Low Mean Relative Error Research Articles

A Thorough Review of Vehicle Detection and Distance Estimation Using Deep Learning in Autonomous Cars

Autonomous vehicle technologies are rapidly advancing, and one key factor contributing to this progress is the enhanced precision in vehicle detection and distance calculation. Deep Learning Networks (DLNs) have emerged as powerful tools to address this challenge, offering remarkable capabilities in accurately detecting and estimating vehicle positions. This study comprehensively reviews DLN applications for vehicle detection and distance estimation. It examines prominent DLN models such as YOLO, R-CNN, and SSD, evaluating their performance on widely used datasets such as KITTI, PASCAL VOC, and COCO. Analysis results indicate that YOLOv5, developed by Farid et al. achieves the highest accuracy level with a mAP (mean Average Precision) of 99.92%. Yang et al. showcased that YOLOv5 performs exceptionally in detection and distance estimation tasks, with a mAP of 96.4% and a low mean relative error (MRE) of 10.81% for distance estimation. These achievements highlight the potential of DLNs to enhance the accuracy and reliability of vehicle detection systems in autonomous vehicles. The study also emphasizes the importance of backbone architectures like DarkNet 53 and ResNet in determining model efficiency. The choice of the appropriate model depends on the specific task requirements, with some models prioritizing real-time detection and others prioritizing accuracy. In conclusion, developing DLN-based methods is crucial in advancing autonomous vehicle technology. Research and development remain crucial in ensuring road safety and efficiency as autonomous vehicles become more common in transportation systems.

Adaptive center constraint for joint release rate estimation and model correction: Multi-scenario validation against wind tunnel experiments

Adaptive center constraint for joint release rate estimation and model correction: Multi-scenario validation against wind tunnel experiments

What not to do in facial infrared thermographic measurements: A post data enhancement

What not to do in facial infrared thermographic measurements: A post data enhancement

Reconfigurable Ring Antenna Sensor for Detection of Adulteration in Liquids

In this research paper, a novel reconfigurable ring antenna sensor is presented to detect adulteration in liquids. The suggested antenna sensor is designed to detect and predict salt and sugar concentrations in water and adulterant concentrations in milk. The proposed antenna works in five modes and reconfigurability is achieved using pin diodes. The adulteration in liquids is determined by analyzing the connection between concentration and fluctuation in the reflection coefficient. Calibration equations and Mean relative error (MRE) of different solutions are obtained using reflection coefficients and concentrations. The investigation demonstrates that salt and sugar concentrations could be predicted using a ring sensor with mean relative errors of 2.17% and 2.67% respectively. The sensor is used to detect and predict the concentration of several adulterants in milk like water, caustic soda, sodium carbonate, ammonium sulfate, and urea. The value of MRE for adulterated milk is 2.2% for water, 2.33% for caustic soda, 2.33% for sodium carbonate, 2% for ammonium sulfate, and 3% for urea. The results show that the proposed ring antenna sensor has considerably good sensitivity and accuracy, which makes the system attractive for detecting adulteration in liquids with the lowest mean relative error.

Spatial Variability of Selected Soil Chemical Parameters in Low-yielding Paddy Production Block in Mahaweli System H, Sri Lanka

