Interaction Analytics of Software Factory Recordings

abstract: A human communications research project at Arizona State University aurally recorded the daily interactions of aware and consenting employees and their visiting clients at the Software Factory, a software engineering consulting team, over a three year period. The resulting dataset contains valuable insights on the communication networks that the participants formed however it is far too vast to be processed manually by researchers. In this work, digital signal processing techniques are employed to develop a software toolkit that can aid in estimating the observable networks contained in the Software Factory recordings. A four-step process is employed that starts with parsing available metadata to initially align the recordings followed by alignment estimation and correction. Once aligned, the recordings are processed for common signals that are detected across multiple participants’ recordings which serve as a proxy for conversations. Lastly, visualization tools are developed to graphically encode the estimated similarity measures to efficiently convey the observable network relationships to assist in future human communications research.Dissertation/ThesisMasters Thesis Electrical Engineering 201

PERFORMANCE ANALYSIS OF AUDIO AND VIDEO SYNCHRONIZATION USING SPREADED CODE DELAY MEASUREMENT TECHNIQUE

The audio and video synchronization plays an important role in speech recognition and multimedia communication. The audio-video sync is a quite significant problem in live video conferencing. It is due to use of various hardware components which introduces variable delay and software environments. The synchronization loss between audio and video causes viewers not to enjoy the program and decreases the effectiveness of the programs. The objective of the synchronization is used to preserve the temporal alignment between the audio and video signals. This paper proposes the audio-video synchronization using spreading codes delay measurement technique. The performance of the proposed method made on home database and achieves 99% synchronization efficiency. The audio-visual signature technique provides a significant reduction in audio-video sync problems and the performance analysis of audio and video synchronization in an effective way. This paper also implements an audio- video synchronizer and analyses its performance in an efficient manner by synchronization efficiency, audio-video time drift and audio-video delay parameters. The simulation result is carried out using Matlab simulation tools and Simulink. It is automatically estimating and correcting the timing relationship between the audio and video signals and maintaining the Quality of Service

Recognition of Activities of Daily Living Based on Environmental Analyses Using Audio Fingerprinting Techniques: A Systematic Review

An increase in the accuracy of identification of Activities of Daily Living (ADL) is very important for different goals of Enhanced Living Environments and for Ambient Assisted Living (AAL) tasks. This increase may be achieved through identification of the surrounding environment. Although this is usually used to identify the location, ADL recognition can be improved with the identification of the sound in that particular environment. This paper reviews audio fingerprinting techniques that can be used with the acoustic data acquired from mobile devices. A comprehensive literature search was conducted in order to identify relevant English language works aimed at the identification of the environment of ADLs using data acquired with mobile devices, published between 2002 and 2017. In total, 40 studies were analyzed and selected from 115 citations. The results highlight several audio fingerprinting techniques, including Modified discrete cosine transform (MDCT), Mel-frequency cepstrum coefficients (MFCC), Principal Component Analysis (PCA), Fast Fourier Transform (FFT), Gaussian mixture models (GMM), likelihood estimation, logarithmic moduled complex lapped transform (LMCLT), support vector machine (SVM), constant Q transform (CQT), symmetric pairwise boosting (SPB), Philips robust hash (PRH), linear discriminant analysis (LDA) and discrete cosine transform (DCT).This work was supported by FCT project UID/EEA/50008/2013 (Este trabalho foi suportado pelo projecto FCT UID/EEA/50008/2013). The authors would also like to acknowledge the contribution of the COST Action IC1303—AAPELE—Architectures, Algorithms and Protocols for Enhanced Living Environments

Online circuit breaker monitoring system

Circuit breakers are used in a power system to break or make current flow through power system apparatus. Reliable operation of circuit breakers is very important to the well-being of the power system. Historically this is achieved by regular inspection and maintenance of the circuit breakers. An automated online circuit breaker monitoring system is proposed to monitor condition, operation and status of high and medium voltage circuit breakers. By tracking equipment condition, this system could be used to perform maintenance only when it is needed. This could decrease overall maintenance cost and increase equipment reliability. Using high accurate time synchronization, this system should enable development of system-wide applications that utilize the data recorded by the system. This makes possible tracking sequence of events and making conclusions about their effect on-line. This solution also enables reliable topology analysis, which can be used to improve power flow analysis, state estimation and alarm processing

