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Abstract Neuromarketing is an emerging research field that aims to understand consumers’ decision-making processes when choosing which product to buy. This information is highly sought after by businesses looking to improve their marketing strategies by understanding what leaves a positive or negative impression on consumers. It has the potential to revolutionize the marketing industry by enabling companies to offer engaging experiences, create more effective advertisements, avoid the wrong marketing strategies, and ultimately save millions of dollars for businesses. Therefore, good documentation is necessary to capture the current research situation in this vital sector. In this article, we present a systematic review of EEG-based Neuromarketing. We aim to shed light on the research trends, technical scopes, and potential opportunities in this field. We reviewed recent publications from valid databases and divided the popular research topics in Neuromarketing into five clusters to present the current research trend in this field. We also discuss the brain regions that are activated when making purchase decisions and their relevance to Neuromarketing applications. The article provides appropriate illustrations of marketing stimuli that can elicit authentic impressions from consumers' minds, the techniques used to process and analyze recorded brain data, and the current strategies employed to interpret the data. Finally, we offer recommendations to upcoming researchers to help them investigate the possibilities in this area more efficiently in the future.

This paper presented the design and performance analysis of a high-gain balanced antipodal Vivaldi antenna (BAVA) integrated with a parasitic patch and a half-spherical shaped dielectric lens, specifically designed for microwave and millimeter-wave applications. Constructed with a Teflon substrate (εr = 2.1, tanδ = 0.0002) and energized by a 50-Ω microstrip feedline, the innovative design incorporated a parasitic patch and three different types of dielectric lenses: half-circular, conical, and half-spherical shaped. The half-spherical dielectric lens significantly enhanced performance, achieving a gain of 20.54 dB, side lobe level (SLL) of −18.15 dB, and a maximum beam squint of 1.59°. Additionally, it exhibited a 98% radiation efficiency, a front-to-back ratio of 31.75 dB, and a half-power beamwidth (HPBW) of 6.7°. The incorporation of a parasitic patch and a half-spherical shaped dielectric lens enhanced the antenna's performance. Comparative analysis with existing antennas underscored the superiority of the proposed design, positioning it as a promising solution for microwave and millimeter-wave communication systems, radar, and sensing applications.

This paper presents a dual-band 4-element multiple-input-multiple-output (MIMO) antenna featuring orthogonal circular polarization (CP) for Sub-6 GHz 5G applications. The single antenna combines a non-uniform width elliptical ring slot and a feed network, utilizing two L-shaped secondary feed lines to generate CP within the targeted frequency bands (3.55−3.80 GHz and 4.60−4.80 GHz). Using the single antenna as a unit antenna, a 4-element MIMO configuration is devised, employing a mirror technique for element placement to achieve orthogonal CP. This placement method effectively enhances coverage area and enlarges the 3-dB axial ratio bandwidth in the lower band (3.40–3.80 GHz). The common ground connections among elements are established via four strip lines. The antenna shows a good diversity performance considering the envelope correlation coefficient (ECC) result of less than 0.04 and isolation greater than 15 dB. The simulated gains of the MIMO antenna are 4 and 5 dBic in the respective bands. The single and MIMO antennas are fabricated, and the antennas’ performances are evaluated. A good agreement is observed between the measured and simulated results. The dual CP and diversity performances of the MIMO antenna make it more efficient for 5G wireless applications.