Although water availability for paddy production in Mahaweli System H is abundant, paddy yield is lower in Nochchiyagama (278 km2), chosen for soil assessment. Paddy yield data were used to identify the low-yielding division using the area-weighted average. Twenty-five random locations were generated, and soil samples were collected for pH, soil conductivity (EC), salinity, and total dissolved solids (TDS) analysis. ArcGIS software was used to create spatial distribution maps and related geostatistical analyses for each parameter. Vector files were created with their associated properties, and thematic maps were generated using spatial interpolation techniques, such as universal kriging (UK), ordinary kriging (OK), and inverse distance weighted (IDW) methods of interpolation techniques to identify the best interpolation method for soil chemical parameters mapping. The entire Nochchiyagama land was observed to have a slightly acidic pH (5.6-5.9) range that may have affected rice crop growth due to nutrient mobility and uptake issues. The spatial interpolation evaluation suggests that at least two-thirds of the area observed for lower TDS levels (591-654 mg/L) is potentially unsafe paddy production. Elevated levels of EC (3.1-7.24 dS/m) along with TDS may lead to physiological drought due to interferences in ion uptake. In overall, spatial interpolation evaluation indicators suggest that the UK method was observed with a lower mean relative error (MRE) than the other two interpolations. However, EC distribution showed low MRE in both IDW and OK interpolation techniques. The IDW method was observed to have a lower RMSE (Root mean square error). The UK spatial interpolation performed better for TDS and salinity predictions than other methods. This study found consistent regional differences in low paddy yields in Mahaweli system H using the UK method for analyzing soil chemical parameters. 
 Keywords: Electrical conductivity, Interpolation errors, Salinity, Spatial interpolation

Microstrip and Metamaterial Embedded Patch Antenna Sensors for Determination of Moisture in Rice, Wheat, and Pulse Grains

Moisture Content determination inside the grains is essential for grain processing activities including harvesting, storing, inspecting, and transporting. In this paper, microstrip and metamaterial embedded patch sensors were developed to determine the moisture content and mean relative error (MRE) of rice, wheat, and pulse. The sensors were simulated in HFSS, prototypes were fabricated and the measurement was done using VNA and anechoic chamber. The Microstrip sensor was operating at a frequency of 5.2 GHz and the metamaterial-embedded sensor was operating at 4.5 GHz. Calibration equations were obtained by using the values of reflection coefficients and moisture content ranging from 11.76% to 25%. For analyzing the efficacy of designed sensors, predicted moisture content (PMC) and actual moisture content (AMC) have been compared and the lowest mean relative error has been determined. The analysis shows that the metamaterial embedded sensor has better sensitivity and accuracy than the microstrip sensor. The lowest mean relative error in moisture prediction for metamaterial sensor was 1.07% for rice, 1.13% for wheat, and 1.47% for pulse respectively. A comparison of the proposed metamaterial embedded sensor with earlier designed sensor in literature was also presented in this work and it was found that the proposed sensor had more accuracy and sensitivity than earlier designs.

Comparison of variable extraction methods using surface field data and its key influencing factors: A case study on aboveground biomass of Pinus densata forest using the original bands and vegetation indices of Landsat 8

Comparison of variable extraction methods using surface field data and its key influencing factors: A case study on aboveground biomass of Pinus densata forest using the original bands and vegetation indices of Landsat 8

Sensitivity of an optical feedback interferometer for acoustic waves measurements.

This work presents a sensitivity study on the use of an optical feedback interferometer to measure acoustic pressure from plane waves. The sensitivity is established by linearising the interferometer's governing equations. It is shown to be independent of the acoustic wave frequency but dependent on configuration parameters such as the optical feedback parameter or the length of the laser through which the acoustic wave passes. Experimental validation is carried out using three acoustic waveguides in the 0.5-18 kHz range. The sensitivity obtained enables broadband acoustic pressure measure with a low mean relative error in comparison with a reference condenser microphone.

Insights into the estimation of surface tensions of mixtures based on designable green materials using an ensemble learning scheme

