Signal Capture Research Articles

Droplet Impedance Feedback-Enabled Microsampling Microfluidic Device for Precise Chemical Information Monitoring.

Microelectrodes have transformed our understanding of spatiotemporal responses to electrical stimulation. However, biological signals are often molecular, complicating the capture of intricate chemical signals. The microfluidic chip developed in this paper accurately measures droplet volume by using impedance analysis. The utilization of droplet volume as a feedback signal for precise microsampling pressure control ensures that microsampling remains unaffected by droplet volume influence. Once the microsampling is complete, chemiluminescence detection enables high temporal resolution and continuous and sensitive monitoring of chemical information within the droplets. Experimental verification shows that the chip can avoid volume influence through impedance feedback, achieving consistent and stable microampling at the nanoliter level (0-3 nL). In just 0.3 s, it can perform sensitive chemiluminescence detection of H2O2 and glucose within droplets. The linear detection ranges for these analytes are 10-50,000 and 20-600 μM, respectively, with the limit of detection being 0.648 and 0.334 μM. The significance of this chip lies in its ability to reveal changes in both electrical and chemical signals during transient biological processes. Its potential applications are numerous, encompassing a wide range of emerging areas such as single-cell analysis, cell communication, and cellular immunity.

Multiple covalent modification enables nylon fiber biosensor with robust scrub-resistant and signal-capture ability for multiscenario health monitoring and security warning

Nylon fibers have great potentials in smart textiles due to excellent wear resistance, resilience, and chemical stability, whereas poor combination between fibers and conductive materials causes discontinuous signal capture. In this work, nylon fibers/di-aldehyde cellulose nanocrystals/polypyrrole (NFACP) biosensors with robust scrub-resistant and signal-capture ability were fabricated by interfacial multiple covalent reactions. The best NFACP0.2 biosensor exhibited high conductivity (354 S/m), robust mechanical strength and stretching-releasing dynamic durability. Especially, its textile sensors still possessed high sensitivity and excellent sensing performance after repeated washing and friction. Moreover, NFACP0.2 biosensor was designed into various multifunctional health monitoring and security warning systems for “stress reducing exercise” enthusiasts, high-altitude activities, and deep-sea exploration, demonstating great potentials of conductive nylon fiber biosensor in flexible electronics.

Designing a practical physical framework for AI-enhanced sound detection and localization in vehicles

This paper presents the practical framework of an AI-implemented system currently in development for the detection and localization of external sound events to the vehicle. We delve into the initial block of the automotive project, analyzing sounds of interest and the noise we encounter, addressing the strategic arrangement of the four microphones on the sides of the vehicle, conceived as the sound input system. This configuration not only optimizes signal capture but also acts as sensitive ears, allowing the system to effectively analyze the acoustic environment. To mitigate wind interference, we have designed a microphone placement system that considers the vehicle's aerodynamics. The strategic location and implementation of aerodynamic protectors significantly minimize the impact of wind on the microphones, ensuring a sharper capture of acoustic signals. Complementing this arrangement, we apply advanced noise elimination processes, further enhancing the quality of the captured signals. Although in its early stages, this project aspires to provide effective solutions for vehicular acoustic perception, marking the beginning of a journey towards innovation in the detection and localization of sounds in mobile environments.

Analysis of electrode performance on amplitude integrated electroencephalography in neonates: evaluation of a new electrode aCUP-E vs. liquid gel electrodes.

Neonatologists and clinical neurophysiologists face challenges with the current electrodes used for long-duration amplitude-integrated electroencephalography (aEEG) in neonatal intensive care units (NICU), limiting the capacity to diagnose brain damage. The objectives of this study were to develop methods for comparing the performance of different electrodes to be used in aEEG. The comparison was done between a newly designed neonate-specific electrode, aCUP-E, with commercial liquid gel electrodes used in amplitude-integrated electroencephalography (aEEG). The comparison included impedance stability, electrode survival, recording quality, usability, and satisfaction of NICU staff. aEEG recordings with bipolar montage was used, with one hemisphere fitted with commercial electrodes and the other with aCUP-E electrodes, alternated among subjects. Continuous impedance and raw EEG data were collected over a minimum of 24 h, and signal processing was performed using Python and MATLAB. aCUP-E electrodes demonstrated superior performance, including: Increased impedance stability and electrode survival, enhanced recording quality with fewer artifacts, high correlation in signal capture between electrodes during optimal brain activity segments, higher signal-to-noise ratio (SNR) across varying impedance levels, greater staff satisfaction and ease of use. Moreover, Kaplan-Meier curves indicated a higher survival rate for aCUP-E electrodes over 24 h compared to commercial electrodes. Impedance variability analysis showed statistically significant stability improvements for aCUP-E. aCUP-E electrodes outperform commercial liquid gel electrodes in impedance stability, electrode survival, and recording quality. These results suggest that aCUP-E electrodes could significantly enhance aEEG utilization in diagnosing and treating neonatal brain conditions in NICUs. Future improvements to the aCUP-E electrode may further reduce artifacts and increase electrode longevity, potentially leading to a significant improvement in neonatal brain monitoring by means of aEEG.

