UAV-UGV Collaborative Localisation with Minimum Sensing.

This paper presents a novel methodology to localise Unmanned Ground Vehicles (UGVs) using Unmanned Aerial Vehicles (UAVs). The UGVs are assumed to be operating in a Global Navigation Satellite System (GNSS)-denied environment. The localisation of the ground vehicles is achieved using UAVs that have full access to the GNSS. The UAVs use range sensors to localise the UGV. One of the major requirements is to use the minimum number of UAVs, which is two UAVs in this paper. Using only two UAVs leads to a significant complication that results an estimation unobservability under certain circumstances. As a solution to the unobservability problem, the main contribution of this paper is to present a methodology to treat the unobservability problem. A Constrained Extended Kalman Filter (CEKF)-based solution, which uses novel kinematics and heuristics-based constraints, is presented. The proposed methodology has been assessed based on the stochastic observability using the Posterior Cramér-Rao Bound (PCRB), and the results demonstrate the successful operation of the proposed localisation method.

Aspergillus fumigatus binding IgA and IgG1 are increased in bronchoalveolar lavage fluid of horses with neutrophilic asthma.

Introduction: Equine asthma (EA) is a common lower airway disease in horses, but whether its pathogenesis is allergic is ambiguous. Extrinsic stimuli like hay dust induce acute exacerbation of clinical signs and sustained local neutrophilic inflammation in susceptible horses. Aspergillus fumigatus is an EA stimulus, but it is unclear if it merely acts as an IgE-provoking allergen. We aimed to comprehensively analyze immunoglobulin (Ig) isotypes in EA, elucidating their binding to different A. fumigatus antigens, and their quantities systemically in serum and locally in bronchoalveolar lavage fluid (BALF). Methods: Serum and BALF from healthy horses (HE, n = 18) and horses with mild-moderate asthma (MEA, n = 20) or severe asthma (SEA, n = 24) were compared. Ig isotype (IgG1, IgG3/5, IgG4/7, IgG6, IgA, and IgE) binding to nine antigens (A. fumigatus lysate, and recombinant Asp f 1, Asp f 7, Asp f 8, dipeptidyl-peptidase 5, class II aldolase/adducin domain protein, glucoamylase, beta-hexosaminidase, and peptide hydrolase) was compared by enzyme-linked immunosorbent assays. Total Ig isotype contents were determined by bead-based assays. Results: MEA and SEA differed from HE but hardly from each other. Compared to HE, asthmatic horses showed increased anti-A. fumigatus binding of IgG (BALF and serum) and IgA (BALF). Serum and BALF IgE binding and total IgE contents were similar between HE and EA. Single antigens, as well as A. fumigatus lysate, yielded similar Ig binding patterns. Serum and BALF IgG1 binding to all antigens was increased in SEA and to several antigens in MEA. Serum IgG4/7 binding to two antigens was increased in SEA. BALF IgA binding to all antigens was increased in SEA and MEA. Total BALF IgG1 and IgG4/7 contents were increased in SEA, and serum IgG4/7 content was increased in MEA compared to HE. Yet, total isotype contents differentiated EA and HE less clearly than antigen-binding Ig. Discussion: A. fumigatus immunogenicity was confirmed without identification of single dominant antigens here. A. fumigatus provoked elevated BALF IgG1 and IgA binding, and these isotypes appear relevant for neutrophilic EA, which does not support allergy. BALF Ig isotype differentiation beyond IgE is crucial for a comprehensive analysis of immune responses to fungi in EA pathogenesis.

Incidence and Prognostic Factors of Radial Artery Occlusion in Transradial Coronary Catheterization.

