Attention deficit disorder is a frequently observed symptom in individuals with autism spectrum disorder (ASD). This condition can present significant obstacles for those affected, manifesting in challenges such as sustained focus, task completion, and the management of distractions. These issues can impede learning, social interactions, and daily functioning. This complexity of symptoms underscores the need for tailored approaches in both educational and therapeutic settings to support individuals with ASD effectively. In this study, we have expanded upon our initial virtual reality (VR) prototype, originally created for attention therapy, to conduct a detailed statistical analysis. Our objective was to precisely identify and measure any significant differences in attention-related outcomes between sessions and groups. Our study found that heart rate (HR) and electrodermal activity (EDA) were more responsive to attention shifts than temperature. The ‘Noise’ and ‘Score’ strategies significantly affected eye openness, with the ASD group showing more responsiveness. The control group had smaller pupil sizes, and the ASD group’s pupil size increased notably when switching strategies in Session 1. Distraction log data showed that both ‘Noise’ and ‘Object Opacity’ strategies influenced attention patterns, with the ‘Red Vignette’ strategy showing a significant effect only in the ASD group. The responsiveness of HR and EDA to attention shifts and the changes in pupil size could serve as valuable physiological markers to monitor and guide these interventions. These findings further support evidence that VR has positive implications for helping those with ASD, allowing for more tailored personalized interventions with meaningful impact.

Designing and developing autonomous vehicles that are capable of moving safely on roads by sensing the environment has motivated researchers to focus on pedestrian detection systems so they can detect people as fast and accurately as possible. However, for pedestrian detection, it is crucial to consider not only the pedestrians themselves but their color as well, because color has the advantage of being invariant to changes in scaling, rotation, and partial occlusion. Therefore, considering skin color detection for implementing pedestrian detection systems is an essential required step in ensuring autonomous vehicles are further incorporated into our society. Detecting human skin has proven to be a challenging problem because skin color can vary dramatically in its appearance due to many factors such as illumination, race, imaging conditions, and others. Recently it has been noted that pedestrian detection systems for autonomous vehicles perform poorly at detecting people with darker skin tones. Such findings indicate that there is a larger problem that is causing these issues: algorithmic bias. Algorithmic bias in pedestrian detection systems could be the leading factor of their poor performance due to the methods implemented and datasets used. Unfortunately, the algorithmic bias in this context has not been considered closely and it seems that many studies do not cover this aspect closely when discussing pedestrian detection systems for autonomous vehicles. To alleviate this, we attempt to explore different techniques that can be used to detect pedestrians while minimizing bias. In our experiment, we use both a YOLO v3 convolutional neural network and K-Means clustering for classifying skin-tones.

With the rapid development of commercial drones, drone detection and classification have emerged and grown recently. Drone detection works to detect unmanned aerial vehicles (UAVs). Usually, systems for drone detection utilize a combination of one or more sensors and some methodology. Many unique technologies and methods are used to detect drones. However, each type of technology offers its benefits and limitations. Most approaches use computer vision or machine learning, but one methodology that has not been given much attention is Sensor Fusion. Sensor Fusion has less uncertainty than most methods, making it suitable for drone detection. In this paper, we propose an artificial neural network-based detection system that uses a deep neural network (DNN) to process the RF data and a convolutional neural network (CNN) to process image data. The features from CNNs and DNNs are concatenated and input into another DNN, which outputs a single prediction score of drone presence. Our model achieved a validation accuracy of 75% that shows the feasibility of a sensor fusion based technique for drone detection.

The UNet is one of the most well-known convolutional neural network (CNN) architectures used for biomedical image segmentation. Unfortunately, the 2D variant is typically discouraged for volumetric brain tumor segmentation due to slices being correlated with one another. Thus, 3D-Unets have become prevalent in the annual Multimodal Brain tumor Segmentation Challenge (BRaTS) hosted by the Perelman School of Medicine of University of Pennsylvania (UPenn). However, with unique data preprocessing and generator techniques, 2DUnets may achieve competitive accuracy and performance with 3D-Unets. Furthermore, the addition of residual blocks (R) and squeeze-and-excitation (SE) blocks in the upsampling portion of 2D-Unets could further speed up performance and minimize computational costs without sacrificing f1 or Jaccard's similarity score. This reveals that 2D-UNets for 3D biomedical image segmentation are still valuable. This paper involves the detailed comparison between 2D-Unets and 2D-SE-RUNets for the purposes of segmenting a whole high-grade glioma (HGG) using the metrics of Jaccard's similarity, recall, specificity, and precision. Results indicate that the 2D-SE-RUNet model is superior to the traditional 2D UNet due to efficieny, which can benefit those looking to save computational costs and time.

The recent Covid-19 pandemic instigated many changes in our way of life within the United States, and slowly but surely we are working towards mitigating the virus. Due to Covid-19, there are higher demands for models to accurately forecast the number of Covid-19 cases that factor mandated guidelines such as social-distancing. Many scholarly and corporate research entities are investigating ways to achieve this goal preemptively; Unfortunately, current models are not yet able to accurately model future Covid-19 cases that factor in various guidelines; What is lacking with these models is an understanding of crucial factors affecting spread, accuracy, availability of reported cases on a small scale, and quantifiable metrics for how social distancing and quarantine efforts mitigate the spread. Therefore, the goal of this study is to produce a mathematical model to directly aid policy decisions by comparing predicted models of various decisions and social distancing protocols. This model can be applied on top of existing models to factor in more imminent data and produce predictive curves, indicating troughs and peaks of new daily Covid-19 cases with comparatively high accuracy, which can aid in analysis. These predictive curves can, therefore, be generated using data corresponding to projected responses to proposed guidelines and compared to each other to choose the optimal solution for “flattening the curve” of the Covid19 infection rate. We use an LSTM-RNN model with ANN Regression in an attempt to predict future Covid-19 cases. Our model achieved comparable results, but further improvements could be implemented for more optimal results.