安卓平台上的医用图像信息采集系统的开发设计

为了探讨在安卓平台上构建医用图像采集系统的开发个案,分析通过以智能手机、平板电脑为核心安卓设备通过拍照获得化验单数据后进行文本识别并提交智慧医疗系统的解决方案。本文首先通过二值化算法形成低阈值图像数据,使用卷积神经元网络算法对文本进行逐一识别,使用K-means算法对识别后的单字文本进行字段记录值的整合并形成元数据库服务于其他智慧医疗系统模块。在使用9000组数据对神经元网络进行前期训练的前提下,该系统的识别准确率达到了99.5%以上。本系统具有一定的可行性,对未来智慧医疗的系统开发有实践意义。     

Reducing over- and under-estimation of the a priori SNR in speech enhancement techniques

Most speech enhancement methods based on short-time spectral modification are generally expressed as a spectral gain depending on the estimate of the local signal-to-noise ratio (SNR) on each frequency bin. Several studies have analyzed the performance of a priori SNR estimation algorithms to improve speech quality and to reduce speech distortions. In this paper, we concentrate on the analysis of over- and under estimation of the a priori SNR in speech enhancement and noise reduction systems. We first show that conventional approaches such as the decision-directed approach proposed by Ephraïm and Malah lead to a biased estimator for the a priori SNR. To reduce this bias, our strategy relies on the introduction of a correction term in the a priori SNR estimate depending on the current state of both the available a posteriori SNR and the estimated a priori one. The proposed solution leads to a bias-compensated a priori SNR estimate, and allows to finely estimating the output speech signal to be very close to the original one on each frequency bin. Such refinement procedure in the a priori SNR estimate can be inserted in any type of spectral gain function to improve the output speech quality. Objective tests under various environments in terms of the Normalized Covariance Metric (NCM) criterion, the Coherence Speech Intelligibility Index (CSII) criterion, the segmental SNR criterion and the Perceptual Evaluation of Speech Quality (PESQ) measure are presented showing the superiority of the proposed method compared to competitive algorithms.     

扫描地图的计算机自动识别

扫描地图的计算机自动识别是当今地图制图学科的前沿课题，也是其重要的研究方向。为此，本文首先将模式识别技术引入到地图制图领域中，提出了四种地图符号的计算机描述方法，然后又讨论了扫描地图的信息获取与处理，并具体地分析了扫描地图的计算机识别方法及其识别系统．最后还给出了相应的符号识别实验。     

基于AMBE-2000的单兵语音通信终端设计

单兵语音通信终端由TMS320C5416 DSP、AMBE-2000、A/D、flash存储器及MCU等电路组成,软件分主控制器与DSP部分.主控制器将程序与数据移至DSP内部RAM,加载后对语音芯片等初始化并设定通道.系统主循环含键盘处理、命令输出、接收命令模块.其DSP分主程序、无线网络跳频算法、及相关通信等模块.     

Influence of ozone-limonene reactions on perceived air quality

This study conducted short-term assessments of perceived air quality (PAQ) for six different realistic concentrations of ozone and limonene, separately or together, in room air. The impact of filtration and the influence of the ozone generation method were also examined. The evaluations were made in four identical 40 m3 low-polluting test offices ventilated at 1.4 h(-1) or in two identical 30 m3 stainless-steel chambers ventilated at 1.9 h(-1). Concentrations of ozone, total volatile organic compounds and size-fractionated particles were continuously monitored in each experiment. The results indicate that, for each of the six conditions, the PAQ was poorer when ozone and limonene were present together compared with when only ozone or only limonene was present. In the test offices a correlation was observed between the number of secondary organic aerosols produced by a given ozone/limonene condition and the sensory pollution load for that condition. The particles themselves do not appear to be the primary causative agents, but instead are co-varying surrogates for sensory offending gas-phase species. PRACTICAL IMPLICATIONS: Although the health consequences of long-term exposures to the products of ozone-initiated indoor chemistry remain to be determined, we judge that the sensory offending nature of selected products provides an additional reason to limit indoor ozone levels. Devices that emit ozone at significant rates should not be used indoors. Ozone-filtration of make-up air should also be beneficial in mechanically ventilated buildings located in regions that repeatedly violate outdoor ozone standards. Additionally, the use of limonene containing products should be curtailed during periods when indoor ozone levels are elevated.     