Background/Objectives: Radial artery occlusion (RAO) is the most common complication of transradial coronary catheterization. In this study, we aimed to evaluate the incidence of RAO and identify the risk factors that predispose patients to it. Methods: We conducted an investigator-initiated, prospective, multicenter, open-label study involving 1357 patients who underwent cardiac catheterization via the transradial route for angiography and/or a percutaneous coronary intervention (PCI). Univariate and multivariate logistic regression analyses were performed to identify potential predictors of RAO occurrence. Additionally, a subgroup analysis only for patients undergoing PCIs was performed. Results: The incidence of RAO was 9.5% overall, 10.6% in the angiography-only group and 6.2% in the PCI group. Independent predictors of RAO were as follows: (i) the female gender (aOR = 1.72 (1.05-2.83)), (ii) access site cross-over (aOR = 4.33 (1.02-18.39)), (iii) increased total time of the sheath in the artery (aOR = 1.01 (1.00-1.02)), (iv) radial artery spasms (aOR = 2.47 (1.40-4.36)), (v) the presence of a hematoma (aOR = 2.28 (1.28-4.06)), (vi) post-catheterization dabigatran use (aOR = 5.15 (1.29-20.55)), (vii) manual hemostasis (aOR = 1.94 (1.01-3.72)) and (viii) numbness at radial artery ultrasound (aOR = 8.25 (1.70-40)). Contrariwise, two variables were independently associated with increased odds for radial artery patency (RAP): (i) PCI performance (aOR = 0.19 (0.06-0.63)), and (ii) a higher dosage of intravenous heparin per patient weight (aOR = 0.98 (0.96-0.99)), particularly, a dosage of >50 IU/kg (aOR = 0.56 (0.31-1.00)). In the PCI subgroup, independent predictors of RAO were as follows: (i) radial artery spasms (aOR = 4.48 (1.42-14.16)), (ii) the use of intra-arterial nitroglycerin as a vasodilator (aOR = 7.40 (1.67-32.79)) and (iii) the presence of symptoms at echo (aOR = 3.80 (1.46-9.87)), either pain (aOR = 2.93 (1.05-8.15)) or numbness (aOR = 4.66 (1.17-18.57)). On the other hand, the use of intra-arterial verapamil as a vasodilator (aOR = 0.17 (0.04-0.76)) was independently associated with a greater frequency of RAP. Conclusions: The incidence of RAO in an unselected, all-comers European population after transradial coronary catheterization for angiography and/or PCIs is similar to that reported in the international literature. Several RAO prognostic factors have been confirmed, and new ones are described. The female gender, radial artery trauma and manual hemostasis are the strongest predictors of RAO. Our results could help in the future identification of patients at higher risk of RAO, for whom less invasive diagnostic procedures maybe preferred, if possible.

Adaptive RAO ensembled dichotomy technique for the accurate parameters extraction of solar PV system.

The parameter extraction process for PV models poses a complex nonlinear and multi-model optimization challenge. Accurately estimating these parameters is crucial for optimizing the efficiency of PV systems. To address this, the paper introduces the Adaptive Rao Dichotomy Method (ARDM) which leverages the adaptive characteristics of the Rao algorithm and the Dichotomy Technique. ARDM is compared with the several recent optimization techniques, including the tuna swarm optimizer, African vulture's optimizer, and teaching-learning-based optimizer. Statistical analyses and experimental results demonstrate the ARDM's superior performance in the parameter extraction for the various PV models, such as RTC France and PWP 201 polycrystalline, utilizing manufacturer-provided datasheets. Comparisons with competing techniques further underscore ARDM dominance. Simulation results highlight ARDM quick processing time, steady convergence, and consistently high accuracy in delivering optimal solutions.

Radial Artery Reconstruction After Iatrogenic Cannulation-Related Occlusion: A Case Report.

A 59-year-old male, with a history of angiogram via the left radial artery during the workup for multi-trauma, presented to the hand clinic with a 14-day history of progressive critical ischemia in the left thumb and index finger, along with dry gangrene of the distal index fingertip. Radial artery occlusion was confirmed on imaging. The patient underwent radial artery thrombectomy, arterial reconstruction with vein graft, and amputation of the index fingertip. Postoperatively, perfusion to the thumb and index finger was restored, resulting in the resolution of associated pain and hypersensitivity. This case demonstrates the delayed presentation of ischemia following radial artery cannulation, which was successfully managed with radial artery thrombectomy and a saphenous vein graft.

A novel optimal identification of various solar PV cell parameters by using MRDT controller.

At present, Renewable Energy Sources (RES) utilization keeps on increasing because of their merits are more availability in the atmosphere, easy energy harvesting, less maintenance expenses, plus more reliability. Here, the solar power generation systems are utilized for supplying the energy to the local consumers. The accurate, and efficient solar power supply to the customers is a very important factor to meet the peak load demand. The accurate power generation of the sunlight system completely depends on its accurate parameters extraction. In this work, a Modified Rao-based Dichotomy Technique (MRAODT) is introduced to identify the actual parameters of the different PV cells which are PWP 201 polycrystalline, plus RTC France. The proposed MRAODT method is compared with the other existing algorithms which are the teaching and learning algorithm, African vultures, plus tuna intelligence algorithm. Finally, from the simulation results, the MRAODT gives superior performance when associated with the other controllers in terms of parameters extraction time, accuracy in the PV cells parameters identification, plus convergence time of the algorithm.

