Federated Learning (FL) enables decentralized model training while preserving data privacy, however, real-world deployments are often constrained by Periodic Deterministic Communication (PDC) schedules, where communication between clients and the central server occurs at fixed intervals due to bandwidth limitations, energy constraints, or regulatory restrictions. These rigid schedules introduce fundamental challenges, including delayed model updates, model drift, inefficient convergence, and heightened sensitivity to non-IID data distributions, which undermine FL performance in practical settings. To address these limitations, we propose Federated Nested Quasi-Newton Optimization (FedNQN), a novel framework that accelerates convergence and enhances FL robustness under PDC constraints. FedNQN integrates curvature-aware central acceleration with variance-controlled local adaptation, ensuring stable learning dynamics despite restricted communication. At the global level, second-order curvature information accelerates model updates, compensating for infrequent synchronization, while local updates leverage variance-controlled optimizations to mitigate drift and adapt to heterogeneous data distributions. This coordinated optimization strategy enhances convergence speed, improves model accuracy, and maintains computational efficiency, making FL more adaptable to real-world constraints. Extensive experiments on benchmark datasets validate FedNQN’s effectiveness, demonstrating superior performance over state-of-the-art FL methods in terms of stability, scalability, and resilience to communication inefficiencies.

Federated learning (FL) in Intelligent Internet of Things (IIoT) environments faces critical challenges, including sparse client participation, non-IID local data distributions, and unreliable communication, which lead to slow convergence and high variance in global updates. To address these issues, we propose adaptive central federated momentum optimization (ACFMO), an optimization framework that enhances FL efficiency and stability under constrained participation. ACFMO integrates an adaptive central acceleration mechanism that dynamically adjusts momentum updates based on real-time client availability, preventing instability and ensuring smoother global model updates. Additionally, a variance-controlled local updating strategy refines client contributions, mitigating high variance caused by infrequent and heterogeneous updates. Extensive experiments across diverse FL scenarios demonstrate that ACFMO significantly accelerates convergence, reduces communication overhead, and improves model stability compared to state-of-the-art FL methods, making it particularly well-suited for real-world IIoT deployments where network and computational resources are constrained.

In the rapidly advancing ubiquitous intelligence society, the role of data as a valuable resource has become paramount. As a result, there is a growing need for the development of autonomous economic agents (AEAs) capable of intelligently and autonomously trading data. These AEAs are responsible for acquiring, processing, and selling data to entities such as software companies. To ensure optimal profitability, an intelligent AEA must carefully allocate its portfolio, relying on accurate return estimation and well-designed models. However, a significant challenge arises due to the sensitive and confidential nature of data trading. Each AEA possesses only limited local information, which may not be sufficient for training a robust and effective portfolio allocation model. To address this limitation, we propose a novel data trading market where AEAs exclusively possess local market information. To overcome the information constraint, AEAs employ federated learning (FL) that allows multiple AEAs to jointly train a model capable of generating promising portfolio allocations for multiple data products. To account for the dynamic and ever-changing revenue returns, we introduce an integration of the histogram of oriented gradients (HoGs) with the discrete wavelet transformation (DWT). This innovative combination serves to redefine the representation of local market information to effectively handle the inherent nonstationarity of revenue patterns associated with data products. Furthermore, we leverage the transform domain of local model drifts in the global model update process, effectively reducing the communication burden and significantly improving training efficiency. Through simulations, we provide compelling evidence that our proposed schemes deliver superior performance across multiple evaluation metrics, including test loss, cumulative return, portfolio risk, and Sharpe ratio.

We consider a setting engaging in collaborative learning with other machines where each individual machine has its own interests. How to effectively collaborate among machines with diverse requirements to maximize the profits of each participant poses a challenge in federated learning (FL). Our studies are motivated by the observation that in FL the global model attempts to acquire knowledge from each individual machine, while aggregating all local models into one optimal solution may not be desirable for some machines. To effectively leverage the knowledge of others while obtaining the customized solution for individual machine, we propose the accelerated federated training procedures with diversified global models. Based on the federated stochastic variance reduced gradient (FSVRG) framework, we propose the model-based grouping mechanism with adaptive central acceleration (MA-FSVRG) and gradients-based grouping mechanism with adaptive central acceleration (GA-FSVRG) to tackle the challenges of heterogeneous demands. The simulation results demonstrate the advantages of the proposed MA-FSVRG and GA-FSVRG over the state-of-the-art FL baselines. MA-FSVRG exhibits greater stability in performance and significant cost savings in local computation expenses compared to GA-FSVRG. On the other hand, GA-FSVRG attains higher test accuracy and faster convergence speed, particularly in scenarios with limited individual machine participation.

In the realm of healthcare data analysis, privacy concerns have been tackled by the federated learning (FL) framework. However, in the situation that heterogeneous healthcare Internet of Things (IoT) devices collect different types of data, applying FL becomes difficult. To train a model leveraging diverse healthcare IoT devices, we propose an advanced collaborative learning framework to fill the gap. With the proposed collaborative learning framework, individual IoT devices project their sensed features into a carefully developed latent space, which are transmitted to a central server. For privacy preservation, the latent local features are encoded within this space, while the samples’ labels remain securely stored in the individual IoT devices. Collaboratively, the deep neural network model is trained by both the central server and the diverse IoT devices. The central server handles the computationally intensive training processes, while the individual IoT devices evaluate the model’s performance and initiate back-propagation based on their locally stored labels. Experimental results demonstrate that the proposed collaborative learning framework achieves performance similar to centralized training and significantly outperforms individual training while preserving data privacy.

