Existing pose estimation algorithms suffer from either low performance or heavy computation cost. In this letter, we present an approach to improve the attractive algorithm called Orthogonal Iteration. A new form of fundamental equations is derived which reduces the computation cost significantly. And paraperspective camera model is used instead of weak perspective camera model during initialization which improves the stability. Experiment results validate the accuracy and stability of the proposed algorithm and show that its computational complexity is favorably compare to the O(n) non-iterative algorithm.

A differential pair of convergent and divergent lenses with adjustable lens spacing (“differential lens”) was devised as a varifocal lens and was successfully integrated into an object-space telecentric lens to build a focus mechanism with constant magnification. This integration was done by placing the front principal point of the varifocal lens at the rear focal point of the telecentric lens within a practical tolerance of positioning. Although the constant-magnification focus mechanism is a parallel projection system, a system for perfect perspective projection imaging without shifting the projection center during focusing could be built simply by properly setting this focus mechanism between an image-taking lens with image-space telecentricity and an image sensor. The focus resolution experimentally obtained was 0.92 µm (σ) for the parallel projection system with a depth range of 1.0 mm and this was 0.25 mm (σ) for the perspective projection system with a range from 120 to 350 mm within a desktop space. A marginal image resolution of 100 lp/mm was obtained with optical distortion of less than 0.2% in the parallel projection system. The differential lens could work up to 55 Hz for a sinusoidal change in lens spacing with a peak-to-valley amplitude of 425 µm when a tiny divergent lens that was plano-concave was translated by a piezoelectric positioner. Therefore, images that were entirely in focus were generated at a frame rate of 30 Hz for an object moving at a speed of around 150 mm/s in depth within the desk top space. Thus, three-dimensional (3-D) imaging that provided 3-D resolution based on fast focusing was accomplished in both microscopic and macroscopic spaces.

With the wide usage of multispectral images, a fast efficient multidimensional clustering method becomes not only meaningful but also necessary. In general, to speed up the multidimensional images' analysis, a multidimensional feature vector should be transformed into a lower dimensional space. The Hilbert curve is a continuous one-to-one mapping from N-dimensional space to one-dimensional space, and can preserves neighborhood as much as possible. However, because the Hilbert curve is generated by a recurve division process, 'Boundary Effects' will happen, which means data that are close in N-dimensional space may not be close in one-dimensional Hilbert order. In this paper, a new efficient approach based on the space-filling curves is proposed for classifying multispectral satellite images. In order to remove 'Boundary Effects' of the Hilbert curve, multiple Hilbert curves, z curves, and the Pseudo-Hilbert curve are used jointly. The proposed method extracts category clusters from one-dimensional data without computing any distance in N-dimensional space. Furthermore, multispectral images can be analyzed hierarchically from coarse data distribution to fine data distribution in accordance with different application. The experimental results performed on LANDSAT data have demonstrated that the proposed method is efficient to manage the multispectral images and can be applied easily.

In this letter, we analyze the influence of motion and out-of-focus blur on both frequency spectrum and cepstrum of an iris image. Based on their characteristics, we define two new discriminative blur features represented by Energy Spectral Density Distribution (ESDD) and Singular Cepstrum Histogram (SCH). To merge the two features for blur detection, a merging kernel which is a linear combination of two kernels is proposed when employing Support Vector Machine. Extensive experiments demonstrate the validity of our method by showing the improved blur detection performance on both synthetic and real datasets.

To improve the robustness and transparency of spread spectrum (SS) based watermarking, this paper presents a new informed embedding strategy, which we call selective host-interference cancellation. We show that part of the host-interference in SS-based watermarking is beneficial to blind watermark extraction or detection, and can be utilized rather than removed. Utilizing this positive effect of the host itself can improve the watermark robustness without significantly sacrificing the media fidelity. The proposed strategy is realized by selectively applying improved SS (ISS) modulation to traditional SS watermarking. Theoretically, the error probability of the new method under additive white Gaussian noise attacks is several orders of magnitude lower than that of ISS for high signal-to-watermark ratios, and the required minimum watermark power is reduced by 3dB. Experiments were conducted on real audio signals, and the results show that our scheme is robust against most of common attacks even in high-transparency or high-payload applications.

