Output Estimation Research Articles

Dy3+-doped lithium aluminoborate glass for luminescent light guides with high luminance

Lithium aluminoborate glass optically activated with the lanthanide ion dysprosium is investigated for its potential as luminescent light guide. For this, ray-tracing simulations are performed on the basis of transmission, photoluminescence, and quantum efficiency measurements. The luminous flux at the end of the light guide depends significantly on its length as well as on the roughness of the output face. The best results are obtained for a light guide length of 70–80 mm with the side faces of the light guide coated with a 100 % reflecting mirror and a rough output face with Lambertian scattering characteristic. An additional coating of the input face with a half-transmitting mirror is investigated. The mirror is transmissive for the excitation wavelength of 388 nm but reflective for the emission bands in the visible spectral range. For this light guide, a luminance of approximately 20 cd/mm 2 is achieved for an excitation power density of 1 W/mm 2 . The geometry of the light guide (cuboid/cylinder) has only a slight effect on the maximum luminance value. • Analytical estimate of luminescence output of dysprosium-doped light guides. • Ray-tracing simulations for different lengths, surface properties, and geometries. • Maximum luminance of approximately 20 cd/mm 2 for 388-nm excitation.

Multifidelity emulation for the matter power spectrum using Gaussian processes

ABSTRACT We present methods for emulating the matter power spectrum by combining information from cosmological N-body simulations at different resolutions. An emulator allows estimation of simulation output by interpolating across the parameter space of a limited number of simulations. We present the first implementation in cosmology of multifidelity emulation, where many low-resolution simulations are combined with a few high-resolution simulations to achieve an increased emulation accuracy. The power spectrum’s dependence on cosmology is learned from the low-resolution simulations, which are in turn calibrated using high-resolution simulations. We show that our multifidelity emulator predicts high-fidelity (HF) counterparts to percent-level relative accuracy when using only three HF simulations and outperforms a single-fidelity emulator that uses 11 simulations, although we do not attempt to produce a converged emulator with high absolute accuracy. With a fixed number of HF training simulations, we show that our multifidelity emulator is ≃100 times better than a single-fidelity emulator at $k \le 2 \, h\textrm {Mpc}{^{-1}}$, and ≃20 times better at $3 \le k \lt 6.4 \, h\textrm {Mpc}{^{-1}}$. Multifidelity emulation is fast to train, using only a simple modification to standard Gaussian processes. Our proposed emulator shows a new way to predict non-linear scales by fusing simulations from different fidelities.

An automated image analysis system for estimating fecundity in portunid crabs

Robust assessment of exploited crustacean stocks requires estimates of reproductive output in relation to size and environmental variability. An automated, high-throughput image analysis approach was developed for estimating fecundity in crabs (using Blue Swimmer Crab, Portunus armatus). The approach employed a waterproofed flatbed scanner, whereby images of egg samples were digitised, followed by automated analysis of each image using customised computer macros. Paired counts (of the same samples) were also conducted using traditional counting of eggs under a compound microscope. Trials demonstrated that the high-throughput technique was 6 times faster than the microscope technique, and simultaneously provided information on egg size. Egg count data collected using the high-throughput technique was modelled to inform the collection of samples for estimation of fecundity in Blue Swimmer Crab. This simulation found that conducting egg counts on as few as 10 egg masses per month produced a 90% probability that the estimate of mean eggs-per-gram-of-egg-mass was within 15% of the actual mean (for upscaling across the population). Quantifying temporal variation in fecundity, and potential environmental drivers of this variation, is important in the assessment of crab stocks and ensuring sustainable commercial and recreational harvest. This method of counting eggs has broad applications for other highly fecund, egg-bearing species.

Modeling sub-boreal forest canopy bulk density in Minnesota, USA, using synthetic aperture radar and optical satellite sensor data

