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Microwave resonators working as sensors can detect only a single analyte at a time. To address this issue, a TE20-mode substrate-integrated waveguide (SIW) resonator is exploited, owing to its two distinct regions of high-intensity electric fields, which can be manipulated by loading two chemicals. Two microfluidic channels with unequal fluid-carrying capacities, engraved in a polydimethylsiloxane (PDMS) sheet, can perturb the symmetric electric fields even if loaded with the two extreme cases of dielectric [ethanol (E), deionized water (DI)] and [deionized water, ethanol]. The four layers of the sandwiched structure considered in this study consisted of a top conductive pattern and a bottom ground, both realized on a Rogers RT/Duroid 5880. PDMS-based channels attached with an adhesive serve as the middle layers. The TE20-mode SIW with empty channels resonates at 8.26 GHz and exhibits a 􀀀25 dB return loss with an unloaded quality factor of Q ≈ 28. We simultaneously load E and DI and demonstrate the detection of the four possible combinations: [E, DI], [DI, E], [E, E], and [DI, DI]. The performance of our proposed method showed increases in sensitivity (MHz/εr) of 7.5%, 216%, and 1170% compared with three previously existing multichannel microwave chemical sensors. [ABSTRACT FROM AUTHOR]

In this study, a high-Q circular substrate-integrated waveguide (SIW) cavity resonator is proposed as a non-contact and non-invasive radio frequency (RF) sensor for chemical sensing applications. The design of the structure utilizes SIW technology along with a circular shape to achieve a high unloaded Q factor, which is one of the important requirements for RF sensors. The resonant frequency of the proposed circular SIW cavity sensor changes when a liquid material or a chemical (microliters) is inserted in the sensitive area of the structure. The sensing of liquid materials with different permittivities is accomplished via the perturbation of the electric fields in the SIW configuration. When a microwell that is 4 mm in radius is installed vertically through the center of the bare circular SIW cavity, the operating frequency varies from 5.26 to 5.34 GHz. Similarly, when the microwell contains ethanol, the frequency shifts from 5.26 to 5.18 GHz, and the amplitude of reflection coefficient is shifted from -29 dB to -17 dB; when the microwell contains mixing deionized (DI)-water, the frequency moves from 5.26 to 4.98 GHz (which is also 0% Ethanol in our study), and the amplitude of reflection coefficient is shifted from -29 dB to -8 dB. A high unloaded Q factor is maintained throughout all experimental results. To demonstrate our idea, different concentrations of ethanol are tested and recorded. The experimental validation yields a close agreement between the simulations and the measurements. [ABSTRACT FROM AUTHOR]

The significant improvements observed in the field of bulk-production of printed microchip technologies in the past decade have allowed the fabrication of microchip printing on numerous materials including organic and flexible substrates. Printed sensors and electronics are of significant interest owing to the fast and low-cost fabrication techniques used in their fabrication. The increasing amount of research and deployment of specially printed electronic sensors in a number of applications demonstrates the immense attention paid by researchers to this topic in the pursuit of achieving wider-scale electronics on different dielectric materials. Although there are many traditional methods for fabricating radio frequency (RF) components, they are time-consuming, expensive, complicated, and require more power for operation than additive fabrication methods. This paper serves as a summary/review of improvements made to the additive printing technologies. The article focuses on three recently developed printing methods for the fabrication of wireless sensors operating at microwave frequencies. The fabrication methods discussed include inkjet printing, three-dimensional (3D) printing, and screen printing. [ABSTRACT FROM AUTHOR]