Nowadays, clustering is a popular tool for exploratory data analysis, with one technique being K-means clustering. Determining the appropriate number of clusters is a significant problem in K-means clustering because the results of the k-means technique depend on different numbers of clusters. Automatic determination of the appropriate number of clusters in a K-means clustering application is often needed in advance as an input parameter to the K-means algorithm. We propose a new method for automatic determination of the appropriate number of clusters using an extended co-occurrence matrix technique called a tri-co-occurrence matrix technique for multispectral imagery in the pre-clustering steps. The proposed method was tested using a dataset from a known number of clusters. The experimental results were compared with ground truth images and evaluated in terms of accuracy, with the numerical result of the tri-co-occurrence providing an accuracy of 84.86%. The results from the tests confirmed the effectiveness of the proposed method in finding the appropriate number of clusters and were compared with the original co-occurrence matrix technique and other algorithms.

Traditional two-microphone noise reduction algorithms to deal with highly nonstationary directional noises generally use the direction of arrival or phase difference information. The performance of these algorithms deteriorate when diffuse noises coexist with nonstationary directional noises in realistic adverse environments. In this paper, we present a two-channel noise reduction algorithm using a spatial information-based speech estimator and a spatial-information-controlled soft-decision noise estimator to improve the noise reduction performance in realistic non-stationary noisy environments. A target presence probability estimator based on Bayes rules using both phase difference and magnitude squared coherence is proposed for soft-decision of noise estimation, so that they can share complementary advantages when both directional noises and diffuse noises are present. Performances of the proposed two-microphone noise reduction algorithm are evaluated by noise reduction, log-spectral distance (LSD) and word recognition rate (WRR) of a distant-talking ASR system in a real room's noisy environment. Experimental results show that the proposed algorithm achieves better noises suppression without further distorting the desired signal components over the comparative dual-channel noise reduction algorithms.

This letter proposes an automatic IQ imbalance compensation technique for quadrature modulators by means of spectrum measurement of RF signal using a spectrum analyzer. The analyzer feeds back only magnitude information of the frequency spectrum of the signal. To realize IQ imbalance compensation, the conventional method of steepest descent is modified; the descent direction is empirically determined and a variable step-size is introduced for accelerating convergence. The experimental results for a four-channel transmitter operating at 11 GHz are presented for verification.

This paper describes a blind watermarking scheme through cyclic signal processing. Due to various rapid networks, there is a growing demand of copyright protection for multimedia data. As efficient watermarking of images, there exist two major approaches: a quantization-based method and a correlation-based method. In this paper, we proposes a correlation-based watermarking technique of three-dimensional (3-D) polygonal models using the fast Fourier transforms (FFTs). For generating a watermark with desirable properties, similar to a pseudonoise signal, an impulse signal on a two-dimensional (2-D) space is spread through the FFT, the multiplication of a complex sinusoid signal, and the inverse FFT. This watermark, i.e., spread impulse signal, in a transform domain is converted to a spatial domain by an inverse wavelet transform, and embedded into 3-D data aligned by the principle component analysis (PCA). In the detection procedure, after realigning the watermarked mesh model through the PCA, we map the 3-D data on the 2-D space via block segmentation and averaging operation. The 2-D data are processed by the inverse system, i.e., the FFT, the division of the complex sinusoid signal, and the inverse FFT. From the resulting 2-D signal, we detect the position of the maximum value as a signature. For 3-D bunny models, detection rates and information capacity are shown to evaluate the performance of the proposed method.

This paper proposes an adaptive bandwidth control scheme for the private wireless networks. Carrier sense multiple access with collision avoidance (CSMA/CA), which is commonly used within the private networks, is not efficient in terms of spectral efficiency due to its strict collision avoidance process. In order to relax the collision avoidance rule, this paper employs dynamic spectrum control (DSC), in which a certain number of discrete spectra having the higher channel gain is selected in a selfish manner with each link allowing a partial band interference. Such interference may be suppressed by the equalizer at the receiver. Aiming at optimal selection of the bandwidth for the selfish DSC according to channel realizations, in the sense of throughput maximization, this paper proposes a channel capacity maximization-based BAR control scheme. Computer simulations validate that the proposed scheme achieves high throughput efficiency.

