In differentiated services, packet classification is used to categorize incoming packets into multiple forwarding classes based on pre-defined filters and make information accessible for quality of service. Although numerous algorithms have presented novel data structures to improve the search performance of packet classification, the performance of these algorithms are usually limited by the characteristics of filter databases. In this paper, we use a different approach of filter preprocessing to enhance the search performance of packet classification. Before generating the searchable data structures, we cluster filters in a bottom-up manner. The procedure of the filter clustering merges filters with high degrees of similarity. The experimental results show that the technique of filter clustering could significantly improve the search performance of Pruned Tuple Space Search, a notable hash-based algorithm. As compared to the prominent existing algorithms, our enhanced Pruned Tuple Space Search also has superior performance in terms of speed and space.

We propose a moving picture coding by lapped transform and an edge adaptive deblocking filter to reduce the blocking distortion. We apply subband coding (SBC) with lapped transform (LT) and zero pruning set partitioning in hierarchical trees (zpSPIHT) to encode the difference picture. Effective coding using zpSPIHT was achieved by quantizing and pruning the quantized zeros. The blocking distortion caused by block motion compensated prediction is reduced by an edge adaptive deblocking filter. Since the original edges can be detected precisely at the reference picture, an edge adaptive deblocking filter on the predicted picture is very effective. Experimental results show that blocking distortion has been visually reduced at very low bit rate coding and better PSNRs of about 1.0 dB was achieved.

A low-voltage operational capability near 1 V along with low noise and distortion characteristics have been realized in a passive sigma-delta modulator. To achieve low-voltage operation, the dc voltage in signal paths in the switched-capacitor-filter section was set to be 0.2 V so that sufficient gate-to-source voltages were obtained for metal-oxide-semiconductor (MOS) switches in signal paths without using a gate-voltage boosting technique. In addition, the input switch that connects the input signal from the outside to the inside of an integrated circuit chip was replaced by a passive resistor to eliminate a floating switch, and gain coefficients in the feedback and input paths were modified so that the bias voltage of the digital-to-analog converter could be set to VDD and 0 V to easily activate MOS switches. As the signal swing becomes small under low-voltage operational circumstances, correlated double sampling was used to suppress the offset voltage and the 1/f noise that appeared at the input of a comparator. The modulator was fabricated using a standard CMOS 0.18-µm process, and the measured results show that the modulator realized 77 dB of dynamic range for 40 kHz of signal bandwidth with a 40 MHz sampling rate while dissipating 2 mW from a 1.1 V supply voltage.

The fast H∞ filter is developed by one of the authors, and its practical use in industries is expected. This paper derives a linear propagation model of numerical errors in the recursive variables of the fast H∞ filter, and then theoretically analyzes the stability of the filter. Based on the analyzed results, a numerical stabilization method of the fast H∞ filter is proposed with the error feedback control in the backward prediction. Also, the effectiveness of the stabilization method is verified using numerical examples.

The proportionate normalized least mean square algorithm (PNLMS) greatly improves the convergence of the sparse impulse response. It exploits the shape of the impulse response to decide the proportionate step gain for each coefficient. This is not always suitable. Actually, the proportionate step gain should be determined according to the difference between the current estimate of the coefficient and its optimal value. Based on this idea, an approach is proposed to determine the proportionate step gain. The proposed approach can improve the convergence of proportionate adaptive algorithms after a fast initial period. It even behaves well for the non-sparse impulse response. Simulations verify the effectiveness of the proposed approach.

In this paper, we propose a robust room inverse filtering algorithm for speech dereverberation based on a kurtosis maximization. The proposed algorithm utilizes a new normalized kurtosis function that nonlinearly maps the input kurtosis onto a finite range from zero to one, which results in a kurtosis warping. Due to the kurtosis warping, the proposed algorithm provides more stable convergence and, in turn, better performance than the conventional algorithm. Experimental results are presented to confirm the robustness of the proposed algorithm.

This letter proposes closed form solutions to the L2-sensitivity minimization of second-order state-space digital filters with real poles. We consider two cases of second-order digital filters: distinct real poles and multiple real poles. In case of second-order digital filters, we can express the L2-sensitivity of second-order digital filters by a simple linear combination of exponential functions and formulate the L2-sensitivity minimization problem by a simple polynomial equation. As a result, the minimum L2-sensitivity realizations can be synthesized by only solving a fourth-degree polynomial equation, which can be analytically solved.