Federated learning (FL) deployed in the edge network environment is a promising approach for combining the separated training results based on the isolated local data sensed by various Internet of Things (IoT) devices. However, the limited computing resources for the training of various application models in each edge server and the communication burden among the edge server and numerous IoT devices greatly impact the realization of IoT intelligence. In this article, we propose transform-domain FL schemes based on discrete cosine transform (DCT-FA) and discrete wavelet transform (DWT-FA) to achieve better training efficiency and reduce the communication burden for IoT devices. Furthermore, when the amount of training data is limited, we propose to combine time-domain features and frequency-domain features in FL (CDCT-FA) that turns out to achieve much higher test accuracy. From the experimental results, the transform-domain FL schemes are shown to be promising, given the different constraints and requirements of various IoT intelligence applications.

A new expression of evaluating l2-sensitivity for generalized direct-form II structure of two-dimensional (2-D) separable-denominator digital filters is derived and analyzed. Unlike the previous work, l2-sensitivity is analyzed for the filter structure instead of its state-space realization. Then the resulting l2-sensitivity measure is compared with that deduced in a recent study of generalized direct-form II state-space realization of 2-D separable in denominator digital filters. In a numerical example, the new l2-sensitivity measure is minimized with respect to free parameters subject to l2-scaling constraints by an exhaustive search over a set of discrete values of finite cardinality, and the results are compared with those obtained by existing techniques.

The design of perfect-reconstruction cosine-modulated filter banks is revisited by applying new linearization techniques for the time-domain quadratic perfect reconstruction constraints. The new techniques are analyzed to explain why they can provide improved approximation accuracy hence improved performance with insignificant increase in complexity. A design example is presented for performance demonstration and comparison.

In this paper, l2-sensitivity for the generalized direct-form II state-space realization of two-dimensional (2-D) separable-denominator digital filters is analyzed and minimized with respect to free parameters subject to l2-scaling constraints. Improved l2-sensitivity for 2-D separable-denominator digital filters is investigated by taking 0 and ±1 elements into account. An l2-sensitivity measure for the generalized direct-form II state-space realization of 2-D separable-denominator digital filters is deduced by applying improved l2-sensitivity. The l2-sensitivity measure is then minimized with respect to free parameters subject to l2-scaling constraints by employing an exhaustive search over a set of discrete values of finite cardinality. A numerical example is included to illustrate the validity and effectiveness of the proposed technique.

The paper is the first of a series of research investigations on robust digital filters which refer to filters that offer optimal performance under variations of filter parameters. We begin with quantitative characterization of performance robustness of a digital filter against parameter uncertainties. This is followed by several properties of the proposed robust performance measures and design formulations of robust FIR filters in L2(least-squares) and L∞(minimax) sense as nonsmooth convex problems. We present an accelerated subgradient algorithm for the design of L∞-robust FIR filters with technical details involved in implementing the proposed algorithm. A numerical example is included for illustration of the proposed design method and performance evaluation in comparison with conventional minimax FIR filters.

In this paper, we explore generalized direct-form II realization of two-dimensional separable-denominator digital filters of order (m,n) based on the concept of polynomial operators, where we deal with a model with 3(m + n) + mn + 1 fixed parameters plus m + n free parameters. An l2-scaling method is introduced by utilizing different coupling coefficients at different branch nodes to avoid overflow. An expression for the roundoff noise gain in the realization is also examined. It is shown that the roundoff noise gain can be minimized with respect to the m + n free parameters by means of exhaustive search in a finite element space.

This paper presents an enhanced Steiglitz-McBride (SM) procedure for the design of stable minimax IIR digital filters. It is well known that minimax design of IIR filters is typically initiated with a nonconvex formulation, followed by a procedure to relax the original design problem to a sequence of convex sub-problems to be solved iteratively. We proposed an enhanced SM procedure that leads to improved convex relaxation relative to the conventional SM techniques, hence to improved designs. In addition, we make an observation that the state-of-the-art convex stability constraint based on strictly positive realness is equivalent to that deduced from an enhanced version of Rouché theorem from complex analysis. Design examples are presented to evaluate the new design algorithm.

The joint optimization problem of high-order error feedback and realization for minimizing roundoff noise at the filter output subject to-scaling constraints for two-dimensional (2-D) separable-denominator digital filters is investigated. Linear algebraic techniques that convert the problem at hand into an unconstrained optimization problem are explored, and an efficient quasi-Newton algorithm is then applied to solve the unconstrained optimization problem iteratively. Closed-form formulas for fast and accurate gradient evaluation are derived. A numerical example is presented to demonstrate the validity and effectiveness of the proposed technique.