Cognitive radio is an emerging technology to further improve the efficiency of spectrum use. Due to the nature of the technology, it has many facets, including its enabling technologies, its implementation issues and its regulatory implications. In ITU-R (International Telecommunications Union – Radiocommunication sector), cognitive radio systems are currently being studied so that ITU-R can have a clear picture on this new technology and its potential regulatory implications, from a viewpoint of global spectrum management. This paper introduces the recent results of the ITU-R studies on cognitive radio on both regulatory and technical aspects. This paper represents a personal opinion of the author, but not an official view of the ITU-R.

Allowing WLANs to exploit opportunistic spectrum access (OSA) is a promising approach to alleviate spectrum congestion problems in overcrowded unlicensed ISM bands, especially in highly dense WLAN deployments. In this context, novel channel assignment mechanisms jointly considering available channels in both unlicensed ISM and OSA-enabled licensed bands are needed. Unlike classical schemes proposed for legacy WLANs, channel assignment mechanisms for OSA-enabled WLAN should face two distinguishing issues: channel prioritization and spectrum heterogeneity. The first refers to the fact that additional prioritization criteria other than interference conditions should be considered when choosing between ISM or licensed band channels. The second refers to the fact that channel availability might not be the same for all WLAN Access Points because of primary users' activity in the OSA-enabled bands. This paper firstly formulates the channel assignment problem for OSA-enabled WLANs as a Binary Linear Programming (BLP) problem. The resulting BLP problem is optimally solved by means of branch and bound algorithms and used as a benchmark to develop more computationally efficient heuristics. Upon such a basis, a novel channel assignment algorithm based on weighted graph coloring heuristics and able to exploit both channel prioritization and spectrum heterogeneity is proposed. The algorithm is evaluated under different conditions of AP density and primary band availability.

In this paper, we study the problem of distributed spectrum allocation under a vertical spectrum sharing scenario in a cognitive radio network. The secondary users share the spectrum licensed to the primary user by observing the activity statistics of the primary users, and regulate their transmission strategy in order to abide by the spectrum sharing etiquette. When the primary user is inactive in a subset of the available frequency bands, from the perspective of the secondary users the problem reduces to a distributed horizontal spectrum sharing. For a specific class of networks, the latter problem is addressed by the recently proposed GADIA algorithm [1]. In this paper, we present analytical and numerical results on the performance of the GADIA algorithm in conjunction with the above-mentioned vertical spectrum sharing scenario. These results reveal near-optimal performance guarantees for the overall vertical spectrum sharing scenario.

This work studies the benefits of heterogeneous cellular networks with overlapping picocells in a large macrocell. We consider three different strategies for resource allocation and cell association. The first model employs a spectrum overlapping strategy with an SINR-based cell association. The second model avoids the interference between macrocell and picocell through a spectrum splitting strategy. Furthermore, picocell range expansion is also considered in this strategy to enable a load balancing between the macrocell and picocells. The last model is a hybrid one, called as fractional spectrum splitting strategy, where spectrum splitting strategy is only applied at the picocell-edge, while the picocell-inner reuses the spectrum of the macrocell. We constructs resource allocation optimization problem for these strategies to maximize the system rate. Our results show that in terms of system rate, all the three strategies outperform the performance of macrocell-only case, which shows the benefit of heterogeneous networks. Moreover, fractional spectrum splitting strategy provides highest system rate at the expense of outage user rate degradation due to inter-macro-pico interference. Spectrum overlapping model provides the second highest system rate gain and also improves outage user rate owing to full spectrum reuse and the benefit of macro diversity, while spectrum splitting model achieves a moderate system rate gain.

This paper proposes a sensing method for TV signals of DVB-T standard to realize effective TV White Space (TVWS) Communication. In the TVWS technology trial organized by the Infocomm Development Authority (iDA) of Singapore, with regard to the sensing level and sensing time, detecting DVB-T signal at the level of -120 dBm over an 8 MHz channel with a sensing time below 1 second is required. To fulfill such a strict sensing requirement, we propose a smart sensing method which combines feature detection and energy detection (CFED), and is also characterized by using dynamic threshold selection (DTS) based on a threshold table to improve sensing robustness to noise uncertainty. The DTS based CFED (DTS-CFED) is evaluated by computer simulations and is also implemented into a hardware sensing prototype. The results show that the DTS-CFED achieves a detection probability above 0.9 for a target false alarm probability of 0.1 for DVB-T signals at the level of -120 dBm over an 8 MHz channel with the sensing time equals to 0.1 second.