BackgroundNational estimates of canopy bulk density (CBD; kg m−3) for fire behavior modeling are generated and supported by the LANDFIRE program. However, locally derived estimates of CBD at finer scales are preferred over national estimates if they exist, as the absolute accuracy of the LANDFIRE CBD product is low and varies regionally. Active sensors (e.g., lidar or radar) are better suited for this task, as passive sensors are ill equipped to detect differences among key vertical fuel structures, such as coniferous surface fuels (≤2 m high) and canopy fuels above this threshold—a key categorical fuel distinction in fire behavior modeling. However, previous efforts to map CBD using lidar sensor data in the Superior National Forest (SNF) of Minnesota, USA, yielded substandard results. Therefore, we use a combination of dormant-season synthetic aperture radar (SAR) and optical satellite sensor data to (1) expand detectability of coniferous fuels among mixed forest canopies to improve the accuracy of CBD modeling and (2) better understand the influence of surface fuels in this regard. Response variables included FuelCalc output and indirect estimates of maximum burnable fuel based on canopy gap fraction (CGF) measured at ground level and 2 m above ground level.ResultsSAR variables were important predictors of CBD and total fuel density (TFD) in all independent model calibrations with ground data, in which we define TFD as the sum of CBD and primarily live coniferous surface fuel density (SFD) 0 to 2 m above ground. Exploratory estimates of TFD appeared biased to the presence of sapling-stage conifer fuel on measures of CGF at the ground level. Thus, modeling efforts to calibrate SFD with satellite sensor data failed. Both CGF-based and FuelCalc-based field estimates of CBD yielded close unity with satellite-calibrated estimates, although substantial differences in data distributions existed. Estimates of CBD from the widest CGF zenith angle range (0 to 38°) correlated best with FuelCalc-based CBD estimates, while both resulted in maximum biomass values that exceeded those considered typical for the SNF. Model results from the narrowest zenith angle range (0 to 7°) produced estimates of CBD that were more in line with values considered typical. LANDFIRE’s estimates of CBD were weakly, but significantly (P = 0.05), correlated to both narrow- and wide-angle CGF-based estimates of CBD, but not with FuelCalc-based estimates.ConclusionsThe combined use of field estimates of CBD, based on indirect measures of CGF according to Keane et al. (Canadian Journal of Forest Research 35:724–739, 2005), with SAR and optical satellite sensor data demonstrates the potential of this method for mapping CBD in the Upper Midwest, USA. Results suggested that the presence of live, coniferous surface fuels neither confounds remote detection nor precludes mapping of CBD in this region using SAR satellite sensor data, as C- and L-band idiosyncrasies likely limit the visibility of these smaller understory fuels from space. Nevertheless, research using direct measures of burnable SFD for calibrations with SAR satellite sensor data should be conducted to more definitively answer this remote detection question, as we suspect substantial bias among measures of CGF from ground level when estimating SFD as the difference between TFD and CBD.

Solar power plant generation forecasting using NARX neural network model: A case study

New technologies have been developed and adopted to generate energy from renewable sources to satisfy the increasing demand without causing environmental damage. However, estimating the power output of inherently intermittent, weather-driven, and non-dispatchable renewable energy sources is a major scientific and societal concern. In this study, a neural network model to enable short-to-middle term forecasts of a photovoltaic (PV) power system is provided. Using historical weather and power generation data, a non-linear autoregressive network with exogenous input (NARX) model is built to forecast the non-linear photovoltaic system output. The performance of the model is then analyzed by different statistical evaluation parameters. It is shown that the PV system power output estimation method can be successfully employed.

Estimation of Mechanical Power Output Employing Deep Learning on Inertial Measurement Data in Roller Ski Skating.

The ability to optimize power generation in sports is imperative, both for understanding and balancing training load correctly, and for optimizing competition performance. In this paper, we aim to estimate mechanical power output by employing a time-sequential information-based deep Long Short-Term Memory (LSTM) neural network from multiple inertial measurement units (IMUs). Thirteen athletes conducted roller ski skating trials on a treadmill with varying incline and speed. The acceleration and gyroscope data collected with the IMUs were run through statistical feature processing, before being used by the deep learning model to estimate power output. The model was thereafter used for prediction of power from test data using two approaches. First, a user-dependent case was explored, reaching a power estimation within 3.5% error. Second, a user-independent case was developed, reaching an error of 11.6% for the power estimation. Finally, the LSTM model was compared to two other machine learning models and was found to be superior. In conclusion, the user-dependent model allows for precise estimation of roller skiing power output after training the model on data from each athlete. The user-independent model provides less accurate estimation; however, the accuracy may be sufficient for providing valuable information for recreational skiers.

Differentiable PAC-Bayes Objectives with Partially Aggregated Neural Networks.