Precise estimation of the physical properties of both ionic liquids (ILs) and their mixtures is crucial for engineers to successfully design new industrial processes. Among these properties, surface tension is especially important. It’s not only necessary to have knowledge of the properties of pure ILs, but also of their mixtures to ensure optimal utilization in a variety of applications. In this regard, this study aimed to evaluate the effectiveness of Stochastic Gradient Boosting (SGB) tree in modeling surface tensions of binary mixtures of various ionic liquids (ILs) using a comprehensive dataset. The dataset comprised 4010 experimental data points from 48 different ILs and 20 non-IL components, covering a surface tension range of 0.0157–0.0727 N m−1 across a temperature range of 278.15–348.15 K. The study found that the estimated values were in good agreement with the reported experimental data, as evidenced by a high correlation coefficient (R) and a low Mean Relative Absolute Error of greater than 0.999 and less than 0.004, respectively. In addition, the results of the used SGB model were compared to the results of SVM, GA-SVM, GA-LSSVM, CSA-LSSVM, GMDH-PNN, three based ANNs, PSO-ANN, GA-ANN, ICA-ANN, TLBO-ANN, ANFIS, ANFIS-ACO, ANFIS-DE, ANFIS-GA, ANFIS-PSO, and MGGP models. In terms of the accuracy, the SGB model is better and provides significantly lower deviations compared to the other techniques. Also, an evaluation was conducted to determine the importance of each variable in predicting surface tension, which revealed that the most influential factor was the mole fraction of IL. In the end, William’s plot was utilized to investigate the model's applicability range. As the majority of data points, i.e. 98.5% of the whole dataset, were well within the safety margin, it was concluded that the proposed model had a high applicability domain and its predictions were valid and reliable.

Quantitative analysis of polycyclic aromatic hydrocarbons (PAHs) in water by surface-enhanced Raman spectroscopy (SERS) combined with Random Forest

Polycyclic aromatic hydrocarbons (PAHs) have strong carcinogenicity, teratogenicity, mutagenicity and other adverse effects on human beings. They are one of the most dangerous pollutants, which have attracted great attention in the past decades. In this work, aiming at the actual problems that water environment is polluted and human health is threatened by PAHs, surface enhanced Raman spectroscopy (SERS) combined with Random Forest (RF) calibration models were used to quantitative analysis of phenanthrene and fluoranthene in water. Firstly, the SERS data was collected after samples mixed with Ag NPs, after 31 PAHs samples were prepared. Secondly, it was discussed how spectral preprocessing integration strategies affect on the prediction performance of the RF calibration models. And then, the effect of mutual information (MI) variable selection method on the performance of RF calibration models was explored. Finally, the RF calibration models were established for phenanthrene and fluoranthene. For the prediction set, a lowest mean relative error (MRE) and a largest determination coefficient (R2) were obtained. For quantitative analysis of phenanthrene, the final prediction performance results show that R2p is 0.9780, and MREp is 0.0369 based on the D1st-WT-RF calibration model. For fluoranthene, WT-D1st-MI-RF is a better calibration model, and corresponding to R2p and MREp are 0.9770 and 0.0694, respectively. Hence, a rapid and accurate quantitative method of PAHs is established for the real-time detection of water environmental pollution, which is intended to provide new ideas and methods for the quantitative analysis of PAHs in water.

Tumor growth monitoring in breast cancer xenografts: A good technique for a strong ethic.

PurposeAlthough recent regulations improved conditions of laboratory animals, their use remains essential in cancer research to determine treatment efficacy. In most cases, such experiments are performed on xenografted animals for which tumor volume is mostly estimated from caliper measurements. However, many formulas have been employed for this estimation and no standardization is available yet.MethodsUsing previous animal studies, we compared all formulas used by the scientific community in 2019. Data were collected from 93 mice orthotopically xenografted with human breast cancer cells. All formulas were evaluated and ranked based on correlation and lower mean relative error. They were then used in a Gompertz quantitative model of tumor growth.ResultsSeven formulas for tumor volume estimation were identified and a statistically significant difference was observed among them (ANOVA test, p < 2.10−16), with the ellipsoid formula (1/6 π × L × W × (L + W)/2) being the most accurate (mean relative error = 0.272 ± 0.201). This was confirmed by the mathematical modeling analysis where this formula resulted in the smallest estimated residual variability. Interestingly, such result was no longer valid for tumors over 1968 ± 425 mg, for which a cubic formula (L x W x H) should be preferred.Main findingsWhen considering that tumor volume remains under 1500mm3, to limit animal stress, improve tumor growth monitoring and go toward mathematic models, the following formula 1/6 π × L × W x (L + W)/2 should be preferred.

