Multisensor Data Fusion for Reliable Obstacle Avoidance

In this work, we propose a new approach that combines data from multiple sensors for reliable obstacle avoidance. The sensors include two depth cameras and a LiDAR arranged so that they can capture the whole 3D area in front of the robot and a 2D slide around it. To fuse the data from these sensors, we first use an external camera as a reference to combine data from two depth cameras. A projection technique is then introduced to convert the 3D point cloud data of the cameras to its 2D correspondence. An obstacle avoidance algorithm is then developed based on the dynamic window approach. A number of experiments have been conducted to evaluate our proposed approach. The results show that the robot can effectively avoid static and dynamic obstacles of different shapes and sizes in different environments.Comment: In the 11th International Conference on Control, Automation and Information Sciences (ICCAIS 2022), Hanoi, Vietna

Improving TDWZ Correlation Noise Estimation: A Deep Learning based Approach

Transform domain Wyner-Ziv video coding (TDWZ) has shown its benefits in compressing video applications with limited resources such as visual surveillance systems, remote sensing and wireless sensor networks. In TDWZ, the correlation noise model (CNM) plays a vital role since it directly affects to the number of bits needed to send from the encoder and thus the overall TDWZ compression performance. To achieve CNM with high accurate for TDWZ, we propose in this paper a novel CNM estimation approach in which the CNM with Laplacian distribution is adaptively estimated based on a deep learning (DL) mechanism. The proposed DL based CNM includes two hidden layers and a linear activation function to adaptively update the Laplacian parameter. Experimental results showed that the proposed TDWZ codec significantly outperforms the relevant benchmarks, notably by around 35% bitrate saving when compared to the DISCOVER codec and around 22% bitrate saving when compared to the HEVC Intra benchmark while providing a similar perceptual quality

Adaptive Quantization Parameter Estimation for HEVC Based Surveillance Scalable Video Coding

Visual surveillance systems have been playing a vital role in human modern life with a large number of applications, ranging from remote home management, public security to traffic monitoring. The recent High Efficiency Video Coding (HEVC) scalable extension, namely SHVC, provides not only the compression efficiency but also the adaptive streaming capability. However, SHVC is originally designed for videos captured from generic scenes rather than from visual surveillance systems. In this paper, we propose a novel HEVC based surveillance scalable video coding (SSVC) framework. First, to achieve high quality inter prediction, we propose a long-term reference coding method, which adaptively exploits the temporal correlation among frames in surveillance video. Second, to optimize the SSVC compression performance, we design a quantization parameter adaptation mechanism in which the relationship between SSVC rate-distortion (RD) performance and the quantization parameter is statistically modeled by a fourth-order polynomial function. Afterwards, an appropriate quantization parameter is derived for frames at long-term reference position. Experiments conducted for a common set of surveillance videos have shown that the proposed SSVC significantly outperforms the relevant SHVC standard, notably by around 6.9% and 12.6% bitrate saving for the low delay (LD) and random access (RA) coding configurations, respectively while still providing a similar perceptual decoded frame quality

Příspěvek ke studiu vlivu manganu na kinetiku oxidace příměsí v železe v proudu kyslíku

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一中間子交換模型による中間子－中間子相互作用

博士(工学)岐阜大学(Gifu University

Vliv manganu na kinetiku oxydace příměsí v železe v proudu kyslíku

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電腦輔助音樂教學研究--音符與休止符認知教學之成效分析

[[abstract]]本研究是探討電腦輔助國中音樂教學成效，並了解國中學生對於電腦輔助音樂教學所 抱持的態度。因此選擇了「音符與休止符」的教學軟體，對國中一年級學生進行實驗 。實驗採取前測末測實驗組與控制組設計實行，態度則以態度問卷調查。從教學實驗 和態度問卷所獲得的結果，以二因子共變數分析與百分比方式綜合整理分析。本研究 所獲之結論敘述如下： 壹、兩組學生的末測分數的平均數都大於前測，而實驗組的學習成就優於控制組。 貳、本研究中性別、智商、學科成績、音樂興趣、課外音樂經驗等因素，對學習成就 沒有顯著的影響。 參、本研究的實驗組學生對於電腦輔助音樂教學抱持著正面、積極的態度。 肆、本研究中性別、智商、學科成績、音樂興趣、課外音樂經驗、家中擁有電腦等因 素，對學習態度沒有顯著的影響。 伍、本研究工具「音符與休止符」軟體，使大部份的學生感到滿意。 陸、電腦輔助教學可以增加學生對音樂的興趣，改善樂理課程實施時的學習之情境。