We study a class of composite filters (C-filters), each is composed of a prototype filter and a shaping filter in cascade, where the shaping filter is constructed by cascading several complementary comb filters. In particular, the problem of designing a C-filter with equiripple passband and least-squares stopband subject to peak stopband gain is formulated and an algorithm for designing such a class of linear-phase FIR C-filters is proposed. The algorithm is based on an alternating convex optimization strategy in that the prototype and shaping filters are optimized in separate steps which are coupled and carried out in a sequential manner to yield a satisfactory design. Design example is presented to illustrate the algorithm and demonstrate the performance of the C-filter relative to its conventional FIR counterparts.

This paper deals with the problem of minimizing weighted pole and zero sensitivity subject to l2-scaling constraints for state-space digital filters. A measure for pole and zero sensitivity is presented and an efficient iterative technique for minimizing this measure subject to l2-scaling constraints is developed by relaxing the constraints into a single constraint on matrix trace and solving the relaxed problem with an effective matrix iteration scheme. A numerical example is included to demonstrate the validity and effectiveness of the proposed technique.

We present a unified approach to minimax design of interpolated and frequency-response-masking FIR filters which are well-known classes of computationally efficient digital filter. The highly nonconvex minimax designs of these filters are carried out by jointly optimizing the subfilters involved using a convex-concave procedure (CCP). We explain why CCP is well-suited for the design problems at hand and present two design instances to demonstrate its performance.

This paper investigates the problem of minimizing weighted pole and zero sensitivity for state-space digital filters. A new measure for pole and zero sensitivity is proposed and an efficient iterative technique for minimizing this measure is developed by employing a quasi-Newton algorithm. A novel and simple method is also explored for obtaining the optimal coordinate transformation matrix which minimizes a zero sensitivity measure subject to minimal pole sensitivity. A numerical example is presented to demonstrate the validity and effectiveness of the proposed technique.

The optimization problem of error-feedback and realization to minimize the roundoff noise in a closed-loop system with full-order state observer feedback subject to l2-scaling constraints is investigated. It is shown that joint optimization of error-feedback and realization is not needed when utilizing a general-matrix error-feedback loop because the optimal error-feedback matrix, in closed-form, can be determined in advance. An analytic technique is developed for obtaining the optimal realization that minimizes the roundoff noise subject to l2-scaling constraints. A numerical example is presented to demonstrate the validity and effectiveness of the proposed technique.

We study a class of composite digital filters, each has a dominating FIR component of order N and an extremely simple IIR component of order r with r-C N that are connected in parallel. We show that a constrained optimization setting known as convex-concave procedure (CCP) is naturally suited for the design of stable composite filters where the FIR and IIR components are jointly optimized in frequency-weighted minimax sense. A design algorithm based on successive CCP is presented and numerical examples are included to demonstrate that even with r =2 composite filters offer substantial performance improvement relative to their FIR-alone counterparts.

Joint optimization of error feedforward, high-order error feedback and state-space realization for minimizing roundoff noise at filter output subject to l2-scaling constraints for state-space digital filters is investigated. Linear algebraic techniques that convert the problems at hand into an unconstrained optimization problem are explored, and an efficient quasi-Newton algorithm is then applied to solve the unconstrained optimization problem iteratively. In this connection, closed-form formulas are derived for fast and accurate gradient evaluation. Finally, a numerical example is presented to demonstrate that the high-order error feedback does offer much improved performance and that the proposed joint optimization is superior relative to a sequentially optimized system where the state-space coordinate transformation as well as error feedforward and high-order error feedback matrices are optimized separately.

Sparsity and incoherence are the two key ingredients in compressive sensing (CS). Given a sparsifying dictionary D, the projection matrix P must be as incoherent with D as possible for the CS system to be efficient. Thus the design of projection matrix is naturally a problem of minimizing the coherence between P andD. Unfortunately, this turns out to be a nonconvex, nonsmooth, large-scale problem even for a CS system of moderate size. In this paper, the above-mentioned problem is investigated in a formulation where the problem is converted into a sequence of nonsmooth but convex subproblems. A subgradient projection algorithm is proposed to solve the nonsmooth subproblems that converges to a projection matrix with improved performance. The performance of the proposed algorithm is evaluated by simulations and comparisons with several existing techniques.

Sparse digital filters are of importance as they offer improved implementation efficiency relative to their nonsparse counterparts. This paper examines the design of minimax sparse IIR filters from a sparse representation point of view. The result is a new algorithm that accomplishes a design with three phases - identification of the whereabouts of zero coefficients; optimal design subject to the sparsity constraint; and performance enhancement by further dimension reduction of the working subspace. A design example is presented for illustrating the new algorithm.

A new algorithm for the reconstruction of images with sparse gradient is proposed. The algorithm is based on the minimization of the so called total-variation (TV) regularized squared error and is especially suited for image reconstruction from a small number of measurements. The algorithm is developed based on a generalized TV norm and uses a sequential conjugate-gradient method. Simulation results are presented which demonstrate that the proposed algorithm yields significantly improved reconstruction performance for images with sparse gradient and requires significantly reduced computational effort relative to the log-barrier based TV-regularized least-squares algorithm.