Being one of the main communication medium, speech contains necessary information about the emotional state of a human. Accurate emotion recognition is crucial for enhancing human-machine interactions, highlighting the importance of a strong Speech Emotion Recognition (SER) system. SER system classifies the human emotional state based on speaker’s utterances in different catagories such as sad, happy, neutral, angry, surprise, calm and so on. This research introduces a novel SER approach that utilizes cochleagram and spectrogram features to capture relevant speech patterns for the classifier network. The network integrates a hybrid model that combines Convolutional Neural Networks (CNN) for feature extraction with Gated Recurrent Units (GRU) to handle temporal dependencies. Furthermore, to improve the performance of this network, a multi-head attention mechanism has been incorporated following the GRU layer. Despite increasing interest in SER, there is a notable lack of studies using Bangla language datasets, revealing a significant gap in current research. To address this gap, evaluation of the model has been conducted on the augmented BanglaSER (Bangla Speech Emotion Recognition) dataset in which the model has achieved a notable accuracy of 92.04% in categorizing five distinct emotions: angry, surprise, happy, neutral, and sad. Additionally, to further evaluate the performance of the SER model, English language based RAVDESS (Ryerson Audio-Visual Database of Emotional Speech) dataset has also been employed into the proposed model. This attempt has provided 82.40% accuracy in classifying eight diverse emotions that includes fear, disgust, calm along with the emotions of BanglaSER. Moreover, a comparative analysis of the proposed model with existing SER approaches is carried out to demonstrate it’s stability and robustness. The incorporation of two individual features as inputs into the attention guided hybrid neural network showcases the efficacy of the proposed SER system, offering a promising approach for precise and efficient emotion categorization from speech signals.

Recently, the transformer-based model e.g., the vision transformer (ViT) has been extensively used in computer vision tasks. The superior performance of the ViT leads to the requirement of an enormous dataset and the complexity of calculating self-attention between patches is quadratic in nature. To acknowledge these two concerns, this paper proposes a novel shifted patch tokenization swin transformer (SPT-Swin) for the image classification task. The shifted patch tokenization (SPT) compensates for the data deficiency by increasing the data samples based on spatial information of the image patches while the swin transformer provides linear computational complexity by calculating self-attention between the shifted window based patches. For model validation, the SPT-Swin framework is trained on popular benchmark image datasets such as ImageNet-1K, CIFAR-10 and CIFAR-100, and the classification accuracies are found 89.45%, 95.67% and 92.95% respectively. Moreover, the comparative analysis of the proposed model with the existing state-of-the-art models shows that the classification performances are improved by 7.05%, 4.14%, and 8.30% for the ImageNet-1K, CIFAR-10 and CIFAR-100 datasets respectively. Therefore, our proposed SPT-based data augmentation technique with the core swin transformer model could be a data-efficient linear complex-able model for future computer vision tasks.

Electroencephalogram (EEG) is the graphical representation of Brain’s electrical activity. Mental stress can be detected in many ways and EEG is one of them. Regular mental stress gives rise to many mental disorders and it may cause various physiological and psychological diseases. As a result, early-stage detection of stress is very important. In this research, brain activity was recorded through EEG headset during inducing different levels of stress from audio-visual stimulus. Again, for better interaction between humans and machines, it is essential to analyze the power spectrum of the brain in response to different audio and visual stimulus. To better evaluate visual and auditory stress, an automated system is designed to differentiate among various audio and visual evoked potentials. This may further help for designing different assistive devices for the people having visual and hearing disability. In this paper, we proposed a framework to classify different levels of stress in response to audio and visual stimuli and also classified between these two stimuli by analyzing EEG signals. Raw EEG data was collected in lab environment and the necessary pre-processing steps were applied for denoising. By extracting robust features from the denoised audio and visual data, binary and multi-level stress were classified. A binary classification between audio and visual stimuli was also successfully done in this research. We achieved highest accuracy for binary stress classification 97.14% from visual stimuli, whereas we achieved 94.51% accuracy for auditory stimuli. Again, we achieved the accuracy for four level stress classification 89.59% for visual stimuli and 82.63% for audio stimuli.

