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Blind Procedures02:07

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Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
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Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
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为视障人士开发完全自主离线辅助系统:以隐私为首要的方法

Fitsum Yebeka Mekonnen1,2, Mohammad F Al Bataineh3, Dana Abu Abdoun3

  • 1Department of Mechanical and Aerospace Engineering, United Arab Emirates University, Al Ain 15551, United Arab Emirates.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
概括

本研究介绍了针对视力受损人士的离线人工智能系统,使用Raspberry Pi 5上的开源工具来实现隐私和可访问性. 该系统提供实时对象检测,识别和语音控制,无需依赖云.

关键词:
在OCR中,OCR是OCR.拉斯伯派 (Raspberry Pi) 是一款非常有价值的小米电脑.辅助技术是指辅助技术的使用.面部识别系统是面部识别系统.对象检测检测对象检测对象检测线下的人工智能线下的人工智能语音识别功能 语音识别功能

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科学领域:

  • 辅助技术 辅助技术 辅助技术
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 视力障碍对独立性和环境相互作用构成重大挑战.
  • 当前的辅助技术往往依赖于云处理,在低资源环境中构成隐私风险和可访问性问题.

研究的目的:

  • 开发和评估一个完全离线的,保护隐私的视力障碍者辅助系统.
  • 探索在辅助应用的低功耗边缘硬件上集成开源AI模型.

主要方法:

  • 整合了YOLOv8用于对象检测,Tesseract用于OCR,以及VOSK用于在Raspberry Pi 5上离线语音命令控制.
  • 实现面部识别与语音引导记录和Piper用于音频反.
  • 开发一个独立于云基础设施的无手,多式联络交互系统.

主要成果:

  • 该系统在受控评估中实现了75-90%的检测和识别准确度.
  • 经过证明的分秒响应时间,确保实时的环境意识.
  • 证实了在边缘硬件上使用开源工具实现有效的辅助功能.

结论:

  • 开源的人工智能模型可以为视力受损者提供有效的离线辅助系统.
  • 开发的平台优先考虑用户隐私,低延迟和可负担性.
  • 这种方法适用于隐私敏感和资源有限的环境.