Dielectric rod arrays in a metallic waveguide alter the propagation modes and group velocities of electromagnetic waves. We have focused on TE30-to-TE10 mode converters and investigated how their behavior varies with frequency. A mode converter is proposed that passes the TE10 mode at frequencies lower than 2fc, and converts the TE30 mode into the TE10 mode for frequencies higher than 3fc.

We propose a high-speed and low-complexity architecture for the very large-scale integration (VLSI) implementation of the maximum a posteriori probability (MAP) algorithm suited to the double binary turbo decoder. For this purpose, equation manipulations on the conventional Linear-Log-MAP algorithm and architectural optimization are proposed. It is shown by synthesized simulations that the proposed architecture improves speed, area and power compared with the state-of-the-art Linear-Log-MAP architecture. It is also observed that the proposed architecture shows good overall performance in terms of error correction capability as well as decoder hardware's speed, complexity and throughput.

This paper presents a novel method for reconstructing 3D geometry of camera motion and human-face model from a video sequence. The approach combines the concepts of Powell's line minimization with gradient descent. We adapted the line minimization with bracketing used in Powell's minimization to our method. However, instead of bracketing and searching deep down a direction for the minimum point along that direction as done in their line minimization, we achieve minimization by bracketing and searching for the direction in the bracket which provides a lower energy than the previous iteration. Thus, we do not need a large memory as required by Powell's algorithm. The approach to moving in a better direction is similar to classical gradient descent except that the derivative calculation and a good starting point are not needed. The system's constraints are also used to control the bracketing direction. The reconstructed solution is further improved using the Levenberg Marquardt algorithm. No average face model or known-coordinate markers are needed. Feature points defining the human face are tracked using the active appearance model. Occluded points, even in the case of self occlusion, do not pose a problem. The reconstructed space is normalized where the origin can be arbitrarily placed. To use the obtained reconstruction, one can rescale the computed volume to a known scale and transform the coordinate system to any other desired coordinates. This is relatively easy since the 3D geometry of the facial points and the camera parameters of all frames are explicitly computed. Robustness to noise and lens distortion, and 3D accuracy are also demonstrated. All experiments were conducted with an off-the-shelf digital camera carried by a person walking without using any dolly to demonstrate the robustness and practicality of the method. Our method does not require a large memory or the use of any particular, expensive equipment.

In multiuser multiple-input multiple-output (MU-MIMO) wireless downlink systems, block diagonalization (BD) is a technique, where the transmit precoding matrix of each user is designed such that its subspace lies in the null space of all the other remaining users, so that multiuser interference (MUI) is completely canceled. In low signal to noise power ratio (SNR) or low signal to interference plus noise power ratio (SINR) environments, regularized BD, that lets some MUI remain and maximizes the sum rate capacity of the BD MIMO channel, was also proposed. One of the problems of both the approaches is high complexity of computation due to a lot of singular value decomposition (SVD) processes. In this paper we propose new BD techniques utilizing QR decomposition (QRD) which can be practically achieved by Gram-Schmidt orthogonalization (GSO) with lower complexity compared to the conventional method employing SVD. We can show that the performance of the proposed approaches is close to the conventional approaches, while the proposed approaches have much lower complexity.

In this letter, a generalized sidelobe canceller (GSC) with robustness against carrier frequency offset (CFO) is proposed for the uplink MC-CDMA system. It has been shown that a CFO will cause the spreading code mismatch and desired signal cancellation. By incorporating the corrected quiescent weight of the upper branch and blocking matrix of the lower branch, we create an efficient GSC that offers strongly counters the effect of the CFO. Significant performance improvement of the proposed GSC is demonstrated by simulation results.

