AI-based Real-time Classification of Human Activity using Software Defined Radios

The Universal Software Radio Peripheral (USRP) from Ettus Research is a software defined radio (SDR) that has emerged as an affordable and capable radio used in both teaching and research applications. Despite its widespread use, precise specification of the transmit power of USRP SDR boards is not readily available in the manufacturer data sheets, and in this paper we present an experimental study of the transmit power for the B-series USRP SDR board from Ettus Research. Specifically, a USRP B200 SDR is used to setup extensive measurements of the output transmit power over its covered frequency range and the power variation with frequency along with its dependence on the transmit gain parameter in the USRP hardware driver (UHD) is evaluated. A discussion of the possible causes that lead to variations in the transmit power is also presented.

Systematic literature review of machine learning based software development effort estimation models

In the field of data-driven healthcare systems, wireless-based human activity monitoring in an indoor environment is getting popular. Localization of human activities can help patients to get remote healthcare. Wearable devices are employed in modern healthcare monitoring systems, despite the fact that they can be inconvenient and expensive. Several studies have shown that analyzing variations in Channel State Information (CSI) can be used to identify activities in an indoor environment and monitor health utilizing radio frequencies. The results of an experiment using Software Defined Radio (SDR) to identify and localize human activities while a human subject performed six different classes of activities (no-activity, leaning forward, sitting, standing, walking-Rx, walking-Tx). The CSI extracted from the communication channel between two Universal Software Radio Peripherals (USRP) devices communicating and operating as SDRs was used to collect a total of 2100 sample data. The Extra Tree (ET) classifier outperformed the prior benchmark approaches in localizing six activities across the 21 classes. The proposed ET classifier can identify if the room is occupied and a human subject’s walking directions in two separate zones, as well as localize six distinct activities inside the room with an accuracy of 91% and 100% respectively.

Human motion detection and activity recognition are becoming vital for the applications in smart homes. Traditional Human Activity Recognition (HAR) mechanisms use special devices to track human motions, such as cameras (vision-based) and various types of sensors (sensor-based). These mechanisms are applied in different applications, such as home security, Human–Computer Interaction (HCI), gaming, and healthcare. However, traditional HAR methods require heavy installation, and can only work under strict conditions. Recently, wireless signals have been utilized to track human motion and HAR in indoor environments. The motion of an object in the test environment causes fluctuations and changes in the Wi-Fi signal reflections at the receiver, which result in variations in received signals. These fluctuations can be used to track object (i.e., a human) motion in indoor environments. This phenomenon can be improved and leveraged in the future to improve the internet of things (IoT) and smart home devices. The main Wi-Fi sensing methods can be broadly categorized as Received Signal Strength Indicator (RSSI), Wi-Fi radar (by using Software Defined Radio (SDR)) and Channel State Information (CSI). CSI and RSSI can be considered as device-free mechanisms because they do not require cumbersome installation, whereas the Wi-Fi radar mechanism requires special devices (i.e., Universal Software Radio Peripheral (USRP)). Recent studies demonstrate that CSI outperforms RSSI in sensing accuracy due to its stability and rich information. This paper presents a comprehensive survey of recent advances in the CSI-based sensing mechanism and illustrates the drawbacks, discusses challenges, and presents some suggestions for the future of device-free sensing technology.

The universal software radio peripheral (USRP) is a versatile software defined radio (SDR) platform, developed by Ettus Research™, which is intended for a wide variety of applications ranging from communication links to RADAR. We have investigated another application of the USRP by implementing a transceiver capable of interrogating passive, wireless surface acoustic wave (SAW) sensors centered at 915MHz. Interrogation of wideband orthogonal frequency coded (OFC) SAW sensors imposes strict requirements on the timing and synchronization of the transceiver. In the standard mode of operation, samples are generated and streamed between the USRP and host computer, introducing latency and bandwidth limitations due to the sampling bus. To achieve the performance required for this application, the USRP FPGA has been modified to introduce new functionality. Extraction of the sensor temperature is accomplished with a custom matched filter correlator. The system is capable of interrogating multiple sensors and can quickly reconfigure the USRP. Demonstration of the USRP wireless sensor system is achieved by interrogating wireless SAW OFC sensors at 915MHz and extracting the sensor temperature.