Recent advancements in indoor positioning systems are based on infrastructure-free solutions, aimed at improving the location accuracy in complex indoor environments without the use of specialized resources. A popular infrastructure-free solution for indoor positioning is a calibration-based positioning, commonly known as fingerprinting. Fingerprinting solutions require extensive and error-free surveys of environments to build radio-map databases, which play a key role in position estimation. Fingerprinting also requires random updates of the database, when there are significant changes in the environment or a decrease in the accuracy. The calibration of the fingerprinting database is a time-consuming and laborious effort that prevents the extensive adoption of this technique. In this paper, we present a systematic LOCALIzation approach, "LOCALI", for indoor positioning, which does not require a calibration database and extensive updates. The LOCALI exploits the floor plan/wall map of the environment to estimate the target position by generating radio maps by integrating path-losses over certain trajectories in complex indoor environments, where triangulation using time information or the received signal strength level is highly erroneous due to the fading effects caused by multi-path propagation or absorption by environmental elements or varying antenna alignment. Experimental results demonstrate that by using the map information and environmental parameters, a significant level of accuracy in indoor positioning can be achieved. Moreover, this process requires considerably lesser effort compared to the calibration-based techniques. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a frequency-switchable complementary split-ring resonator (CSRR)-loaded quarter-mode substrate-integrated-waveguide (QMSIW) band-pass filter. For frequency switching, a microfluidic channel and liquid metal are used. The liquid metal used is eutectic gallium-indium (EGaIn), consisting of 24.5% indium and 75.5% gallium. The microfluidic channels are built using the elastomer polydimethylsiloxane (PDMS) and three-dimensional-printed microfluidic channel frames. The CSRR-loaded QMSIW band-pass filter is designed to have two states. Before the injection of the liquid metal, the measured center frequency and fractional bandwidths are 2.205 GHz and 6.80%, respectively. After injection, the center frequency shifts from 2.205 GHz to 2.56 GHz. Although the coupling coefficient is practically unchanged, the fractional bandwidth changes from 6.8% to 9.38%, as the CSRR shape changes and the external quality factor decreases. After the removal of the liquid metal, the measured values are similar to the values recorded before the liquid metal was injected. The repeatability of the frequency-switchable mechanism is, therefore, verified. [ABSTRACT FROM AUTHOR]

This research proposes a substrate-integrated waveguide (SIW) cavity sensor to detect several chemicals using the millimeter-wave frequency range. The frequency response of the presented SIW sensor is switched by filling a very small quantity of chemical inside of the fluidic channel, which also causes a difference in the effective permittivity. The fluidic channel on this structure is either empty or filled with a chemical; when it is empty the structure resonates at 17.08 GHz. There is always a different resonant frequency when any chemical is injected into the fluidic channel. The maximum amount of chemical after injection is held in the center of the SIW structure, which has the maximum magnitude of the electric field distribution. Thus, the objective of sensing chemicals in this research is achieved by perturbing the electric fields of the SIW structure. [ABSTRACT FROM AUTHOR]

The proposed structure in this research is constructed on substrate integrated waveguide (SIW) technology and has a mechanism that produces 16 different and distinct resonant frequencies between 2.45 and 3.05 GHz by perturbing a fundamental TE10 mode. It is a unique method for producing multiple resonances in a radio frequency planar structure without any extra circuitry or passive elements is developed. The proposed SIW structure has four vertical fluidic holes (channels); injecting eutectic gallium indium (EGaIn), also known commonly as liquid metal (LM), into these vertical channels produces different resonant frequencies. Either a channel is empty, or it is filled with LM. In total, the combination of different frequencies produced from four vertical channels is 16. [ABSTRACT FROM AUTHOR]

The lumbar spine plays a very important role in our load transfer and mobility. Vertebrae localization and segmentation are useful in detecting spinal deformities and fractures. Understanding of automated medical imagery is of main importance to help doctors in handling the time-consuming manual or semi-manual diagnosis. Our paper presents the methods that will help clinicians to grade the severity of the disease with confidence, as the current manual diagnosis by different doctors has dissimilarity and variations in the analysis of diseases. In this paper we discuss the lumbar spine localization and segmentation which help for the analysis of lumbar spine deformities. The lumber spine is localized using YOLOv5 which is the fifth variant of the YOLO family. It is the fastest and the lightest object detector. Mean average precision (mAP) of 0.975 is achieved by YOLOv5. To diagnose the lumbar lordosis, we correlated the angles with region area that is computed from the YOLOv5 centroids and obtained 74.5% accuracy. Cropped images from YOLOv5 bounding boxes are passed through HED U-Net, which is a combination of segmentation and edge detection frameworks, to obtain the segmented vertebrae and its edges. Lumbar lordortic angles (LLAs) and lumbosacral angles (LSAs) are found after detecting the corners of vertebrae using a Harris corner detector with very small mean errors of 0.29° and 0.38°, respectively. This paper compares the different object detectors used to localize the vertebrae, the results of two methods used to diagnose the lumbar deformity, and the results with other researchers. [ABSTRACT FROM AUTHOR]