In this paper, a relatively simple and ultra-sensitive Photonic crystal fiber (PCF) based surface plasmon resonance (SPR) sensor is proposed for detecting different analyte refractive indices (RIs) ranging from 1.33 to 1.43 over a wide range of wavelength spectrum spanning $0.55~\mu $ m to $3.50~\mu $ m. The comprehensive finite-element simulations indicate that it is possible to achieve remarkable sensing performances such as wavelength sensitivity (WS) and figure of merit (FOM) as high as 123,000 nm/RIU and 683 RIU1, respectively, and extremely low value of wavelength resolution (WR) about $8.13\times 10^{-7}$ RIU. A novel artificial neural network (ANN) model is proposed which helps to accurately predict the RIs by carefully examining the simulation data. The mean square error (MSE) and prediction accuracy ( $R^{2}$ ) values for the ANN model are found about 0.0097 and 0.9987, respectively, indicating the high prediction capability of the proposed ANN model. Due to its exceptional sensitivity and precise detection capabilities, the proposed sensor has the potential to serve as a viable option for sensing analyte RI. Additionally, the sensor could be utilized for identifying cancerous cells and detecting urinary tract infections in humans.

Floods stand out as one of the most expensive natural calamities, causing harm to both lives and properties for millions of people globally. The increasing frequency and intensity of flooding underscores the need for accurate and timely flood mapping methodologies to enhance disaster preparedness and response. Earth observation data obtained through satellites offer comprehensive and recurring perspectives of areas that may be prone to flooding. This paper shows the suitability of high-resolution PlanetScope imagery as an efficient and accessible approach for flood mapping through a case study in South Chickamauga Creek (SCC), Chattanooga, Tennessee, focusing on a significant flooding event in 2020. The extent of the flood water was delineated and mapped using image classification and density slicing of Normalized Difference Water Index (NDWI). The obtained results indicate that PlanetScope imagery performed well in flood mapping for a narrow creek like SCC, achieving an overall accuracy of more than 90% and a Kappa coefficient of over 0.80. The findings of this research contribute to a better understanding of the flood event in Chattanooga and demonstrate that PlanetScope imagery can be utilized as a very useful resource for accurate and timely flood mapping of streams with narrow widths.

Brain tumor detection and categorization of its subtypes are essential for early diagnosis and improving patient outcomes. This research presents a cutting-edge approach that employs advanced data augmentation and deep learning methodologies for brain tumor classification. For this work, a dataset of 6982 MRI images from the IEEE Data Port was considered, in which a total of 5712 images of four classes (1321 glioma, 1339 meningioma, 1595 no tumor, and 1457 pituitary) were used in the training set and a total of 1270 images of the same four classes were used in the testing set. A Wasserstein Generative Adversarial Network was implemented to generate synthetic images to address class imbalance, resulting in a balanced and consistent dataset. A comparison was conducted between various data augmentation metholodogies demonstrating that Wasserstein Generative Adversarial Network-augmented results perform excellently over traditional augmentation (such as rotation, shift, zoom, etc.) and no augmentation. Additionally, a Gaussian filter and normalization were applied during preprocessing to reduce noise, highlighting its superior accuracy and edge preservation by comparing its performance to Median and Bilateral filters. The classifier model combines parallel feature extraction from modified InceptionV3 and VGG19 followed by custom attention mechanisms for effectively capturing the characteristics of each tumor type. The model was trained for 64 epochs using model checkpoints to save the best-performing model based on validation accuracy and learning rate adjustments. The model achieved a 99.61% accuracy rate on the testing set, with precision, recall, AUC, and loss of 0.9960, 0.9960, 0.0153, and 0.9999, respectively. The proposed architecture’s explainability has been enhanced by t-SNE plots, which show unique tumor clusters, and Grad-CAM representations, which highlight crucial areas in MRI scans. This research showcases an explainable and robust approach for correctly classifying four brain tumor types, combining WGAN-augmented data with advanced deep learning models in feature extraction. The framework effectively manages class imbalance and integrates a custom attention mechanism, outperforming other models, thereby improving diagnostic accuracy and reliability in clinical settings.

