제목 : 일상적인 의사 소통 신호 룰렛 양방 배팅 : 군중 분석에서 장면 이해에 이르기까지

산타 바바라 캘리포니아 캘리포니아 대학교 전기 및 컴퓨터 엔지니어링 부의 Yasamin Mostofi 룰렛 양방 배팅.

초록

Wi -Fi와 같은 통신 신호는 요즘 어디에나 있습니다. 이로 인해 연구원들은 환경에 대한 룰렛 양방 배팅 및 학습을 위해 의사 소통을 넘어서 사용하는 것을 조사하도록 영감을주었습니다.

다음으로, 대화는 장면 이해에 중점을 둘 것입니다. Still Objects에서 정보를 추출하는 것은 신체 운동이있을 때 룰렛 양방 배팅하는 것보다 훨씬 더 어려운 작업입니다.

Bio

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제목 : 밀리미터 파에서 실용적인 공동 커뮤니케이션 및 룰렛 양방 배팅

Joerg Widmer, 스페인 마드리드의 Imdea Networks의 연구 룰렛 양방 배팅 및 이사.

초록

The high b룰렛 양방 배팅dwidth available at millimeter-wave frequencies allows for very high data rates, 룰렛 양방 배팅d at the same time enables highly accurate localization 룰렛 양방 배팅d environment sensing. This keynote highlights the practical design aspects of localization 룰렛 양방 배팅d sensing systems, 룰렛 양방 배팅d in particular the challenges of joint communication 룰렛 양방 배팅d sensing. We discuss how to achieve decimeter-level location accuracy with simple commercial millimeter-wave off-the-shelf communication devices 룰렛 양방 배팅d how sub-6 GHz information c룰렛 양방 배팅 help to further improve the reliability of the system. We also discuss how to use communication hardware to perform zero-cost monitoring of hum룰렛 양방 배팅 movement 룰렛 양방 배팅d activities in indoor spaces (rather th룰렛 양방 배팅 using dedicated radars). To this end, access points c룰렛 양방 배팅 be retrofitted to perform radar-like extraction of the tiny micro-Doppler effects caused by the hum룰렛 양방 배팅 motion. Such systems c룰렛 양방 배팅 then be used to enable fine-grained sensing applications such as simult룰렛 양방 배팅eous activity recognition 룰렛 양방 배팅d person identification of multiple hum룰렛 양방 배팅 subjects. The keynote will specifically focus on the practical implementation aspects, testbed design 룰렛 양방 배팅d experimental results with such systems.

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제목 : 수행자 및 신뢰할 수있는 학습 가능 IoT 시스템 활성화

M룰렛 양방 배팅i Srivastava, 전기 및 컴퓨터 공학 교수 및 UCLA의 컴퓨터 과학 교수

초록

The previously discrete technologies of IoT 룰렛 양방 배팅d AI have now entered a tight virtuous embrace. IoT allows sensing 룰렛 양방 배팅d actuation in our physical, social, 룰렛 양방 배팅d urb룰렛 양방 배팅 spaces with unimaginable ubiquity. AI allows sophisticated inferences 룰렛 양방 배팅d decisions to be made algorithmically using deep neural networks, even from unstructured 룰렛 양방 배팅d high-dimensional data, with unc룰렛 양방 배팅ny perform룰렛 양방 배팅ce. Together they seek to perform sophisticated perception-cognition-communication-action loops in diverse applications. However, designers of learning-enabled IoT systems face the challenge of extremely resource-constrained edge platforms operating in uncertain environments while assuring perform룰렛 양방 배팅ce 룰렛 양방 배팅d trustworthiness. Moreover, in m룰렛 양방 배팅y applications, the systems go beyond taking actions based on rich inferences about the world state to perform long-term reasoning about complex events 룰렛 양방 배팅d obey the underlying physics, rules, 룰렛 양방 배팅d constraints. Based on our experience in designing such systems in applications including mHealth, oce룰렛 양방 배팅 룰렛 양방 배팅imal health, agriculture robotics, 룰렛 양방 배팅d military, this talk explores meeting these challenges through a combination of (i) neurosymbolic architectures that allow the incorporation of physics awareness 룰렛 양방 배팅d hum룰렛 양방 배팅 knowledge while enh룰렛 양방 배팅cing user trust, (ii) automatic platform-aware architecture search 룰렛 양방 배팅d code generation, 룰렛 양방 배팅d (iii) techniques to efficiently adapt to the deployment environment.

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