The basis pursuit denoising refers to the solution of an ℓ1-ℓ2minimization formulation which is well known as an effective method for signal denoising. In this paper we investigate an ℓp-ℓ2formulation with p ∈ (0, 1) for denoising. Based on an analysis of the discontinuity of the global minimizer of the ℓp-ℓ2problem with respect to regularization parameter, we propose two smoothed ℓp-ℓ2solvers for orthogonal basis and overcomplete dictionary respectively. Experimental studies that evaluate the performance of the proposed solvers with various parameter settings are also presented.

Implementing a variable fractional delay (VFD) filter in Farrow model is costly as each coefficient of a VFD filter is a polynomial rather than a numerical scalar as in a conventional digital filter. This paper presents a method for the design of VFD filters with sparse coefficients which admits efficient implementation. The design is accomplished in two phases with the first phase identifying locations in polynomial impulse response that are suitable to be set to zero and the second phase optimizing the remaining nonzero coefficients so as for the VFD filter to best approximate a desired frequency response. Performance evaluation and comparison of the proposed algorithm relative to an equivalent nonsparse counterpart are also presented.

A new algorithm for the reconstruction of so called block-sparse signals in a compressive sensing framework is presented. The algorithm is based on minimizing an ℓ2/p-norm regularized l2error. The minimization is carried out by using a sequential conjugate-gradient algorithm where the line search involved is carried out using a technique based on Banach's fixed-point theorem. Simulation results are presented which show that for large-size data the proposed algorithm yields improved reconstruction performance and requires a reduced amount of computation relative to several known algorithms.

Coefficient sparsity of digital filters is of importance as it is directly related to implementation efficiency and cost. To date most work in the field has been focused on finite-impulse response filters. In this paper, the design of sparse digital filters is investigated for a class of IIR filters. We propose a two-phase algorithm that promotes coefficient sparsity, maintains filter's stability, and optimizes its frequency response to approximate a desired frequency response in minimax sense. A design example is presented to illustrate the proposed algorithm and compare its performance relative to its nonsparse IIR and FIR counterparts.

A new algorithm for signal reconstruction in a compressive sensing framework is presented. The algorithm is based on minimizing an unconstrained regularized ℓpnorm with p 〈 1 in the null space of the measurement matrix. The unconstrained optimization involved is performed by using a quasi-Newton algorithm in which a new line search based on Banach's fixed-point theorem is used. Simulation results are presented, which demonstrate that the proposed algorithm yields improved reconstruction performance and requires a reduced amount of computation relative to several known algorithms.

This paper studies the optimization problem for joint precoding design in a multi-antenna downlink channel using relaying. We formulate the joint source and relay precoding design by aiming at sum capacity maximization. Since this problem is in general nonconvex, we first convert this problem into standard convex quadratic programs, and then propose an iterative joint precoding optimization algorithm by utilizing efficient quadratic programming approaches. We observe that the iterative method always yields optimal precoding matrices which diagonalize the compound channel of the backward (source-to-relay) and the forward (relay-to-destination) links at high SNR regimes. Motivated by this observation, we further develop an efficient structured precoding design. Simulation results are presented to verify the effectiveness of our proposed precoding schemes.

Owing to its spectral efficiency, bidirectional relaying is a promising candidate for information exchange in multiple-user cooperative networks. When the network is limited by resource constraints, amplify-and-forward (AaF) relay protocol is often the choice due to its simplicity and ease of use. Power allocation for AaF protocol has being extensively studied in unidirectional relay networks but how it can be implemented in two-way multiple-relay multiple-user networks has yet to be addressed. In this paper, we consider the adaptive power allocation in bidirectional AaF multiple-relay multiple-user networks. We show that when the multiple-user interference can be removed by a robust channel assignment algorithm, power allocation by maximizing the instantaneous sum rate or minimizing the symbol error rate can be suitably casted as a geometric programming (GP) problem. Simulation results show adaptive power allocation by GP outperforms that of equal power allocation scheme particularly when there is a single serving relay, and the gain can be as substantial when there are multiple serving relays.

This paper investigates the joint precoding optimization problem for a relay-assisted multi- antenna downlink system. Aiming at multiuser sum capacity maximization, we first propose an iterative optimization algorithm which exploits quadratic programming approaches. We observe that the iterative method always yields the optimized precoding matrices which diagonalize the compound channel of the system at high SNR regimes. Inspired by this observation, we further develop an efficient source and relay precoding strategy by diagonalizing the compound channel of the backward (source-to- relay) and the forward (relay-to-destination) links. Simulation results verify the effectiveness of our proposed precoding schemes.

Optimal scheduling for concurrent transmissions in rate-nonadaptive wireless networks is NP-hard. Optimal scheduling in rate-adaptive wireless networks is even more difficult, because, due to mutual interference, each flow's throughput in a time slot is unknown before the scheduling decision of that slot is finalized. The capacity bound derived for rate-nonadaptive networks is no longer applicable either. In this paper, we first formulate the optimal scheduling problems with and without minimum per-flow throughput constraints. Given the hardness of the problems and the fact that the scheduling decisions should be made within a few milliseconds, we propose two simple yet effective searching algorithms which can quickly move towards better scheduling decisions. Thus, the proposed scheduling algorithms can achieve high network throughput and maintain long-term fairness among competing flows with low computational complexity. For the constrained optimization problem involved, we consider its dual problem and apply Lagrangian relaxation. We then incorporate a dual update procedure in the proposed searching algorithm to ensure that the searching results satisfy the constraints. Extensive simulations are conducted to demonstrate the effectiveness and efficiency of the proposed scheduling algorithms which are found to achieve throughputs close to the exhaustive searching results with much lower computational complexity.