Early detection for carbohydrate antigen-19-9 based on surface enhanced Raman spectroscopy aptamer sensor

BackgroundIncipient screening for cervical cancer is crucial for treatment and improving prognosis. In this work, SERS based aptamer sensor (aptasensor) has been developed and applied to high-sensitivity detection of cervical cancer tumor marker (Carbohydrate antigen19-9 (CA19-9)) with a microporous chip, which was used as surface enhanced Raman scattering (SERS) analysis platform. MethodsThe aptamer of CA19-9 was immobilized on the gold nanodumbbell as a capture probe and the gold nanobipyramid containing complementary aptamer chains of the signal molecule Cy5 was modified as a signal probe. SERS aptasensor was constructed by combining signal probes and capture probes. It caused signal molecule Cy5 were sandwiched between the signal probes and capture probes; the constructed SERS aptasensor generated a strong SERS signal of Cy5. Based on the strong specificity of the aptamer, the CA19-9 aptamer binds specifically to CA19-9 when CA19-9 exist in the detection environment. The signal probe was released, resulting in a decrease in the SERS signal of the detection environment. ResultsUnder the optimal detection conditions, the detection limit of the aptasensor was calculated to be 1.16 × 10−3 U/mL and limit of quantification is equal to 3.87 × 10−3 U/mL. In addition, a standard cervical cancer bearing mouse model was constructed and the SERS spectra of the serum of mice at different stages were measured. The CA19-9 content in the serum of mice at different stages was calculated. Compared with the results of enzyme-linked immunosorbent assay (ELISA), relative error (RE) values (2.54 %, −6.093 %, −4.922 %, and 3.04 %) of the CA19-9 concentrations detected by the two methods on the day of 0, 7, 14 and 28. ConclusionThe proposed SERS aptasensor provides a new and reliable platform for early cervical cancer screening, and its low RE value proves that it will be a promising CA19-9 detection method.

Enabling SENSE accelerated 2D CSI for hyperpolarized carbon-13 imaging

As hyperpolarized (HP) carbon-13 (13C) metabolic imaging is clinically translated, there is a need for easy-to-implement, fast, and robust imaging techniques. However, achieving high temporal resolution without decreasing spatial and/or spectral resolution, whilst maintaining the usability of the imaging sequence is challenging. Therefore, this study looked to accelerate HP 13C MRI by combining a well-established and robust sequence called two-dimensional Chemical Shift Imaging (2D CSI) with prospective under sampling and SENSitivity Encoding (SENSE) reconstruction. Due to the low natural abundance of 13C, the sensitivity maps cannot be pre-acquired for the reconstruction. As such, the implementation of sodium (23Na) sensitivity maps for SENSE reconstructed 13C CSI was demonstrated in a phantom and in vivo in the pig kidney. Results showed that SENSE reconstruction using 23Na sensitivity maps corrected aliased images with a four-fold acceleration. With high temporal resolution, the kidney spectra produced a detailed metabolic arrival and decay curve, useful for further metabolite kinetic modelling or denoising. Metabolic ratio maps were produced in three pigs demonstrating the technique’s ability for repeat metabolic measurements. In cases with unknown metabolite spectra or limited HP MRI specialist knowledge, this robust acceleration method ensures comprehensive capture of metabolic signals, mitigating the risk of missing spectral data.