Bias-reduced neural networks for parameter estimation in quantitative MRI.

PURPOSE: To develop neural network (NN)-based quantitative MRI parameter estimators with minimal bias and a variance close to the Cramér-Rao bound. THEORY AND METHODS: We generalize the mean squared error loss to control the bias and variance of the NN's estimates, which involves averaging over multiple noise realizations of the same measurements during training. Bias and variance properties of the resulting NNs are studied for two neuroimaging applications. RESULTS: In simulations, the proposed strategy reduces the estimates' bias throughout parameter space and achieves a variance close to the Cramér-Rao bound. In vivo, we observe good concordance between parameter maps estimated with the proposed NNs and traditional estimators, such as nonlinear least-squares fitting, while state-of-the-art NNs show larger deviations. CONCLUSION: The proposed NNs have greatly reduced bias compared to those trained using the mean squared error and offer significantly improved computational efficiency over traditional estimators with comparable or better accuracy.

Towards non-invasive tissue hydration measurements with optical coherence tomography.

The attenuation coefficient ( µ OCT ) measured by optical coherence tomography (OCT) has been used to determine tissue hydration. Previous dual-wavelength OCT systems could not attain the needed precision, which we attribute to the absence of wavelength-dependent scattering of tissue in the underlying model. Assuming that scattering can be described using two parameters, we propose a triple/quadrupole-OCT system to achieve clinically relevant precision in water volume fraction. In this study, we conduct a quantitative analysis to determine the necessary precision of µ OCT measurements and compare it with numerical simulation. Our findings emphasize that achieving a clinically relevant assessment of a 2% water fraction requires determining the attenuation coefficient with a remarkable precision of 0.01 m m - 1 . This precision threshold is influenced by the chosen wavelength for attenuation measurement and can be enhanced through the inclusion of a fourth wavelength range.

Photon Phase Delay Sensing with Sub-Attosecond Uncertainty.

The application of statistical estimation theory to Hong-Ou-Mandel interferometry led to enticing results in terms of the detection limit for photon reciprocal delay and polarisation measurement. In the following paper, a fully fibre-coupled setup operating in the telecom wavelength region proves to achieve, for the first time, in common-path Hong-Ou-Mandel-based interferometry, a detection limit for photon phase delay at the zeptosecond scale. The experimental results are then framed in a theoretical model by calculating the Cramer-Rao bound (CRB) and, after comparison with the obtained experimental results, it is shown that our setup attains the optimal measurement, nearly saturating CRB.

Cramér-Rao Lower Bound for Magnetic Field Localization around Elementary Structures.

The determination of a mobile terminal's position with high accuracy and ubiquitous coverage is still challenging. Global satellite navigation systems (GNSSs) provide sufficient accuracy in areas with a clear view to the sky. For GNSS-denied environments like indoors, complementary positioning technologies are required. A promising approach is to use the Earth's magnetic field for positioning. In open areas, the Earth's magnetic field is almost homogeneous, which makes it possible to determine the orientation of a mobile device using a compass. In more complex environments like indoors, ferromagnetic materials cause distortions of the Earth's magnetic field. A compass usually fails in such areas. However, these magnetic distortions are location dependent and therefore can be used for positioning. In this paper, we investigate the influence of elementary structures, in particular a sphere and a cylinder, on the achievable accuracy of magnetic positioning methods. In a first step, we analytically calculate the magnetic field around a sphere and a cylinder in an outer homogeneous magnetic field. Assuming a noisy magnetic field sensor, we investigate the achievable positioning accuracy when observing these resulting fields. For our analysis, we calculate the Cramér-Rao lower bound, which is a fundamental lower bound on the variance of an unbiased estimator. The results of our investigations show the dependency of the positioning error variance on the magnetic sensor properties, in particular the sensor noise variance and the material properties, i.e., the relative permeability of the sphere with respect to the cylinder and the location of the sensor relative to the sphere with respect to the cylinder. The insights provided in this work make it possible to evaluate experimental results from a theoretical perspective.

Bias-Reduced Neural Networks for Parameter Estimation in Quantitative MRI.