Is sparsity an issue in filter design problems? and why is it important? How a digital filter can be designed to have a sparse impulse response for efficient implementation while achieving improved performance relative to its non-sparse counterpart? In an attempt to address these questions, this paper comes up with a design technique for optimal linear-phase FIR filters with sparse impulse responses.

An optimized numerical mapping scheme for achieving improved location of exons in DNA sequences using digital filters is proposed. Characteristic numerical values for the four nucleotides, referred to as pseudo-EIIP values, are obtained using a training procedure where the location accuracy is maximized using a quasi-Newton algorithm based on the Broyden-Fletcher-Goldfarb-Shanno updating formula. A training set of 80 DNA sequences is chosen from the HMR195 database. The objective function for the optimization procedure is formulated using the so-called receiver operating characteristic (ROC) technique and the procedure is initialized using electron-ion interaction potential (EIIP) values. Unbiased testing of the optimized characteristic values is carried out using a set of DNA sequences that has no overlap with the training set. Simulation results show that the pseudo-EIIP values yield more accurate exon locations than those obtained using the actual EIIP values.

One of the most attractive features of ultra-wideband impulse radio is the collection of rich multipath with the transmission of ultra-short pulses. Exploiting the rich multipath diversity with channel estimating Rake receivers enables significant energy capture, higher performance and flexibility than suboptimal receivers. Although data-aided (DA) maximum likelihood (ML) channel estimator shows a promising performance, its implementation is restricted by the Nyquist sampling criterion. The emerging theory of compressed sensing (CS) describes a novel framework to jointly compress and detect a sparse signal with fewer samples than the traditional Nyquist criterion. In this paper, we propose a CS-ML channel estimator which combines the compression framework of CS for sampling rate reduction while retaining the noise statistics formulation of ML to achieve a reliable performance. Simulation assessment indicates that, with far fewer measurements, the performance of our proposed scheme supersedes that of the 4-norm minimization estimator of CS and can be as close as the ML, but with a reduction in complexity.

This paper investigates the problem of reducing the deviation from a desired transfer function caused by the coefficient quantization errors of a three-dimensional (3-D) separable-denominator digital filter. First, a 3-D transfer function with separable denominator is represented with the cascade connection of three one-dimensional (1-D) transfer functions by applying a minimal decomposition technique. Next, the multiinput multi-output (MIMO) 1-D transfer function located in the middle of the cascade connection is realized by a minimal state-space model and then the l2/l2-sensitivity of the model is analyzed. Third, a technique for the optimal synthesis of the minimal state-space model is developed so as to minimize the l2-sensitivity subject to l2-scaling constraints. Finally, a numerical example is given to demonstrate the validity and effectiveness of the proposed technique.

This paper presents a new method for the design of two-channel conjugate quadrature (CQ) filter banks in which halfband filter and spectrum factorization are not required. Instead, a CQ filter is directly optimized subject to the perfect reconstruction and possibly other constraints (such as number of vanishing moments (VM)). We develop a design strategy in that the solution is approached sequentially with each update confined to within a small vicinity of the current iterate where the problem at hand behaves like a convex one, thus the update can be obtained as a solution of a convex problem. Four design scenarios are considered, namely the least squares designs with or without VM requirement, and equiripple designs with or without VM requirement. The simulation studies demonstrate that the proposed method is reliable to design high-order CQ filters with improved performance.

A filtering technique for the location of hot spots in proteins proposed recently is applied for the location of exons in DNA sequences. The technique involves conversion of a DNA character sequence into a numerical sequence using the electron-ion interaction potential values and then filtering the numerical sequence using a narrowband bandpass digital filter whose passband is centered at the period-3 frequency, i.e., 2π/3. The strength of the bandpass-filtered signal as a function of nucleotide location is then detected using a lowpass filter. A plot of the signal power versus location reveals the presence of exons as distinct peaks. Simulations have shown that the technique leads to more accurate exon locations than another computational technique based on the short-time discrete Fourier transform. Furthermore, the amount of computation required is reduced by as much as 97 percent thereby rendering the technique suitable for the processing of long DNA sequences, even complete genomes.

In this paper, we present an analysis on the coefficient sensitivity (CS) of a second-order cone programming (SOCP) based method for the design of FRM filters and show that the method is guaranteed to produce FRM filters with low CS as long as the CS of the initial FRM filter is low. Moreover, we present an enhanced SOCP-based design method that incorporates a constraint on the CS measure S12recently introduced by Y. C. Lim et al. Our formulation shows that SOCP provides a suitable design setting for FRM filters, where the CS is taken into account. A design example illustrates the ability of the proposed method to produce FRM filters with very low CS without sacrificing the filter’s other performance.