We make two related contributions motivated by the challenge of training stochastic neural networks, particularly in a PAC–Bayesian setting: (1) we show how averaging over an ensemble of stochastic neural networks enables a new class of partially-aggregated estimators, proving that these lead to unbiased lower-variance output and gradient estimators; (2) we reformulate a PAC–Bayesian bound for signed-output networks to derive in combination with the above a directly optimisable, differentiable objective and a generalisation guarantee, without using a surrogate loss or loosening the bound. We show empirically that this leads to competitive generalisation guarantees and compares favourably to other methods for training such networks. Finally, we note that the above leads to a simpler PAC–Bayesian training scheme for sign-activation networks than previous work.

Fault detection for uncertain nonlinear systems via recursive observer and tight threshold

This paper presents an fault detection (FD) method for a class of uncertain nonlinear systems with unmatched nonlinear fault functions and disturbances. A recursive FD observer is designed with predetermined and small output estimation error. The nonlinear observer gain function is achieved by introducing predetermined output estimation accuracy-dependent nonnegative functions. Combining Lyapunov functions, it is shown that the absolute value of the residual signal is equal or lesser than tight threshold before fault occurrence. The FD scheme is proposed following fault detectability analysis, simulation example indicates the validity of the proposed method.

Effect of marker dosage frequency and spot fecal sampling frequency in the prediction accuracy of fecal output using chromic oxide and titanium dioxide in grazing BON steers.

This study aimed to evaluate the effect of two marker dosage frequencies and two spot fecal sampling frequencies on the variability of fecal output estimation and fecal recovery rate in grazing Blanco Orejinegro (BON) steers with chromium (Cr) or titanium (Ti) as external markers. Four steers (230.5 ± 14.4kg BW) were used in a 4 × 4 Latin square design in a split-plot arrangement with two markers: Cr or Ti, two marker dosage frequencies: 10.0g Cr2O3 or TiO2/steer once daily (DF1) or 5.0g Cr2O3 or TiO2/steer twice daily (DF2), and two fecal sampling frequencies: spot sampling once daily (SF1) or spot sampling twice daily (SF2). Steers were equipped with fecal collection bags to evaluate the actual fecal output (FOR). Fecal marker concentration (FMC) was affected by marker dosage frequency × fecal sampling frequency interaction (P = 0.032): SF1DF1 allowed higher FMC (2.26g/kg DM) than SF2DF1 (1.95g/kg DM). Chromium marker allowed higher estimated fecal output (FOe) values when compared to FOR (P < 0.001). Both Ti mean and SD bias (+ 0.074 ± 0.160kg) were lower than Cr mean and SD bias (+ 0.312 ± 0.272kg). The Ti fecal output prediction showed lower both RSR (0.586 vs 1.401) and MSPE (0.029 vs 0.167) than Cr prediction. The titanium external marker allowed fecal output estimation with more accuracy in grazing beef cattle trials. A protocol including a once-daily marker dosage associated with a once-daily fecal sampling is plausible, allowing good fecal recovery rates and accurate fecal output estimation.

Prediction of energy generated from composite cycle power plant in smart cities

In smart cities, it is significant to predict the energy generated from composite cycle power plants, in order to reduction the efforts and to extreme profit from the megawatt hours during the operation, which is consider from the important topics in mechanical engineering. The most significant component of a power plant with a composite cycle is the gas turbine, which generates the entire electrical energy via a fuel and distributes it to homes, schools and other institutions around the cities. In this paper artificial neural networks, regression machine learning with decision tree method is utilized to develop a model that can be able to predictive the estimate of electrical energy output of a composite cycle power plant. The basis load functioning of plant is impacted through four key characteristics, such as surroundings temperature, relative humidity, pressure of atmospheric, and generated steam pressure, which are utilized in the dataset as input variables. These variables have an impact on electrical energy output, which is the desired variable. The input and target variables are included in the dataset, obtained from an open online source. The decision tree algorithm produced the best results, with a mean absolute error of (0.009) and a root mean square error of (0.022).