Coastal aquifers are vital for sustaining ecosystems and providing freshwater for agriculture and domestic use. However, rising groundwater salinity in these regions demands innovative approaches for accurate modeling and prediction. In recent decades, machine learning (ML) and deep learning (DL) techniques have been extensively used across various water resource management fields, often yielding promising outcomes. This review examines the growing usage of ML and DL techniques in modeling groundwater salinity in coastal aquifers. The analysis is based on 104 peer-reviewed journal articles including the review articles indexed in PubMed, ScienceDirect, Google Scholar, Scopus, and SpringerLink using keywords like "saltwater intrusion", "machine learning", “deep learning”, and "groundwater salinity". The review discusses recent advancements, challenges, and future directions for improving ML model accuracy and applicability. Studies reveal an increasing reliance on ML techniques to predict groundwater salinity and manage seawater intrusion across various coastal geological settings. Researchers have employed a range of ML models, from traditional methods like Artificial Neural Networks and Adaptive Neuro Fuzzy Inference Systems to advanced DL-based models like Convolutional Neural Networks and Long Short-Term Memory Networks, often using ensemble methods to enhance accuracy. Key findings include the top-tier output of ensemble and optimized models, including Random Forest and Grasshopper Optimization Algorithm-XGBoost. These studies highlight the importance of model selection, input variable ranking, and computational efficiency, demonstrating ML's global relevance in environmental management. The review highlights the prospects of ML, DL, and ensemble models to redefine groundwater management in coastal aquifers, offering more effective and sustainable solutions to the challenges posed by groundwater salinity and saltwater intrusion. Future advancements will focus on further integrating DL, explainable artificial intelligence, and ensemble approaches, along with other innovative techniques, to explore sparsely studied variables, model new or unique study areas, and apply ML methods for managing groundwater salinity.

Machine learning (ML) techniques play a significant role in detecting abnormal link operations in wireless networks. However, during the design and development process of a ML-based model, numerous factors including input data transformation, scaling methods, choice of ML techniques and their associated parametrization, influence the final model performance. In this paper, we discuss the design and development process of wireless link anomaly detection models followed by the model ranking process for selecting the most suitable models given the required application criteria. Under this premise, we consider four different data representations, four anomaly types, six scaling methods and six distinct ML techniques along with their associated parametrization trials leading to over twenty thousand anomaly detection models for wireless links. We show that the most suitable supervised ML models are able to detect anomalies with an F1 score of at least 88% and up to perfect score, whereas unsupervised ML models can attain an F1 score of up to 96% depending on the anomaly type, and on average, amongst 20,352 available models, the supervised SVM model can be readily adopted to efficiently detect any type of anomaly with an F1 score of at least 94%, particularly when trained on encoded data representations using the combination of "mean and deviation scaling" and "non-linear RBF kernel".

Summary A meticulous interpretation of steady-state or unsteady-state relative permeability (Kr) experimental data is required to determine a complete set of Kr curves. In this work, different machine learning (ML) models were developed to assist in a faster estimation of these curves from steady-state drainage coreflooding experimental runs. These ML algorithms include gradient boosting (GB), random forest (RF), extreme gradient boosting (XGB), and deep neural network (DNN) with a main focus on and comparison of the two latter algorithms (XGB and DNN). Based on existing mathematical models, a leading-edge framework was developed where a large database of Kr and capillary pressure (Pc) curves were generated. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from these simulation runs, mainly pressure drop along with other conventional core analysis data, were used to estimate analytical Kr curves based on Darcy’s law. These analytically estimated Kr curves along with the previously generated Pc curves were fed as features into the ML model. The entire data set was split into 80% for training and 20% for testing. The k-fold cross-validation technique was applied to increase the model’s accuracy by splitting 80% of the training data into 10 folds. In this manner, for each of the 10 experiments, nine folds were used for training and the remaining fold was used for model validation. Once the model was trained and validated, it was subjected to blind testing on the remaining 20% of the data set. The ML model learns to capture fluid flow behavior inside the core from the training data set. In terms of applicability of these ML models, two sets of experimental data were needed as input; the first was the analytically estimated Kr curves from the steady-state drainage coreflooding experiments, while the other was the Pc curves estimated from centrifuge or mercury injection capillary pressure (MICP) measurements. The trained/tested model was then able to estimate Kr curves based on the experimental results fed as input. Furthermore, to test the performance of the ML model when only one set of experimental data is available to an end user, a recurrent neural network (RNN) algorithm was trained/tested to predict Kr curves in the absence of Pc curves as an input. The performance of the three developed models (XGB, DNN, and RNN) was assessed using the values of the coefficient of determination (R2) along with the loss calculated during training/validation of the model. The respective crossplots along with comparisons of ground truth vs. artificial intelligence (AI)-predicted curves indicated that the model is capable of making accurate predictions with an error percentage between 0.2% and 0.6% on history-matching experimental data for all three tested ML techniques. This implies that the AI-based model exhibits better efficiency and reliability in determining Kr curves when compared to conventional methods. The developed ML models by no means replace the need to conduct drainage coreflooding or centrifuge experiments but act as an alternative to existing commercial platforms that are used to interpret experimental data to predict Kr curves. The two main advantages of the developed ML models are their capability of predicting Kr curves within a matter of a few minutes as well as with limited intervention from the end user. The results also include a comparison between classical ML approaches, shallow neural networks, and DNNs in terms of accuracy in predicting the final Kr curves. The research presented here is an extension of the state-of-the-art framework proposed by Mathew et al. (2021). However, the two main aspects of the current study are the application of deep learning for the prediction of Kr curves and the application of feature engineering. The latter not only reduces the training/testing time for the ML models but also enables the end user to obtain the final predictions with the least set of experimental data. The various models discussed in this research work currently focus on the prediction of Kr curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.