An improved technique for the location of hot spots in proteins based on the use of a bandpass notch (BPN) filter is described. The BPN filter is designed by specifying a stability margin and then minimizing the area under the amplitude response so as to achieve maximum selectivity for the chosen stability margin. Preliminary results indicate that the use of a BPN filter leads to improved hot-spot prediction compared to results obtained with classical filters investigated earlier by the authors such as inverse-Chebyshev filters. The results also show that the improved technique yields predictions that are consistent with results based on biological methodologies. In addition, the technique reveals certain new ‘potential’ hot-spot locations which could be investigated further by the biological community.

Orthogonal frequency division multiplexing (OFDM) modulation is widely used in communication systems to meet the demand for ever increasing data rates. Characteristics of the transmitted signal can be employed for blind channel identification. In this paper, we propose a blind polynomial channel estimation algorithm using noncircular second-order statistics of the received OFDM signal. A set of polynomial equations are then formulated based on the correlation of the received signal. The solution of these equations provides an estimate of the channel coefficients. Results are presented which show that the proposed algorithm provides performance comparable to the least minimum mean square error (LMMSE) solution with low computational complexity. The performance is near-optimal for large OFDM systems.

Orthogonal frequency division multiplexing (OFDM) modulation is widely used in communication systems to meet the demand for ever increasing data rates. In this paper, a low complexity joint semiblind detection algorithm for OFDM systems over time-varying channels is proposed based on the channel correlation and noise variance. The problem is relaxed to a continuous non-convex quadratic programming problem. Then an iterative method is utilized to deduce a sequence of reduced-size quadratic programming problems. These are solved by limiting the search in the 2-dimensional subspace. Furthermore, a low-bit descent search is employed to improve the system performance. Results are given which demonstrate that the proposed algorithm provides comparable performance with lower computational complexity than that of a sphere decoder.

Sequential quadratic programming is used to design high-performance quincunx filter banks for image coding, where the resulting filter banks have perfect reconstruction, linear phase, high coding gain, good frequency selectivity, and certain prescribed vanishing-moment properties. Design examples are presented and compared to various previously proposed filter banks. The new filter banks are shown to be highly effective for image coding, outperforming previously proposed quincunx filter banks in most cases, and outperforming the well-known 9/7 filter bank in some limited cases.

In a rapidly fading environment, Doppler spread caused by user mobility destroys the orthogonality among OFDM subcarriers, resulting in intercarrier interference. In this paper, a low complexity ICI reduction algorithm which can be applied to QAM signal constellations is proposed. A combinatorial optimization problem for ICI suppression is formulated and then relaxed to a quadratic programming problem. A successive method is then utilized to deduce a sequence of reduced-size QP problems, which is solved by limiting the search in the 2- dimensional subspace spanned by its steepest-descent and Newton directions to reduce the computational complexity. Furthermore, a low-bit descent search is employed to enhance the system performance. The proposed algorithm is shown to provide excellent performance with low computational complexity.

The minimization problem of a frequency-weighted l2-sensitivity measure subject to l2-scaling constraints for two-dimensional (2D) state-space digital filters is formulated. First, an iterative method for solving the constrained optimization problem in question is developed by introducing a Lagrange function and applying some matrix-theoretic techniques as well as an efficient bisection method. The optimal filter structure is then synthesized so as to minimize the frequency-weighted l2-sensitivity subject to l2-scaling constraints. Finally, a numerical example is presented to illustrate the utility of the proposed technique.

Polynomial programming (PP) deals with a class of optimization problems where both the objective function and constraint functions are multivariable polynomials. PP covers several popular classes of convex optimization problems such as linear, convex quadratic, semidefinite, and second-order cone programming problems, it also includes a good many non-convex problems that are encountered in engineering analysis and design. This paper describes a preliminary attempt to apply a recently developed PP algorithm to the design of FIR digital filters with discrete coefficients. Computer simulations are presented to demonstrate the efficiency of the PP-based algorithm and its ability to provide globally or near-globally optimal designs.

In this paper, we investigate several efficient interpolation techniques for pilot symbol assisted channel estimation in OFDM. The interpolation methods studied include two dimensional (2-D) separable lowpass sine interpolator with Kaiser window, 2-D separable Deslauriers-Dubuc (DD) interpolation and 2-D discrete Fourier transform (DFT) based lowpass interpolation. The performances of these interpolators are compared with those of the well known minimum mean-square error (MMSE) 2-D separable Wiener filter and the perfect channel state information. It is shown that the Kaiser window based interpolator and DD interpolation are simple, robust, and outperform the 2-D DFT based lowpass interpolation as well as several existing interpolation methods proposed in the literature. These two schemes are suitable candidates for use in 1-D and 2-D channel estimation

Peak-to-average power-ratio (PAPR) reduction for OFDM systems is investigated in a probabilistic framework. A new constellation extension technique is developed whereby the data for each subcarrier are represented either by points in the original constellation or by extended points. An optimal representation of the OFDM signal is achieved by using a de-randomization algorithm where the conditional probability involved is handled by using the Chernoff bound and the evaluation of the many hyperbolic cosine functions involved is replaced by a tight upper bound for these functions. The proposed algorithm can be used by itself or be combined with a selective rotation technique described in the paper and with other known algorithms such as the coordinate descent optimization and selective mapping algorithms to achieve further performance enhancements at the cost of a slight increase in the computational complexity. When compared with other existing PAPR-reduction algorithms, the enhanced algorithm offers improved PAPR-reduction performance and improved computational complexity although, the transmit power is increased somewhat

A new technique is proposed for the design of high-performance quincunx filter banks for the application of image coding. This method yields linear-phase perfect-reconstruction systems with high coding gain, good analysis/synthesis filter frequency responses, and prescribed vanishing moment properties. Examples of filter banks designed with this technique are presented and shown to be highly effective for image coding.