The SARS-CoV-2 Coronavirus disease, also known as the COVID-19 pandemic, has engendered the biggest challenge to human life for the last two years. With a rapid increase in the spread of the Omicron variant across the world, and to contain the spread of COVID-19 in general, it is crucial to rapidly identify this viral infection with minimal logistics. To achieve this, a novel plastic optical fiber (POF) U-shaped probe sensing method is presented for accurate detection of SARS-CoV-2, commonly known as the COVID-19 virus, which has the capability to detect new variants such as Omicron. The sample under test can be taken from oropharyngeal or nasopharyngeal via specific POF U-shaped probe with one end that is fed with a laser source while the other end is connected to a photodetector to receive the response and postprocess for decision-making. The study includes detection comparison with two types of POF with diameters of 200 and 500 µm. Results show that detection is better when a smaller-diameter POF is used. It is also seen that the proposed test bed and its envisaged prototype can detect the COVID-19 variants within 15 min of the test. The proposed approach will make the clinical diagnosis faster, cheaper and applicable to patients in remote areas where there are no hospitals or clinical laboratories due to poverty, geographic obstacles, or other factors. [ABSTRACT FROM AUTHOR]

In this work, fabrication of gold coated glass substrate, growth of ZnO nanorods and potentiometric response of lactic acid are explained. The biosensor was developed by immobilizing the lactate oxidase on the ZnO nanorods in combination with glutaraldehyde as a cross linker for lactate oxidase enzyme. The potentiometric technique was applied for the measuring the output (EMF) response of L-lactic acid biosensor. We noticed that the present biosensor has wide linear detection range of concentration from 1 × 10-4-1 × 100 mM with acceptable sensitivity about 41.33 ± 1.58 mV/decade. In addition, the proposed biosensor showed fast response time less than 10 s, a good selectivity towards L-lactic acid in presence of common interfering substances such as ascorbic acid, urea, glucose, galactose, magnesium ions and calcium ions. The present biosensor based on immobilized ZnO nanorods with lactate oxidase sustained its stability for more than three weeks. [ABSTRACT FROM AUTHOR]

With the popularization of Internet-of-things (IoT) and wireless communication systems, a diverse set of applications in smart cities are emerging to improve the city-life. These applications usually require a large coverage area and minimal operation and maintenance cost. To this end, the recently emerging backscatter communication (BC) is gaining interest in both industry and academia as a new communication paradigm that provides high energy efficient communications that may even work in a battery-less mode and, thus, it is well suited for smart city applications. However, the coverage of BC in urban area deployments is not available, and the feasibility of its utilization for smart city applications is not known. In this article, we present a comprehensive coverage study of a practical cellular carrier-based BC system for indoor and outdoor scenarios in a downtown area of a Helsinki city. In particular, we evaluate the coverage outage performance of different low-power and wide area technologies, i.e., long range (LoRa) backscatter, arrow band-Internet of Things (NB-IoT), and Bluetooth low energy (BLE) based BC at different frequencies of operation. To do so, we carry out a comprehensive campaign of simulations while using a sophisticated three-dimensional (3D) ray tracing (RT) tool, ITU outdoor model, and 3rd generation partnership project (3GPP) indoor hotspot model. This study also covers the energy harvesting aspects of backscatter device, and it highlights the importance of future backscatter devices with high energy harvesting efficiency. The simulation results and discussion provided in this article will be helpful in understanding the coverage aspects of practical backscatter communication system in a smart city environment. [ABSTRACT FROM AUTHOR]

Liquid materials' characterization using commercial probes and radio frequency techniques is expensive and complex. This study proposes a compact and cost-effective radio frequency sensor system to measure the dielectric constant using a three-material calibration. The simplified approach measures reflection coefficient magnitudes for all four materials rather than the complex values in conventional permittivity detection systems. We employ a sensor module based on a circular substrate-integrated waveguide with measured unloaded quality factor = 910 to ensure measurement reliability. Miniaturized quarter-mode substrate-integrated waveguide resonators are integrated with four microfluidic channels containing three known materials and one unknown analyte. Step-wise measurement and linearity ensures maximum 4% error for the dielectric constant compared with results obtained using a high-performance commercial product. [ABSTRACT FROM AUTHOR]

Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR). The use of these signals provides challenges of performance improvement within a broader set of emotion classes in a less constrained real-world environment. To overcome these challenges, we propose a computational framework of 2D Convolutional Neural Network (CNN) architecture for the arrangement of 14 channels of EEG, and a combination of Long Short-Term Memory (LSTM) and 1D-CNN architecture for ECG and GSR. Our approach is subject-independent and incorporates two publicly available datasets of DREAMER and AMIGOS with low-cost, wearable sensors to extract physiological signals suitable for real-world environments. The results outperform state-of-the-art approaches for classification into four classes, namely High Valence—High Arousal, High Valence—Low Arousal, Low Valence—High Arousal, and Low Valence—Low Arousal. Emotion elicitation average accuracy of 98.73 % is achieved with ECG right-channel modality, 76.65% with EEG modality, and 63.67% with GSR modality for AMIGOS. The overall highest accuracy of 99.0% for the AMIGOS dataset and 90.8% for the DREAMER dataset is achieved with multi-modal fusion. A strong correlation between spectral- and hidden-layer feature analysis with classification performance suggests the efficacy of the proposed method for significant feature extraction and higher emotion elicitation performance to a broader context for less constrained environments. [ABSTRACT FROM AUTHOR]

Recognizing human physical activities from streaming smartphone sensor readings is essential for the successful realization of a smart environment. Physical activity recognition is one of the active research topics to provide users the adaptive services using smart devices. Existing physical activity recognition methods lack in providing fast and accurate recognition of activities. This paper proposes an approach to recognize physical activities using only2-axes of the smartphone accelerometer sensor. It also investigates the effectiveness and contribution of each axis of the accelerometer in the recognition of physical activities. To implement our approach, data of daily life activities are collected labeled using the accelerometer from 12 participants. Furthermore, three machine learning classifiers are implemented to train the model on the collected dataset and in predicting the activities. Our proposed approach provides more promising results compared to the existing techniques and presents a strong rationale behind the effectiveness and contribution of each axis of an accelerometer for activity recognition. To ensure the reliability of the model, we evaluate the proposed approach and observations on standard publicly available dataset WISDM also and provide a comparative analysis with state-of-the-art studies. The proposed approach achieved 93% weighted accuracy with Multilayer Perceptron (MLP) classifier, which is almost 13% higher than the existing methods. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) has achieved significant importance in tracking different physical or environmental conditions using wireless sensor nodes. Such types of networks are used in various applications including smart cities, smart building, military target tracking and surveillance, natural disaster relief, and smart homes. However, the limited power capacity of sensor nodes is considered a major issue that hampers the performance of a WSN. A plethora of research has been conducted to reduce the energy consumption of sensor nodes in traditional WSN, however the limited functional capability of such networks is the main constraint in designing sophisticated and dynamic solutions. Given this, software defined networking (SDN) has revolutionized traditional networks by providing a programmable and flexible framework. Therefore, SDN concepts can be utilized in designing energy-efficient WSN solutions. In this paper, we exploit SDN capabilities to conserve energy consumption in a traditional WSN. To achieve this, an energy-aware multihop routing protocol (named EASDN) is proposed for software defined wireless sensor network (SDWSN). The proposed protocol is evaluated in a real environment. For this purpose, a test bed is developed using Raspberry Pi. The experimental results show that the proposed algorithm exhibits promising results in terms of network lifetime, average energy consumption, the packet delivery ratio, and average delay in comparison to an existing energy efficient routing protocol for SDWSN and a traditional source routing algorithm. [ABSTRACT FROM AUTHOR]

Wi-Fi positioning based on fingerprinting has been considered as the most widely used technology in the field of indoor positioning. The fingerprinting database has been used as an essential part of the Wi-Fi positioning system. However, the offline phase of the calibration involves a laborious task of site analysis which involves costs and a waste of time. We offer an indoor positioning system based on the automatic generation of radio maps of the indoor environment. The proposed system does not require any effort and uses Wi-Fi compatible Internet-of-Things (IoT) sensors. Propagation loss parameters are automatically estimated from the online feedback of deployed sensors and the radio maps are updated periodically without any physical intervention. The proposed system leverages the raster maps of an environment with the wall information only, against computationally extensive techniques based on vector maps that require precise information on the length and angles of each wall. Experimental results show that the proposed system has achieved an average accuracy of 2 m, which is comparable to the survey-based Wi-Fi fingerprinting technique. [ABSTRACT FROM AUTHOR]