In this paper, we utilize low temperature co-fired ceramic technology (LTCC) to realize a modified short-circuited stub bandpass filter suitable for ultra-wideband (UWB) applications. By modifying the conventional short-circuited stub bandpass filter structure with stubs and connecting lines of lower characteristic impedances, the number of stubs has been reduced from 5 to 2 on a high dielectric constant substrate (∈ r = 40). A wireless local area network (WLAN) stopband in the frequency range of 5.15 to 5.825 GHz has been inserted into the filter characteristic using three short-circuited coupled lines. The filter is fabricated and measurement results show that it has an insertion loss less than 1.0 dB and return loss better than 10 dB in the pass bands. A bandwidth ratio of 109.49% has been achieved. Measurement results agree well with simulation results. The dimensions of the filter are 480.57 mm3.

A new adaptive algorithm is proposed by introducing some modifications to the recursive least squares (RLS) algorithm. Except for the noise variance, the proposed algorithm does not require any statistics or knowledge of the desired signal, thus, it is suitable for adaptive filtering for channel estimation in code division multiple access (CDMA) systems in cases where the standard RLS approach cannot be used. A theoretical analysis demonstrates the convergence of the proposed algorithm, and simulation results for CDMA channel estimation show that the proposed algorithm outperforms existing channel estimation schemes.

The purpose of this study is to show a class of near shift-invariant and orientation-selective transform based on even-stacked cosine-modulated filter banks (ECFBs) which originally have been proposed by Lin and Vaidyanathan. It is well-known that ECFBs can be designed by the modulation of just one prototype filter and guarantee the linear phase property. We extend this class to delay-less oversampled ECFB and show two additional attractive features; high directional selectivity and near shift-invariant property. In this paper, these properties are verified by theoretical analysis and demonstrations.

A design of the tunable superconducting power filter is described. The filter consists of superconducting microwave cavities with a mechanical tuning method. The electromagnetic simulations using niobium cavity suggested that there were conditions where the resonator with high-unloaded Q can realize a fractional center frequency change of more than 10% by using a Nb rod moving in the cavity. The simulations approximated the resonant frequency dependence of the rod moved by a cryogenic actuator in the tunable cavity experiment. In addition, the simulation of the power handling capability showed a feasibility of the value more than 50 dBW.

Various observation models have been introduced into the object tracking community, and combining them has become a promising direction. This paper proposes a novel approach for estimating the confidences of different observation models, and then effectively combining them in the particle filter framework. In our approach, spatial Likelihood distribution is represented by three simple but efficient parameters, reflecting the overall similarity, distribution sharpness and degree of multi peak. The balance of these three aspects leads to good estimation of confidences, which helps maintain the advantages of each observation model and further increases robustness to partial occlusion. Experiments on challenging video sequences demonstrate the effectiveness of our approach.

Due to the distributed nature, mobile ad-hoc networks (MANETs) are vulnerable to various attacks, resulting in distrusted communications. To achieve trusted communications, it is important to build trusted routes in routing algorithms in a self-organizing and decentralized fashion. This paper proposes a trusted routing to locate and to preserve trusted routes in MANETs. Instead of using a hard security mechanism, we employ a new dynamic trust mechanism based on multiple constraints and collaborative filtering. The dynamic trust mechanism can effectively evaluate the trust and obtain the precise trust value among nodes, and can also be integrated into existing routing protocols for MANETs, such as ad hoc on-demand distance vector routing (AODV) and dynamic source routing (DSR). As an example, we present a trusted routing protocol, based on dynamic trust mechanism, by extending DSR, in which a node makes a routing decision based on the trust values on its neighboring nodes, and finally, establish a trusted route through the trust values of the nodes along the route in MANETs. The effectiveness of our approach is validated through extensive simulations.

In this paper, we propose a novel stochastic gradient algorithm for efficient adaptive filtering. The basic idea is to sparsify the initial error vector and maximize the benefits from the sparsification under computational constraints. To this end, we formulate the task of algorithm-design as a constrained optimization problem and derive its (non-trivial) closed-form solution. The computational constraints are formed by focusing on the fact that the energy of the sparsified error vector concentrates at the first few components. The numerical examples demonstrate that the proposed algorithm achieves the convergence as fast as the computationally expensive method based on the optimization without the computational constraints.