The minimization problem of an L/sub 2/-sensitivity measure subject to L/sub 2/-scaling constraints on the dynamic range for multi-input/multi-output (MIMO) linear discrete-time systems is formulated. An iterative technique is developed to solve the constrained optimization problem directly. The proposed solution method largely relies on the use of a Lagrange function and some matrix-theoretic techniques. A numerical example is presented to illustrate the utility of the proposed technique.

A new constellation extension technique for peak-to-average power-ratio reduction (PAPR) in orthogonal frequency-division multiplexing systems is proposed. Two new algorithms for PAPR reduction are developed by applying the so-called method of conditional probability (MCP) and coordinate descent optimization (CDO). Our simulations demonstrate that the proposed algorithms outperform several existing algorithms and the performance can be further improved by combining the MCP, CDO, and the selective mapping algorithms

A method for designing FIR digital filters, with each coefficient a sum of signed power-of-2 terms, by sphere relaxation is proposed. To justify the design methodology, we present an analysis showing that the globally optimal design always lies in a vicinity of the optimal FIR filter with continuous coefficients. The design problem at hand is then addressed using a new relaxation method in which the constraints characterizing the binary nature of the design variables are relaxed to a single sphere type constraint on the corresponding continuous variables. This yields a simple nonconvex continuous optimization problem whose solution can be calculated considerably faster than previously reported relaxation methods. Design examples are presented to demonstrate that the proposed algorithm offers near-optimal designs with small fraction of design complexity relative to that required by the existing methods.

A method for the design of IIR digital filters with pole radius constraint based on the argument principle (AP) from complex analysis is proposed. Unlike the stability constraints available in the literature which are sufficient but not necessary stability constraints, the proposed AP-based stability condition is both sufficient and necessary. We show that weighted least-squares and minimax designs of robustly stable IIR filters can be accomplished in convenient convex programming settings where the proposed stability criterion can easily be incorporated as a single equality constraint that does not depend on any parameters other then filter's denominator coefficients. Design examples are presented to illustrate the usefulness of the proposed algorithms.

A design algorithm based on second-order cone programming (SOCP) for minimax design of 2-D FIR filters with low group delay is proposed. SOCP is a special class of convex programming problems that can be carried out considerably more efficiently than the popular semidefinite programming. The simulation studies presented in this paper also confirm this in a filter design context. The proposed algorithm is compared favorably with a recently proposed design method based on sequential quadratic programming. The proposed design method is expected to be a useful utility for 2-D digital filter designers whose interest is not limited to linear phase responses and low order filters.

The problem of minimizing an L/sub 2/-sensitivity measure subject to L/sub 2/-norm dynamic-range scaling constraints for state-space digital filters is considered. A novel iterative technique is developed to solve the constraint optimization problem directly. The proposed solution method is largely based on the use of a Lagrange function and some matrix-theoretic techniques. Computer simulation results are also presented to demonstrate the effectiveness of the proposed technique.

In the case of one-dimensional filter banks, symmetric extension is a commonly used technique for constructing nonexpansive transforms of finite-length sequences. In this paper, we show how symmetric extension can be extended to the case of two-dimensional filter banks based on quincunx sampling. In particular, we show how, for filter banks of this type, one can construct nonexpansive transforms for input sequences defined on arbitrary rectangular regions.

The minimization of roundoff noise subject to l/sub 2/-norm dynamic-range scaling constraints in two-dimensional (2-D) state-space digital filters is considered by using joint error feedback and coordinate transformation optimization. An iterative approach for minimizing the roundoff noise under l/sub 2/-norm dynamic-range scaling constraints is developed by jointly optimizing a scalar error-feedback matrix and a coordinate transformation matrix. A numerical example is presented to illustrate the utility of the proposed technique.

A new multiuser detector for direct-sequence code-division multiple-access communication systems is described. The proposed detector divides the received signal stream into a number of overlapping windows and decorrelates them window by window. This scheme is justified by an analysis of the convergence and decay rate of the impulse response. Based on the results, a signal-adapted criterion is developed which enables one to determine the window length according to the near-far situation of a practical system. A performance analysis and simulation results show that a small to moderate window length is usually sufficient to yield a performance that is close to or even better than that of the ideal decorrelating detector.

Semidefinite programming (SDP) has recently been found useful in designing various types of FIR digital filters. This paper describes a SDP-based method for the design of stable minimax IIR filters. A single linear matrix inequality (LMI) constraint assures the filter's stability and fits nicely into the SDP-based design setting. Design efficiency and performance of the proposed method an illustrated by simulations and comparisons to a well-known design by Deczky (1972).

