Adaptive Signal Processing

Non-orthogonal multiple access (NOMA) scheme has gained remarkable consideration from researchers as it is a favorable technique for the future release of 5G and beyond. Recently proposed beamspace MIMO NOMA for mmWave communication has shown further improvement in its spectrum efficiency by employing beamforming with the aid of instantaneous channel state estimation. But, practically the downlink and the uplink channel state information (CSI) are not identical as the channel reciprocity is no more valid in the fast-varying environment of mmWave communication. Thus, this increases the transmission overhead due to pilot transmission in both uplink and downlink communication. To overcome this issue, we propose a semi-blind technique to design beamforming in downlink power-domain NOMA for mmWave communication which only requires statistical CSI. In the first step, the sum outage probability is derived by using the exact characterization of ratio of the indefinite quadratic form (IQF). In the second step, a heuristic approach with the aid of the interior point (IP) algorithm is developed to find the optimal solution for beam vectors by minimizing the derived sum outage probability. Moreover, a closed-form statistical beamforming solution is derived which is established on statistical signal-to-leakage-noise-ratio (SLNR) maximization. These two proposed methods are compared with the classical principle eigenvector-based solution. Monte Carlo simulations are used to validate the derived analytical expression of outage probability. The results of optimization demonstrate that the performance of both proposed beamforming is higher than the classical eigenvector-based solution. However, the heuristic-based beamforming algorithm via interiorpoint optimization is significantly better than the SLNR-based solution.

Purpose: Electrooculography (EOG) serves as a vital non-invasive technique for neuro-ophthalmological diagnostics and human-computer interface (HCI) development, yet its parameters may vary with demographic and physiological factors. Methods: This prospective study enrolled 50 healthy participants, stratified by age into three groups: child/adolescent (12-22 years, n=20), adult (23-40 years, n=24), and a pilot elderly group (≥40 years, n=6). EOG signals were recorded using a BIOPAC MP36 system, adhering to protocols informed by International Society for Clinical Electrophysiology of Vision (ISCEV) standards for electro-oculography. Standardized tasks assessed saccadic latency, blink dynamics, and signal-tonoise ratio (SNR) in horizontal and vertical planes. In adult females (n=12), menstrual cycle phase was determined via serum progesterone (≥3 ng/mL for luteal) and estradiol assays, synchronized with testing. Results: Adolescents exhibited significantly higher saccadic latency variability (210±30 ms; p=0.03 versus adults) and 40% lower vertical SNR, reflecting immature oculomotor control. Adult women in the luteal phase showed an 18% reduction in horizontal SNR (p=0.04), with estradiol levels inversely correlating with fixation duration (Spearman ρ=-0.58, p<0.05). Elderly participants demonstrated prolonged saccadic latency (p_adj=0.01) and 30% reduced blink amplitude compared to adults, indicating age-related declines in EOG signal quality. Conclusions: Age, gender, and menstrual cycle-related hormonal fluctuations systematically modulate key EOG parameters. These findings underscore the need for demographic-specific normative data in clinical electrophysiology and adaptive HCI applications, aligning with ISCEV recommendations for standardized interpretation.

The paper introduces evolving connectionist systems (ECOS) as an effective approach to building on-line, adaptive intelligent systems. ECOS evolve through incremental, hybrid (supervised/unsupervised), on-line learning. They can accommodate new input data, including new features, new classes, etc. through local element tuning. New connections and new neurons are created during the operation of the system. The ECOS framework is presented and illustrated on a particular type of evolving neural networks -evolving fuzzy neural networks (EFuNNs). EFuNNs can learn spatial-temporal sequences in an adaptive way, through one pass learning. Rules can be inserted and extracted at any time of the system operation. The characteristics of ECOS and EFuNNs are illustrated on a case study of adaptive, phoneme-based spoken language recognition.

We have described an adaptive signal processing method that allows fine graded control of a cursor in three-dimensions from cortical signals . Here we describe application of the same signal processing method to direct cortical control of a robotic arm for a variety of tasks. Ou r subject w as extensiv ely traine d in controlling a computer cursor in a 3D virtual environment. We applied the mapping between cortical activity and cursor motion to endpoint control of a robotic arm. This algorithm was refined further as the animal continued to make 3D point-to-point movements of the braincontrolled robot. The animal then used the cortically-controlled robot to retrieve food placed at arbitrary locations within the workspace and deliver the food to a hopper. Finally, the animal learned to use the cortically-controlled robot to deliver food directly to its mouth.