Purpose: To develop neural network (NN)-based quantitative MRI parameter estimators with minimal bias and a variance close to the Cramér-Rao bound. Theory and Methods: We generalize the mean squared error loss to control the bias and variance of the NN's estimates, which involves averaging over multiple noise realizations of the same measurements during training. Bias and variance properties of the resulting NNs are studied for two neuroimaging applications. Results: In simulations, the proposed strategy reduces the estimates' bias throughout parameter space and achieves a variance close to the Cramér-Rao bound. In vivo, we observe good concordance between parameter maps estimated with the proposed NNs and traditional estimators, such as non-linear least-squares fitting, while state-of-the-art NNs show larger deviations. Conclusion: The proposed NNs have greatly reduced bias compared to those trained using the mean squared error and offer significantly improved computational efficiency over traditional estimators with comparable or better accuracy.

Immunoproteomics enable broad identification of new Aspergillus fumigatus antigens in severe equine asthma.

Introduction: Severe equine asthma (SEA) is a common chronic disease of adult horses with characteristic recurrent airway obstruction and similarities to neutrophilic asthma in humans. As an extrinsic stimulus, hay dust exposure is a major risk factor and induces acute exacerbation in susceptible horses. However, single inducing agents of SEA have hardly been identified on a molecular basis. Aspergillus fumigatus (A. fumigatus) is a common mold species in hay and has been described as a major provoking agent of SEA. Methods: Aiming to identify disease-relevant antigens, we analyzed A. fumigatus using an immunoproteomics approach on two-dimensional immunoblots of A. fumigatus protein probed with serum from environmentally matched asthmatic and healthy horses (n=5 pairs). A. fumigatus binding serum immunoglobulins (Pan-Ig), and the isotypes IgG4/7 and IgG3/5 were quantified for each protein spot and then compared between asthmatic and healthy horses. Results and discussion: For 21 out of 289 spots serum immunoglobulin (Ig) binding was different between the two groups for Pan-Ig or the isotypes. If differences were detected, Pan-Ig and IgG4/7 binding to the proteins were lower, while IgG3/5 binding was higher in asthmatic than healthy horse sera. Proteins were extracted from the 21 spots of interest and analyzed by liquid chromatography mass spectrometry. Eight prioritized proteins (candidate antigens) were expressed as recombinant proteins. Some of these have been previously described as major or minor A. fumigatus allergens, alongside other proteins, most with hydrolase activity. Recombinant candidate antigens were tested on 1D immunoblots to confirm their relevance as antigens by serum antibody binding. Four proteins (beta-hexosaminidase, class II aldolase/adducin domain protein, glucoamylase, peptide hydrolase B0XX53) showed different antibody binding characteristics between asthmatic and healthy horses and are likely relevant antigens in SEA. Their identification can provide the basis for innovative diagnostics, prevention, or therapeutic approaches. Additionally, a more profound understanding of SEA and its potential underlying mechanisms can be established. Elevated serum IgG3/5 antibodies correlate with T helper cell 2 responses in other equine pathologies, and the recombinant SEA antigens developed here can become instrumental in analyzing the involvement of SEA-specific T cell responses and Ig responses in future studies.

Effectiveness and Safety of Left Distal Transradial Access in Coronary Procedures in the Caribbean.

INTRODUCTION: This retrospective study investigated the effectiveness and safety of left distal transradial access (LDTRA) in patients with cardiovascular disease in Trinidad undergoing coronary angiography (CAG) or percutaneous coronary intervention (PCI). METHOD: Procedural parameters, including technical success and safety outcomes such as vascular complications and radial artery occlusion (RAO), were assessed in 111 consecutive patients undergoing CAG or PCI from January 2023 to June 2023 at the Eric Williams Medical Sciences Complex, Trinidad and Tobago. Eighty-eight patients underwent LDTRA, while 23 received left transradial access (LTRA). RESULTS: There was no difference in procedural success with LDTRA compared to LTRA, 90.9% vs. 100%, p-value 0.202, non-significant (ns). LDTRA was associated with shorter fluoroscopy times (8.4 ± 6.8 minutes vs. 12.4 ± 7.7 minutes, p-value = 0.02), procedural duration (26.7 ± 18 minutes vs. 35.8 ± 20 minutes, p-value = 0.04), and hemostasis time (142 ± 41 minutes vs. 186 ± 44 minutes, p-value < 0.05). There were no significant differences in procedural-related complications (8% for LDTRA vs. 4.3% for LTRA, p-value = 0.476, ns). There were no reported cases of RAO. In the subgroup of patients with prior coronary artery bypass grafting (CABG), the fluoroscopy and procedure times were similar for both access sites; however, LDTRA was associated with a shorter hemostasis time (128 ± 30 minutes vs. 194 ± 39 minutes, p-value = 0.01). CONCLUSIONS: LDTRA is effective and safe for coronary procedures and is associated with a shorter hemostasis time. This study may prove clinically pertinent in a limited-resource Caribbean setting.