Enhanced runoff simulation by precise capture of snowmelt variation signals with satellite-based snow products in a high-elevation basin

Hydrological models stand as a pivotal instrument for runoff simulation, while encountering notable uncertainties in output due to intricate terrain conditions and limited ground-based observations, especially in high-elevation basins. Leveraging satellite-based images presents a promising avenue for deciphering the hydrological model’s state variables. In pursuit of refining runoff simulation, this study developed a two-step enhancement strategy, which involved (1) calibrating snow-related parameters in the hydrological model using remotely sensed snow cover area (SCA) and snow water equivalent (SWE) and (2) capturing snowmelt runoff through the hydrological model and image-based products. Coupled with the Variable Infiltration Capacity (VIC) model, we adopted this strategy as a case study in the Dadu River Basin, China. The results indicated (1) the daily Nash-Sutcliffe Efficiency (NSE) of runoff simulation reached 0.84 by the enhancement strategy, markedly surpassing the strategy reliant on soil parameters with a single calibrated reference (daily NSE of 0.66), and exhibited comparability to the strategy incorporating snow parameters but calibrated solely based on observed discharge (daily NSE of 0.83). (2) the enhancement strategy demonstrated hydrological consistency with snowmelt information derived from imagery. Specifically, multi-year average contributions of model’s and image-based snowmelt calculations were 31.8% and 33.4%, respectively. Additionally, simulated dates of snow accumulation and ablation appeared an average deviation of approximately one week compared to the imagery results. This study elucidates a potential methodological approach for offering valuable insights into hydrological processes within analogous high-elevation basins globally.

Preliminary insights on fast GNSS signal capture using SFT and FFT frequency shift

Global Navigation Satellite Systems (GNSS) are the cornerstone of modern navigation and positioning technology. In this paper, we introduce an innovative approach to GNSS signal acquisition, specifically designed to enhance the performance and speed of the signal lock-on process with real-world applications in mind. Our proposed algorithm leverages advanced mathematical tools, including Fast Fourier Transform (FFT) and Inverse FFT, to streamline the acquisition process. Notably, it eliminates the need for an Intermediate Frequency (IF) down mixer, simplifying hardware and potentially reducing power consumption in GNSS receivers. One key breakthrough is using the FFT's "circle shift" to handle Doppler frequency offsets resulting from relative satellite-receiver motion. This method significantly reduces the computational burden during Doppler frequency search, ensuring a faster and more efficient lock-on to satellite signals. Furthermore, we exploit the sparse nature of GNSS signals in the reverse FFT calculation, utilizing a Sparse Fourier Transform (SFT) for efficient processing. This innovation allows quicker reverse FFT computations, pivotal in the acquisition process. Through extensive simulations, we demonstrate the superior performance of our improved algorithm. Its speed and reliability make it well-suited for many practical GNSS applications, such as vehicle navigation, precision agriculture, surveying, and geolocation services. By enhancing acquisition efficiency, our approach contributes to more accurate, responsive, and energy-efficient GNSS positioning, benefiting users in both civilian and industrial sectors.

One-stage estimation of cyclic arrival rates using license plate recognition data

Traffic arrival on urban roads usually reveals time-varying rates due to the effects of signal control, motivating us to investigate the distribution of arrival rates within the signal cycle. However, such information is not readily available as only a portion of arrival traffic can be observed in the field. Previous studies have utilized different data sources and proposed probability-based models to solve the estimation problem. However, these studies rely heavily on the assumption that the observed vehicles are evenly distributed in the arrival traffic flow and exact signal timings are known. This study uses license plate recognition (LPR) data collected at adjacent signalized intersections to estimate cyclic arrival rates in a historical period. A probability-based model is formulated by parameterizing the vehicle arrival distribution as a mixture of circular distributions. This facilitates us to describe similar traffic arrival patterns over different signal cycles and capture multiple arrival platoons from different signal phases within the signal cycle. The proposed model is validated in a field experiment with different arrival patterns and traffic conditions. Model performance under both full and partial monitoring of LPR sensors at the upstream intersection is analyzed. The robustness of the proposed model with unevenly sampled observations in arrival traffic flow is also investigated.

Soft Systems Methodology in Standardizing the Method for Applying Dolphin-Assisted Therapies in Neurodivergent Patients: Case Study of Delfiniti Mexico

Dolphin-assisted therapy (DAT) currently lacks a standard for their application, making it difficult to collect the consistent data necessary for comparative studies and the development of new evidence-based therapeutic strategies. Due to their high social component, DAT requires a standardized method that identifies the elements that affect them, understands their complex situations, and proposes solutions to the challenges. This study aims to establish the first steps towards standardizing DAT, using the Soft Systems Methodology (SSM) as the central approach. SSM is suitable for addressing complex and ambiguous problems that involve multiple actors and perspectives. Through SSM, the study seeks to visualize problems, clarify conflict relationships that hinder standardization, and propose effective solutions. To establish an initial standard method, a time and motion study is performed to identify activities that disrupt the sequence of operations and the capture of EEG signals collected before, during, and after DAT. SSM allows for summarizing the current system situation, identifying and analyzing problems, clarifying challenges, and proposing pertinent solutions to achieve the standardization of this therapy. This methodology facilitates the identification of critical points and the development of intervention strategies that could improve the efficiency and effectiveness of the therapeutic process, establishing a more coherent framework for the implementation of DAT. Thus, the contribution of this work is based on systems thinking to strategic management, as it demonstrates the potential role of systems thinking, specifically SSM, in analyzing complex problems, improving strategy mapping, fostering strategic decision making, and planning for the future in the context of strategic management.