Adaptive least mean square (LMS) predictors with independently low-order cascaded structures, such as the cascaded forward LMS (CFLMS) and cascaded forward-backward LMS (CFBLMS), have proven effective in combating the misadjustment and eigenvalue spread effects of linear predictors. Further developing this cascade structure, we study the fixed-point implementation of CFBLMS with applications to speech signals. Moreover, two groups of predictors with a total of six cases are compared. Group 1 employs the transversal structure for LMS, CFLMS, and CFBLMS algorithms. Group 2 employs the lattice structure for LMS, CFLMS, and CFBLMS algorithms. Experimental results show that, in group 1, the performance degradation of CFBLMS and CFLMS predictors becomes significant when the number of bits is reduced to 8, while that of the LMS predictor becomes significant when the number of bits is reduced to 9. On the other hand, in group 2, the performance degradation of CFBLMS and CFLMS predictors becomes significant when the number of bits is reduced to 5, while that of the LMS predictor becomes significant when the number of bits is reduced to 6. In both groups, the performances of CFBLMS and CFLMS are significantly superior to that of LMS, and CFBLMS is superior to CFLMS, in terms of the rate of convergence, misadjustment, and mean-square error (MSE).

Further developing the idea of a cascade structure for
adaptive linear prediction with independently adapting low-order
stages, we develop a new implementation where the single stages
use the forward-backward linear prediction algorithm. Combining
the advantages of the cascade structure, which is less affected by
the eigenvalue spread and mode coupling, with the reduced misadjustment
of the forward-backward predictor delivers a new structure
with improved performance. This new structure is capable of
faster and more accurate tracking of nonstationary inputs in the
early stage of the adaptation.

The influence of temporarily correlated source activities on neuromagnetic reconstruction by adaptive beamformer techniques was investigated. It is known that the spatial filter weight of an adaptive beamformer cannot perfectly block correlated signals. This causes two major influences on the reconstruction results: time course distortions and reductions in reconstructed signal intensities. Our theoretical analysis and numerical experiments both showed that the reduction in signal intensity for sources with a medium degree of correlation is small. The time-course distortion for such sources, however, may be discernible. Our analysis also showed that the magnitude correlation coefficient between two correlated sources can be accurately estimated by using the beamformer outputs. A method of retrieving the original time courses using estimated correlation coefficients was developed. Our numerical experiments demonstrated that reasonably accurate time courses can be retrieved from considerably distorted time courses even when the signal-to-noise ratio is low.

Video streaming over the Internet and packet-based wireless networks is sensitive to packet loss, which can severely damage the quality of the received video. To protect the transmitted video data against packet loss, application-layer forward error correction (FEC) is commonly used. Typically, for a given source block, the channel code rate is fixed in advance according to an estimation of the packet loss rate. However, since network conditions are difficult to predict, determining the right amount of redundancy introduced by the channel encoder is not obvious. To address this problem, we consider a general framework where the sender applies rateless erasure coding to every source block and keeps on transmitting the encoded symbols until it receives an acknowledgment from the receiver indicating that the block was decoded successfully. Within this framework, we design transmission strategies that aim at minimizing the expected bandwidth usage while ensuring successful decoding subject to an upper bound on the packet loss rate. In real simulations over the Internet, our solution outperformed standard FEC and hybrid ARQ approaches. For the QCIF Foreman sequence compressed with the H.264 video coder, the gain in average peak signal to noise ratio over the best previous scheme exceeded 3.5 decibels at 90 kilobits per second.

The influence of temporarily correlated source activities on neuromagnetic reconstruction by adaptive beamformer techniques was investigated. It is known that the spatial filter weight of an adaptive beamformer cannot perfectly block correlated signals. This causes two major influences on the reconstruction results: time course distortions and reductions in reconstructed signal intensities. Our theoretical analysis and numerical experiments both showed that the reduction in signal intensity for sources with a medium degree of correlation is small. The time-course distortion for such sources, however, may be discernible. Our analysis also showed that the magnitude correlation coefficient between two correlated sources can be accurately estimated by using the beamformer outputs. A method of retrieving the original time courses using estimated correlation coefficients was developed. Our numerical experiments demonstrated that reasonably accurate time courses can be retrieved from considerably distorted time courses even when the signal-to-noise ratio is low.

In this paper we have used the most useful processing and analysis methods to classify two congenital heart defects. The most important congenital valve disease is the congenital aort stenosis(AS) and of septum diseases is Ventricular Septal Defect(VSD). In this research, we managed to fuzzy classification of three groups of healthy, and AS, and VSD patients with the use of PCG.

