Bulletin of the Center for Children's Books 48 (04) 1994

published or submitted for publicatio

John Brown University disaster shelter competition

John Brown University hosted the 6th annual Disaster Shelter Relief Competition in April 2017 for which the team built a prototype shelter and proposed a camp plan. Both the shelter and the camp plan were designed to house refugees coming into Greece from the Middle East. The shelter would accommodate a family of four and the camp plan was designed to hold 1250 shelters, or 5000 people. The shelter was built on site at John Brown University and was required to take less than two hours to fully construct. This report summarizes the work the team did for the competition, including a review of existing shelter designs currently in use, a description of the method of design of the prototype, validation that the prototype meets the criteria, a discussion of the cultural appropriateness of the shelter to the scenario, suggested modifications and improvements that can be made, photos and drawings of the prototype, and the camp plan

Detection of visual field defects using Eye Movement Pediatric Perimetry in children with intracranial lesions:feasibility and applicability

The study aimed at evaluating the feasibility of Eye Movement Pediatric Perimetry (EMPP) among children in detecting Visual Field Defects (VFDs) associated with Intracranial Lesions (IL). Healthy controls (n = 35) and patients diagnosed with IL (n = 19) underwent a comprehensive clinical evaluation followed by a Goldmann Visual Field (GVF) and a customised EMPP protocol. During EMPP, all the participants were encouraged to fixate on a central target and initiate Saccadic Eye Movement (SEM) responses towards randomly appearing peripheral stimuli. The SEM responses were recorded using an eye-tracking device and further inspected to calculate Performance Scores (PS), Saccadic Reaction Times (SRTs), and an EMPP Index (EMPI). The mean age (years) of the controls and cases were 7.3 (SD: 1.5) and 9.4 (SD: 2.4) respectively. Among the controls, the older children (≥7 years) showed statistically significantly faster SRTs (p = 0.008) compared to the younger group. The binocular EMPP measurements compared between the controls and the cases revealed no statistically significant differences in PS (p = 0.34) and SRT (p = 0.51). EMPP failed in 4 children because of data loss or unacceptably poor PS whereas GVF failed in 7 children due to unreliable subjective responses. Of the 16 reports, with regard to the central 30-degree VF, 63% of the outputs obtained from both methods were comparable. EMPP is a reliable method to estimate and characterise the central 30-degree VF in greater detail in children with IL. EMPP can supplement the conventional methods, especially in those children who fail to complete a long duration GVF test

Prediction of Hourly Cooling Energy Consumption of Educational Buildings Using Artificial Neural Network

Predicating the required building energy when it is in the design stage and before being constructed considers a crucial step for in charge people. Hence, the main aim of this research is to accurately forecast the needed building cooling energy per hour for educational buildings at University of Technology in Iraq. For this purpose, the feed forward artificial neural network (ANN) has been selected as an efficient technique to develop such a predication system.  Firstly, the main building parameters have been investigated and then only the most important ones were chosen to be used as inputs to the ANN model. However, due to the long time period that is required to collect actual consumed building energy in order to be employed for ANN model training, the hourly analysis program (HAP), which is a building simulation software, has been utilized to produce a database covering the summer months in Iraq. Different training algorithms and range of learning rate values have been investigated, and the Bayesian regularization backpropagation training algorithm and 0.05 learning rate were found very suitable for precise cooling energy prediction. To evaluate the performance of the optimized ANN model, mean square error (MSE) and correlation coefficient (R) have been adopted. The MSE and R indices for the predication results proved that the optimized ANN model is having a high predication accuracy with 5.99*10-6 and 0.9994, respectively

Sound Event Localization, Detection, and Tracking by Deep Neural Networks

In this thesis, we present novel sound representations and classification methods for the task of sound event localization, detection, and tracking (SELDT). The human auditory system has evolved to localize multiple sound events, recognize and further track their motion individually in an acoustic environment. This ability of humans makes them context-aware and enables them to interact with their surroundings naturally. Developing similar methods for machines will provide an automatic description of social and human activities around them and enable machines to be context-aware similar to humans. Such methods can be employed to assist the hearing impaired to visualize sounds, for robot navigation, and to monitor biodiversity, the home, and cities. A real-life acoustic scene is complex in nature, with multiple sound events that are temporally and spatially overlapping, including stationary and moving events with varying angular velocities. Additionally, each individual sound event class, for example, a car horn can have a lot of variabilities, i.e., different cars have different horns, and within the same model of the car, the duration and the temporal structure of the horn sound is driver dependent. Performing SELDT in such overlapping and dynamic sound scenes while being robust is challenging for machines. Hence we propose to investigate the SELDT task in this thesis and use a data-driven approach using deep neural networks (DNNs). The sound event detection (SED) task requires the detection of onset and offset time for individual sound events and their corresponding labels. In this regard, we propose to use spatial and perceptual features extracted from multichannel audio for SED using two different DNNs, recurrent neural networks (RNNs) and convolutional recurrent neural networks (CRNNs). We show that using multichannel audio features improves the SED performance for overlapping sound events in comparison to traditional single-channel audio features. The proposed novel features and methods produced state-of-the-art performance for the real-life SED task and won the IEEE AASP DCASE challenge consecutively in 2016 and 2017. Sound event localization is the task of spatially locating the position of individual sound events. Traditionally, this has been approached using parametric methods. In this thesis, we propose a CRNN for detecting the azimuth and elevation angles of multiple temporally overlapping sound events. This is the first DNN-based method performing localization in complete azimuth and elevation space. In comparison to parametric methods which require the information of the number of active sources, the proposed method learns this information directly from the input data and estimates their respective spatial locations. Further, the proposed CRNN is shown to be more robust than parametric methods in reverberant scenarios. Finally, the detection and localization tasks are performed jointly using a CRNN. This method additionally tracks the spatial location with time, thus producing the SELDT results. This is the first DNN-based SELDT method and is shown to perform equally with stand-alone baselines for SED, localization, and tracking. The proposed SELDT method is evaluated on nine datasets that represent anechoic and reverberant sound scenes, stationary and moving sources with varying velocities, a different number of overlapping sound events and different microphone array formats. The results show that the SELDT method can track multiple overlapping sound events that are both spatially stationary and moving