Prediction and optimization of the performance in an osmotic microbial fuel cell for water purification and bioelectricity production using a developed process model

To better understand and mathematically express the process of osmotic microbial fuel cells (OsMFCs), a model describing the integration of electrogen growth kinetics and osmotic mass transfer was developed and validated in this study. Low root mean square error (RMSE) and mean relative error (MRE) were obtained for simulated water flux , voltage generation, and chemical oxygen demand (COD) removal, which revealed a reasonable accuracy of the developed model. The local sensitivity analysis indicated that operating parameters such as draw solution concentration, anode hydraulic retention time (HRT) and influent COD concentration played important roles in influencing the key performance of OsMFCs. At the benchmark settings of anode HRT (4 h), influent COD (880 mg L −1 ), and external resistance (3000 Ω), a voltage increment of approximately 9.4% was predicted with an increased draw solution concentration from ∼0 to 3.0 mol L −1 (sodium chloride, NaCl), revealing the merit brought by the combined effects of water flux-facilitated proton advection and the high conductivities of electrolytes in OsMFCs. Shortening the anode HRT from 14 to 0.5 h at a fixed draw solution concentration of 0.5 mol L −1 was able to achieve approximately 9.3% increment in voltage output, whereas, the COD removal compromised from 99.2% to less than 20%. High COD removal rate of over 95% was obtained when the examined influent COD was as low as approximately 300 mg L −1 . The optimization of combined parameters indicated that the optimized operation was at a draw solution concentration of ∼1.0 mol L −1 (NaCl), anode HRT ranging between 4 and 8 h, and a low influent COD of ∼300 mg L −1 at the model settings. • A model describing the hybrid processes in an OsMFC was developed and optimized. • The effect of proton advection on voltage generation was quantitatively described. • The effect of operating parameters was investigated using the validated model. • OsMFC operation was optimized considering both individual and combined parameters.

Comparative analysis of interpolation methods for rainfall mapping in the Faria catchment, Palestine

The availability of rainfall data is essential for the prediction of extreme rainfall events, hydrological modeling, and the design of water resources systems. In Palestine, the limited available rain gauges and the available rainfall data are insufficient to precisely characterize the areal rainfall distribution over the entire area. Hence, it is essential to find proper methods to utilize the available rainfall data at the existing raingauges to predict rainfall values at certain locations where rainfall measurements are not available. This study aims at identifying the most suitable interpolation method that can be used to produce interpolated rainfall surfaces at the Faria catchment, Palestine. Faria catchment (320 km2), located in the northeastern part of the West Bank, Palestine, is characterized by high temporal and spatial rainfall variations. Six, GIS-based, spatial interpolation methods have been tested by visual observation and cross-validation. These methods are: Inverse Distance Weighted, Spline completely Regularized, Spline with Tension, Ordinary Kriging, Universal Kriging, and Regression. Available seasonal rainfall data recorded at 16 locations for two rainy seasons; 2016/2017 and 2017/2018 were utilized. Results indicate that the Universal Kriging method has slightly a higher correlation coefficient and lower mean relative error than the other methods for the two seasons that make the method the most optimal one for mapping rainfall distribution in the Faria catchment. Moreover, the spatial distribution of raingauges between the two seasons affects the performance of different interpolation methods in the catchment.