This paper studies a sparse signal recovery task in time-varying (time-adaptive) environments. The contribution of the paper to sparsity-aware online learning is threefold; first, a Generalized Thresholding (GT) operator, which relates to both convex and non-convex penalty functions, is introduced. This operator embodies, in a unified way, the majority of well-known thresholding rules which promote sparsity. Second, a non-convexly constrained, sparsity-promoting, online learning scheme, namely the Adaptive Projection-based Generalized Thresholding (APGT), is developed that incorporates the GT operator with a computational complexity that scales linearly to the number of unknowns. Third, the novel family of partially quasi-nonexpansive mappings is introduced as a functional analytic tool for treating the GT operator. By building upon the rich fixed point theory, the previous class of mappings helps us, also, to establish a link between the GT operator and a union of linear subspaces; a non-convex object which lies at the heart of any sparsity promoting technique, batch or online. Based on such a functional analytic framework, a convergence analysis of the APGT is provided. Furthermore, extensive experiments suggest that the APGT exhibits competitive performance when compared to computationally more demanding alternatives, such as the sparsity-promoting Affine Projection Algorithm (APA)-and Recursive Least Squares (RLS)based techniques.

Separation of mixed and overlapped images is a frequently arising problem in image processing. For example separation of overlapped fingerprints obtained from any crime scene, in which we get a mixture which consists of two or more than two fingerprints and for identification we need to separate them. In this paper, it is assumed that original signals are identifiable and mutually statistically independent at the time of mixing, and the problem is solved by applying ICA in frequency domain. To apply ICA in frequency domain EASI algorithm was extended to separate complex valued signals.

A fault location technology for low voltage distribution networks (LVDN) is described. Technical details and performance of the intelligent fault location (IFL) system and its usefulness in inaccessible cable situations are presented. The intelligence ...

In this paper, we present mean-squared convergence analysis for the partial-update normalized least-mean square (PU-NLMS) algorithm with closed-form expressions for the case of white input signals. The formulae presented here are more accurate than the ones found in the literature for the PU-NLMS algorithm. Thereafter, the ideas of the partial-update NLMS-type algorithms found in the literature are incorporated in the framework of set-membership filtering, from which data-selective NLMS-type algorithms with partial-update are derived. The new algorithms, referred to herein as the set-membership partial-update normalized least-mean square (SM-PU-NLMS) algorithms, combine the data-selective updating from set-membership filtering with the reduced computational complexity from partial updating. A thorough discussion of the SM-PU-NLMS algorithms follows, whereby we propose different update strategies and provide stability analysis and closed-form formulae for excess mean-squared error (MSE). Simulation results verify the analysis for the PU-NLMS algorithm and the good performance of the SM-PU-NLMS algorithms in terms of convergence speed, final misadjustment, and computational complexity.

In applications that demand high resolution images, it is often not feasible nor sometimes possible to acquire images of such high resolution by using current CCD camera with its inherent resolution. Instead, image processing methods may be used to construct a high resolution image from multiple, degraded, low resolution images. This paper presented a novel robust high resolution reconstruction method that consists of a high accuracy hierarchical registration, interpolation and postprocessing. This algorithm is found to be effective, robust and shows significant improvement over conventional interpolation algorithms. The success of this algorithm is demonstrated through various simulated and real images.

In applications that demand high resolution images, it is often not feasible nor sometimes possible to acquire images of such high resolution by using current CCD camera with its inherent resolution. Instead, image processing methods may be used to construct a high resolution image from multiple, degraded, low resolution images. This paper presented a novel robust high resolution reconstruction method that consists of a high accuracy hierarchical registration, interpolation and postprocessing. This algorithm is found to be effective, robust and shows significant improvement over conventional interpolation algorithms. The success of this algorithm is demonstrated through various simulated and real images.

This paper introduces several new least mean-square (LMS) algorithms based on error normalization procedure. Different minimization approaches and techniques were used in developing the proposed algorithms. Some of these algorithms are selected and applied to an adaptive noise canceller setup with different stationary noise power levels. Simulation results, carried out using a real speech, clearly demonstrate the superiority of the proposed algorithms over other standard LMS algorithms. Smaller values of steady-state excess mean-square error and better tracking capabilities are obtained using these new algorithms.

A hybrid adaptive algorithm is developed for an active noise control system that leverages the stability of the filtered-input normalized least mean squares (FxNLMS) adaptive algorithm, with the high convergence speed of the filtered-input recursive least squares (FxRLS) adaptive algorithm. This algorithm is motivated by practical issues in implementing a real-time active noise control system. It leads to fast initial convergence with low, stable steady-state error while being limited by the computational capability of hardware. It gives better convergence speed than either the FxNLMS or FxRLS algorithm individually, lower residual error, and a lower overall computational complexity than the FxRLS algorithm, when appropriate filter lengths are chosen. Experimental results are presented for the implementation of the hybrid algorithm to cancel functional magnetic resonance imaging (fMRI) acoustic noise in an fMRI test-bed.