Accurate Estimation Method Research Articles

Accuracy of the Copernicus High-Resolution Layer Forest Type (HRL FTY) assessed with domestic NFI sampling plots in Poland

Abstract Over the past years, several remote sensing maps of land cover have been produced, but they still exhibit certain differences compared to the real land use that reduce their value for climate and carbon cycle modelling as well as for national estimates of forest carbon stocks and their change. This paper outlines a straightforward framework for evaluating map accuracy and estimating uncertainty in land cover area, specifically for forest-related land cover maps in Poland for the year 2018. The study compares stratified field-based data from the National Forest Inventory (NFI) with remote sensing data on forest variables, at the pixel level, in order to identify suitable methods for accuracy and area uncertainty estimation. Additionally, the paper introduces and presents a variety of accuracy metrics applicable to assess overall uncertainties in GHG inventories. The results indicate that the High-Resolution Layer Forest Type (HRL FTY) product (part of the broader Copernicus Land Monitoring Service [CLMS] portfolio), assessed using NFI field-based information, achieved an overall accuracy (OA) of 69.2%. This metric varies among particular nature protection forms, with the highest observed ones in Natura 2000 sites of 70.45%. The primary source of map errors was associated with distinguishing between broad-leaved and coniferous forest areas. Improving future maps necessitates more precise differentiation between species to better support national forest monitoring systems for the purpose of greenhouse gas (GHG) inventories where information on the spatial distribution and variability of forests sources, biodiversity assessment, threat prevention, estimation of carbon content is becoming an important part of the associated reporting system.

Non-destructive method for evaluating local integrity of model piles using electromagnetic waves

The objective of this study is to suggest a more accurate integrity estimation method in drilled shafts using electromagnetic waves propagating through transmission lines. Model piles, whose dimensions were 3.0 m in length, 0.2 m in height, and 0.2 m in width, were prepared. In the center of model piles along the length, the rebar and transmission line with 0, 1, 2, or 3 connectors were installed. In the middle of model piles, defect segments with length of 1.0 m were prepared The defect segments were filled with air, sands with different water contents, and water. The experimental results show that the velocity of the electromagnetic waves are dependent on the properties of the medium surrounding the transmission line. Although defects were detected in the transmission line without the connector, the location and size of defect were not clearly identified. However, for the transmission line with connectors, the location and size of sound and defect segments were more clearly identified using the velocity of electromagnetic waves determined between the connectors. This study suggests that transmission line with connectors may be effectively used for a more accurate evaluation of the integrity of drilled shafts.

Monitoring drought induced photosynthetic and fluorescent variations of potatoes by visible and thermal imaging analysis

Accurate determination of photosynthetic parameters is critical to evaluate crop physiological and growth processes. This study aimed to estimate photosynthetic and fluorescence variables of potatoes by visible and thermal imaging fusion analysis. Two-years pot experiments were conducted in a climate chamber with different irrigation treatments. Multi-modal image features of crop canopy were extracted from both visible and thermal images by color component extraction, discrete wavelet transformation, gray level co-occurrence matrix, and local binary pattern algorithms. Extracted features were subsequently employed to build partial least squares regression (PLSR) models for estimation of transpiration rate (Tr), net photosynthetic rate (An), stomatal conductance (GSW), electron transport rate (ETR), and maximum photochemical efficiency under Photosystem II (Fv'/Fm'). Results showed that Mask Region-Convolutional Neural Network (Mask R-CNN) performed satisfactorily on canopy segmentation with intersection over union of 87.29 % and 86.93 % in visible and thermal images, respectively. Three different types of models that either using only visible image features (PLSRRGB), or only thermal image features (PLSRT) or both visible and thermal image features (PLSRRGB+T) as inputs were compared. Results showed that PLSRRGB+T had superior estimation performance in terms of R2 and RMSE. It achieved the highest R2 of 0.85 with An and the lowest R2 of 0.66 with GSW for Zhongshu 5, while it had the highest R2 of 0.86 with Fv'/Fm', and the lowest R2 of 0.71 with Tr and An for D681. This implied the potential of visible and thermal image-driven method for quick and accurate estimation of photosynthetic traits of crops grown in controlled environment.

Betterment of a displacement retrieval method based on data fitting in a near-half-fringe zone.