A deep learning method for cyanobacterial harmful algae blooms prediction in Taihu Lake, China

Cyanobacterial Harmful Algae Blooms (CyanoHABs) in the eutrophic lakes have become a global environmental and ecological problem. In this study, a CNN-LSTM integrated model for predicting the CyanoHABs area was proposed and applied to the prediction of the CyanoHABs area in Taihu Lake. Firstly, the time-series data of the CyanoHABs area in Taihu Lake for 20 years were accurately obtained using MODIS images from 2000 to 2019 based on the FAI method. Then, a principal component analysis was performed on the daily meteorological data for the month before the outbreak of CyanoHABs in Taihu Lake from 2000 to 2019 to determine the meteorological factors closely related to the outbreak of CyanoHABs. Finally, the features of CyanoHABs area and meteorological data were extracted by Convolutional Neural Networks (CNN) model and used as the input of Long Short Term Memory Network (LSTM). An integrated CNN-LSTM model approach was constructed for predicting the CyanoHABs area. The results show that high R2 (0.91) and low mean relative error (17.42%) verified the validity of the FAI index to extract the CyanoHABs area in Taihu Lake; the meteorological factors closely related to the CyanoHABs outbreak in Taihu Lake are mainly temperature, relative humidity, wind speed, and precipitation; the CNN-LSTM integrated model has better prediction effect for both training and test sets compared with the CNN and LSTM models. This study provides an effective method for predicting temporal changes in the CyanoHABs area and offers new ideas for scientific and effective regulation of inland water safety.

Validation of a full-plasma integrated modeling approach on ASDEX Upgrade

In this work we present the extensive validation of a refined version of the integrated model based on engineering parameters (IMEP) introduced in reference (Luda et al 2020 Nucl. Fusion 60 036023). The modeling workflow is now fully automated, computationally faster thanks to the reduced radial resolution of the TGLF calculation, and it includes the modeling of the toroidal rotation, which was still taken from experimental measurements in our previous work. The updated model maintains the same accuracy as its previous version when tested on the cases presented in the initial publication. The confined plasma, from the magnetic axis to the separatrix, is simulated without using any experimental information from profiles measurements, and the inputs of IMEP are the same engineering parameters used when programming a plasma discharge. The model validation database consists of 50 ASDEX Upgrade (AUG) stationary (over a few energy confinement time) H-mode phases, which largely cover the entire AUG operational domain. The prediction of IMEP is compared with experimental measurements and with scaling laws, such as the IPB98(y,2), the ITPA20-IL, and AUG specific regressions. This modeling framework has proven to be very accurate over the entire set of 50 cases, with a significantly lower mean relative error with respect to each of the scaling laws considered, accurately reproducing the change in pedestal and core confinement caused by a change in plasma current, heating power, fueling rate, triangularity, magnetic field, NBI voltage (i.e. the effect of a change in the core particle source), and heating mix (e.g. correctly predicting the effect on confinement caused by a change in T e/T i). Plasma confinement is correctly described by IMEP also for two particular operating regimes, such as the ITER baseline scenario, and the QCE regime (quasi continuous exhaust, also referred as type-II and small ELMs). This work clearly demonstrates the power of this approach in pulling out physics mechanisms to interpret subtle interdependencies and that a 1D integrated model can reproduce experimental results over very large parameter variations with a higher accuracy than any statistical regression. This approach has therefore the potential to improve the prediction of the fusion performance in future tokamak reactors.

A Predictive Analysis of Wall Stress in Abdominal Aortic Aneurysms Using a Neural Network Model.