Prediction of Photovoltaic Panel Power Outputs using Time Series and Artificial Neural Network Methods

Solar energy is one of the renewable energy sources that has been in high demand in the last decades. With the increasing penetration of photovoltaic (PV) systems in around the world, accurate estimation of the power output of PV systems has become an important issue. Since PV systems directly convert sunlight into electrical energy, PV power output varies depending on environmental conditions. In order to deal with the periodic and non-stationary problems of PV output power, modelling methods are widely use for forecasting. The main purpose of this study is to lead an assessment of forecasting of the PV power outputs in short-time. For this purpose, data are obtained from experimental activities carried out on a real 250 kWp PV stystem, which is located in T.C Tekirdağ Namık Kemal University, Süleymanpaşa district of Tekirdağ province. All parametres are measured hourly with three times according to inclination of the panel setups (0˚, 30˚,60˚). In this sense, this study differs from the previously studies in literature, as it expands the forecasting model with considering of different panel angle. In the first stage, the significant variables for predicting PV power output are identified based on both correlation analysis and stepwise regression analysis. The findings are shown that solar radiation and angle of inclination of the panel are significant predictors of the generation of PV power. In the second stage, three different model are proposed based on Time Series Analysis (TSA) and Artificial Neural Network (ANN) approaches in order to predict power production of PV system. Furthermore, the accuracies of the models are analyzed in order to better understand the intrinsic errors caused and to evaluate its potential in energy forecasting applications. All models are compared in terms of the correlation coefficient (R), coefficient of determination (R2), mean absolute percentage error (MAPE). The results of analyses show that the ANN models have higher accuracy than the TSA model for forecasting PV power.

Perioperative Hemodynamic Monitoring: An Overview of Current Methods.

Perioperative hemodynamic monitoring is an essential part of anesthetic care. In this review, we aim to give an overview of methods currently used in the clinical routine and experimental methods under development. The technical aspects of the mentioned methods are discussed briefly. This review includes methods to monitor blood pressures, for example, arterial pressure, mean systemic filling pressure and central venous pressure, and volumes, for example, global end-diastolic volume (GEDV) and extravascular lung water. In addition, monitoring blood flow (cardiac output) and fluid responsiveness (preload) will be discussed.

Optimizing thermoelectric generators based on Mg2(Si, Sn) alloys through numerical simulations

• A Mg 2 (Si, Sn) based thermoelectric generator (TEG) system is optimized and analysed using the modeling approach. • The performance of TEG module is notably determined by the geometry of thermoelectric leg and thermal contact resistances. • An effective heat sink system is designed with optimal number of fins and fin spacing. • A conversion efficiency of 8.35% and power output of 18.90 W is produced from the optimized model. Thermoelectric generator (TEG) that works in medium and high temperature ranges has appeared as a necessity in combating with the waste heat management from various industries. Therefore, design of TEG with thermoelectric materials capable to exhibit high capacity for thermoelectric power generation is of emerging technological interest. It is known that alloys based on Mg 2 (Si, Sn) and PbTe thermoelectric materials are best suited for the intermediate temperature scale (400–900 K). For constructing a TEG, higher manganese silicides (HMS) are often used for integration with n-type Mg 2 X (Si, Ge, Sn) alloys. The thermo-mechanical property-mismatch reasons out the option for such a combination in fabrication of a sustainable thermoelectric generator. This makes one focus on evolving a generation system that comprises of both n- and p-type thermo elements prepared from suitable Mg 2 X (Si, Ge, Sn) alloys. The present research analyses the feasibility of designing a Mg 2 (Si, Sn) alloy based thermoelectric generator by modeling approach. It presents a comprehensive take on the effect of thermoelectric leg dimensions and contact resistances on the output voltage, output power and efficiency, with alteration in the operating temperature span. The COMSOL modeling results indicate that the power output reduces considerably with increment in the thermoelectric leg length, while the conversion efficiency enhances. Contrarily, augmenting the cross-sectional area of a thermoelectric leg follows an opposite trend i.e., it increases the power output and decreases the conversion efficiency. The power output and the conversion efficiency values diminish when contact resistances are considered in the modeling study. This study also incorporates an efficient heat exchanger system including contact resistances and conductive and convective heat losses for accurate estimation of power output and efficiency of the optimized TEG module.

Comparing Doppler Echocardiography and Thermodilution for Cardiac Output Measurements in a Contemporary Cohort of Comatose Cardiac Arrest Patients Undergoing Targeted Temperature Management.