Raising the speed and accuracy of displacement retrieval by overcoming the weakness of time-consuming parameter estimation and a complex displacement retrieval algorithm is of key importance in improving the real-time quality of optical feedback self-mixing-based displacement measurement sensor. This paper proposes a high-speed and accurate simultaneous estimation method of displacement, optical feedback factor, and linewidth enhancement factor based on data fitting near a half-fringe cooperating with the fringe-counting method. The displacement measurement accuracy of the proposed method is about 16nm. The high estimation speed ensured by the principle of the reported method constitutes the main character of the method; besides, the method can be applied to any motion, as well as a harmonic vibration.

YENİ NESİL DENTAL YAŞ TAYİNİ YÖNTEMLERİNE GÜNCEL BİR BAKIŞ: BİR DERLEME

In forensic odontology, dental findings play a significant role in determining age and gender. Due to their anatomical structures and durability, teeth have a critical function in identification, even when exposed to mechanical, chemical, and physical effects. However, most of the estimated dental age determination methods are generally invasive, costly, and require procedures like tooth extraction. Additionally, misinterpretations can occur in 2-dimensional imaging due to distortions and magnifications. Recently, three-dimensional imaging technologies, especially Cone Beam Computed Tomography (CBCT), Computed Tomography (CT), and micro-CT, are being used to investigate the potential relationship between the volumetric data of the tooth and pulp and age. CBCT allows researchers to comprehensively evaluate pulp changes and explain the relationship between chronological age and pulp volume. With the 3D images obtained by CBCT, the volumetric measurements of the tooth and pulp can be objectively evaluated and can be used as a criterion for estimated dental age. This review aims to identify, investigate, and summarize the most reliable, practical, and accurate methods for age estimation based on current data and methods.

Fast and Accurate Calorie Count Prediction from Food Images using a Convolution Neural Network Method

In the past ten years, with advances in deep learning techniques, automated object recognition has come very near to human levels of accuracy. Fast, automatic, and consistent image-based food calorie calculation is becoming a requirement with the increasing growth of overweight and other lifestyle- related disorders throughout the globe. Accurate and insightful solutions may be provided in the form of a mobile app with the aid of a deep learning-based automatic object recognition system. However, real-time image processing necessitates a high level of processing speed, which is a major consideration for such applications. Although several researchers have looked at estimating calories based on pictures of food, there is currently no image-driven, lightweight, rapid, and accurate food calorie estimation method. In this study, we offer a technique for recognizing common meals captured with a mobile phone camera by using Convolution Neural Networks (CNN) with optimum parameters. Calories and other nutritional information may be deduced from the known food class after the food items have been identified. Our research shows that our suggested method not only guarantees precision but also has the potential to greatly streamline the complex, time-consuming, and labor-intensive processes now used for estimating calorie intake by turning them together into real-time automation techniques.

An effective routability-driven packing algorithm for large-scale heterogeneous FPGAs

With the growing complexity and scale of FPGA architectures, challenges posed by heterogeneity and routability have become more pronounced in FPGA packing. Conventional FPGA packing algorithms are seldom considered routability and may lead to unroutable results. This paper addresses these challenges by proposing an effective routability-driven packing algorithm tailored for large-scale heterogeneous FPGAs. The proposed algorithm consists of four major parts: (1) a LUT-FF pairing method is presented to pack LUTs and FFs into LUT-FF pairs. (2) A novel BLE packing method is presented to deal with the complex clock constraints, meanwhile forming more HCLB-friendly BLEs. (3) A congestion-aware HCLB packing technique is proposed to produce placement-friendly netlists without degrading routability. (4) An effective and accurate routing congestion estimation method is proposed to guide the packing process. Based on the ISPD 2017 clock-aware FPGA placement contest benchmarks, experimental results show that our algorithm outperforms three state-of-the-art FPGA placers by 9.8%, 8.2%, and 3.4% on routed wirelength, respectively.

