In this letter we present steady-state analyses of a gradient algorithm (GA) for second-order adaptive infinite impulse response (IIR) notch filters. A method for deriving more accurate estimation mean square error (MSE) expressions than the recently proposed method is presented. The method is based on the estimation error power spectral density (PSD). Moreover, an expression for the estimation bias for the adaptive IIR notch filter with constrained poles and zeros is shown to be obtained from the estimation MSE expression. Simulations are presented to confirm the validity of the analyses.

A second-order CMOS continuous-time bandpass filter with a tuneable 4-12 MHz center frequency (fc) is presented. The Design has been done by using a new second-order block which is based on Gm-C method. This Gm-C filter achieves a dynamic range of 30 dB for 1% IM3, and Q equal to 58 at 12 MHz, while dissipating only 10.5 mW from 3.3 V power supply in 0.35 µm CMOS process. The on-chip indirect automatic tuning circuit uses a phase-locked loop which sets filter center frequency to an external reference clock.

The noise robustness of an audio fingerprinting system is one of the most important issues in music information retrieval by the content-based audio identification technique. In a real environment, sound recordings are commonly distorted by channel and background noise. Recently, Philips published a robust and efficient audio fingerprinting system for audio identification. To extract a robust and efficient audio fingerprint, Philips applied the first derivative (differential) to the frequency-time sequence of the perceptual filter-bank energies. In practice, however, the noise robustness of Philips' audio fingerprinting scheme is still insufficient. In this paper, we introduce an extension method of the audio fingerprinting scheme for the enhancement of noise robustness. As an alternative to frequency filtering, a type of band-pass filter, instead of a high-pass filter, is used to achieve robustness to background noise in a real situation. Our experimental results show that the proposed filter improves the noise robustness in audio identification.

Movement of the thighs is an important factor for studying gait cycle. In this paper, a hybrid hidden Markov model (HMM)/Kalman filter (KF) scheme is proposed to track the hip angle during gait cycles. Within such a framework, HMM and KF work in parallel to estimate the hip angle and detect major gait events. This approach has been applied to study gait features of different subjects and compared with video based approach. Experimental results indicate that 1.) the swing angle of the hip can be detected with simple hardware configuration using biaxial accelerometers and 2.) the hip angle can be tracked for different subjects within the error range of -5°+5°.

The minimization problem of an L2-sensitivity measure subject to L2-norm dynamic-range scaling constraints is formulated for a class of two-dimensional (2-D) state-space digital filters. First, the problem is converted into an unconstrained optimization problem by using linear-algebraic techniques. Next, the unconstrained optimization problem is solved by applying an efficient quasi-Newton algorithm with closed-form formula for gradient evaluation. The coordinate transformation matrix obtained is then used to synthesize the optimal 2-D state-space filter structure that minimizes the L2-sensitivity measure subject to L2-norm dynamic-range scaling constraints. Finally, a numerical example is presented to illustrate the utility of the proposed technique.

A distributed denial-of-service (DDoS) attack presents a very serious threat to the stability of the Internet. In a typical DDoS attack, a large number of compromised hosts are amassed to send useless packets to jam a victim or its Internet connection, or both. Defense against DDoS attacks as well as identification of their sources comprise demanding challenges in the realm of Internet security studies. In this paper, effective measures are proposed for detecting attacks in routers through the use of queuing models, which help detect attacks closer to the attack sources. Utilizing these measures, an effective DDoS attack detection and packet-filtering scheme is proposed. The suggested approach is a cooperative technique among routers intended to protect the network from persistent and severe congestion arising from a rapid increase in attack traffic. Through computer simulations, it is shown that the proposed scheme can trace attacks near to the attack sources, and can effectively filter attack packets.

An HEMT CCD (charge-coupled-device) matched filter for spread-spectrum communication was developed. For higher data rates, it was fabricated using a two-phase CCD based on HEMT technology. It operates at 1.6 GHz, and its calculated data rate is 100 Mbps with a PN data length of 16 bits (PN data rate is 1.6 GHz). And it attains a charge transfer efficiency (CTE) of 0.975 at 2 GHz. The HEMT CCD matched filter dissipates 173 mW from a 10-Vp-p supply, and its chip size is 0.961.03 mm. It will thus be useful for optical communication and other high-data-rate applications utilizing spread-spectrum (SS) communication.