An All-in-One Array of Pressure Sensors and sEMG Electrodes for Scoliosis Monitoring.

Scoliosis often occurs in adolescents and seriously affects physical development and health. Traditionally, medical imaging is the most common means of evaluating the corrective effect of bracing during treatment. However, the imaging approach falls short in providing real-time feedback, and the optimal corrective force remains unclear, potentially slowing the patient's recovery progress. To tackle these challenges, an all-in-one integrated array of pressure sensors and sEMG electrodes based on hierarchical MXene/chitosan/polydimethylsiloxane (PDMS)/polyurethane sponge and MXene/polyimide (PI) is developed. Benefiting from the microstructured electrodes and the modulus enhancement of PDMS, the sensor demonstrates a high sensitivity of 444.3 kPa-1 and a broad linear detection range (up to 81.6kPa). With the help of electrostatic attraction of chitosan and interface locking of PDMS, the pressure sensor achieves remarkable stability of over 100000 cycles. Simultaneously, the sEMG electrodes offer exceptional stretchability and flexibility, functioning effectively at 60% strain, which ensures precise signal capture for various human motions. After integrating the developed all-in-one arrays into a commercial scoliosis brace, the system can accurately categorize human motion and predict Cobb angles aided by deep learning. This study provides real-time insights into brace effectiveness and patient progress, offering new ideas for improving the efficiency of scoliosis treatment.

EEG-based Sleep Deprivation Classification: A Performance Analysis of Channel Selection on Classifier Accuracy

This study analyses the effect of electroencephalogram (EEG) channel selection on the classification accuracy of sleep deprivation using four distinct classifiers: Random Forest (RF), k-Nearest Neighbours (k-NN), Support Vector Machine (SVM), and Artificial Neural Network (ANN). In this study, the EEG data from ten male individuals in good health were collected. Two distinct sets of EEG channels—a limited frontal channel set (Fp1, Fp2) and a thorough 19-channel set—were used to compare the performance of the classifiers. According to our findings, the k-NN classifier produced the greatest classification accuracy of 99.7% when applied to the 19-channel EEG signals. In contrast, both SVM and ANN classifiers were able to obtain the greatest accuracy of 94% with the frontal channels. Though there are not many gaps, these results imply that employing a larger range of EEG channels greatly improves the classification accuracy of sleep deprivation. The present study emphasizes the significance of channel selection in EEG-based sleep deprivation investigations by showcasing the significant advantages of full EEG signal capture over minimum channel configurations.

Benchmarking and Optimization of Methods for the Detection of Identity-By-Descent in High-Recombining Plasmodium falciparum Genomes.

Genomic surveillance is crucial for identifying at-risk populations for targeted malaria control and elimination. Identity-by-descent (IBD) is increasingly being used in Plasmodium population genomics to estimate genetic relatedness, effective population size (N e ), population structure, and signals of positive selection. Despite its potential, a thorough evaluation of IBD segment detection tools for species with high recombination rates, such as P. falciparum, remains absent. Here, we perform comprehensive benchmarking of IBD callers - probabilistic (hmmIBD, isoRelate), identity-by-state-based (hap-IBD, phased IBD) and others (Refined IBD) - using population genetic simulations tailored for high recombination, and IBD quality metrics at both the IBD segment level and the IBD-based downstream inference level. Our results demonstrate that low marker density per genetic unit, related to high recombination relative to mutation, significantly compromises the accuracy of detected IBD segments. In genomes with high recombination rates resembling P. falciparum, most IBD callers exhibit high false negative rates for shorter IBD segments, which can be partially mitigated through optimization of IBD caller parameters, especially those related to marker density. Notably, IBD detected with optimized parameters allows for more accurate capture of selection signals and population structure; IBD-based N e inference is very sensitive to IBD detection errors, with IBD called from hmmIBD uniquely providing less biased estimates of N e in this context. Validation with empirical data from the MalariaGEN Pf 7 database, representing different transmission settings, corroborates these findings. We conclude that context-specific evaluation and parameter optimization are essential for accurate IBD detection in high-recombining species and recommend hmmIBD for quality-sensitive analysis, such as estimation of N e in these species. Our optimization and high-level benchmarking methods not only improve IBD segment detection in high-recombining genomes but also enhance overall genomic analysis, paving the way for more accurate genomic surveillance and targeted intervention strategies for malaria.