2OC: A General Automated Orientation and Orthorectification Method for Corona KH-4B Panoramic Imagery

Due to a lack of geographical reference information, complex panoramic camera models, and intricate distortions, including radiation, geometric, and land cover changes, it can be challenging to effectively apply the large number (800,000+) of high-resolution Corona KH-4B panoramic images from the 1960s and 1970s for surveying-related tasks. This limitation hampers their significant potential in the remote sensing of the environment, urban planning, and other applications. This study proposes a method called 2OC for the automatic and accurate orientation and orthorectification of Corona KH-4B images, which is based on generalized control information from reference images such as Google Earth orthophoto. (1) For the Corona KH-4B panoramic camera, we propose an adaptive focal length variation model that ensures accuracy and consistency. (2) We introduce a robust multi-source remote sensing image matching algorithm, which includes an accurate primary orientation estimation method, a multi-threshold matching enhancement strategy based on scale, orientation, and texture (MTE), and a model-guided matching strategy. These techniques are employed to extract high-accuracy generalized control information for Corona images with significant geometric distortions and numerous weak texture areas. (3) A time-iterative Corona panoramic digital differential correction method is proposed. The orientation and orthorectification results of KH-4B images from multiple regions, including the United States, Russia, Austria, Burkina Faso, Beijing, Chongqing, Gansu, and the Qinghai–Tibet Plateau in China, demonstrate that 2OC not only achieves automation but also attains a state-of-the-art level of generality and accuracy. Specifically, the standard deviation of the orientation is less than 2 pixels, the mosaic error of orthorectified images is approximately 1 pixel, and the standard deviation of ground checkpoints is better than 4 m. In addition, 2OC can provide a longer time series analysis of data from 1962 to 1972, benefiting various fields such as environmental remote sensing and archaeology.

High-Precision DOA Estimation Based on Synthetic Aperture and Sparse Reconstruction.

The direction-of-arrival (DOA) estimation is predominantly influenced by the antenna's aperture size. However, space constraints on flight platforms often necessitate the use of antennas with smaller apertures and fewer array elements. This inevitably imposes limitations on the DOA estimation's resolution and degrees of freedom. To address these precision constraints, we introduce an accurate DOA estimation method based on spatial synthetic aperture model. This method adopts a two-stage strategy to ensure both efficiency and precision in DOA estimation. Initially, the orthogonal matching pursuit (OMP) reconstruction algorithm processes the original aperture data, providing a rough estimate of target angles that guides the aircraft's flight direction. Subsequently, the early estimations merge with the aircraft's motion space samples, forming equivalent spatially synthesized array samples. The refined angle estimation then employs the OMP-RELAX algorithm. Moreover, with the off-grid issue in mind, we devise an estimation method integrating Bayesian parameter estimation with dictionary sequence refinement. The proposed technique harnesses the spatial synthetic aperture for pinpoint estimation, effectively addressing the challenges of atomic orthogonality and angular off-grid on estimation accuracy. Importantly, the efficiency of deploying sparse reconstruction for angle estimation is bolstered by our phased strategy, eliminating the necessity for fine grid analysis across the entire observation scene. Moreover, the poor estimation accuracy caused by coherent source targets and angular-flickering targets is improved by sparse reconstruction. Through simulation and experiment, we affirm the proposed method's efficacy in angle estimation. The results indicate that target angle estimation errors are limited to within 1°. Furthermore, we assess the impact of variables such as target state, heading angle, spatial sampling points, and target distance on the estimation accuracy of our method, showcasing its resilience and adaptability.

Damage Analysis and Quality Control of Carbon-Reinforced Concrete Beams Based on In Situ Computed Tomography Tests

Carbon-reinforced concrete (CRC) is increasingly utilized in construction, due to its unique properties, such as corrosion resistance, high-tensile strength, and durability. Understanding its behavior under different loads is crucial to ensuring its safe and effective use in various construction applications. In this study, three-point bending tests were performed in combination with large-scale in situ computed tomography (CT). This paper presents the related three- and four-dimensional evaluation methods, with emphasis on crack width and quality control. The focus was on large CRC beams, with cross-sectional sizes of up to 80 mm by 160 mm. Such dimensions require extremely high energy during a CT scan. Therefore, a new experimental setup with energies of up to 8 MeV was used in this study. However, such high energies posed new challenges to the analysis methods. Therefore, two methods (digital volume correlation and grayscale profile analysis) for accurate crack width estimation were adapted and applied to the 3D reconstructions. In addition, a photogrammetric stereo image sequence was acquired and analyzed, using digital image correlation to cross-validate the results derived from the 3D crack width estimates. The 3D CT images also played a key role in the quality control measures, including the localization of the carbon-reinforcement and the assessment of porosity within the concrete structure.