Target Q coverage is needed to secure the stability of data collection in WSN. The targets may have different level of importance then the multiple-target coverage scheme must schedule sensors according to each target's weight to increase the network lifetime. The schedule scheme previously proposed for weighted coverage uses an iterative solution to solve the problem but it has long computation time. We propose a heuristic greedy-TQC algorithm to use the residual energy of sensors to generate multiple scheduling cover sets. A simulation shows a dramatic reduction in computation time. The greedy-TQC algorithm is suitable for the frequently topology-changing WSN and for the often changing targets' weights in WSN.

In this letter, we propose a power allocation scheme to optimize the ergodic secrecy rate of multiple-input single-output (MISO) fading wiretap channels with a probabilistic constraint, using the statistical channel state information (CSI) of the eavesdropper (CSI-E). The analytical expressions of the false secrecy probability are derived and used as constraints in the rate maximization problem. Moreover, we obtain a suboptimal solution by formulating the power allocation problem as a Rayleigh quotient problem.

Solution-based organic field-effect transistors (OFETs) with low parasitic capacitance have been fabricated using a self-aligned method. The self-aligned processes using a cross-linking polymer gate insulator allow fabricating electrically stable polymer OFETs with small overlap area between the source-drain electrodes and the gate electrode, whose frequency characteristics have been investigated by impedance spectroscopy (IS). The IS of polymer OFETs with self-aligned electrodes reveals frequency-dependent channel formation process and the frequency response in FET structure.

This letter considers a sum-rate maximization problem with user scheduling wherein each user has a minimum-rate requirement in multiple-input-multiple-output broadcast channel. The multiuser strategy used in the user scheduling is a joint transceiver scheme with block diagonal geometric mean decomposition. Since optimum solution to the user scheduling problem generally requires exhaustive search, we propose a suboptimum user scheduling algorithm with each user's minimum-rate requirement as the main constraint. In order to satisfy maximum sum-rate and minimum-rate constraints simultaneously, we additionally consider power allocation for scheduled users. Simulation results show that the proposed user scheduling algorithm, together with the user power allocation, achieves sum-rate close to the exhaustive search, while also guarantees minimum-rate requirement of each user.

In this paper, we propose an energy efficient MAC protocol for wireless sensor networks. In sensor networks, reducing energy consumption is one of the critical issues for extending network lifetime. One good solution to resolve this issue is introducing listen-sleep cycles, allowing sensor nodes to turn their transceiver off during sleep periods, which was adopted by S-MAC [1]. However, in S-MAC, due to the synchronized scheduling, transmission collisions will increase in heavy traffic situations, resulting in energy waste and low throughput. Hence, in this paper, we propose probabilistic scheduled MAC (PS-MAC), in which each node determines ‘listen’ or ‘sleep’ pseudo-randomly based on its own pre-wakeup probability and pre-wakeup probabilities of its neighbor nodes in each time slot. This allows the listen-sleep schedule of nodes in each transmitter and receiver pair to be synchronized, while maintaining those of the rest of nodes to be asynchronous. Therefore, collisions can be reduced even under heavy traffic conditions, resulting in reduced energy waste and high throughput. In addition, by dynamically adjusting the pre-wakeup probabilities of sensor nodes based on the change of the network environment, system throughput and latency can be further improved. Simulation results show that PS-MAC provides significant energy savings, low delay, and high network throughput.

This paper presents a low-complexity multi-mode fast Fourier transform (FFT) processor for Digital Video Broadcasting-Terrestrial 2 (DVB-T2) systems. DVB-T2 operations need 1K/2K/4K/8K/16K/32K-point multiple mode FFT processors. The proposed architecture employs pipelined shared-memory architecture in which radix-2/22/23/24 FFT algorithms, multi-path delay commutator (MDC), and a novel data scaling approach are exploited. Based on this architecture, a novel low-cost data scaling unit is proposed to increase area efficiency, and an elaborate memory configuration scheme is designed to make single-port SRAM without degrading throughput rate. Also, new scheduling method of twiddle factor is proposed to reduce the area. The SQNR performance of 32K-point FFT mode is about 45.3 dB at 11-bit internal word length for 256QAM modulation. The proposed FFT processor has a lower hardware complexity and memory size compared to conventional FFT processors.