Chattanooga, Tennessee is one of many cities in the Southeastern United States that is experiencing rapid urban growth. As these metropolitan areas continue to grow larger, more and more of Earth’s unique temperate forest, an ecosystem of enormous cultural, ecological, and recreational significance in the Southeastern United States, is destroyed to make way for new urban development. This research takes advantage of the extensive temporal archive of multispectral satellite imagery provided by the Landsat program to conduct a 37-year analysis of urban forest canopy cover across the City of Chattanooga. A time series of seven Landsat 5 scenes and three Landsat 8 scenes were acquired between 1984 and 2021 at an interval of five years or less. Each multispectral image was processed digitally and classified into a four-class thematic raster using a supervised hybrid classification scheme with a support vector machine (SVM) algorithm. The obtained results showed a loss of up to 43% of urban forest canopy and a gain of up to 134% urban land area in the city. Analyzing the multidecade spatiotemporal forest canopy in a rapidly expanding metropolitan center, such as Chattanooga, could help direct sustainable development efforts towards areas urbanizing at an above-average rate.

Urban tree canopy (UTC) provides urban residents with numerous benefits, including positive mental and physical health, the mitigation and prevention of urban heat islands, and a sense of place. Numerous studies have shown that as the wealth of a community decreases, so does the amount of UTC found in the community; thus, wealthier communities are more likely to enjoy the benefits that urban forests provide. Emerging technologies in remote sensing and GIS are allowing for new opportunities to study and understand the relationships between urban neighborhoods and UTC. In this study, land cover data for Chattanooga, Tennessee were derived from high-resolution (50 cm) multispectral imagery to assess the previously unknown extent and distribution of UTC and to measure the extent of UTC by neighborhood and census block group level. Using exploratory regression analysis, variables representing income, population density, race, educational attainment, and urban heat islands were analyzed to investigate the influence of UTC on neighborhood characteristics. This study found that UTC represented half of the total land cover composition, the tree equity was not as profound as shown in other cities, and the lack of UTC likely influences the prevalence of urban heat islands. This study also shows the importance and utility of using high-resolution imagery and land cover to assess and understand the impact and distribution of UTC in urban environments.

The demand for an effective system that combines cutting-edge technologies with medical research to improve healthcare systems has increased with the development of medical technology. The most fundamental form of disease prevention is taking the right medication when needed. With the right care, many fatal diseases can be cured or prevented. Therefore, it is crucial to follow the doctor's recommended drug plan. Healthcare experts now have serious concerns about patients not being able to take their prescribed medications on time, particularly elderly patients. Due to age-related memory loss, people who have been given multiple prescriptions at once over an extended period of time are more likely to forget to take their medication on time or to take the wrong medication. Sometimes, a patient's inability to take the right medication at the right time might have a major impact on their health. Aside from being forgetful, patients, especially the elderly and illiterate, may not be able to read the name stated on medical containers, leading to the consumption of the wrong medication. These errors contribute to non-adherence to pharmaceuticals, which is detrimental to the patient's health. As a result, there is a significant problem that hinders the success of the treatment. The medication reminder system is intended for people who frequently take medications or vitamin supplements in order to handle this. In order to help an elderly person properly take their medication and help the patient have a healthy life, we have created a ground-breaking portable multifunctional medicine reminder kit with phone calls. Other intelligent characteristics of the smart medicine reminder include the capacity to show the time, date, and day in real time, the detection of smoke, the measurement of air humidity and temperature in the room, the measurement of heartbeats per second, the patient's body temperature, and the oxygen saturation level.

Abstract Deep learning-based models such as deep neural network (DNN) and convolutional neural network (CNN) have recently been established as state-of-the-art for enumerating electric fields from transcranial magnetic stimulation coil. One of the main challenges related to this electric field enumeration is the prediction time and accuracy. Despite the low computational cost, the performance of the existing prediction models for electric field enumeration is quite inefficient. This study proposes a 1D CNN-based bi-directional long short-term memory (BiLSTM) model with an attention mechanism to predict electric field induced by a transcranial magnetic stimulation coil. The model employs three consecutive 1D CNN layers followed by the BiLSTM layer for extracting deep features. After that, the weights of the deep features are redistributed and integrated by the attention mechanism and a fully connected layer is utilized for the prediction. For the prediction purpose, six input features including coil turns of single wing, coil thickness, coil diameter, distance between two wings, distance between head and coil position, and angle between two wings of coil are mapped with the output of the electric field. The performance evaluation is conducted based on four verification metrics (e.g. R2, MSE, MAE, and RMSE) between the simulated data and predicted data. The results indicate that the proposed model outperforms existing DNN and CNN models in predicting the induced electrical field with R2 = 0.9992, MSE = 0.0005, MAE = 0.0188, and RMSE = 0.0228 in the testing stage.