Usually an FIR filter is used to model the physical paths in an active noise control system. However, the order of the filter to be modeled is a key factor for determining the computational load for the adaptive algorithms associated with active noise control (ANC), particularly for multi-channel algorithms. In this letter, the relationships among the filter's order, the plant modeling error and the location of poles for the transfer functions of the physical paths in an ANC system are theoretically examined and numerical examples are given to verify the theoretical results.

Real-time object detection or recognition technology becomes more important for various intelligent vision systems. Processing models for object detection or recognition from natural images should tolerate pattern deformations and pattern position shifts. The hierarchical convolutional neural networks are considered as a promising model for robust object detection/recognition. This model requires huge computational power for a large number of multiply-and-accumulation operations. In order to apply this model to robot vision or various intelligent real-time vision systems, its LSI implementation is essential. This paper proposes a new algorithm for reducing multiply-and-accumulation operation by sorting neuron outputs by magnitude. We also propose an LSI architecture based on this algorithm. As a proof of concept for our LSI architecture, we have designed, fabricated and tested two test LSIs: a sorting LSI and an image-filtering LSI. The sorting LSI is designed based on the content addressable memory (CAM) circuit technology. The image-filtering LSI is designed for parallel processing by analog circuit array based on the merged/mixed analog-digital approach. We have verified the validity of our LSI architecture by measuring the LSIs.

A reduced-sample-rate (RSR) sigma-delta-pipeline (SDP) analog-to-digital converter architecture suitable for high-resolution and high-speed applications with low oversampling ratios (OSR) is presented. The proposed architecture employs a class of high-order noise transfer function (NTF) with a novel pole-zero locations. A design methodology is developed to reach the optimum NTF. The optimum NTF determines the location of the non-zero poles improving the stability of the loop and implementing the reduced-sample-rate structure, simultaneously. Unity gain signal transfer function to mitigate the analog circuit imperfections, simplified analog implementation with reduced number of operational transconductance amplifiers (OTAs), and novel, aggressive yet stable NTF with high out of band gain to achieve larger peak signal-to-noise ratio (SNR) are the main features of the proposed NTF and ADC architecture. To verify the usefulness of the proposed architecture, NTF, and design methodology, two different cases are investigated. Simulation results show that with a 4th-order modulator, designed making use of the proposed approach, the maximum SNDR of 115 dB and 124.1 dB can be achieved with only OSR of 8, and 16 respectively.

A new stereo echo canceler with input slides and counter-lateralization is proposed. Convergence of filter coefficients to the correct echo paths is obtained by pre-processing which delays the input signal periodically by one sample in one of the two channels. The time difference between the two stereo components of the input signals causes a shift of the sound image. This shift is compensated for by presenting the delayed component of the stereo signals to a loudspeaker at a higher intensity, and the other component at a lower intensity. Correct echo-path identification is analytically shown in a more general form than in the preceding literatures. A subjective listening test shows that this method is more effective for vocal musics. The processed signals are scored 0.45 lower than the original input signals, using the ITU-R five-grade impairment scale.

We devised an efficient architecture of deblocking filter and implemented the circuit with 15,400 logic gates and a 16032 dual-port SRAM using 0.25 µm standard cell technology. This circuit can process 88 image frames with 1,280720 pixels per second at 166 MHz. Our circuit requires smaller number of accesses to the external memory than other approaches and hence causes less bus traffic in the SoC design platform.

A novel first order voltage-mode non-inverting and inverting output of all-pass filter using an inverting type current conveyor (ICCII) is given. It is a first announced about a first-order voltage-mode non-inverting and inverting output of all-pass filter at the same configuration in the literature. The proposed circuit is verified using HSPICE simulation with attractive results.

This paper proposes a multiple-input multiple-output (MIMO) eigenmode transmission technique which transmits different data streams on eigenmodes of different multi-path components while suppressing intra and inter-eigenmode interferences by means of a turbo equalization technique. This paper also evaluates the effectiveness of the proposed system in frequency selective fading conditions. Computer simulation results confirms the proposed technique is effective even in high spatial correlation cases.

