Classification of sporting activities using smartphone accelerometers

In this paper we present a framework that allows for the automatic identification of sporting activities using commonly available smartphones. We extract discriminative informational features from smartphone accelerometers using the Discrete Wavelet Transform (DWT). Despite the poor quality of their accelerometers, smartphones were used as capture devices due to their prevalence in today’s society. Successful classification on this basis potentially makes the technology accessible to both elite and non-elite athletes. Extracted features are used to train different categories of classifiers. No one classifier family has a reportable direct advantage in activity classification problems to date; thus we examine classifiers from each of the most widely used classifier families. We investigate three classification approaches; a commonly used SVM-based approach, an optimized classification model and a fusion of classifiers. We also investigate the effect of changing several of the DWT input parameters, including mother wavelets, window lengths and DWT decomposition levels. During the course of this work we created a challenging sports activity analysis dataset, comprised of soccer and field-hockey activities. The average maximum F-measure accuracy of 87% was achieved using a fusion of classifiers, which was 6% better than a single classifier model and 23% better than a standard SVM approach

Breathing feedback system with wearable textile sensors

Breathing exercises form an essential part of the treatment for respiratory illnesses such as cystic fibrosis. Ideally these exercises should be performed on a daily basis. This paper presents an interactive system using a wearable textile sensor to monitor breathing patterns. A graphical user interface provides visual real-time feedback to patients. The aim of the system is to encourage the correct performance of prescribed breathing exercises by monitoring the rate and the depth of breathing. The system is straightforward to use, low-cost and can be installed easily within a clinical setting or in the home. Monitoring the user with a wearable sensor gives real-time feedback to the user as they perform the exercise, allowing them to perform the exercises independently. There is also potential for remote monitoring where the user’s overall performance over time can be assessed by a clinician

Construction and Software Design for a Microcomputer Controlled pH/Ion Titrator

The construction of an automated titration device is described. The major components include an Apple II+ Microcomputer and 8-bit parallel interface. Fisher Accumet, Model 520 Digital pH/lon Meter, Gilmont Micrometer Buret of 2.5 mL capacity, Sigma stepper motor, power supply and driver to operate the buret, and a constant temperature bath of ± 0.005 °C stability. The limitations of the system are 0.001 pH/0.1 mv for the pH/ion sensing system, and 0.125 μL per step for the buret. The system as described is designed to determine equilibrium constants for metal ion-amino acid complexes. By changing the software a variety of different pH and redox titration experiments may be performed. A computer program used to operate the stepper motor driven syringe buret and record the pH from a digital pH meter is described. The program uses both Apple BASIC and assembly language. This is a closed loop operation in which the data from the pH meter is used to control the amount of reagent delivered by the buret. The results are displayed graphically as the titration proceeds. The variance of the pH readings are calculated using an assembly language subroutine and the calculations are done with zero round-off error

E-Quality: An Analysis of Digital Equity Discourse and Co-Production in the Era of COVID-19

The digital divide refers to the social stratification due to an unequal ability to access, adapt, and create knowledge via information and communication technologies (Andreasson, 2015). Digitally disadvantaged individuals have inadequate access to services and resources, exacerbating existing vulnerabilities. The COVID-19 pandemic instigated a new model of digital equity policymaking that leverages co-production between numerous actors. As citizens faced new financial and community constraints and governments reached administrative capacities, both the digital divide and the policymaking process evolved. This inductive study explores how digital equity policymaking shifted to a co-production model (Ostrom, 1996) amid the pandemic. Using a sequential mixed-methods approach, this research considers the interconnections of digital equity, co-production, and crisis policymaking. Digital divide discourse was first examined through a large-scale text analysis of verified tweets. Methods of investigation include natural language processing techniques, regression modeling, and unsupervised machine learning topic modeling. Descriptive and inferential analyses demonstrate a statistically significant increase in policy discourse as well as a diversification of topics, though suggest a disconnect between outputs and on-the-ground needs. Next, semi-structured interviews were conducted with City of Boston policymakers, and the resulting data was open-coded and axially coded to reveal insights into the design and implementation of co-productive solutions. Additionally, interviews detail what conditions contribute to successful outcomes while working with limited time, knowledge, and resources. Analyses reveal that co-productive behavior is critical to coping with the effects of the pandemic and highlight the influential role of community-based organizations. Furthermore, the study provides contextual information on co-production prerequisites that were previously understood, and sheds light on interpersonal conditions that Ostrom does not address. This dissertation contributes to the developing body of scholarly literature on the digital divide in the era of COVID-19. This case study also advances theoretical knowledge, offers methodological innovations, and provides concrete policy recommendations to promote more egalitarian digital use

Unit organization of the topic "Fasteners in mechanical drawing"

Thesis (M.A.)--Boston University, 1949. This item was digitized by the Internet Archive

DETECTION OF SYNTHETIC ANOMALIES ON AN EXPERIMENTALLY GENERATED 5G DATA SET USING CONVOLUTIONAL NEURAL NETWORKS

The research microgrid currently deployed at Marine Corps Air Station, Miramar, is leveraging Verizon’s Non-Standalone (NSA) 5G communications network to provide connectivity between dispersed energy assets and the energy and water operations center (EWOC). Due to its anchor to the Verizon 4G/LTE core, the NSA network does not provide technological avenues for cyber anomaly detection. In this research, we developed a traffic anomaly detection model using supervised machine learning for the energy communication infrastructure at Miramar. We developed a preliminary cyber anomaly detection platform using a convolutional neural network (CNN). We experimentally generated a benign 5G data set using the AT&T 5G cellular tower at the NPS SLAMR facility. We injected synthetic anomalies within the data set to test the CNN and its effectiveness at classifying packets as anomalous or benign. Data sets with varying amounts of anomalous data, ranging from 10% to 50%, were created. Accuracy, precision, and recall were used as performance metrics. Our experiments, conducted with Python and TensorFlow, showed that while the CNN did not perform its best on the data sets generated, it has the potential to work well with a more balanced data set that is large enough to host more anomalous traffic.ONRLieutenant, United States NavyApproved for public release. Distribution is unlimited