Current deep learning-assisted brain tumor classification models sustain inductive bias and parameter dependency problems for extracting texture-based image information. Thereby concerning these problems, the recent development of the vision transformer model has substituted the DL model for classification tasks. However, the high performance of the vision transformer model depends on a large-scale dataset as well as self-attention calculations between the number of image patches which result in a quadratic computational complexity. To address these problems, the vision transformer must be data-efficient to be well-trained with a limited amount of data, and the computational complexity must be linear with the number of image patches. Consequently, this paper presents a novel linear-complexity data-efficient image transformer called LCDEiT for training with small-size datasets by using a teacher-student strategy and linear computational complexity concerning the number of patches using an external attention mechanism. The teacher model comprised a custom gated-pooled convolutional neural network to provide knowledge to the transformer-based student model for the classification of MRI brain tumors. The average classification accuracy and F1-score for two benchmark datasets including Figshare and BraTS-21 are found 98.11% and 97.86% and 93.69% and 93.68% respectively. The results indicate that the proposed model could have a great impact on medical imaging-based diagnosis where data availability and faster computations are the main concern.

Transcranial magnetic stimulation is an electromagnetic induction-based non-invasive therapeutic technique for neurological diseases. For finding new clinical applications and enhancing the efficacy of TMS in existing neurological disorders, the current study focuses on a deep learning-based prediction model as an alternative to time-consuming electromagnetic (EM) simulation software. The main bottleneck of the existing prediction models is to consider very few input parameters of a standard coil such as coil type and coil position for predicting an output of electric field value. To overcome this limitation, a transformer-based prediction model titled as ViTab transformer is developed in this work to predict electric field (E-max), focality or area of stmulation (S-half), and volume of stimulation (V-half) by considering several input parameters such as sources of MRI images, types of coils, coil position, rate of change of current, brain tissues conductivity, and coil distance from the scalp. The proposed framework consists of a vision and a tab transformer to handle both image and tabular-type data. The prediction performance of the offered model is evaluated in terms of coefficient determination, R2 score, for E-max, V-half, and S-half in the testing phase. The obtained result in terms of R2 score for E-max, V-half, and S-half are found 0.97, 0.87, and 0.90 respectively. The results indicate that the suggested ViTab transformer model can predict electric field as well as focality more accurately than the current state-of-the-art methods. The reduced computational time, as well as efficient prediction accuracy, resembles that ViTab transformer can assist the neuroscientist and neurosurgeon prior to providing superior TMS treatment in near future.

The current advancement towards retinal disease detection mainly focused on distinct feature extraction using either a convolutional neural network (CNN) or a transformer-based end-to-end deep learning (DL) model. The individual end-to-end DL models are capable of only processing texture or shape-based information for performing detection tasks. However, extraction of only texture- or shape-based features does not provide the model robustness needed to classify different types of retinal diseases. Therefore, concerning these two features, this paper developed a fusion model called ‘Conv-ViT’ to detect retinal diseases from foveal cut optical coherence tomography (OCT) images. The transfer learning-based CNN models, such as Inception-V3 and ResNet-50, are utilized to process texture information by calculating the correlation of the nearby pixel. Additionally, the vision transformer model is fused to process shape-based features by determining the correlation between long-distance pixels. The hybridization of these three models results in shape-based texture feature learning during the classification of retinal diseases into its four classes, including choroidal neovascularization (CNV), diabetic macular edema (DME), DRUSEN, and NORMAL. The weighted average classification accuracy, precision, recall, and F1 score of the model are found to be approximately 94%. The results indicate that the fusion of both texture and shape features assisted the proposed Conv-ViT model to outperform the state-of-the-art retinal disease classification models.