For V-band applications, this paper presents a fully embedded multi-layer dielectric waveguide filter (DWGF) with very low insertion loss and small size, which does not need any more assemblies such as flip-chip bonding and bond wires. The top and bottom plane are grounded, and therefore, although we make a metal housing, there will be no resonance occurrences. Especially, the proposed structure is very suitable for MMICs interconnection because the in/output pads consist of conductor backed co-planar waveguide (CBCPW). The filter is formed incorporating metallized through holes in low temperature co-fired ceramic (LTCC) substrates with relative dielectric constant of 7.05. The total volume of the filter including transitions is 4.5 mm2.65 mm0.4 mm. A fabricated DWGF with four transitions shows an insertion loss and a return loss of 2.95 dB and less than 15 dB at the center frequency of 62.17 GHz, respectively. According to the authors' knowledge, the proposed filter shows the lowest insertion loss among the embedded multi-layer millimeter-wave filters ever reported for 60 GHz applications.

In this paper, the voice signal processing module has been designed using the micro processor for the use of fully implantable middle ear devices (F-IMEHD). The voice signal processing module for F-IMEHD should be designed to compensate for the hearing loss of hearing impaired person and have the flexibility for compensating various hearing threshold level. So, the voice signal processing module has been designed and implemented to present the various frequency characteristics using the low-power micro processor, MSP430F169. The different voice signal path to the inner ear entrance was considered so that two voice signal would be combined in-phase using an all pass filter with a constant time-delay to improve the vibration of the ossicles.

In this paper, a 2-D iteratively reweighted least squares lattice algorithm, which is robust to the outliers, is introduced and is applied to defect detection problem in textured images. First, the philosophy of using different optimization functions that results in weighted least squares solution in the theory of 1-D robust regression is extended to 2-D. Then a new algorithm is derived which combines 2-D robust regression concepts with the 2-D recursive least squares lattice algorithm. With this approach, whatever the probability distribution of the prediction error may be, small weights are assigned to the outliers so that the least squares algorithm will be less sensitive to the outliers. Implementation of the proposed iteratively reweighted least squares lattice algorithm to the problem of defect detection in textured images is then considered. The performance evaluation, in terms of defect detection rate, demonstrates the importance of the proposed algorithm in reducing the effect of the outliers that generally correspond to false alarms in classification of textures as defective or nondefective.

Affine projection algorithms perform well for acoustic echo cancellation and adaptive equalization. Although these algorithms typically provide fast convergence, they are unduly complex when updating the weights of the associated adaptive filter. In this paper, we propose a new subband affine projection (SAP) algorithm and a facile method for its implementation. The SAP algorithm is derived by combining the affine projection algorithm and the subband adaptive structure with the maximal decimation. In the proposed SAP algorithm, the derived weight-updating formula for the subband adaptive filter has a simple form as compared with the normalized least mean square (NLMS) algorithm. The algorithm gives improved convergence and reduced computational complexity. The efficiency of the proposed algorithm for a colored input signal is evaluated experimentally.

Magnetoencephalography (MEG) is a method to measure a magnetic field generated by electrical neural activity in a brain, and it plays increasingly important role in clinical diagnoses and neurophysiological studies. However, in MEG analysis, the estimation of the brain activity, of the electric current density distribution in a brain which is represented by current dipoles, is problematic. A spatial filter and subsequent reconstruction of the current density distribution estimated by the spatial filter (spatial filtered reconstruction: SFR) are proposed. The spatial filter is designed to be used without prior or temporal information. The proposed spatial filter ensures that it concentrates the current distribution around the activated sources in the conductor. The current distribution estimated by the spatial filter is reconstructed by multiple linear regression. Redundant current dipoles are eliminated, and the current distribution is optimized in the sense of the Mallows Cp statistic. Numerical studies are demonstrated and show successful estimation by SFR in multiple-dipole cases. In single-dipole cases with SNRs of 101 and more, the location of the true dipole was successfully estimated for about 80% of the simulations. The reconstruction with multiple linear regression corrected the location of the maximum current density estimated by the proposed spatial filtering. The dipole on the correct position contributes to more than 70% of the total dipoles in the estimated current distribution in those cases. These results show that the current distribution is effectively localized by SFR. We also investigate the differences among SFR, the LCMV (linearly constrained minimum variance) beamformer and the SAM (synthetic aperture magnetometry), the representatives of spatial filters in MEG analyses. It is indicated that spatial resolution is improved by avoiding dependence on temporal information.

In this paper, we treat a design problem for IIR digital filters described by rational transfer function in discrete space. First, we form the filter design problem using the modified least-squares (MLS) criterion and express it as the quadratic form with respect to the numerator and denominator coefficients. Next, we show the relaxation method using the Lagrange multiplier method in order to search for the good solution efficiently. Additionally we can check the filter stability when designing the denominator coefficients. Finally, we show the effectiveness of the proposed method using a numerical example.