Molecularly imprinted polymers have been used for the creation of an electrochemical sensor for the detection of sucrose, which are modified by using functionalized graphene (fG). Using AIBN as the free radical initiator and sucrose as the template, imprinted polymers are synthesized. The monomer, 4,4′-diisocyanatodiphenylmethane (DPDI), has both proton donor groups (N-H or O-H) and lone-pair donor groups (C=O). By creating H-bonds with electron donor groups (C=O), the proton donor group in this polymer may interact with the sugar molecule serving as its template. The sensor signals have improved as a result of the interaction between the monomer and the template. Thermogravimetric and differential thermal analysis (TGA/DTA) curves, scanning electron microscopy (SEM), and FT-IR spectroscopy have been employed to characterize the fabricated receptors. The fabricated sensor has exhibited a limit of detection of 16 ppb for the target analyte that is highly sensitive, linear, reversible, regenerative, and selective. Moreover, the sensor's stability, reproducibility, and reusability have been evaluated for six months, following the device's manufacturing, and the results revealed similar responses with the percentage error of less than 1%. Most importantly, this sensor has demonstrated a quick response time, which is very sensitive, stable, and selective. [ABSTRACT FROM AUTHOR]

Emotion charting using multimodal signals has gained great demand for stroke-affected patients, for psychiatrists while examining patients, and for neuromarketing applications. Multimodal signals for emotion charting include electrocardiogram (ECG) signals, electroencephalogram (EEG) signals, and galvanic skin response (GSR) signals. EEG, ECG, and GSR are also known as physiological signals, which can be used for identification of human emotions. Due to the unbiased nature of physiological signals, this field has become a great motivation in recent research as physiological signals are generated autonomously from human central nervous system. Researchers have developed multiple methods for the classification of these signals for emotion detection. However, due to the non-linear nature of these signals and the inclusion of noise, while recording, accurate classification of physiological signals is a challenge for emotion charting. Valence and arousal are two important states for emotion detection; therefore, this paper presents a novel ensemble learning method based on deep learning for the classification of four different emotional states including high valence and high arousal (HVHA), low valence and low arousal (LVLA), high valence and low arousal (HVLA) and low valence high arousal (LVHA). In the proposed method, multimodal signals (EEG, ECG, and GSR) are preprocessed using bandpass filtering and independent components analysis (ICA) for noise removal in EEG signals followed by discrete wavelet transform for time domain to frequency domain conversion. Discrete wavelet transform results in spectrograms of the physiological signal and then features are extracted using stacked autoencoders from those spectrograms. A feature vector is obtained from the bottleneck layer of the autoencoder and is fed to three classifiers SVM (support vector machine), RF (random forest), and LSTM (long short-term memory) followed by majority voting as ensemble classification. The proposed system is trained and tested on the AMIGOS dataset with k-fold cross-validation. The proposed system obtained the highest accuracy of 94.5% and shows improved results of the proposed method compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The demand for cloud computing has drastically increased recently, but this paradigm has several issues due to its inherent complications, such as non-reliability, latency, lesser mobility support, and location-aware services. Fog computing can resolve these issues to some extent, yet it is still in its infancy. Despite several existing works, these works lack fault-tolerant fog computing, which necessitates further research. Fault tolerance enables the performing and provisioning of services despite failures and maintains anti-fragility and resiliency. Fog computing is highly diverse in terms of failures as compared to cloud computing and requires wide research and investigation. From this perspective, this study primarily focuses on the provision of uninterrupted services through fog computing. A framework has been designed to provide uninterrupted services while maintaining resiliency. The geographical information system (GIS) services have been deployed as a test bed which requires high computation, requires intensive resources in terms of CPU and memory, and requires low latency. Keeping different types of failures at different levels and their impacts on service failure and greater response time in mind, the framework was made anti-fragile and resilient at different levels. Experimental results indicate that during service interruption, the user state remains unaffected. [ABSTRACT FROM AUTHOR]