This article proposes a deep neural network (DNN) model to predict the electric field induced by a transcranial magnetic stimulation (TMS) coil under high-amplitude and low-frequency current pulse conditions. The DNN model is comprised of an input layer with 6 neurons, three non-linear hidden layers with a total of 1088 neurons, and a linear single output layer. The model is developed in Google Colaboratory environment with TensorFlow framework using six features including coil turns of single wing, coil thickness, coil diameter, distance between two wings, distance between head and coil position, and angle between two wings of coil as the inputs and electric field as the output. The model performance is evaluated based on four verification statistic metrics such as coefficient of determination (R2), mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) between the simulated and predicted values. The proposed model provides an adequate performance with $\text{R}^{2} =0.766$ , MSE = 0.184, MAE = 0.262, and RMSE = 0.429 in the testing stage. Therefore, the model can successfully predict the electric field in an assembly TMS coil without the aid of electromagnetic simulation software that suffers from an extensive computational cost.

By using computer-aided arrhythmia diagnosis tools, electrocardiogram (ECG) signal plays a vital role in lowering the fatality rate associated with cardiovascular diseases (CVDs) and providing information about the patient’s cardiac health to the specialist. Current advancements in deep-learning-based multivariate time series data analysis, such as ECG data classification include LSTM, Bi-LSTM, CNN, with Bi-LSTM, and other sequential networks. However, these networks often struggle to accurately determine the long-range dependencies among data instances, which can result in problems such as vanishing or exploding gradients for longer data sequences. To address these shortcomings of sequential models, a hybrid arrhythmia classification system using recurrence along with a self-attention mechanism is developed. This system utilizes convolutional layers as a part of representation learning, designed to capture the salient features of raw ECG data. Then, the latent embedded layer is fed to a self-attention-assisted transformer encoder model. Because the ECG data are highly influenced by absolute order, position, and proximity of time steps due to interdependent relationships among immediate neighbors, a component of recurrence using Bi-LSTM is added to the encoder model to address this characteristic of the data. The model performance indices such as classification accuracy and F1-score were found to be 99.2%. This indicates that the combination of recurrence along with self-attention-assisted architecture produces improved classification of arrhythmia from raw ECG signal when compared with the state-of-the-art models.

As a noninvasive neuromodulation technique, transcranial magnetic stimulation (TMS) has already exhibited a great impact in clinical application and scientific research. This study presents a finite element method-based simulation of the Halo-V assembly (HVA) coil placed on the five-shell spherical human head model to examine the distributions of induced electric and magnetic fields. The performance of the designed HVA coil is evaluated by comparing the simulation results with the commercially available Halo-FO8 (HFA) assembly coil and standard single coils including the Halo and V coils. The simulation results indicate that the HVA coil shows an improved focality in terms of electric field distribution than the other single and assembly stimulation coils. Additionally, the effects of a magnetic shield plate and magnetic core on the designed HVA coil are investigated. Results indicate that the magnetic shield plate and magnetic core are proficient in further improving the stimulation focality. Therefore, the HVA TMS coil results in a safe and effective stimulation with enhanced focality of the target region as compared to the existing assembly coil.

Enid Lake is one of the largest reservoirs located in Yazoo River Basin, the largest basin in the state of Mississippi. The lake was impounded by Enid Dam on the Yocona River in Yalobusha County and covers an area of 30 square kilometers. It provides significant natural and recreational resources. The soils in this region are highly erodible, resulting in a large amount of fine-grained cohesive sediment discharged into the lake. In this study, a 3D numerical model was developed to simulate the free surface hydrodynamics and transportation of cohesive sediment with a median diameter of 0.0025 to 0.003 mm in Enid Lake. Flow fields in the lake are generally induced by wind and upstream river inflow, and the sediment is also introduced from the inflow during storm events. The general processes of sediment flocculation and settling were considered in the model, and the erosion rate and deposition rate of cohesive sediment were calculated. In this model, the sediment simulation was coupled with flow simulation. In this research, remote sensing technology was applied to estimate the sediment concentration at the lake surface and provide validation data for numerical model simulation. The model results and remote sensing data help us to understand the transport, deposition and resuspension processes of cohesive sediment in large reservoirs due to wind-induced currents and upstream river flows.