Chaotic Orthogonal Composite Sequence for 5G NR Time Service Signal Capture Algorithm

Establishing a national comprehensive PNT (Positioning, Navigation, and Timing) system has become a consensus among major countries worldwide. As a crucial component in completing the entire PNT system, the 5G NR (new radio) time service signal plays a vital role. This paper proposes a 5G NR time service signal that uses a spread spectrum system, shares the 5G signal frequency band, but does not occupy the bandwidth of the 5G communication signal. This timing service signal has relatively low power, making it appear “submerged” within the power of the 5G communication signal. The spread spectrum code for this timing signal employs the chaotic orthogonal composite sequence proposed in this paper. Compared to traditional spread spectrum sequences, this sequence offers better security than m-sequences, improved autocorrelation than Walsh sequences, and an effective suppression of the short-period characteristics exhibited when the Skew Tent-Map chaotic sequence takes special values. This paper simulates the capture of the 5G NR time service signal in an environment with a signal-to-noise ratio of 10 dB using an FFT-based parallel code phase search algorithm, successfully capturing the 5G NR time service signal and verifying the feasibility of the proposed chaotic orthogonal composite sequence as a spread spectrum code.

Cloud-Connected Patch-Worn Auscultation Device for Chest Sound Monitoring.

AbstractIn recent years, wearable electrocardiograms have risen in popularity as a solution for personal monitoring of heart activity. However, this technology has limitations in diagnostic capability and structural function monitoring. Meanwhile, auscultation of the heart remains a fundamental tool for physicians in diagnosis and monitoring of heart disease largely unaddressed in a convenient wearable format. The present work outlines a promising system currently under investigation, allowing user-initiated 10-second chest-sound recordings to be transmitted over Bluetooth-Low-Energy, with an innovative package design providing inherent noise reduction and a high signal-to-noise ratio. The device has been tested on healthy individuals, and system response has been validated against calibrated electrocardiogram recording equipment to analyze signal capture fidelity.Abstract Figure

Flexible piezoelectret film sensor for noncontact mechanical signal capture by multiple transmission media

Flexible piezoelectret film sensor for noncontact mechanical signal capture by multiple transmission media

Research on partial discharge signal denoising based on Kalman-WPT

Abstract Oil-immersed insulating paper will produce partial discharge under high-frequency pulse voltage conditions, so suppressing or reducing the generation of partial discharge signals is an important method for stabilizing the operation of power equipment. However, it has been found in experiments that various interference signals can affect the generation, capture, and measurement of partial discharge signals during various analyses. Therefore, how to capture partial discharge signals effectively and accurately through denoising algorithms has become the focus of this series of experiments. Kalman filtering and wavelet packet transform are the most basic and commonly used methods for denoising at this stage. By analyzing and comparing the basic principles and characteristics of the two methods, the author proposes a Kalman-WPT joint denoising method and conducts simulation analysis using MATLAB 2022b. Multiple controlled experiments have shown that the signal-to-noise ratio of selected discharge signals has increased from 1.3052 for Kalman filtering and 8.9553 for wavelet packet transform to 10.3702, which is sufficient to prove that this method enhances the denoising effect on the original partial discharge signal, reduces waveform distortion, and greatly improves signal detection accuracy.

Size and isotope analysis of individual nanoparticles by multi-collector ICP-MS using “event-triggered signal capture” with a high-speed oscilloscope

