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Communication as a Conduit for Successful Parent Engagement: Early Childhood Teachers Speak Up
No full textThis dissertation delves into the crucial realm of teacher-parent communication, recognizing its pivotal role in establishing a foundation of trust with families. This study employs a qualitative approach to emphasize the bidirectional and continuous nature of effective communication. It adopts a case study methodology, focusing on ten early childhood teachers within a Michigan school district. Through in-depth interviews guided by a common question - “How do you perceive and describe communication with parents?” - the research unveils two central themes: 1) the identification of effective communication strategies for building trust and 2) the exploration of collaborative learning, professional development, and new teacher experiences. The findings shed light on teachers perspectives regarding communication with families, revealing insights into professional development, new teacher learning, mentorship, empathy, administrative support, and collaborative learning. These discoveries contribute to a nuanced understanding of teacher-parent communication and hold promise for shaping future practices and policies in education and family involvemen
Hybrid machine learning approaches for SOC and RUL estimation in battery management systems
No full textWith the fast development of electric vehicles (EVs), new technologies are needed to manage batteries more efficiently to optimize performance and more profound and longer battery use. A significant problem that must be solved successfully is accurate estimation of the State-of-Charge (SoC) to avoid fully discharging a battery. It shortens battery life and prolongs the time it takes to charge the battery. This dissertation introduces a new approach that uses Edge Computing and real-time predictive analytics to assess the status of EV batteries and send alerts when necessary, thus facilitating energy efficiency. The Edge Impulse platform is used to predict the Remain Useable Life (RUL) of batteries with enhanced accuracy using EON-Tuner and DSP processing blocks, enhancing computational capability and making it feasible for edge devices. Since traditional SoC estimations include tools like Kalman filters and Extended Kalman filters, which are effective but have a considerable drawback in estimating the SoC with changing battery parameters, this study proposes a multi-variable optimization method. The method enhances performance prediction after key parameters are iteratively adjusted, thus resolving the emergence hypotheses of most existing techniques. The system was designed and tested on Jupyter Notebook, and performance indicators of accuracy, MSE, and efficiency further validated the design. This study helps ensure proper energy use and long battery life for e-vehicles, which promotes clean energy us
Comprehensive analysis of physical layer performance for DSRC in NLOS V2V scenarios
No full textMotivated by the development of the vehicle-to-everything (V2X) communications, both the dedicated short-range communications (DSRC) and the cellular V2X (C-V2X) involved in the radio access technologies (RATs) are experiencing extensive evolution to support advanced vehicular applications and scenarios. Both the DSRC and the C-V2X support the vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-pedestrian (V2P) communications to directly transmit and receive data between the vehicles, infrastructures, and pedestrians. In addition, the C-V2X can support the vehicle-to-network (V2N) communications via the cellular networks. However, the C-V2X suffers from the challenge in transmitting data within the permissible latency with the higher cellular network traffic load. On the contrary, in the DSRC with short-range wireless technologies, vehicles can directly communicate with each other to exchange information and to largely extend their awareness range beyond autonomous on-board capabilities. On one hand, IEEE 802.11p is more robust and mature with the large-scale field trials performed worldwide and can provide safety and service applications for the intelligent transportation system (ITS) in the vehicular communications. On the other hand, IEEE 802.11bd was proposed as the amendment to IEEE 802.11p with the evolution for IEEE-based V2X communications by enhancing the reliability, throughput, and transmission range. The contributions of the proposed work over the previous work are as follows. Firstly, extensive physical layer (PHY) metrics, containing the packet error rate (PER), packet reception ratio (PRR), output packet inter-arrival time (IAT), and output effective data rate, are sufficiently adopted to accomplish the thorough PHY evaluation which can avoid the limitation brought by the partial PHY metrics. Secondly, various multi-antenna configurations, including the multiple-input multiple-output (MIMO), single-input multiple-output (SIMO), and multiple-input single-output (MISO) systems, are added to remedy the incomplete analysis on antenna configurations induced by the only single-input single-output (SISO) configuration. Finally, considerably