The Tennessee River in the United States is one of the most ecologically distinct rivers in the world and serves as a great resource for local residents. However, it is also one of the most polluted rivers in the world, and a leading cause of this pollution is storm water runoff. Satellite remote sensing technology, which has been used successfully to study surface water quality parameters for many years, could be very useful to study and monitor the quality of water in the Tennessee River. This study developed a numerical turbidity estimation model for the Tennessee River and its tributaries in Southeast Tennessee using Landsat 8 satellite imagery coupled with near real-time in situ measurements. The obtained results suggest that a nonlinear regression-based numerical model can be developed using Band 4 (red) surface reflectance values of the Landsat 8 OLI sensor to estimate turbidity in these water bodies with the potential of high accuracy. The accuracy assessment of the estimated turbidity achieved a coefficient of determination (R2) value and root mean square error (RMSE) as high as 0.97 and 1.41 NTU, respectively. The model was also tested on imagery acquired on a different date to assess its potential for routine remote estimation of turbidity and produced encouraging results with R2 value of 0.94 and relatively high RMSE.

A 2×2 slot array antenna fed by microstrip line for circular polarization operated in the S band frequency range is designed in this paper. Single cross slot with single port feed as well as dual port feed is taken into consideration for realizing circular polarization and combining these two processes, the slot array is designed with single feed for circular polarization. The antennas are designed on a Teflon glass fiber substrate of thickness 0.8 mm. The slot array dimension is 120×142×1.636 mm³. Smith chart of single cross slot antenna with single feed as well as dual feed has a dip at 2.69 and 2.53 GHz respectively indicate the capability of realizing circular polarization in the S band frequency range. The return loss of the slot array antenna is -58 dB shows good input impedance matching of the antenna. A dip in the smith chart of the slot array shows circular polarization near 2.4 GHz ensuring wireless applications as well. Axial ratio is found to be less than 1 dB in the resonance frequency. The impedance bandwidth percentage of the slot array antenna is 12.24%. The simulation is done by using keysight advanced design system (ADS) software. [ABSTRACT FROM AUTHOR]

Soil moisture monitoring and characterization of the spatial and temporal variability of this hydrologic parameter at scales from small catchments to large river basins continues to receive much attention, reflecting its critical role in subsurface-land surface-atmospheric interactions and its importance to drought analysis, irrigation planning, crop yield forecasting, flood protection, and forest fire prevention. Synthetic Aperture Radar (SAR) data acquired at different spatial resolutions have been successfully used to estimate soil moisture in different semi-arid areas of the world for many years. This research investigated the potential of linear multiple regressions and Artificial Neural Networks (ANN) based models that incorporate different geophysical variables with Radarsat 1 SAR fine imagery and concurrently measured soil moisture measurements to estimate surface soil moisture in Nash Draw, NM. An artificial neural network based model with vegetation density, soil type, and elevation data as input in addition to radar backscatter values was found suitable to estimate surface soil moisture in this area with reasonable accuracy. This model was applied to a time series of SAR data acquired in 2006 to produce soil moisture data covering a normal wet season in the study site.

Soil moisture monitoring and characterization of the spatial and temporal variability of this hydrologic parameter at scales from small catchments to large river basins continues to receive much attention, reflecting its critical role in subsurface-land surface-atmospheric interactions and its importance to drought analysis, irrigation planning, crop yield forecasting, flood protection, and forest fire prevention. Synthetic Aperture Radar (SAR) data acquired at different spatial resolutions have been successfully used to estimate soil moisture in different semi-arid areas of the world for many years. This research investigated the potential of linear multiple regressions and Artificial Neural Networks (ANN) based models that incorporate different geophysical variables with Radarsat 1 SAR fine imagery and concurrently measured soil moisture measurements to estimate surface soil moisture in Nash Draw, NM. An artificial neural network based model with vegetation density, soil type, and elevation data as input in addition to radar backscatter values was found suitable to estimate surface soil moisture in this area with reasonable accuracy. This model was applied to a time series of SAR data acquired in 2006 to produce soil moisture data covering a normal wet season in the study site. [ABSTRACT FROM AUTHOR]