Accurate quantitative elemental and isotope analysis of nanoparticles at the single-particle level is crucial for better understanding their origin, properties and behaviors. Single particle inductively coupled plasma-mass spectrometry (spICP-MS) has emerged as a promising technique for nanoparticle analysis. However, challenges persist in obtaining accurate and consistent element profiles and ratios for small-sized nanoparticles by conventional quadrupole (QMS) or time-of-flight mass analyzers (TOF-MS) due to their low level and transient nature. In this paper, we present a novel analytical method for single nanoparticle analysis using multiple collector ICP-MS (MC-ICP-MS) combined with a modern high-speed digital oscilloscope. The single particle events are acquired using an “event-triggered signal capture” (ETSC) technique, which enables the simultaneously capture and visualization of multiple isotopes of transient individual particle profiles with nanosecond time resolution. This greatly facilitates precise and efficient analysis of nanoparticles. The minimum detectable particle size is calculated to be as small as 8 nm (∼1 ag 109Ag) for AgNPs. Based on the 109/107Ag ratios obtained from 2000 particles, the precisions of 109/107Ag ratio measurements on 20 nm, 40 nm, 60 nm, 80 nm and 100 nm were approximately 0.086 (SD), 0.063 (SD), 0.051 (SD), 0.040 (SD), and 0.029 (SD), which is limited by counting statistics of the isotopic signals. Furthermore, the achieved standard error of 109/107Ag can be reduced to sub-permil level (0.7 ‰) even for the measurement of 20 nm AgNPs (N = 17,000). These results demonstrate that the ETSC provides a unique method for isotope analysis of single particles, holding great potential for enhancing our understanding of nanoparticles.

Bionic fusion perspective: Audiovisual-motivated integration network for solar irradiance prediction

Accurate and reliable prediction of solar irradiance (SI) is an important requirement to develop solar energy while a challenging task due to stochastic and nonlinear data characteristics. Additionally, most deep networks show powerful prediction capabilities but lack the supports from biological science, reflecting that bionically-inspired networks in SI analysis are still not enough explored. To this end, this paper proposes an audiovisual-motivated Transformer-CNN integration network, called ATI-net, for predicting SI. The audiovisual cognition gives a superior design framework for ATI-net with signal capture, signal analysis, and prediction blocks. In the first block, through mimicking the function of both eye and ear in external signal conversion, multi-scale features are extracted by incorporating multi-branch convolutions with varying kernels, where the Mish function addresses the problem that traditional ReLU function stops learning when the input is negative. In the second block, through mimicking the function of left and right hemispheres in neuronal signal analysis, two structures triggered by Transformers and convolutions are designed to remember temporal evolutionary rules, where residual connections are beneficial to mine deep information and avoid forgetting. In the third block, through mimicking the function of a higher brain region in generating understanding, the above information is integrated to make the SI prediction. Besides, the nonlinear dependencies and linear relationships are independently extracted and integrated into the ATI-net, which not only reduces information interference but is consistent with the “divide and conquer” idea. Experimental results show that the ATI-net outperforms 18 benchmarks, and average improvements of root mean squared error (RMSE) are 26.28% and 26.01% for two datasets, respectively. In summary, the ATI-net is one of the reliable alternatives to SI prediction.

Improved soil moisture estimation and detection of irrigation signal by incorporating SMAP soil moisture into the Indian Land Data Assimilation System (ILDAS)

Land surface models have facilitated the estimation of soil moisture over a range of spatiotemporal scales. However, limitations in model parameterization and under-representation of anthropogenic processes restrict their ability to estimate local-scale soil moisture variability, especially over irrigated areas. Assimilation of satellite-based soil moisture retrievals into land surface models can be a viable approach to overcome these constraints, specially over highly irrigated countries such as India, where such applications are rare. Additionally, large-scale validation of modeled soil moisture has been limited over India till now due to lack of a representative station network. By assimilating Soil Moisture Active Passive (SMAP)-based estimates into the state-of-the-art Indian Land Data Assimilation System (ILDAS) and combining with a new soil moisture station network of more than 200 stations, this study demonstrates improved soil moisture estimations and capture of irrigation signals over the region. The Noah-MP land surface model is forced by multiple local and global meteorological datasets and Ensemble Kalman Filter (EnKF) is used for assimilation of soil moisture. Comparison of open-loop and data assimilated soil moisture against station soil moisture data shows relative spatial mean improvement of 0.0178 in correlation and 0.0029 m3/m3 in RMSE. Further statistical comparison with in-situ data has also shown better results over most of the stations, as evident from improved correlations and reduced unbiased RMSE after assimilation. Finally, the climatology of soil moisture over the different irrigation fractions reveals that data assimilated outputs over irrigated grid cells tend to have higher soil moisture during dry winter season, demonstrating the ability to capture irrigation signals. These findings quantify the value of data assimilation in improving soil moisture estimates and the ability to capture unmodeled processes such as irrigation, which lays the science groundwork for upcoming space missions such as NASA ISRO Synthetic Aperture Radar (NISAR).