different packet sizes and modulation and coding schemes (MCSs) are discussed under the urban and highway non-line-of-sight (NLOS) scenarios to uncover the impact of each parameter on the PHY performance which cannot be found in the fixed parameter or slightly different parameters. Some important conclusions obtained from a complete MATLAB-based PHY simulation are as follows. Firstly, the multi-antenna systems are more advantageous in reducing the PER, increasing the PRR and transmission coverage, decreasing the output packet IAT, and elevating the output effective data rate, compared to the SISO system, above the distance threshold and below the signal-to-noise ratio (SNR) threshold. Secondly, the packet size and the MCS should be adjusted simultaneously in different applications to accommodate their high-reliability, low-latency, or high-throughput requirement. Finally, the urban NLOS scenario with the lower Doppler effect is more tolerant than the highway NLOS scenario in the V2V communications due to its lower PER, larger PRR and transmission coverage, smaller output packet IAT, and higher output effective data rate
Starting college during COVID: examining whether summer bridge programs’ initiatives, goals, and outcomes translate to a remote environment
No full textThis dissertation investigates the experiences and outcomes of students who participated in remote summer bridge programs during the COVID-19 pandemic which was the 2020-2021 and 2021-2022 academic years. The study aims to evaluate the impact of these programs on overall student persistence. Student persistence is the participants’ attributions, retention, self-efficacy, and sense of belonging. The remote summer bridge program cohorts will be compared by in-person cohorts in years previously and following. Utilizing a sequential mixed-methods approach, the research examines quantitative data on retention rates and GPAs, alongside qualitative insights into students’ personal growth, confidence, and sense of belonging. The findings reveal that remote summer bridge programs enhanced students’ preparedness for college-level work and fostered strong social connections and community despite negative attributions about the remote facilitation. Lastly, recommendations are provided for incorporating remote learning into future summer bridge programs and suggests longitudinal assessments to evaluate the long-term impact on student success
An AI-driven strategy for non-invasive fault detection: techniques, applications, and innovations
No full textThis paper will provide a non-invasive fault detection solution with Artificial Intelligence (AI) techniques. The system uses already available information to collect visual, audio, and vibration data for diagnostics. Because the early signs of fault are numerous, subtle, complex, and difficult to classify and detect with mathematics and signal processing, the collected diagnostic data will be processed and analyzed for unique patterns and features to be used as input to AI tools. These features will be used to train the AI tools (Alexnet, Googlenet, Hybridnet, Yamnet, LSTM, Fuzzy Logic). The subtle characteristics are learned through training; when completed, the trained AI tools can detect them in real time. Each collected data will be classified as a fault with a soft value between [0-1]. Some faults are better detected by visual images than by vibration or audio, while others are better with audio and/or by vibration because the fault is embedded deep inside a machine. For example, an Instrumental Panel (IP) showing engine rpm, and a speedometer complemented by engine sound and wheel vibrations can reveal non-obvious anomalies that might not be shown solely on the IP. Sound and vibration would be able to provide the early telltale signs of anomalies inside the engine and wheels. These theories are experimented with and validated on an actual vehicle and will show that a non-invasive fault detection solution is a viable solution to early fault detectio
The Use of Artificial Intelligence for Melanoma Detection
No full textWith an increase in the uses of Artificial Intelligence (AI) and Machine Learning (ML), the possibilities of computer science in other fields are endless. Therefore, having developed an application in Python (Programming Language), that uses AI and ML to detect if an image of a skin spot uploaded by a patient or doctor is or is not potentially melanoma with percentage certainty can aid in the early detection phase and save valuable time. Namely, Convolutional Neural Networks (CNN) techniques have been implemented through the help of TensorFlow and Keras, open-source frameworks that aid the development of CNNs, since they are the standard for distinguishing the classification of an image. Through the application’s diagnosis, this means that doctors can reevaluate what the best course of action should be to save their patient’s life. This project provides a better understanding of how these Artificial Intelligence applications can be used within the medical field. This thesis will also aid others in the same area of expertise to build upon their own applications through this project as a reference