Measuring cardiac output is used to guide treatment during postresuscitation care. The aim of this study was to compare Doppler echocardiography (Doppler-CO) with thermodilution using pulmonary artery catheters (PAC-CO) for cardiac output estimation in a large cohort of comatose out-of-hospital cardiac arrest (OHCA) patients undergoing targeted temperature management (TTM). Single-center substudy of 141 patients included in the TTM trial randomly assigned to 33 or 36°C for 24 hours after OHCA. Per protocol, PAC-CO and Doppler-CO were measured simultaneously shortly after admission and again at 24 and 48 hours. Linear correlation was assessed between methods and positive predictive value (PPV) and negative predictive value (NPV) of Doppler to estimate low cardiac output (<3.5 L/min) was calculated. A total of 301 paired cardiac output measurements were available. Average cardiac output was 5.28 ± 1.94 L/min measured by thermodilution and 4.06 ± 1.49 L/min measured by Doppler with a mean bias of 1.22 L/min (limits of agreements -1.92 to 4.36 L/min). Correlation between methods was moderate (R2 = 0.36). Using PAC-CO as the gold standard, PPV of a low cardiac output measurement (<3.5 L/min) by Doppler was 33%. However, the NPV was 92%. Hypothermia at 33°C did not negatively affect the correlations of CO methods. In the lowest quartile of Doppler, 13% had elevated lactate (>2 mmol/L). In the lowest quartile of thermodilution, 36% had elevated lactate (>2 mmol/L). In ventilated OHCA patients, the two methods for estimating cardiac output correlated moderately and there was a consistent underestimation of Doppler-CO. Absolute cardiac output values from Doppler-CO should be interpreted with caution. However, Doppler can be used to exclude low cardiac output with high accuracy. TTM at 33°C did not negatively affect the correlation or bias of cardiac output measurements. ClinicalTrials.gov ID: NCT01020916.

From Potential GDP to Structural Balance: A Theoretical Reassessment and New Evidence for Italy

ABSTRACT Since the Maastricht Treaty, increasingly complex fiscal rules have been progressively introduced aimed at closely regulating and deeply affecting the budgetary policies of all signatory countries. One of the main reforms, enacted since 2005, has been to interpret national budget balances in structural terms, forcing each member state to comply with a Medium-Term Objective (MTO) based on the general principle of a zero-structural budget balance. Starting from the standard European Commission methodology for calculating the structural budget, our paper shows that, by replacing the estimate of the potential output based on the NAWRU with a different notion and measure of potential output, the results in terms of fiscal policy space are significantly different. This outcome points out the distinct possibility that the actual fiscal policies of the member states of the EU may be driven and constrained by a measure that seriously limits their flexibility by several percentage points. While our empirical calculations refer to Italy, the point we address is general and relates to all member states, particularly those that in the past have been forced to restrictive policies while in recession.

Technical note: using an automated head chamber system to administer an external marker to estimate fecal output by grazing beef cattle.

The objective of this experiment was to determine if titanium dioxide (TiO2) dosed through an automated head chamber system (GreenFeed; C-Lock Inc., Rapid City, SD, USA) is an acceptable method to measure fecal output. The GreenFeed used on this experiment had a 2-hopper bait dispensing system, where hopper 1 contained alfalfa pellets marked with 1% titanium dioxide (TiO2) and hopper 2 contained unmarked alfalfa pellets. Eleven heifers (BW = 394 ± 18.7 kg) grazing a common pasture were stratified by BW and then randomized to either 1) dosed with TiO2-marked pellets by hand feeding (HFD; n = 6) or 2) dosed with TiO2-marked pellets by the GreenFeed (GFFD; n = 5) for 19 d. During the morning (0800), all heifers were offered a pelleted, high-CP supplement at 0.25% of BW in individual feeding stanchions. The HFD heifers also received 32 g of TiO2-marked pellets at morning feeding, whereas the GFFD heifers received 32 g of unmarked pellets. The GFFD heifers received a single aliquot (32 ± 1.6 g; mean ± SD) of marked pellets at their first visit to the GreenFeed each day with all subsequent 32-g aliquots providing unmarked pellets; HFD heifers received only unmarked pellets. Starting on d 15, fecal samples were collected via rectal grab at feeding and every 12 h for 5 d. A two-one sided t-test method was used to determine agreement and it was determined that the fecal output estimates by HFD and GFFD methods were similar (P = 0.04). There was a difference (P < 0.01; Bartlett's test for homogenous variances) in variability between the dosing methods for HFD and GFFD (SD = 0.1 and 0.7, respectively). This difference in fecal output variability may have been due to variability of dosing times-of-day for the GFFD heifers (0615 ± 6.2 h) relative to the constant dosing time-of-day for HFD and constant 0800 and 2000 sampling times-of-day for all animals. This research has highlighted the potential for dosing cattle with an external marker through a GreenFeed configured with two (or more) feed hoppers because estimated fecal output means were similar; however, consideration of the increased variability of the fecal output estimates is needed for future experimental designs.