Human beings tend to incrementally learn from the rapidly changing environment without comprising or forgetting the already learned representations. Although deep learning also has the potential to mimic such human behaviors to some extent, it suffers from catastrophic forgetting due to which its performance on already learned tasks drastically decreases while learning about newer knowledge. Many researchers have proposed promising solutions to eliminate such catastrophic forgetting during the knowledge distillation process. However, to our best knowledge, there is no literature available to date that exploits the complex relationships between these solutions and utilizes them for the effective learning that spans over multiple datasets and even multiple domains. In this paper, we propose a continual learning objective that encompasses mutual distillation loss to understand such complex relationships and allows deep learning models to effectively retain the prior knowledge while adapting to the new classes, new datasets, and even new applications. The proposed objective was rigorously tested on nine publicly available, multi-vendor, and multimodal datasets that span over three applications, and it achieved the top-1 accuracy of 0.9863% and an F1-score of 0.9930. [ABSTRACT FROM AUTHOR]

The environment of underground coal mines has challenging properties that makes this zone inadaptable for a stable communication system. Additionally, various deteriorating physical parameters strongly affect the performance of wireless networks, which leads to limited network coverage and poor quality of data communication. This study investigates the communication capability in underground coal mines by optimizing the wireless link to develop a stable network for an underground hazardous environment. A hybrid channel-modeling scheme is proposed to characterize the environment of underground mines for wireless communication by classifying the area of a mine into the main gallery and sub-galleries. The complex segments of mine are evaluated by categorizing the wireless links for the line-of-sight (LOS) zones and hybrid modeling is employed to examine the characteristics of electromagnetic signal propagation. For hybrid channel modeling, the multimode waveguide model and geometrical optic (GO) model are used for developing an optimal framework that improves the accessibility of the network in the critical time-varying environment of mines. Moreover, the influence of various deteriorating factors is analyzed using 2.4 GHz to 5 GHz frequency band to study its relationship with the vital constraints of an underground mine. The critical factors such as path loss, roughness loss, delay spread, and shadow fading are examined under detailed analysis with variation in link structure for the mine. [ABSTRACT FROM AUTHOR]

Clustering is the most common method for organizing unlabeled data into its natural groups (called clusters), based on similarity (in some sense or another) among data objects. The Partitioning Around Medoids (PAM) algorithm belongs to the partitioning-based methods of clustering widely used for objects categorization, image analysis, bioinformatics and data compression, but due to its high time complexity, the PAM algorithm cannot be used with large datasets or in any embedded or real-time application. In this work, we propose a simple and scalable parallel architecture for the PAM algorithm to reduce its running time. This architecture can easily be implemented either on a multi-core processor system to deal with big data or on a reconfigurable hardware platform, such as FPGA and MPSoCs, which makes it suitable for real-time clustering applications. Our proposed model partitions data equally among multiple processing cores. Each core executes the same sequence of tasks simultaneously on its respective data subset and shares intermediate results with other cores to produce results. Experiments show that the computational complexity of the PAM algorithm is reduced exponentially as we increase the number of cores working in parallel. It is also observed that the speedup graph of our proposed model becomes more linear with the increase in number of data points and as the clusters become more uniform. The results also demonstrate that the proposed architecture produces the same results as the actual PAM algorithm, but with reduced computational complexity. [ABSTRACT FROM AUTHOR]

Proximal hyperspectral sensing tools could complement and perhaps replace destructive traditional methods for accurate estimation and monitoring of various morpho-physiological plant indicators. In this study, we assessed the potential of thermal imaging (TI) criteria and spectral reflectance indices (SRIs) to monitor different vegetative growth traits (biomass fresh weight, biomass dry weight, and canopy water mass) and seed yield (SY) of soybean exposed to 100%, 75%, and 50% of estimated crop evapotranspiration (ETc). These different plant traits were evaluated and related to TI criteria and SRIs at the beginning bloom (R1) and full seed (R6) growth stages. Results showed that all plant traits, TI criteria, and SRIs presented significant variations (p < 0.05) among irrigation regimes at both growth stages. The performance of TI criteria and SRIs for assessment of vegetative growth traits and SY fluctuated when relationships were analyzed for each irrigation regime or growth stage separately or when the data of both conditions were combined together. TI criteria and SRIs exhibited a moderate to strong relationship with vegetative growth traits when data from different irrigation regimes were pooled together at each growth stage or vice versa. The R6 and R1 growth stages are suitable for assessing SY under full (100% ETc) and severe (50% ETc) irrigation regimes, respectively, using SRIs. The overall results indicate that the usefulness of the TI and SRIs for assessment of growth, yield, and water status of soybean under arid conditions is limited to the growth stage, the irrigation level, and the combination between them. [ABSTRACT FROM AUTHOR]