Derivatization-free conventional and synchronous spectrofluorimetric estimation of atenolol and amlodipine

Fixed-dose combinations for treatment of hypertension are observed in many dosages in the global market because of their high efficacy compared to single component dosage forms. One of these effective combinations is atenolol/amlodipine which is usually administered to patients with hypertension. Hence, development of facile, accurate, and sensitive methods for simultaneous estimation of atenolol and amlodipine is of great importance for quality control testing and pharmacokinetic studies. In our study, we developed two spectrofluorimetric methods to estimate both compounds in different pharmaceuticals. The first method is based on the estimation of atenolol and amlodipine by double-scan conventional spectrofluorimetry where the fluorescence intensities of atenolol and amlodipine were measured at 299 and 434 nm after excitation at 274 and 358 nm, respectively. The second method depends on synchronous spectrofluorimetric measurements at Δλ = 70 nm, where atenolol is assayed at 266 nm and amlodipine is assayed at 363 nm. Methods’ optimizations were carried out to select the optimum conditions that render high selectivity and sensitivity. Such optimizations included assessment of solvents, surfactants, buffer volumes and pHs. The conventional spectrofluorimetric method was rectilinear over concentration range of 30.0–300.0 ng mL−1 for atenolol and 0.25–7.00 µg mL−1 for amlodipine while the synchronous spectrofluorimetric method showed linearity over the ranges of 0.60–6.00 µg mL−1 for atenolol and 0.25–7.00 µg mL−1 for amlodipine with low detection limits (≤0.12 µg mL−1) for both compounds in the two methods. It is the first work that demonstrates estimation of atenolol and amlodipine in their combinations by conventional and synchronous spectrofluorimetry. Both methods were applied to estimate atenolol and amlodipine in different pharmaceuticals with high %recovery and low %RSD.

Relationship between Leaf Area Index and Yield Components in Farmers’ Paddy Fields

Estimation of rice yield components is required to optimize cultivation management in fields. The leaf area index (LAI) can be a parameter for this estimation, but it has not been evaluated in farmers’ fields. In this study, we analyzed the relationship between the LAI and yield components using data collected over a five-year period in farmers’ fields for the cultivar Hitomebore. Leaf area dynamics (LAD) were parameterized by fitting a growth function to the time-series data of LAI measured using a canopy analyzer. The contribution of LAD to yield components was analyzed using multiple regression. The LAIs at five points during the growing season (effective integrated temperatures of 200, 400, 600, 800, and 1000 °Cd) were calculated using the growth function and the relationship between them and the yield components were analyzed using linear regression. The results of the multiple regression analysis showed that all function parameters significantly affected the yield components at the 5% probability level, with the greatest contribution from the LAI. The LAI at effective integrated temperatures of 400 to 600 °Cd significantly affected most of the yield components. However, the correlation coefficients between the LAI and yield components were not high (R = 0.18–0.61). The LAIs at almost all periods significantly affected the grain number per panicle and 1000-grain weight at the 5% probability level. These results suggest that the LAI could be used for monitoring trends in yield components, while further research on the development of accurate estimation methods is needed.

Timing Synchronization Based on Supervised Learning of Spectrogram for OFDM Systems

This paper proposes a supervised convolutional neural network (CNN) based symbol timing synchronization method using the spectrogram image for preamble-less orthogonal frequency division multiplexing (OFDM) systems. With the development of mobile terminals, OFDM has become an increasingly widespread fundamental technology for wireless communications. While OFDM can achieve high-speed transmission, it is sensitive to synchronization timing for decoding. Thus, the accurate synchronization timing estimation method has become essential for reliable communication. Conventional synchronization timing estimation methods without the preamble lack investigations of estimation accuracy under varying environments, comprehensive performance evaluation, and robustness to Doppler shift. Focusing on the spectrum fluctuations observed when synchronization errors occur, our proposed approach is to train the CNN using spectrogram images to find accurate synchronization points even in noisy and fluctuating environments. The simulation results show that the proposed method achieves better synchronization accuracy than other existing methods. Furthermore, it shows the near-optimal bit error rate (BER) characteristics and superior processing time for BER in an environment close to realistic settings, such as broader synchronization timing and various Doppler shifts.

Age estimates derived from hard parts of swordfish Xiphias gladius from the north-western Mediterranean Sea.