Ambient temperature and solar irradiance forecasting prediction horizon sensitivity analysis

Selecting the correct weather forecasting technique is a crucial task when planning an efficient solar energy generation system. Estimating accurate solar photovoltaic systems power output depends on the correct modeling of solar irradiance and ambient temperature, evidencing the need for a framework to select the correct technique to forecast these parameters. This paper presents a review of the forecasting methods to predict solar irradiance and ambient temperature, considering the sensitivity to the forecasting horizon. The methodology includes estimating an interval for ambient temperature and solar irradiance by using the Mean Absolute Error as the percentage of variation in these parameters. To provide context, the study considers best-case and worst-case scenarios for four cities, estimating the power output for a sample array and analyzing the differences between the cases. The power output estimation of the PV array varied between 36% and 50% (on average) for the short-term prediction, and 54% to 95% for the long-term. The changes in the location produced an average variation of 43% in terms of power production, and up to 187% in economic value (USD) for the short term, and 44.5% and 187% for the long term. The results suggest a marked sensitivity to the variation in the forecasting horizon and significance with regards to location selection (considering the changes in solar irradiation and the cost of electricity).

Non-invasive Oscillometry-Based Estimation of Cardiac Output - Can We Use It in Clinical Practice?

While invasive thermodilution techniques remain the reference methods for cardiac output (CO) measurement, there is a currently unmet need for non-invasive techniques to simplify CO determination, reduce complications related to invasive procedures required for indicator dilution CO measurement, and expand the application field toward emergency room, non-intensive care, or outpatient settings. We evaluated the performance of a non-invasive oscillometry-based CO estimation method compared to transpulmonary thermodilution. To assess agreement between the devices, we used Bland–Altman analysis. Four-quadrant plot analysis was used to visualize the ability of Mobil-O-Graph (MG) to track CO changes after a fluid challenge. Trending analysis of CO trajectories was used to compare MG and PiCCO® calibrated pulse wave analysis over time (6 h). We included 40 patients from the medical intensive care unit at the Charité – Universitätsmedizin Berlin, Campus Benjamin Franklin between November 2019 and June 2020. The median age was 73 years. Forty percent of the study population was male; 98% was ventilator-dependent and 75% vasopressor-dependent at study entry. The mean of the observed differences for the cardiac output index (COI) was 0.7 l∗min–1*m–2 and the lower, and upper 95% limits of agreement (LOA) were -1.9 and 3.3 l∗min–1*m–2, respectively. The 95% confidence interval for the LOA was ± 0.26 l∗min–1*m–2, the percentage error 83.6%. We observed concordant changes in CO with MG and PiCCO® in 50% of the measurements after a fluid challenge and over the course of 6 h. Cardiac output calculation with a novel oscillometry-based pulse wave analysis method is feasible and replicable in critically ill patients. However, we did not find clinically applicable agreement between MG and thermodilution or calibrated pulse wave analysis, respectively, assessed with established evaluation routine using the Bland–Altman approach and with trending analysis methods. In summary, we do not recommend the use of this method in critically ill patients at this time. As the basic approach is promising and the CO determination with MG very simple to perform, further studies should be undertaken both in hemodynamically stable patients, and in the critical care setting to allow additional adjustments of the underlying algorithm for CO estimation with MG.

A novel method for parameter identification combining analytical method and optimization algorithm

Single-diode model is widely used in photovoltaic system modelling and output estimation because of its simplicity and accuracy. This paper proposed a novel method for parameter identification and performance estimation by using measured data. The proposed method combines the analytical method and optimization algorithm to extract the model parameters and considers the deviations of different operating conditions between the measured and estimated data in calculation procedure, which reduces the dimensions of the search space and save computational cost for parameter optimization. The accuracy and effectiveness of the proposed method are validated by large number of experimental data under wide range environmental conditions.

Blind Signal Estimation Using Structured Subspace Technique

This brief addresses the blind signal estimation of a single input multiple output (SIMO) finite impulse response (FIR) system. The proposed method estimates directly the input signal with no need for the channel estimation stage or estimation of the second-order statistics. This is achieved by exploiting the Toeplitz structure inherent in the signal’s linear model, to build a quadratic cost function. Such methods are needed to lessen the latency and wave the error propagation phenomena. The simulation results are compared to other methods and are shown to highlight appealing advantages under adverse scenarios, specifically, short-burst communications and poor channel diversity.