Semidefinite programming (SDP) has recently attracted a great deal of research interest. Among other things, the optimization tool was proven to be applicable to design various types of FIR digital filters. This paper describes an attempt on extending the SDP approach to 2-D IIR filters. It is shown that a stable 2-D IIR filter design in the minimax sense can be formulated as an iterative SDP problem. Stability constraints are expressed as linear matrix inequalities which fit nicely into the SDP-based design setting. Unlike the 1-D case, an alternating iteration scheme is used to deal with parameter nonlinearity encountered in the separable denominator polynomials. A design example is presented to illustrate the proposed method.

A new linear multiuser detector for binary signaling in code-division multiple-access communication systems is described. The new detector directly minimizes the bit-error rate (BER) subject to a set of reasonable constraints. It is shown that any local minimum of the constrained BER cost function is a global minimum; hence a robust constrained minimization algorithm always leads to a detector with good performance. Although the proposed detector cannot be shown to be optimal among linear multiuser detectors because of the constraints imposed, our analysis and simulations indicate that it always outperforms the decorrelating detector and is optimal for most realistic systems.

This paper outlines an efficient optimization method to determine the optimal power allocation plan that guarantees the quality of service contracts for multimedia traffic while ensuring minimal power consumption in a systematic manner. Both perfect and imperfect power-controlled cases in a multicell operating environment have been considered. This method may also be used to determine the capacity region of the integrated network for a given set of system parameters.

A recursive algorithm for updating linear detectors in code-division multiple-access systems is proposed. Based on this algorithm, a window-based implementation with a signal-based criterion for determining the window length is developed. Performance analysis and numerical experiments are conducted that show the merits of the proposed implementation method.

This letter presents a simple method to simultaneously optimize a multiplicity of design parameters for the adaptive automatic repeat request strategy previously reported, and subsequently provides a quantitative measurement that reflects the appropriateness of the selected parameters. An exact analytical expression that allows us to compute the throughput crossover probability between the two different protocols is derived. The results provide fundamental insights into how these key parameters interact and determine the system performance.

A new method is proposed for the design of two-channel linear-phase perfect reconstruction QMF banks. The analysis lowpass filter is first designed by a conventional method, and then the synthesis lowpass filter is obtained by using a null-space projection approach. This method is then extended to the design of two-channel perfect reconstruction QMF banks with low reconstruction delay, which are desirable in some applications. Two design examples are given to illustrate the proposed methods.

In this paper a new manipulator joint velocity observer is presented. The observer is reduced in its order and it is adaptive with respect to unknown dynamic parameters. The velocity estimate produced by the observer is used in an adaptive controller for trajectory tracking. The result is locally asymptotically stable velocity observation errors and locally asymptotically stable position and velocity trajectory tracking errors. Simulations of the proposed scheme on the PUMA-560 show an improvement over a well known adaptive controller which obtains its joint velocity estimates via numerical differentiation experiments demonstrate the usefulness of the proposed observer.>

An iterative procedure proposed by Chen and Lee for the design of quadrature mirror filter (QMF) banks is extended to the design of two types of filter banks, i.e., l-D QMF banks with low reconstruction delay and 2-D nonseparable hexagonal QMF banks. Our simulations show that the extended methods are very efficient and yield good designs.>

Two approaches to the evaluation of the manipulator regressor of a general n-degree-of-freedom (DOF) robot are presented. The first method is an energy-based approach using the Lagrangian formulation of robot dynamics as a starting point. A key fact used in deriving the solution is that the manipulator Lagrangian is linkwise additive. The second approach generates an iterative algorithm for efficient numerical evaluation of the regressor. It is obtained by reformulating the Newton-Euler recursion using vector analysis type techniques. In addition, a modified Slotine-Li algorithm for adaptive motion control is presented and is then applied in a simulation study to a 4-DOF PUMA-type robot, where the manipulator regressor is evaluated using the iterative algorithm proposed.>

The commenters point out an error in the adaptive impedance-control approach for robotic manipulators given in the above-titled paper by W.-S. Lu and Q.-H. Meng (ibid., vol.7, no.3, p.408-15, June 1991). Lu and Meng assert that their adaptive impedance-control scheme can compensate for uncertainty in the force measurements; however, it is shown that it is possible for the uncertainty in the force measurements to cause the parameter estimates to go unbounded. To remedy this possible instability problem, an additional auxiliary controller is suggested. In their reply, Lu and Meng fail to find any error in the Lyapunov analysis of their algorithm. They are not convinced that one should seek a theoretically asymptotically stable solution with a controller that has a highly undesirable feature.>

Two adaptive impedance control algorithms are presented. In this treatment, it is assumed that some parameters in the manipulator dynamics may be uncertain, and the measurements from the wrist force sensor utilized are imprecise. By introducing the concept of target-impedance reference trajectory (TIRT), which characterizes a desired dynamic relation of the end-point with the environment and a refined Lyapunov approach, it is shown that the adaptation mechanisms previously suggested can be injected into N. Hogan's (1987) conventional impedance control scheme. The two resulting algorithms are compared in terms of implementation feasibility as well as computation efficiency. Simulation results are presented to illustrate the proposed algorithms.>