One important issue in cognitive transmission is for multiple secondary users to dynamically acquire spare spectrum from the single primary user. The existing spectrum sharing scheme adopts a deterministic Cournot game to formulate this problem, of which the solution is the Nash equilibrium. This formulation is based on two implicit assumptions. First, each secondary user is willing to fully exchange transmission parameters with all others and hence knows their complete information. Second, the unused spectrum of the primary user for spectrum sharing is always larger than the total frequency demand of all secondary users at the Nash equilibrium. However, both assumptions may not be true in general. To remedy this, the present paper considers a more realistic assumption of incomplete information, i.e., each secondary user may choose to conceal their private information for achieving higher transmission benefit. Following this assumption and given that the unused bandwidth of the primary user is large enough, we adopt a probabilistic Cournot game to formulate an opportunistic spectrum sharing scheme for maximizing the total benefit of all secondary users. Bayesian equilibrium is considered as the solution of this game. Moreover, we prove that a secondary user can improve their expected benefit by actively hiding its transmission parameters and increasing their variance. On the other hand, when the unused spectrum of the primary user is smaller than the maximal total frequency demand of all secondary users at the Bayesian equilibrium, we formulate a constrained optimization problem for the primary user to maximize its profit in spectrum sharing and revise the proposed spectrum sharing scheme to solve this problem heuristically. This provides a unified approach to overcome the aforementioned two limitations of the existing spectrum sharing scheme.

An important and widely considered signal identification technique for cognitive radios is cyclostationarity-based feature detection because this method does not require time and frequency synchronization and prior information except for information concerning cyclic autocorrelation features of target signals. This paper presents the development and experimental evaluation of cyclostationarity-based signal identification equipment. A spatial channel emulator is used in conjunction with the equipment that provides an environment to evaluate realistic spectrum sharing scenarios. The results reveal the effectiveness of the cyclostationarity-based signal identification methodology in realistic spectrum sharing scenarios, especially in terms of the capability to identify weak signals.

No nontrivial optimal sets of frequency-hopping sequences (FHSs) of period 2(2n-1) for a positive integer n ≥ 2 have been found so far, when their frequency set sizes are less than their periods. In this paper, systematic doubling methods to construct new FHS sets are presented under the constraint that the set of frequencies has size less than or equal to 2n. First, optimal FHS sets with respect to the Peng-Fan bound are constructed when frequency set size is either 2n-1 or 2n. And then, near-optimal FHS sets with frequency set size 2n-1 are designed by applying the Chinese Remainder Theorem to Sidel'nikov sequences, whose FHSs are optimal with respect to the Lempel-Greenberger bound. Finally, a general construction is given for near-optimal FHS sets whose frequency set size is less than 2n-1. Our constructions give new parameters not covered in the literature, which are summarized in Table1.

An OFDMA-based (Orthogonal Frequency Division Multiple Access-based) channel access scheme for dynamic spectrum access has the drawbacks of large PAPR (Peak to Average Power Ratio) and large ACI (Adjacent Channel Interference). To solve these problems, a flexible channel access scheme using an overlap FFT filter-bank was proposed based on single carrier modulation for dynamic spectrum access. In order to apply the overlap FFT filter-bank for dynamic spectrum access, it is necessary to clarify the performance of the overlap FFT filter-bank according to the design parameters since its frequency characteristics are critical for dynamic spectrum access applications. This paper analyzes the overlap FFT filter-bank and evaluates its performance such as frequency characteristics and ACI performance according to the design parameters.

AspectM, an aspect-oriented modeling (AOM) language, provides not only basic modeling constructs but also an extension mechanism called metamodel access protocol (MMAP) that allows a modeler to modify the metamodel. MMAP consists of metamodel extension points, extension operations, and primitive predicates for navigating the metamodel. Although the notion of MMAP is useful, it needs tool support. This paper proposes a method for implementing a MMAP-based AspectM support tool. It consists of model editor, model weaver, and model verifier. We introduce the notion of edit-time structural reflection and extensible model weaving. Using these mechanisms, a modeler can easily construct domain-specific languages (DSLs). We show a case study using the AspectM support tool and discuss the effectiveness of the extension mechanism provided by MMAP. As a case study, we show a UML-based DSL for describing the external contexts of embedded systems.