Feasibility of early radial artery occlusion recanalization and reuse through transradial access for neuroendovascular procedures.

BACKGROUND: Radial artery occlusion (RAO) remains a significant limitation of neuroendovascular procedures peformed through transradial access (TRA) when radial artery needs to be reused. Instances of early RAO recanalization to successfully complete neuroendovascular procedures have been rarely documented. MATERIALS AND METHODS: Documents and imaging data were extracted retrospectively for all patients who underwent TRA diagnostic angiography and neuroendovascular procedures in our center from June 2022 to February 2023. The patients with early RAO who required repeat TRA were included. RESULTS: A total of 46 patients underwent repeat TRA, and 13 consecutive patients who experienced early RAO after angiography as confirmed by ultrasonography were enrolled in this study. The occluded radial arteries were successfully recanalized, and subsequent neuroendovascular procedures were carried out successful. During an average follow-up time of 7.1 months, no patients exhibited symptomatic RAO, dissection, hematoma or pseudoaneurysm. CONCLUSIONS: Early RAO recanalization and reused for neuroendovascular procedures through TRA is feasible. A visually guided and stable puncture process plays a crucial role in successfully recanalizing early RAO.

Nonlinear event-based state estimation using particle filter under packet loss.

In this research paper, we investigate the problem of remote state estimation for nonlinear discrete systems. Specifically, we focus on scenarios where event-triggered sensor schedules are utilized and where packet drops occur between the sensor and the estimator. In the sensor scheduler, the SOD mechanism is proposed to decrease the amount of data transmitted from the sensor to a remote estimator and the phenomena of packet drops modeled with random variables obeying the Bernoulli distribution. As a consequence of packet drops, the assumption of Gaussianity no longer holds at the estimator side. By fully considering the non-linearity and non-Gaussianity of the dynamic system, this paper develops an event-trigger particle filter algorithm to relieve the communication burden and achieve an appropriate estimation accuracy. First, we derive an explicit expression for the likelihood function when an event trigger occurs and the possible occurrence of packet dropout is taken into consideration. Then, using a special form of sequential Monte-Carlo algorithm, the posterior distribution is approximated and the corresponding minimum mean-squared error is derived. By contrasting the error covariance matrix with the posterior Cramér-Rao lower bound, the estimator's performance is assessed. An illustrative numerical example shows the effectiveness of the proposed design.

Uncertainty propagation in absolute metabolite quantification for in vivo MRS of the human brain.

PURPOSE: Absolute spectral quantification is the standard method for deriving estimates of the concentration from metabolite signals measured using in vivo proton MRS (1 H-MRS). This method is often reported with minimum variance estimators, specifically the Cramér-Rao lower bound (CRLB) of the metabolite signal amplitude's scaling factor from linear combination modeling. This value serves as a proxy for SD and is commonly reported in MRS experiments. Characterizing the uncertainty of absolute quantification, however, depends on more than simply the CRLB. The uncertainties of metabolite-specific (T1m , T2m ), reference-specific (T1ref , T2ref ), and sequence-specific (TR , TE ) parameters are generally ignored, potentially leading to an overestimation of precision. In this study, the propagation of uncertainty is used to derive a comprehensive estimate of the overall precision of concentrations from an internal reference. METHODS: The propagated uncertainty is calculated using analytical derivations and Monte Carlo simulations and subsequently analyzed across a set of commonly measured metabolites and macromolecules. The effect of measurement error from experimentally obtained quantification parameters is estimated using published uncertainties and CRLBs from in vivo 1 H-MRS literature. RESULTS: The additive effect of propagated measurement uncertainty from applied quantification correction factors can result in up to a fourfold increase in the concentration estimate's coefficient of variation compared to the CRLB alone. A case study analysis reveals similar multifold increases across both metabolites and macromolecules. CONCLUSION: The precision of absolute metabolite concentrations derived from 1 H-MRS experiments is systematically overestimated if the uncertainties of commonly applied corrections are neglected as sources of error.

