Time-domain Type Research Articles

Research on Optimization of Boundary Detection and Dangerous Area Warning Algorithms Based on Deep Learning in Campus Security System

This study designs and implements a boundary detection and dangerous area warning algorithm based on deep learning from the perspective of typified campus security situation resources such as data, information, and knowledge. Based on integrating multiple campus security factors, real-time perception and further prediction of campus security situation can be achieved. Through coordinated operation among various algorithm modules, object intrusion in specific areas can be accurately identified and early warning can be given. The research results show that when an object invades a specific area, the difference coefficient will increase, and the larger the change value in the intrusion area, the larger the corresponding difference coefficient. By using this feature, the threshold of the difference coefficient can be determined. When a region is invaded, the contour length of the foreground will sharply increase. Based on the statistical information of the contour length of the foreground, the threshold can be set to determine whether someone has invaded the region. The deep learning algorithm in this study accurately extracts the contour of moving targets and can identify foreground targets. The real-time performance of the algorithm is also guaranteed, and it has high practical value in intelligent video monitoring. This algorithm greatly improves the efficiency of intrusion detection by utilizing the joint constraints of two types of time-domain and scene-space transformations in monitoring images. This method is not affected by the brightness of the regional environment, nor will it cause misjudgment due to significant differences in brightness of the regional environment. The detection and inference time of deep learning-based detection methods is controlled within 2-3ms, and the FPS value of the detection method is always at a high level, which can quickly increase to over 350frames/s after transmission begins. The detection method based on deep learning has higher detection efficiency.

Survey on computational-intelligence-based UAV path planning

The key objective of unmanned aerial vehicle (UAV) path planning is to produce a flight path that connects a start state and a goal state while meeting the required constraints. Computational intelligence (CI) is a set of nature-inspired computational methodologies and approaches for addressing complex real-world problems for which mathematical or traditional modelling does not perform well. It has been applied in the field of UAVs since it can yield effective, accurate and rapid solutions. This article provides an overview of studies on UAV path planning based on CI methods published in major journals and conference proceedings. We survey relevant studies with respect to different CI algorithms utilized in UAV path planning, the types of time domain in UAV path planning, namely, offline and online, and the types of environment models, namely, 2D and 3D. It is observed that CI methods outperform traditional methods on online and 3D problems. The analysis is useful for identifying key results from UAV path planning research and is leveraged in this article to highlight trends and open issues.

The Comparison of the Effect of Haimming Window and Blackman Window in the Time-Scaling and Pitch-Shifting Algorithms

The real-time pitch shifting process is widely used in various types of music production. The pitch shifting technology can be divided into two major types, the time domain type and the frequency domain type. Compared with the time domain method, the frequency domain method has the advantage of large shifting scale, low total cost of computing and the more flexibility of the algorithm. However, the use of Fourier Transform in frequency domain processing leads to the inevitable inherent frequency leakage effects which decrease the accuracy of the pitch shifting effect. In order to restrain the side effect of Fourier Transform, window functions are used to fall down the spectrum-aliasing. In practical processing, Haimming Window and Blackman Window are frequently used. In this paper, we compare both the effect of the two window functions in the restraint of frequency leakage and the performance and accuracy in subjective based on the traditional phase vocoder[1]. Experiment shows that Haimming Window is generally better than Blackman Window in pitch shifting process.

Real time analysis of lung sounds

This paper describes a real time system for the analysis of pulmonary sounds. The system performs various types of time-domain and spectrographic analysis. It is able to display time-domain waveforms obtained from microphones detecting lung sounds, their power spectra and a real-time linear prediction model instantaneously for the immediate identification of interesting features. Details of the system are presented with examples of clinical research carried out using spectrographic analysis.

Speedier decoding of cyclic codes using chord properties

Finite-field transform quantities, appearing in typical cyclic code decoders, obey conjugacy-based relationships commonly called chord properties. It is demonstrated that these relationships may be used to significantly expedite error estimation by the decoder. The resulting speed-up factor is proportional to the degree of the extension field in which the code design is specified. BCH codes and standard decoder structures, either of the frequency-domain or time-domain type, are used to motivate, develop, and illustrate the underlying principles. Approximate estimates of the complexities and benefits of the proposed techniques are given. Finally, these chord-directed speed-up methods are extended to Reed-Solomon and other cyclic codes that use symbols in an extension field.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>