Accurate age estimates for fish are critical for properly understanding stock dynamics and health; this is particularly true for larger billfishes. Here we determined the most accurate aging estimation methods for swordfish (Xiphias gladius). We compared age estimates obtained from fin-ray sections, otolith sections, whole otoliths, and vertebrae collected from 87 swordfish off the east coast of Corsica. Age estimates from otolith sections were most consistently estimated across different readers (lowest average percentage error), followed by fin-ray sections, third vertebrae, and whole otoliths. When the age estimates from the otolith sections were compared with the other three age sclerochronological methods, we found the average percentage error to be lowest between the otolith section and fin-ray methods. However, age estimates from fin rays proved most useful for estimating swordfish younger than 6 years, as the fin ray-based age diverged from that of the otolith sections as the swordfish aged. Combining fin ray and otolith section techniques, we estimated the growth parameters of 1-12-year-old females (L∞ = 259.412, k = 0.113, t0 = -2.499) and 1-7-year-old males (L∞ = 175.543, k = 0.202, t0 = -2.239). We found that females grew significantly faster than males after 3 years and remained larger thereafter. Our calculated growth rates for this region of the north-western Mediterranean Sea were lower than those of the Atlantic, Pacific, and eastern Mediterranean Sea swordfish populations, and similar to growth rates recorded for the western Mediterranean Sea populations. Our study provides critical knowledge on biological-related parameters to serve as a guide for preserving the swordfish population in the Mediterranean Sea.

Knowledge‐enhanced graph neural networks for construction material quantity estimation of reinforced concrete buildings

AbstractThe construction material quantity (CMQ) is widely concerned in the structural design of reinforced concrete buildings and is often included among the objective functions of computer‐aided optimization design techniques. To minimize construction cost and carbon emissions, an accurate and efficient CMQ estimation method is timely required. In this study, a novel graph neural network (GNN) is proposed, whose architecture and loss function are specifically designed for CMQ estimation. With a heterogeneous feature fusion mechanism, the GNN can automatically extract features from all CMQ‐related information, in contrast to the existing data‐driven methods that rely heavily on manually selected features. By further incorporating a prior knowledge inclusion strategy, the GNN can avoid fundamental errors that might be encountered by purely data‐driven methods. To enrich the diversity of the CMQ dataset, a data augmentation method is proposed incorporating generative adversarial networks and parametric modeling. Numerical experiments and case studies show that the proposed CMQ estimation method is superior to the existing data‐driven methods in terms of accuracy and is 500 times faster than typical commercial structural design software. This study is anticipated to benefit the objective evaluation of computer‐aided design, thereby facilitating the promotion of low‐cost and low‐carbon‐emission building designs.

Size and shape of human mandible: Sex differences and influence of age on sex estimation accuracy

The degree of sexual dimorphism expressed by human bones is of primary importance for the development of accurate methods for sex estimation. The objective of the present study was to investigate sex differences in shape and size of the mandible using geometric morphometric methods. The study also aimed to examine the impact of age on the sex classification ability of the size and shape of the mandible. Computed tomography images of 190 Bulgarians (98 males and 92 females) were used in the study. Polygonal surface models of the skulls were generated and used for digitizing 45 landmarks located on the mandible. The raw three-dimensional coordinates of the landmarks were processed via generalized Procrustes superimposition. The sex differences in mandibular size and shape were evaluated for statistical significance. Multivariate regression was applied for correction of the allometric effect. Principal component analysis, discriminant analysis, and canonical variate analysis were also used in the study. Mandibular size differed significantly between males and females and achieved a sex classification accuracy of 87%. The significance of the sex differences in mandibular shape depended on the type of shape variables used in the analysis. The shape variables provided different classification accuracy: 78% using the Procrustes coordinates and 53% using the regression residuals. The male and female mandibles differed significantly in size and shape, including the allometric component. Mandibular size is a more effective sex indicator than shape. Age has an ambiguous effect on the classification accuracy of the size and shape variables of the mandible.

Adaptive MCMC for Bayesian Variable Selection in Generalised Linear Models and Survival Models.