Recent research in mesoscale hydrology suggests that the size of the reservoirs and the land-use/land-cover (LULC) patterns near them impact the extreme weather [e.g., probable maximum flood (PMF)]. A key question was addressed byW. Yigzaw et al.: How do reservoir size and/or LULC modify extreme flood patterns, specifically PMF via modification of probable maximum precipitation (PMP)? Using the American River watershed (ARW) as a representative example of an impounded watershed with Folsom Dam as the flood control structure, they applied the distributed Variable Infiltration Capacity (VIC) model to simulate the PMF from the atmospheric feedbacks simulated for various LULC scenarios. The current study presents a methodology to extend the impacts of these modified extreme flood patterns on the downstream Sacramento County, California. The research question addressed is, what are the relative effects of downstream flood hazards to population on the American River system under various PMF scenarios for the Folsom Dam? To address this goal, a two-dimensional flood model, the Flood in Two Dimensions-Graphics Processing Unit (Flood2D-GPU), is calibrated using synthetic aperture radar (SAR) and Landsat satellite observations and observed flood stage data. The calibration process emphasized challenges associated with using National Elevation Dataset (NED) digital elevation model (DEM) and topographic light detection and ranging (lidar)-derived DEMs to achieve realistic flood inundation. Following this calibration exercise, the flood model was used to simulate four land-use scenarios (control, predam, reservoir double, and nonirrigation). The flood hazards are quantified as downstream flood hazard zones by estimating flood depths and velocities and its impacts on risk to population using depth-velocity hazard relationships provided by U.S. Bureau of Reclamation (USBR). From the preliminary application ofmethodology in this study, it is evident from comparing downstream flood hazard that similar trends in PMF comparisons reported byW. Yigzaw et al. were observed. Between the control and nonirrigation, the downstream flood hazard is pronounced by -3.90% for the judgment zone and -2.40% for high hazard zones. Comparing the control and predam scenarios, these differences are amplified, ranging between 0.17% and -1.34%. While there was no change detected in the peak PMF discharges between the control and reservoirdouble scenarios, it still yielded an increase in high hazard areas for the latter. Based on this preliminary bottom-up vulnerability assessment study, it is evident that what was observed in PMF comparisons byW. Yigzaw et al. is confirmed in comparisons between control versus predam and control versus nonirrigation.While there was no change detected in the peak PMF discharges between the control and reservoirdouble scenarios, it still yielded a noticeable change in the total areal extents: specifically, an increase in high hazard areas for the latter. Continued studies in bottom-up vulnerability assessment of flood hazards will aid in developing suitable mitigation and adaptation options for a much needed resilient urban infrastructure. [ABSTRACT FROM AUTHOR]

Shallow surficial failures or levee slides in the Mississippi River levee system are very common. There is currently no system to identify or predict the location of these slides before they occur. Studies of slide occurrence mechanisms suggest that probable slide-affected areas are characterized by anomalous vegetation. Compact Airborne Spectrographic Imager II (CASI II) imagery was analysed for selected levee sites in association with slide inventory data and field observations. Normalized Difference Vegetation Index (NDVI), Red edge Vegetation Stress Index (RVSI) and Red Edge Position Index (REP) were calculated from the acquired CASI II imagery. The vegetation indices were used to locate the stressed or anomalous vegetation and predict levee slides. The statistical significance of the predictors was determined by logistic regression. All predictors were found statistically significant for developing a slide prediction model. The slide prediction model was developed by combining the single predictors, categorized vegetation indices, into a model based on all three predictors. Percentage of Search Area Reduction (PSAR) and Failure Index (FI) were used to evaluate the performance of the slide prediction model. Evaluation of the model performance shows that it achieves a maximum FI of 0.43 and PSAR of 99.5. [ABSTRACT FROM AUTHOR]