Modern web applications incorporate many programmatic frameworks and APIs that are often pushed to the client-side with most of the application logic while contents are the result of mashing up several resources from different origins. Such applications are threatened by attackers that often attempts to inject directly, or by leveraging a stepstone website, script codes that perform malicious operations. Web scripting based malware proliferation is being more and more industrialized with the drawbacks and advantages that characterize such approach: on one hand, we are witnessing a lot of samples that exhibit the same characteristics which make these easy to detect, while on the other hand, professional developers are continuously developing new attack techniques. While obfuscation is still a debated issue within the community, it becomes clear that, with new schemes being designed, this issue cannot be ignored anymore. Because many proposed countermeasures confess that they perform better on unobfuscated contents, we propose a 2-stage technique that first relieve the burden of obfuscation by emulating the deobfuscation stage before performing a static abstraction of the analyzed sample's functionalities in order to reveal its intent. We support our proposal with evidence from applying our technique to real-life examples and provide discussion on performance in terms of time, as well as possible other applications of proposed techniques in the areas of web crawling and script classification. Additionally, we claim that such approach can be generalized to other scripting languages similar to JavaScript.

Impulsive noise interference is a significant problem for the Integrated Services Digital Broadcasting for Terrestrial (ISDB-T) receivers due to its effect on the orthogonal frequency division multiplexing (OFDM) signal. In this paper, an adaptive scheme to suppress the effect of impulsive noise is proposed. The impact of impulsive noise can be detected by using the guard band in the frequency domain; furthermore the position information of the impulsive noise, including burst duration, instantaneous power and arrived time, can be estimated as well. Then a time-domain window function with adaptive parameters, which are decided in terms of the estimated information of the impulsive noise and the carrier-to-noise ratio (CNR), is employed to suppress the impulsive interference. Simulation results confirm the validity of the proposed scheme, which improved the bit error rate (BER) performance for the ISDB-T receivers in both AWGN channel and Rayleigh fading channel.

This paper proposes new scheduling algorithms for best effort (BE) traffic classification in business femtocell networks. The purpose of traffic classification is to provide differentiated services to BE users depending on their traffic classes, and the concept of traffic classification is called Inter User Best Effort (IUBE) in CDMA2000 1x Evolution Data Optimized (EVDO) standard. Traffic differentiation is achieved by introducing Grade of Service (GoS) as a quality of service (QoS) parameter into the scheduler's decision metric (DM). New scheduling algorithms are called QoS Round Robin (QoS-RR), QoS Proportionally Fair (QoS-PF), QoS maximum data rate control (DRC) (QoS-maxDRC), QoS average DRC (QoS-aveDRC), QoS exponent DRC (QoS-expDRC), QoS maxDRC-PF (QoS-maxDRC-PF). Two different femtocell throughput experiments are performed using real femtocell devices in order to collect real DRC values. The first experiment examines 4, 8, 12 and 16 IUBE users, while second experiment examines 4 IUBE + 2 Voice over IP (VoIP), 8 IUBE + 2 VoIP, 12 IUBE + 2 VoIP, 16 IUBE + 2 (VoIP) users. Average sector throughput, IUBE traffic differentiation, VoIP delay bound error values are investigated to compare the performance of the proposed scheduling algorithms. In conclusion, QoS-maxDRC-PF scheduler is proposed for business femtocell environment.