Rupture risk assessment of abdominal aortic aneurysms (AAAs) by means of quantifying wall stress is a common biomechanical strategy. However, the clinical translation of this approach has been greatly limited due to the complexity associated with the computational tools required for its implementation. Thus, being able to estimate wall stress using nonbiomechanical markers that can be quantified as a direct outcome of clinical image segmentation would be advantageous in improving the potential implementation of said strategy. In the present work, we investigated the use of geometric indices to predict patient-specific AAA wall stress by means of a novel neural network (NN) modeling approach. We conducted a retrospective review of existing clinical images of two patient groups: 98 asymptomatic and 50 symptomatic AAAs. The images were subject to a protocol consisting of image segmentation, processing, volume meshing, finite element modeling, and geometry quantification, from which 53 geometric indices and the spatially averaged wall stress (SAWS) were calculated. SAWS estimated from finite element analysis was considered the gold standard for the predictions. We developed feed-forward NN models composed of an input layer, two dense layers, and an output layer using Keras, a deep learning library in python. The NN models were trained, tested, and validated independently for both AAA groups using all geometric indices, as well as a reduced set of indices resulting from a variable reduction procedure. We compared the performance of the NN models with two standard machine learning algorithms (MARS: multivariate adaptive regression splines and GAM: generalized additive model) and a linear regression model (GLM: generalized linear model). With the reduced sets of indices, the NN-based approach exhibited the highest mean goodness-of-fit (for the symptomatic group 0.71 and for the asymptomatic group 0.79) and lowest mean relative error (17% for both groups). In contrast, MARS yielded a mean goodness-of-fit of 0.59 for the symptomatic group and 0.77 for the asymptomatic group, with relative errors of 17% for the symptomatic group and 22% for the asymptomatic group. GAM had a mean goodness-of-fit of 0.70 for the symptomatic group and 0.80 for the asymptomatic group, with relative errors of 16% for the symptomatic group and 20% for the asymptomatic group. GLM did not perform as well as the other algorithms, with a mean goodness-of-fit of 0.53 for the symptomatic group and 0.70 for the asymptomatic group, with relative errors of 19% for the symptomatic group and 23% for the asymptomatic group. Nevertheless, the NN models required a reduced set of 15 and 13 geometric indices to predict SAWS for the symptomatic and asymptomatic AAA groups, respectively. This was in contrast to the reduced set of nine and eight geometric indices required to predict SAWS with the MARS and GAM algorithms for each AAA group, respectively. The use of NN modeling represents a promising alternative methodology for the estimation of AAA wall stress using geometric indices as surrogates, in lieu of finite element modeling. The performance metrics of NN models are expected to improve with significantly larger group sizes, given the suitability of NN modeling for "big data" applications.

Estimation of Paddy Rice Nitrogen Content and Accumulation Both at Leaf and Plant Levels from UAV Hyperspectral Imagery

Remote sensing-based mapping of crop nitrogen (N) status is beneficial for precision N management over large geographic regions. Both leaf/canopy level nitrogen content and accumulation are valuable for crop nutrient diagnosis. However, previous studies mainly focused on leaf nitrogen content (LNC) estimation. The effects of growth stages on the modeling accuracy have not been widely discussed. This study aimed to estimate different paddy rice N traits—LNC, plant nitrogen content (PNC), leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA)—from unmanned aerial vehicle (UAV)-based hyperspectral images. Additionally, the effects of the growth stage were evaluated. Univariate regression models on vegetation indices (VIs), the traditional multivariate calibration method, partial least squares regression (PLSR) and modern machine learning (ML) methods, including artificial neural network (ANN), random forest (RF), and support vector machine (SVM), were evaluated both over the whole growing season and in each single growth stage (including the tillering, jointing, booting and heading growth stages). The results indicate that the correlation between the four nitrogen traits and the other three biochemical traits—leaf chlorophyll content, canopy chlorophyll content and aboveground biomass—are affected by the growth stage. Within a single growth stage, the performance of selected VIs is relatively constant. For the full-growth-stage models, the performance of the VI-based models is more diverse. For the full-growth-stage models, the transformed chlorophyll absorption in the reflectance index/optimized soil-adjusted vegetation index (TCARI/OSAVI) performs best for LNC, PNC and PNA estimation, while the three band vegetation index (TBVITian) performs best for LNA estimation. There are no obvious patterns regarding which method performs the best of the PLSR, ANN, RF and SVM in either the growth-stage-specific or full-growth-stage models. For the growth-stage-specific models, a lower mean relative error (MRE) and higher R2 can be acquired at the tillering and jointing growth stages. The PLSR and ML methods yield obviously better estimation accuracy for the full-growth-stage models than the VI-based models. For the growth-stage-specific models, the performance of VI-based models seems optimal and cannot be obviously surpassed. These results suggest that building linear regression models on VIs for paddy rice nitrogen traits estimation is still a reasonable choice when only a single growth stage is involved. However, when multiple growth stages are involved or missing the phenology information, using PLSR or ML methods is a better option.