This paper proposes closed form solutions to the L2-sensitivity minimization subject to L2-scaling constraints for second-order state-space digital filters with real poles. We consider two cases of second-order digital filters: distinct real poles and multiple real poles. The proposed approach reduces the constrained optimization problem to an unconstrained optimization problem by appropriate variable transformation. We can express the L2-sensitivity by a simple linear combination of exponential functions and formulate the L2-sensitivity minimization problem by a simple polynomial equation. As a result, L2-sensitivity is expressed in closed form, and its minimization subject to L2-scaling constraints is achieved without iterative calculations.

Recently, the marginalized particle filter (MPF) has been applied to blind symbol detection problems over selective fading channels. The MPF can ease the computational burden of the standard particle filter (PF) while offering better estimates compared with the standard PF. In this paper, we investigate the application of the blind MPF detector to more realistic situations where the systems suffer from analog imperfections which are non-linear signal distortion due to the inaccurate analog circuits in wireless devices. By reformulating the system model using the widely linear representation and employing the auxiliary variable resampling (AVR) technique for estimation of the imperfections, the blind MPF detector is successfully modified to cope with the analog imperfections. The effectiveness of the proposed MPF detector is demonstrated via computer simulations.

The selection of effective features is especially important in achieving highly accurate speech recognition. Although the mel-cepstrum is a popular and effective feature for speech recognition, it is still unclear that the filterbank adopted in the mel-cepstrum always produces the optimal performance regardless of the phonetic environment of any specific speech recognition task. In this paper, we propose a new cepstral domain feature extraction approach utilizing the entropic distance-based filterbank for highly accurate speech recognition. Experimental results showed that the cepstral features employing the proposed filterbank reduce the relative error by 31% for clean as well as noisy speech compared to the mel-cepstral features.

A band-pass bilateral filter is an improved variant of a bilateral filter that does not have low-pass characteristics but has band-pass characteristics. Unfortunately, its computation time is relatively large since all pixels are subjected to Gaussian calculation. To solve this problem, we pay attention to a nonlinear filter called ε-filter and propose an advanced ε-filter labeled band-pass ε-filter. As ε-filter has low-pass characteristics due to spatial filtering, it does not enhance the image contrast. On the other hand, band-pass ε-filter does not have low-pass characteristics but has band-pass characteristics to enhance the image contrast around edges unlike ε-filter. The filter works not only as a noise reduction filter but also as an edge detection filter depending on the filter setting. Due to its simple design, the calculation cost is relatively small compared to the band-pass bilateral filter. To show the effectiveness of the proposed method, we report the results of some comparison experiments on the filter characteristics and computational cost.

Dramatic gains in channel capacity can be achieved in the closed-loop MIMO system under the assumption that the base station (BS) can acquire the downlink channel state information (CSI) accurately. However, transmitting CSI with high precision is a heavy burden that wastes a lot of uplink bandwidth, while transmitting CSI within a limited bandwidth leads to the degradation of system performance. To address this problem, we propose a zero-overhead downlink CSI feedback scheme based on the hybrid pilot structure. The downlink CSI is contained in the hybrid pilots at mobile terminal (MT) side, fed back to BS via the uplink pilot channel, and recovered from hybrid pilot at BS side. Meanwhile the uplink channel is estimated based on the hybrid pilot at BS side. Since transmitting the hybrid pilots occupies the same bandwidth as transmitting traditional code division multiplexing based uplink pilots, no extra uplink channel bandwidth is occupied. Therefore, the overhead for downlink CSI feedback is zero. Moreover, the hybrid pilots are formed at MT side by superposing the received analog downlink pilots directly on the uplink pilots. Thus the downlink CSI estimation process is unnecessary at MT side, and MT's complexity can be reduced. Numerical Simulations prove that, the proposed downlink CSI feedback has the higher precision than the traditional feedback schemes while the overhead for downlink CSI feedback is zero.

This paper proposes a low complexity noise suppressor with hybrid filterbanks and adaptive time-frequency tiling. An analysis hybrid filterbank provides efficient transformation by further decomposing low-frequency bins after a coarse transformation with a short frame size. A synthesis hybrid filterbank also reduces computational complexity in a similar fashion to the analysis hybrid filterbank. Adaptive time-frequency tiling reduces the number of spectral gain calculations. It adaptively generates tiling information in the time-frequency plane based on the signal characteristics. The average number of instructions on a typical DSP chip has been reduced by 30% to 7.5 MIPS in case of mono signals sampled at 44.1 kHz. A Subjective test result shows that the sound quality of the proposed method is comparable to that of the conventional one.