Kernel biased discriminant analysis (KBDA), as a subspace learning algorithm, has been an attractive approach for the relevance feedback in content-based image retrieval. Its performance, however, still suffers from the “small sample learning” problem and “kernel learning” problem. Aiming to solve these problems, in this paper, we present a new semi-supervised scheme of KBDA (S-KBDA), in which the projection learning and the “kernel learning” are interweaved into a constrained optimization framework. Specifically, S-KBDA learns a subspace that preserves both the biased discriminant structure among the labeled samples, and the geometric structure among all training samples. In kernel optimization, we directly optimize the kernel matrix, rather than a kernel function, which makes the kernel learning more flexible and appropriate for the retrieval task. To solve the constrained optimization problem, a fast algorithm based on gradient ascent is developed. The image retrieval experiments are given to show the effectiveness of the S-KBDA scheme in comparison with the original KBDA, and the other two state-of-the-art algorithms.

We propose a robust and efficient algorithm called ROCKET for clustering large-scale transaction databases. ROCKET is a divisive hierarchical algorithm that makes the most of recent hardware architecture. ROCKET handles the cases with the small and the large number of similar transaction pairs separately and efficiently. Through experiments, we show that ROCKET achieves high-quality clustering with a dramatic performance improvement.

In this paper, we are concerned about content-based networking (CBN) at extreme scales, characterized by a large number of widely spread consumers, heterogeneous consumer requirements, huge volume of publications, and the scarcity of end-to-end bandwidth. We extend CBN with a generic service model that allows consumers to express their interests in future publications including cached content, but also to quantify the maximum amount of information they are willing to consume. We take advantage of this knowledge to pace the dissemination process and therefore, enhance the service efficiency. Early evaluation results show gains of up to 80% compared to a baseline CBN model.

Broadband single-carrier frequency division multiple access (SC-FDMA) uplink using frequency-domain square-root Nyquist filtering is considered. The peak-to-average power ratio (PAPR) of filtered SC signals can be reduced by increasing the filter roll-off factor α. Furthermore, an additional frequency diversity gain can be obtained by making use of the excess bandwidth introduced by the transmit root Nyquist filtering. However, if the carrier-frequency separation is kept the same as in the case of α=0, the adjacent users' signal spectra overlap with the desired users' spectrum and the multiuser interference (MUI) is produced. In this paper, we propose two frequency-domain iterative MUI cancellation schemes which can achieve the frequency diversity gain through spectrum combining. The achievable bit error rate (BER) and throughput performances are evaluated by computer simulation.

We present a scalable approach to automatically discovering particular objects (as opposed to object categories) from a set of images. The basic idea is to search for local image features that consistently appear in the same images under the assumption that such co-occurring features underlie the same object. We first represent each image in the set as a set of visual words (vector quantized local image features) and construct an inverted file to memorize the set of images in which each visual word appears. Then, our object discovery method proceeds by searching the inverted file and extracting visual word sets whose elements tend to appear in the same images; such visual word sets are called co-occurring word sets. Because of unstable and polysemous visual words, a co-occurring word set typically represents only a part of an object. We observe that co-occurring word sets associated with the same object often share many visual words with one another. Hence, to obtain the object models, we further cluster highly overlapping co-occurring word sets in an agglomerative manner. Remarkably, we accelerate both extraction and clustering of co-occurring word sets by Min-Hashing. We show that the models generated by our method can effectively discriminate particular objects. We demonstrate our method on the Oxford buildings dataset. In a quantitative evaluation using a set of ground truth landmarks, our method achieved higher scores than the state-of-the-art methods.

Accurate device models are very important for the design of high-frequency circuits. One of the factors degrading the accuracy of device models appears during the de-embedding procedure. Generally, to obtain device characteristics without parasitic elements such as pads, a de-embedding procedure is essential. However, some errors are introduced during this procedure, which degrades the accuracy of device models. In this paper, we demonstrate that such errors due to de-embedding are cancelled in cascade circuit design, meaning that cascade circuits can be designed without knowing the actual characteristics of devices. Because it is difficult to know the actual characteristics of devices at a high frequency, the cancellation of the de-embedding error is expected to improve the accuracy of device models at high frequencies. After giving a theoretical treatment of de-embedding error cancellation, we report the results of simulations and measurements performed for verification.