Sensitivity evaluation of machine learning-based calibrated transportation mode choice models: A case study of Alexandria City, Egypt

Modern software defined radio (SDR) technologies have enabled surface acoustic wave (SAW) sensor interrogation systems that are small, inexpensive, and fully integrated. The universal software radio peripheral (USRP) is available as a commercial-off-the-shelf (COTS) SDR that can be programmed to fit a wide variety of applications and needs. Recent work at the University of Central Florida has demonstrated the USRP B200 as a SAW sensor interrogator. This paper expands upon previous efforts by implementing a standalone interrogation platform which utilizes the USRP B200 and embedded microcomputer. Recent advances in embedded processing platforms have enabled full Linux environments to be run, enabling high performance computation in a compact package. One such embedded platform, the MinnowBoard MAX, has been employed to perform the complex matched filter correlator post processing techniques as well as handle programming and communication with the USRP. The unit is self contained and can be operated without a traditional Desktop or Laptop. Performance of the interrogator has also been improved by adding and external RF switch and amplifier. The system is demonstrated by interrogating orthogonal frequency coded (OFC) SAW sensors at 915MHz and extracting the sensor temperature.

For the various climatic zones of India, machine learning (ML) models are created in the current work to forecast monthly-average diffuse solar radiation (DSR). The long-term solar radiation data are taken from Indian Meteorological Department (IMD), Pune, provided for 21 cities that span all of India’s climatic zones. The diffusion coefficient and diffuse fraction are the two groups of ML models with dual input parameters (sunshine ratio and clearness index) that are built and compared (each category has seven models). To create ML models, two well-known ML techniques, random forest (RF) and k-nearest neighbours (KNN), are used. The proposed ML models are compared with well-known models that are found in the literature. The ML models are ranked according to their overall and within predictive power using the Global Performance Indicator (GPI). It is discovered that KNN models generally outperform RF models. The results reveal that in diffusion coefficient models perform well than diffuse fraction models. Moreover, functional form 2 is the best followed by form 6. The ML models created here can be effectively used to accurately forecast DSR in various climates.

Techno-economic estimation of a non-cover box solar still with thermoelectric and antiseptic nanofluid using machine learning models

A comprehensive review on detection of plant disease using machine learning and deep learning approaches

We propose a low complexity synchronization method for the 3rd Generation Partnership Project (3GPP) long term evolution (LTE) mobile communication systems. A low complexity detection window, synchronization state machine, and two-stage detection are used to lower the computational complexity. The positions of the detection window and received samples are recorded, and the position and length of the detection window are controlled by the state machine. The positions are compared to decide if the received sample is useful for synchronization signal detection. Such techniques avoid unnecessary computations especially when implementing a receiver on a software defined radio (SDR) platform with general-purpose processors. We analyze the computational complexity of the proposed method and evaluate its performance based on simulations and using a Universal Software Radio Peripheral (USRP)-based SDR platform. The evaluation shows that the proposed method provides reliable and real-time synchronization signals on the considered SDR platform.

Systematic Review of Machine Learning in Diagnosis of Myeloproliferative Neoplasia