With the development of next generation mobile communication and short distance communication, mmWave is becoming more and more critical. The transmission rate and bandwidth of mmWave are greater than that of low frequency band. mmWave can effectively provide large-flow and low-latency service over short distances. Next generation WLAN, such as 802.11ad/ay, already uses mmWave. mmWave uses the directional gain antenna, and beamtracking is performed to determine the new working beam when one end of the communication is displaced. The beamtracking method is designed in detail in 802.11ad /ay: beamtracking is performed after data is sent. This method takes the delay into account, but it is easy to lose packets when nodes move quickly. To address this issue, we design an adaptive beamtracking method (ABT), which adjusts the order of sending data and performing beamtracking according to the number of consecutive beamtracking request. It can take both throughput and delay into account. The simulation results show that the adaptive beamtracking method can achieve the same delay as the beamtracking method in 802.11ad/ay, and the throughput is greater than the beamtracking method in 802.11ad/ay.
Intelligent cabins have become a hot topic in the development of automated vehicles, with a focus on multimodal human-computer interaction. Using knowledge mapping, this paper investigates international literature regarding intelligent cockpits (IC) over the past 22 years. The research characteristics are analyzed through cluster analysis, based on which the future development directions are provided. Since 2019, there has been a significant increase in global publication volume. Researchers have extensively researched new functions and technologies such as gesture interaction, virtual environment, human-machine interface, external human machine interface, and human-machine interaction, with some technologies already being applied in practice. The extension of intelligent cockpit-related concepts has further developed technologies such as electronic skin, health detection, speech emotion recognition, and electromyography control. In the future, nanomaterials with excellent properties will be further integrated and applied in intelligent cabins to adapt to their highly integrated functional module group. Fields such as biology, mathematics and systems science, ophthalmology and neuroscience, physics, and chemistry are involved in the development of IC. The main development features of IC are multimodal interaction, virtual-real combined display, virtual agents, and emotional interaction. Overall, the safety, comfort, and convenience of passengers have always been the prime goals of the research on IC.
Abstract Groundwater is one of the key problems that must be faced and solved in underground engineering. Under special conditions, large-scale water damage accidents will be formed. Therefore, it is of great engineering significance to study the stability of surrounding rock of water-inrush roadway. This study focuses on laboratory experiments to study the preparation of similar materials, establish multiple linear regression equations of the ratio and physical parameters, analyze the influence law of roadway depth of roadway, water inrush height and water inrush time on the stability of surrounding rock of water-inrush roadway, and verify and expand it with numerical simulation. The experimental results show that the sensitivity of each factor to roadway stability is as follows: water inrush height > water inrush time > depth of roadway; In the test within 24 hours after water inrush, the sensitivity of each factor to roadway stability is also the height of water inrush > the time of water inrush > the depth of burial. This conclusion can provide an important basis for the rescue work after roadway water inrush and enrich the relevant test simulation after roadway water inrush.
Electroencephalogram (EEG)-based emotion recognition has been widely used in affective computing. However, the study on improving recognition accuracy across individuals is insufficient. In this study, a new linear domain adaption approach with experiment-level batch normalization and single-layer depthwise convolutional neural network is proposed. In particular, the experiment-level batch normalization and depthwise convolutional neural network can be integrated as a linear mapping with a scaling parameter and a translation parameter. By linear mapping, difference between subjects in different domain can be effectively diminished, and the mapping parameters can be used to further investigate EEG emotion mechanism. The domain adaption experiments are conducted with SJTU emotion EEG dataset and SJTU emotion EEG dataset-IV, which are divided into source domain and target domain to validate the recognition effect across individuals. Multiple traditional machine learning and deep learning classifiers are used to examine the effectiveness of our proposed approach. By mapping the EEG data from source domain to target domain, the increment of recognition accuracy is up to 61.11% when using the support vector machine classifier. The highest recognition accuracy 97.22% is achieved when using the logistic regression classifier. The scaling and translation parameters in the mapping procedure are then analyzed with statistical methods. It is found that EEG signal waves in the same emotion category are highly similar and EEG data have characteristics including integration of channels and hierarchy of frequency bands. In addition, our results indicate that emotion complexity and emotion sensitiveness of brain cortex regions can affect the correlations between channels.
The exponentially growing complexity of modern processors intensifies verification challenges. Traditional pre-silicon verification covers less and less of the design space, resulting in increasing post-silicon validation effort. A critical challenge is the manual debugging of intermittent failures on prototype chips, where multiple executions of a same test do not yield a consistent outcome. We leverage the power of machine learning to support automatic diagnosis of these difficult, inconsistent bugs. During post-silicon validation, lightweight hardware logs a compact measurement of observed signal activity over multiple executions of a same test: some may pass, somemay fail. Our novel algorithm applies anomaly detection techniques similar to those used to detect credit card fraud to identify the approximate cycle of a bug's occurrence and a set of candidate root-cause signals. Compared against other state-of-the-art solutions in this space, our new approach can locate the time of a bug's occurrence with nearly 4x better accuracy when applied to the complex OpenSPARC T2 design.
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