While our world is filled with its own natural sounds that we can't resist
enjoying, it is also chock-full of other sounds that can be irritating, this is
noise. Noise not only influences the working efficiency but also the human's
health. The problem of reducing noise is one of great importance and great
difficulty. The problem has been addressed in many ways over the years. The
current methods for noise reducing mostly rely on the materials and
transmission medium, which are only effective to some extent for the high
frequency noise. However, the effective reduction noise method especially for
low frequency noise is very limited.
Here we come up with a noise reduction system consist of a sensor to detect
the noise in the environment. Then the noise will be sent to an electronic
control system to process the noise, which will generate a reverse phase
frequency signal to counteract the disturbance. Finally, the processed smaller
noise will be broadcasted by the speaker. Through this smart noise reduction
system, even the noise with low-frequency can be eliminated.
The system is also integrated with sleep tracking and music player
applications. It can also remember and store settings for the same environment,
sense temperature, and smart control of home furniture, fire alarm, etc. This
smart system can transfer data easily by Wi-Fi or Bluetooth and controlled by
its APP.
In this project, we will present a model of the above technology which can be
used in various environments to prevent noise pollution and provide a solution
to the people who have difficulties finding a peaceful and quiet environment
for sleep, work or study.

通过使用一个感应器检测噪音、采用电子控制系统处理噪音并产生相反相位频率信号来对抗干扰，再通过扬声器播放处理后的较小噪音，这个智能噪音减震系统可以消除甚至低频噪音。该系统还集成了睡眠追踪和音乐播放器应用，能够通过 Wi-Fi 或蓝牙轻松传输数据并通过其 APP 进行控制。该项目将提出上述技术的模型，可在各种环境中用于防止噪声污染，并为那些难以找到安静环境来睡觉、工作或学习的人们提供解决方案。

主动噪声控制便携设备设计

Active Noise Control Portable Device Design

This paper presents a new multimodal interventional radiology dataset, called
PoCaP (Port Catheter Placement) Corpus. This corpus consists of speech and
audio signals in German, X-ray images, and system commands collected from 31
PoCaP interventions by six surgeons with average duration of 81.4 $\pm$ 41.0
minutes. The corpus aims to provide a resource for developing a smart speech
assistant in operating rooms. In particular, it may be used to develop a speech
controlled system that enables surgeons to control the operation parameters
such as C-arm movements and table positions. In order to record the dataset, we
acquired consent by the institutional review board and workers council in the
University Hospital Erlangen and by the patients for data privacy. We describe
the recording set-up, data structure, workflow and preprocessing steps, and
report the first PoCaP Corpus speech recognition analysis results with 11.52
$\%$ word error rate using pretrained models. The findings suggest that the
data has the potential to build a robust command recognition system and will
allow the development of a novel intervention support systems using speech and
image processing in the medical domain.

该论文介绍了一个名为 PoCaP （Port Catheter Placement）Corpus 的新的多模态介入放射学数据集，旨在为医疗领域开发精准的智能语音助手系统提供资源和支持。研究人员通过征得患者和相关机构的同意获取了语音和音频信号、X 光图像以及操作指令等数据，并利用预训练模型分析了该数据集的言语识别结果，表明这个数据集可以为医学领域开发一套全新的介入支持系统提供可能性。

PoCaP Corpus: 一种用于智能手术室语音助手的多模态数据集，使用介入放射学工作流分析

PoCaP Corpus: A Multimodal Dataset for Smart Operating Room Speech Assistant using Interventional Radiology Workflow Analysis

Various domain users are increasingly leveraging real-time social media data
to gain rapid situational awareness. However, due to the high noise in the
deluge of data, effectively determining semantically relevant information can
be difficult, further complicated by the changing definition of relevancy by
each end user for different events. The majority of existing methods for short
text relevance classification fail to incorporate users' knowledge into the
classification process. Existing methods that incorporate interactive user
feedback focus on historical datasets. Therefore, classifiers cannot be
interactively retrained for specific events or user-dependent needs in
real-time. This limits real-time situational awareness, as streaming data that
is incorrectly classified cannot be corrected immediately, permitting the
possibility for important incoming data to be incorrectly classified as well.
We present a novel interactive learning framework to improve the classification
process in which the user iteratively corrects the relevancy of tweets in
real-time to train the classification model on-the-fly for immediate predictive
improvements. We computationally evaluate our classification model adapted to
learn at interactive rates. Our results show that our approach outperforms
state-of-the-art machine learning models. In addition, we integrate our
framework with the extended Social Media Analytics and Reporting Toolkit
(SMART) 2.0 system, allowing the use of our interactive learning framework
within a visual analytics system tailored for real-time situational awareness.
To demonstrate our framework's effectiveness, we provide domain expert feedback
from first responders who used the extended SMART 2.0 system.

提出了一种新颖的交互式学习框架来提高对实时推文的分类过程，该框架允许用户在实时学习的基础上迭代地纠正推文的相关性以训练分类模型，实验结果表明该方法优于现有的机器学习模型，结合 SMART 系统，可用于实时情境感知并满足不同用户依赖需求。