transformation methods.

Reducing communication complexity is a viable approach to multilevel logic synthesis. A communication complexity based approach was proposed previously. In the previous works, only disjoint input decomposition was considered. However, for certain types of circuits, the circuit size can be reduced by using overlapped decomposition. In this paper, we consider overlapped decompositions. Some design issues for overlapped decompositions such as detecting globals" and deriving subfunctions are addressed. Moreover, the Decomposition Don't Cares (DDC) is considered for improving the decomposed results. By using these techniques together, the area and delay of circuits can be further minimized.

The objective of wireless location is to determine the mobile station (MS) location in a wireless cellular communications system. When signals are propagated through non-line-of-sight (NLOS) paths, the measurements at the base stations (BSs) contain large errors which result in poor detectability of an MS by the surrounding BSs. In those situations, it is necessary to integrate all available heterogeneous measurements to improve location accuracy. This paper presents hybrid methods that combine time of arrival (TOA) at three BSs and angle of arrival (AOA) information at the serving BS to obtain a location estimate for the MS. The proposed methods mitigate the NLOS effect by using the weighted sum of the intersections between three TOA circles and the AOA line without requiring the a priori knowledge of NLOS error statistics. Numerical results show that all positioning methods offer improved estimation accuracy over those which rely on the two circles and two lines. The proposed methods always achieve better location accuracy than the Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) do, regardless of the NLOS error statistics.

Recent top-performing object detectors usually depend on a two-stage approach, which benefits from its region proposal and refining practice but suffers low detection speed. By contrast, one-stage approaches have the advantage of high efficiency while sacrifice their accuracies to some extent. In this paper, we propose a novel single-shot object detection network which inherits the merits of both. Motivated by the idea of semantic enrichment to the convolutional features within a typical deep detector, we propose two novel modules: 1) by modeling the semantic interactions between channels and the long-range dependencies between spatial positions, the self-attending module generates both channel and position attention, and enhance the original convolutional features in a self-guided manner; 2) leveraging the class-discriminative localization ability of classification-trained CNN, the semantic activating module learns a semantic meaningful convolutional response which augments low-level convolutional features with strong class-specific semantic information. The so called self-attending and semantic activating network (ASAN) achieves better accuracy than two-stage methods and is able to fulfil real-time processing. Comprehensive experiments on PASCAL VOC indicates that ASAN achieves state-of-the-art detection performance with high efficiency.

In this paper, we design a new color space YUskin Vskin from YUV color space, based on the principle of skin color with respect to the change of color temperature. Compared with previous work, this color space proved to be the optimal color space for hand segmentation with linear thresholds. We also propose a novel fingertip detection method based on the concomitance between finger and fingernail. The two techniques together improve the performance of hand contour and fingertip extraction in hand gesture recognition.

This paper investigates the wireless information surveillance in a suspicious millimeter wave (mmWave) wireless communication system via the spoofing relay based proactive eavesdropping approach. Specifically, the legitimate monitor in the system acts as a relay to simultaneously eavesdrop and send spoofing signals to vary the source transmission rate. To maximize the effective eavesdropping rate, an optimization problem for both hybrid precoding design and power distribution is formulated. Since the problem is fractional and non-convex, we resort to the Dinkelbach method to equivalently reduce the original problem into a series of non-fractional problems, which is still coupling. Afterwards, based on the BCD-type method, the non-fractional problem is reduced to three subproblems with two introduced parameters. Then the GS-PDD-based algorithm is proposed to obtain the optimal solution by alternately optimizing the three subproblems and simultaneously updating the introduced parameters. Numerical results verify the effectiveness and superiority of our proposed scheme.