Developing an efficient computational scheme for high-dimensional Bayesian variable selection in generalised linear models and survival models has always been a challenging problem due to the absence of closed-form solutions to the marginal likelihood. The Reversible Jump Markov Chain Monte Carlo (RJMCMC) approach can be employed to jointly sample models and coefficients, but the effective design of the trans-dimensional jumps of RJMCMC can be challenging, making it hard to implement. Alternatively, the marginal likelihood can be derived conditional on latent variables using a data-augmentation scheme (e.g., Pólya-gamma data augmentation for logistic regression) or using other estimation methods. However, suitable data-augmentation schemes are not available for every generalised linear model and survival model, and estimating the marginal likelihood using a Laplace approximation or a correlated pseudo-marginal method can be computationally expensive. In this paper, three main contributions are presented. Firstly, we present an extended Point-wise implementation of Adaptive Random Neighbourhood Informed proposal (PARNI) to efficiently sample models directly from the marginal posterior distributions of generalised linear models and survival models. Secondly, in light of the recently proposed approximate Laplace approximation, we describe an efficient and accurate estimation method for marginal likelihood that involves adaptive parameters. Additionally, we describe a new method to adapt the algorithmic tuning parameters of the PARNI proposal by replacing Rao-Blackwellised estimates with the combination of a warm-start estimate and the ergodic average. We present numerous numerical results from simulated data and eight high-dimensional genetic mapping data-sets to showcase the efficiency of the novel PARNI proposal compared with the baseline add-delete-swap proposal.

ANALYTICAL METHOD DEVELOPMENT AND VALIDATION OF RP-HPLC FOR AMLODIPINE BESYLATE AND LISINOPRIL IN COMBINED TABLET DOSAGE FORM BY USING SIMULTANEOUS ESTIMATION METHOD


 
 
 The purpose of this study was to develop and validate a simple, sensitive, accurate, and reproducible reverse phase high performance liquid chromatography (RP-HPLC) method for simultaneous estimation of amlodipine besylate and lisinopril in a Tablet dosage form. The chromatographic measurement was performed on a Phenomenex C18 (250 × 4.6 mm, 5um) column with an optimised Acetate buffer mobile phase: Methanol (65:35). The flow rate was one ml/min, and the detecting wavelength was 221 nm. Triethanolamine was used to adjust the pH to 5. In the concentration range of 4-20 ug/ml, amlodipine besylate and lisinopril showed a linear response of the suggested approach. The correlation coefficients ('r' values) for amlodipine besylate and lisinopril were 0.9998 and 0.9995, respectively, and the retention times were 3.279 for amlodipine besylate and 6.124 for lisinopril, respectively. The developed chromatographic technique was validated for specificity, linearity, precision, accuracy, LOD, and LOQ using ICH Q2(R1) criteria. The analysis results have been validated in accordance to ICH guidelines
 
 

Development and validation of RP-HPLC method for simultaneous estimation of Ertugliflozin and Sitagliptin in bulk drug and tablet dosage form

To treat type 2 diabetes, in a combined tablet dosage form the ertugliflozin and sitagliptin were administered. Considering the less complication and readily availability of HPLC, the main objective of present study was to develop a new, precise, accurate, linear, robust, and economical RP-HPLC method for the simultaneous estimation of ertugliflozin and sitagliptin in tablet dosage form. Effective chromatographic separation of Ertugliflozin and Sitagliptin was achieved on Kromasil C18 (5 µm 250 mm X 4.6 mm) and the mobile phase containing Methanol and 0.1% OPA in water isocratic elution mode at a flow rate of 1.0mL/min. with column temperature at 30 °C and the injection volume was 20 µL at column temperature at 30°C. At an isosbestic wavelength of 212 nm, ertugliflozin and sitagliptin were found to have retention times of 5.30 min. and 2.05 min., respectively. The method was proven to be precise (%RSD 2%), accurate (>90%), and specific for the simultaneous measurement of both drugs in tablets. As a result, the suggested method with excellent specificity, accuracy, precision, linearity and robustness as well as economical was useful for the regular quality control analysis of ertugliflozin and sitagliptin tablets.

Performance Optimization in Frequency Estimation of Noisy Signals: Ds-IpDTFT Estimator.

This research presents a comprehensive study of the dichotomous search iterative parabolic discrete time Fourier transform (Ds-IpDTFT) estimator, a novel approach for fine frequency estimation in noisy exponential signals. The proposed estimator leverages a dichotomous search process before iterative interpolation estimation, which significantly reduces computational complexity while maintaining high estimation accuracy. An in-depth exploration of the relationship between the optimal parameter p and the unknown parameter δ forms the backbone of the methodology. Through extensive simulations and real-world experiments, the Ds-IpDTFT estimator exhibits superior performance relative to other established estimators, demonstrating robustness in noisy conditions and stability across varying frequencies. This efficient and accurate estimation method is a significant contribution to the field of signal processing and offers promising potential for practical applications.