Use of Very High Spatial Resolution Commercial Satellite Imagery and Deep Learning to Automatically Map Ice-Wedge Polygons across Tundra Vegetation Types.

We developed a high-throughput mapping workflow, which centers on deep learning (DL) convolutional neural network (CNN) algorithms on high-performance distributed computing resources, to automatically characterize ice-wedge polygons (IWPs) from sub-meter resolution commercial satellite imagery. We applied a region-based CNN object instance segmentation algorithm, namely the Mask R-CNN, to automatically detect and classify IWPs in North Slope of Alaska. The central goal of our study was to systematically expound the DLCNN model interoperability across varying tundra types (sedge, tussock sedge, and non-tussock sedge) and image scene complexities to refine the understanding of opportunities and challenges for regional-scale mapping applications. We corroborated quantitative error statistics along with detailed visual inspections to gauge the IWP detection accuracies. We found promising model performances (detection accuracies: 89% to 96% and classification accuracies: 94% to 97%) for all candidate image scenes with varying tundra types. The mapping workflow discerned the IWPs by exhibiting low absolute mean relative error (AMRE) values (0.17–0.23). Results further suggest the importance of increasing the variability of training samples when practicing transfer-learning strategy to map IWPs across heterogeneous tundra cover types. Overall, our findings demonstrate the robust performances of IWPs mapping workflow in multiple tundra landscapes.

An Image Processing Algorithm for an Automated Smartphone-based Semen Analyzer

This paper deals with the implementation of an image processing algorithm for an automated semen analyzer. The analysis of semen is the most important diagnostic tool in the initial investigation of male fertility. Semen analysis is best performed in a specialized laboratory with extensive experience in using the approved methods of the World Health Organization. Semen analysis in a specialized laboratory is labor-intensive, expensive, and time-consuming, and the accuracy depends on staff experience only. So, the use of image processing technology becomes very necessary in order to overcome these disadvantages. In this study, to improve the procedures involved in motion detection and prediction algorithms, we study Kalman filter-based object motion prediction. Furthermore, to improve accuracy and quality in the rate of object detection, the visual background extractor (ViBe) is employed. The experimental results of the proposed technique are evaluated and validated for performance and quality analysis from video data based on visual experiments. The experimental result achieved low root mean square error (RMSE) and relative root mean square error (RMSE%) values, demonstrating the effectiveness of the proposed technique for detecting and tracking sperm. The combination of two image processing approaches is proven to be effective in analyzing sperm condition.

A New Remote Sensing Method to Estimate River to Ocean DOC Flux in Peatland Dominated Sarawak Coastal Regions, Borneo

We present a new remote sensing based method to estimate dissolved organic carbon (DOC) flux discharged from rivers into coastal waters off the Sarawak region in Borneo. This method comprises three steps. In the first step, we developed an algorithm for estimating DOC concentrations using the ratio of Landsat-8 Red to Green bands B4/B3 (DOC (μM C) = 89.86 ·e0.27·(B4/B3)), which showed good correlation (R = 0.88) and low mean relative error (+5.71%) between measured and predicted DOC. In the second step, we used TRMM Multisatellite Precipitation Analysis (TMPA) precipitation data to estimate river discharge for the river basins. In the final step, DOC flux for each river catchment was then estimated by combining Landsat-8 derived DOC concentrations and TMPA derived river discharge. The analysis of remote sensing derived DOC flux (April 2013 to December 2018) shows that Sarawak coastal waters off the Rajang river basin, received the highest DOC flux (72% of total) with an average of 168 Gg C per year in our study area, has seasonal variability. The whole of Sarawak represents about 0.1% of the global annual riverine and estuarine DOC flux. The results presented in this study demonstrate the ability to estimate DOC flux using satellite remotely sensed observations.