Shape Representation in Primate Visual Area 4 and Inferotemporal Cortex

The representation of contour shape is an essential component of object recognition, but the cortical mechanisms underlying it are incompletely understood, leaving it a fundamental open question in neuroscience. Such an understanding would be useful theoretically as well as in developing computer vision and Brain-Computer Interface applications. We ask two fundamental questions: “How is contour shape represented in cortex and how can neural models and computer vision algorithms more closely approximate this?” We begin by analyzing the statistics of contour curvature variation and develop a measure of salience based upon the arc length over which it remains within a constrained range. We create a population of V4-like cells – responsive to a particular local contour conformation located at a specific position on an object’s boundary – and demonstrate high recognition accuracies classifying handwritten digits in the MNIST database and objects in the MPEG-7 Shape Silhouette database. We compare the performance of the cells to the “shape-context” representation (Belongie et al., 2002) and achieve roughly comparable recognition accuracies using a small test set. We analyze the relative contributions of various feature sensitivities to recognition accuracy and robustness to noise. Local curvature appears to be the most informative for shape recognition. We create a population of IT-like cells, which integrate specific information about the 2-D boundary shapes of multiple contour fragments, and evaluate its performance on a set of real images as a function of the V4 cell inputs. We determine the sub-population of cells that are most effective at identifying a particular category. We classify based upon cell population response and obtain very good results. We use the Morris-Lecar neuronal model to more realistically illustrate the previously explored shape representation pathway in V4 – IT. We demonstrate recognition using spatiotemporal patterns within a winnerless competition network with FitzHugh-Nagumo model neurons. Finally, we use the Izhikevich neuronal model to produce an enhanced response in IT, correlated with recognition, via gamma synchronization in V4. Our results support the hypothesis that the response properties of V4 and IT cells, as well as our computer models of them, function as robust shape descriptors in the object recognition process

Monitoring of Power System Topology in Real-Time.

Abstrac

An inertial motion capture framework for constructing body sensor networks

Motion capture is the process of measuring and subsequently reconstructing the movement of an animated object or being in virtual space. Virtual reconstructions of human motion play an important role in numerous application areas such as animation, medical science, ergonomics, etc. While optical motion capture systems are the industry standard, inertial body sensor networks are becoming viable alternatives due to portability, practicality and cost. This thesis presents an innovative inertial motion capture framework for constructing body sensor networks through software environments, smartphones and web technologies. The first component of the framework is a unique inertial motion capture software environment aimed at providing an improved experimentation environment, accompanied by programming scaffolding and a driver development kit, for users interested in studying or engineering body sensor networks. The software environment provides a bespoke 3D engine for kinematic motion visualisations and a set of tools for hardware integration. The software environment is used to develop the hardware behind a prototype motion capture suit focused on low-power consumption and hardware-centricity. Additional inertial measurement units, which are available commercially, are also integrated to demonstrate the functionality the software environment while providing the framework with additional sources for motion data. The smartphone is the most ubiquitous computing technology and its worldwide uptake has prompted many advances in wearable inertial sensing technologies. Smartphones contain gyroscopes, accelerometers and magnetometers, a combination of sensors that is commonly found in inertial measurement units. This thesis presents a mobile application that investigates whether the smartphone is capable of inertial motion capture by constructing a novel omnidirectional body sensor network. This thesis proposes a novel use for web technologies through the development of the Motion Cloud, a repository and gateway for inertial data. Web technologies have the potential to replace motion capture file formats with online repositories and to set a new standard for how motion data is stored. From a single inertial measurement unit to a more complex body sensor network, the proposed architecture is extendable and facilitates the integration of any inertial hardware configuration. The Motion Cloud’s data can be accessed through an application-programming interface or through a web portal that provides users with the functionality for visualising and exporting the motion data