汽车牌照自动识别系统在公路收费口的应用

最近及未来的几年 ,上海市境内将建成多条高速公路 ,如何提高高速公路的服务质量 ,出入口的通行能力是一条比较重要的衡量标准。汽车牌照自动识别系统利用先进的摄像技术、图像识别技术和计算机技术 ,对抓拍图像进行正确分割和快速处理 ,自动识别出汽车的牌照号码 ,提升了高速公路的服务水平     

注册建筑师制度在中国的建立及管理统计

文章重点从政策层面比较系统地回顾了中国建设行业注册建筑师执业管理制度从建立到发展演变的过程以及注册资格国际互认方面的情况。     

Recognizing people’s identity in construction sites with computer vision: A spatial and temporal attention pooling network

Several prototype vision-based approaches have been developed to capture and recognize unsafe behavior in construction automatically. Vision-based approaches have been difficult to use due to their inability to identify individuals who commit unsafe acts when captured using digital images/video. To address this problem, we applied a novel deep learning approach that utilizes a Spatial and Temporal Attention Pooling Network to remove redundant information contained in a video to enable a person’s identity to be automatically determined. The deep learning approach we have adopted focuses on: (1) extracting spatial feature maps using the spatial attention network; (2) extracting temporal information using the temporal attention networks; and (3) recognizing a person’s identity by computing the distance between features. To validate the feasibility and effectiveness of the adopted deep learning approach, we created a database of videos that contained people performing their work on construction sites, conducted an experiment, and then performed k-fold cross-validation. The results demonstrated that the approach could accurately identify a person’s identity from videos captured from construction sites. We suggest that our computer-vision approach can potentially be used by site managers to automatically recognize those individuals that engage in unsafe behavior and therefore be used to provide instantaneous feedback about their actions and possible consequences.     

Recognizing well-dominated graphs is coNP-complete

A graph G is well-covered if every minimal vertex cover of G is minimum, and it is well-dominated if every minimal dominating set of G is minimum. Studies on well-covered graphs were initiated in [Plummer, JCT 1970], and well-dominated graphs were first introduced in [Finbow, Hartnell and Nowakow, AC 1988]. Well-dominated graphs are well-covered, and both classes have been widely studied in the literature. The recognition of well-covered graphs has been proved to be coNP-complete by [Chvátal and Slater, AODM 1993] and [Sankaranarayana and Stewart, Networks 1992], but the complexity of recognizing well-dominated graphs has been left open since their introduction. We close this complexity gap by proving that recognizing well-dominated graphs is coNP-complete. This solves a well-known open question (cf. [Levit and Tankus, DM 2017] and [Gözüpek, Hujdurovic and Milanič, DMTCS 2017]), which was first asked in [Caro, Sebő and Tarsi, JAlg 1996]. Although the problem has been open for a long time, our proof is surprisingly simple. Finally, we show that recognizing well-totally-dominated graphs is coNP-complete, answering a question of [Bahadır, Ekim, and Gözüpek, AMC 2021].     

Speech / music classification using speech-specific features

This paper proposes the use of speech-specific features for speech / music classification. Features representing the excitation source, vocal tract system and syllabic rate of speech are explored. The normalized autocorrelation peak strength of zero frequency filtered signal, and peak-to-sidelobe ratio of the Hilbert envelope of linear prediction residual are the two source features. The log mel energy feature represents the vocal tract information. The modulation spectrum represents the slowly-varying temporal envelope corresponding to the speech syllabic rate. The novelty of the present work is in analyzing the behavior of these features for the discrimination of speech and music regions. These features are non-linearly mapped and combined to perform the classification task using a threshold based approach. Further, the performance of speech-specific features is evaluated using classifiers such as Gaussian mixture models, and support vector machines. It is observed that the performance of the speech-specific features is better compared to existing features. Additional improvement for speech / music classification is achieved when speech-specific features are combined with the existing ones, indicating different aspects of information exploited by the former.