Wavelet transformation is one of the most frequent procedures for data denoising, smoothing, decomposition, features extraction, and further related tasks. In order to perform such tasks, we need to select appropriate wavelet settings, including particular wavelet, decomposition level and other parameters, which form the wavelet transformation outputs. Selection of such parameters is a challenging area due to absence of versatile recommendation tools for suitable wavelet settings. In this paper, we propose a versatile recommendation system for prediction of suitable wavelet selection for data smoothing. The proposed system is aimed to generate spatial response matrix for selected wavelets and the decomposition levels. Such response enables the mapping of selected evaluation parameters, determining the efficacy of wavelet settings. The proposed system also enables tracking the dynamical noise influence in the context of Wavelet efficacy by using volumetric response. We provide testing on computed tomography (CT) and magnetic resonance (MR) image data and EMG signals mostly of musculoskeletal system to objectivise system usability for clinical data processing. The experimental testing is done by using evaluation parameters such is MSE (Mean Squared Error), ED (Euclidean distance) and Corr (Correlation index). We also provide the statistical analysis of the results based on Mann-Whitney test, which points out on statistically significant differences for individual Wavelets for the data corrupted with Salt and Pepper and Gaussian noise. [ABSTRACT FROM AUTHOR]

Deployment of efficient and cost-effective parking lots is a known bottleneck for the electric vehicles (EVs) sector. A comprehensive solution incorporating the requirements of all key stakeholders is required. Taking up the challenge, we propose a real-time EV smart parking lot model to attain the following objectives: (a) maximize the smart parking lot revenue by accommodating maximum number of EVs and (b) minimize the cost of power consumption by participating in a demand response (DR) program offered by the utility since it is a tool to answer and handle the electric power usage requirements for charging the EV in the smart parking lot. With a view to achieving these objectives, a linear programming-based binary/cyclic (0/1) optimization technique is developed for the EV charge scheduling process. It is difficult to solve the problems of binary optimization in real-time given that the complexity of the problem increases with the increase in number of EV. We deploy a simplified convex relaxation technique integrated with the linear programming solution to overcome this problem. The algorithm achieves: minimum power consumption cost of the EV smart parking lot; efficient utilization of available power; maximization of the number of the EV to be charged; and minimum impact on the EV battery lifecycle. DR participation provide benefits by offering time-based and incentive-based hourly intelligent charging schedules for the EV. A thorough comparison is drawn with existing variable charging rate-based techniques in order to demonstrate the comparative validity of our proposed technique. The simulation results show that even under no DR event, the proposed scheme results in 2.9% decrease in overall power consumption cost for a 500 EV scenario when compared to variable charging rate method. Moreover, in similar conditions, such as no DR event and for 500 EV arrived per day, there is a 2.8% increase in number of EV charged per day, 3.2% improvement in the average state-of-charge (SoC) of the EV, 12.47% reduction in the average time intervals required to achieve final SoC. [ABSTRACT FROM AUTHOR]

Fusion of the Global Positioning System (GPS) and Inertial Navigation System (INS) for navigation of ground vehicles is an extensively researched topic for military and civilian applications. Micro-electro-mechanical-systems-based inertial measurement units (MEMS-IMU) are being widely used in numerous commercial applications due to their low cost; however, they are characterized by relatively poor accuracy when compared with more expensive counterparts. With a sudden boom in research and development of autonomous navigation technology for consumer vehicles, the need to enhance estimation accuracy and reliability has become critical, while aiming to deliver a cost-effective solution. Optimal fusion of commercially available, low-cost MEMS-IMU and the GPS may provide one such solution. Different variants of the Kalman filter have been proposed and implemented for integration of the GPS and the INS. This paper proposes a framework for the fusion of adaptive Kalman filters, based on Sage-Husa and strong tracking filtering algorithms, implemented on MEMS-IMU and the GPS for the case of a ground vehicle. The error models of the inertial sensors have also been implemented to achieve reliable and accurate estimations. Simulations have been carried out on actual navigation data from a test vehicle. Measurements were obtained using commercially available GPS receiver and MEMS-IMU. The solution was shown to enhance navigation accuracy when compared to conventional Kalman filter. [ABSTRACT FROM AUTHOR]