Empirical optimization of energy bin weights for compressing measurements with realistic photon counting x-ray detectors.

BACKGROUND: Photon counting detectors (PCDs) provide higher spatial resolution, improved contrast-to-noise ratio (CNR), and energy discriminating capabilities. However, the greatly increased amount of projection data in photon counting computed tomography (PCCT) systems becomes challenging to transmit through the slip ring, process, and store. PURPOSE: This study proposes and evaluates an empirical optimization algorithm to obtain optimal energy weights for energy bin data compression. This algorithm is universally applicable to spectral imaging tasks including 2 and 3 material decomposition (MD) tasks and virtual monoenergetic images (VMIs). This method is simple to implement while preserving spectral information for the full range of object thicknesses and is applicable to different PCDs, for example, silicon detectors and CdTe detectors. METHODS: We used realistic detector energy response models to simulate the spectral response of different PCDs and an empirical calibration method to fit a semi-empirical forward model for each PCD. We numerically optimized the optimal energy weights by minimizing the average relative Cramér-Rao lower bound (CRLB) due to the energy-weighted bin compression, for MD and VMI tasks over a range of material area density ρ A , m ${\rho }_{A,m}$ (0-40 g/cm2 water, 0-2.16 g/cm2 calcium). We used Monte Carlo simulation of a step wedge phantom and an anthropomorphic head phantom to evaluate the performance of this energy bin compression method in the projection domain and image domain, respectively. RESULTS: The results show that for 2 MD, the energy bin compression method can reduce PCCT data size by 75% and 60%, with an average variance penalty of less than 17% and 3% for silicon and CdTe detectors, respectively. For 3 MD tasks with a K-edge material (iodine), this method can reduce the data size by 62.5% and 40% with an average variance penalty of less than 12% and 13% for silicon and CdTe detectors, respectively. CONCLUSIONS: We proposed an energy bin compression method that is broadly applicable to different PCCT systems and object sizes, with high data compression ratio and little loss of spectral information.

Fast and robust quantification of uncertainty in non-linear diffusion MRI models.

Diffusion MRI (dMRI) allows for non-invasive investigation of brain tissue microstructure. By fitting a model to the dMRI signal, various quantitative measures can be derived from the data, such as fractional anisotropy, neurite density and axonal radii maps. We investigate the Fisher Information Matrix (FIM) and uncertainty propagation as a generally applicable method for quantifying the parameter uncertainties in linear and non-linear diffusion MRI models. In direct comparison with Markov Chain Monte Carlo (MCMC) sampling, the FIM produces similar uncertainty estimates at much lower computational cost. Using acquired and simulated data, we then list several characteristics that influence the parameter variances, including data complexity and signal-to-noise ratio. For practical purposes we investigate a possible use of uncertainty estimates in decreasing intra-group variance in group statistics by uncertainty-weighted group estimates. This has potential use cases for detection and suppression of imaging artifacts.

An Improved Orthogonal Matching Pursuit Algorithm for CS-Based Channel Estimation.

Wireless broadband transmission channels usually have time-domain-sparse properties, and the reconstruction of these channels using a greedy search-based orthogonal matching pursuit (OMP) algorithm can effectively improve channel estimation performance while decreasing the length of the reference signal. In this research, the improved OMP and SOMP algorithms for compressed-sensing (CS)-based channel estimation are proposed for single-carrier frequency domain equalization (SC-FDE) systems, which, in comparison with conventional algorithms, calculate the path gain after obtaining the path delay and updating the observation matrices. The reliability of the communication system is further enhanced because the channel path gain is calculated using longer observation vectors, which lowers the Cramér-Rao lower bound (CRLB) and results in better channel estimation performance. The developed method can also be applied to time-domain-synchronous OFDM (TDS-OFDM) systems, and it is applicable to the improvement of other matching pursuit algorithms.

Fisher and Shannon Functionals for Hyperbolic Diffusion.

The complexity measure for the distribution in space-time of a finite-velocity diffusion process is calculated. Numerical results are presented for the calculation of Fisher's information, Shannon's entropy, and the Cramér-Rao inequality, all of which are associated with a positively normalized solution to the telegrapher's equation. In the framework of hyperbolic diffusion, the non-local Fisher's information with the x-parameter is related to the local Fisher's information with the t-parameter. A perturbation theory is presented to calculate Shannon's entropy of the telegrapher's equation at long times, as well as a toy model to describe the system as an attenuated wave in the ballistic regime (short times).