Construction heavy machines often perform their routine tasks in the form of cyclic operations (e.g., cycles of digging, swinging, and dumping for a hydraulic excavator), and forecasting those cycle times is an important step toward scheduling, cost estimation, and productivity analysis of construction projects. The current state of research for automated cycle time prediction for construction operations is based on processing kinematic data (e.g., acceleration) or implementing computer vision algorithms. Both methods have certain limitations (e.g., it is necessary to directly attach kinematic sensors to machines, and computer vision algorithms are very sensitive to lighting conditions and occlusions). In addition, current methods predict cycle time values once (usually at the beginning of projects) and do not include an “auto-update” component capable of adjusting results over time and due to variations, such as changes in jobsite conditions or impacts of learning curve, for example. To address these two important issues, the authors propose an audio-based Bayesian system for estimating cycle times of cyclic construction activities. The sounds generated during routine operations of construction equipment and machines are treated as the main source of data for this project, and efficient signal processing and machine learning algorithms are implemented to process recorded audio files at construction jobsites and extract useful information. A robust denoising algorithm was developed to improve the quality of audio files, and Bayesian statistical models are utilized to include historical data for cycle time estimation enhancement. Case studies illustrate that implementing robust audio signal processing techniques, along with a Markov chain–based filter, enable the system to accurately forecast cycle times of construction activities for multiple days of operation.

Magnetic domain walls in patterned permalloy ring elements of different widths and thicknesses have been probed by magnetic force microscopy and micromagnetic simulations. We report the experimental observation of antivortex structures in single-layer magnetic rings, previously only theoretically predicted. Furthermore, vortex-antivortex chains are observed on occasion in wider rings that prefer a circulating flux closure state. It is possible that they are pinned transient states of the relaxation path from the magnetically saturated to the circulating flux closure state. Similar tendencies are seen in micromagnetic simulations implying that vortex-antivortex pair nucleation and annihilation are fundamental parts of the said relaxation. Finally we present a phase diagram of different domain-wall configurations as a function of ring width and thickness.

Recently, many attention-based deep neural networks have emerged and achieved state-of-the-art performance in environmental sound classification. The essence of attention mechanism is assigning contribution weights on different parts of features, namely channels, spectral or spatial contents, and temporal frames. In this paper, we propose an effective convolutional neural network structure with a multichannel temporal attention (MCTA) block, which applies a temporal attention mechanism within each channel of the embedded features to extract channel-wise relevant temporal information. This multi-channel temporal attention structure will result in a distinct attention vector for each channel, which enables the network to fully exploit the relevant temporal information in different channels. The datasets used to test our model include ESC-50 and its subset ESC-10, along with development sets of DCASE 2018 and 2019. In our experiments, MCTA performed better than the single-channel temporal attention model and the non-attention model with the same number of parameters. Furthermore, we compared our model with some successful attention-based models and obtained competitive results with a relatively lighter network.

This book is intended to fill the gap between the "ideal precision" digital signal processing (DSP) that is widely taught, and the limited precision implementation skills that are commonly required in fixed-point processors and field programmable gate arrays (FPGAs). These skills are often neglected at the university level, particularly for undergraduates. We have attempted to create a resource both for a DSP elective course and for the practicing engineer with a need to understand fixed-point implementation. Although we assume a background in DSP, Chapter 2 contains a review of basic theory and Chapter 3 reviews random processes to support the noise model of quantization error. Chapter 4 details the binary arithmetic that underlies fixed-point processors and then introduces fractional format for binary numbers. Chapter 5 covers the noise model for quantization error and the effects of coefficient quantization in filters. Because of the numerical sensitivity of IIR filters, they are used extensiv...