|新任助教講演会(Lectures from New Assistant Professors)|
|日時：||平成29年5月15日(月)3限 (13:30 -- 14:00), 2016/05/15, Monday|
|司会(Chair)：||藤本 まなと (Manato FUJIMOTO)|
|講演者(Presenter)：||Plopski Alexander, インタラクティブメディア研究室 (Interactive Media Designs Lab.)|
|題目(Title)：||Applications of Corneal Imaging with Optical-See-Through Head-Mounted Display
Our eyes are essential for the exploration of our surroundings and help us understand the appearance of the world. At the same time, the eyes also reflect the world and a keen observer can see if we look at a car, talk to another human, or are browsing the internet. The same holds true when we look at virtual content shown on an Optical-See-Through Head-Mounted Display (OST-HMD).
In this talk, I will first introduce previous applications of corneal imaging. I will then show how it can be applied to improve presentation and interaction with the content shown on the OST-HMD. Finally, I will explore some future applications of corneal imaging with OST-HMDs.
|講演者(Presenter)：||藤本 大介 (Daisuke Fujimoto), 情報セキュリティ研究室 (Information Security Engineering Lab.)|
Research introduction on instrumentation security
A number of automatic safety systems such as Automatic Emergency Braking (AEB) for vehicles functions based on the measurement by certain instrumentation devices. Therefore the integrity of the output of instrumentation devices is extremely important for such critical applications. Conventional security technics such as authentication and encryption protect the data output from sensors. The data from sensor itself is converted from analog data with ADC. Instrumentation security considers the security such as falsification and spoofing in ADC phase and analog signal itself. In this talk, I introduce some examples on instrumentation security.
|講演者(Presenter)：||張 任遠 (Renyuan Zhanng), コンピューティング・アーキテクチャ研究室 (Computing Architecture Lab.)|
|題目(Title)：||Analog Computing: One Solution for Efficient Implementations of IoT
The cognitive functions play very important roles in the IoT tasks such as audio processing and visual processing. In these cognitive tasks, the human brain is much superior to traditional very large scale integrated (VLSI) processors or software programs in cognitive functions, since the brain can learn from samples autonomously. Therefore, plenty of machine learning algorithms have been developed to realize the learning operations, which were originally implemented by the software programs. Due to the reasons of power consumption and processing performances, a number of attempts to implement the machine learning algorithms were made by using hardware including graphic processing units (GPUs), field programmable gate array (FPGA), and VLSI circuits. Since many computations in the machine learning algorithms are very complex, the implementation costs including computing time and hardware utilization are greatly concerned. Furthermore, a large amount of iterations are always required by these algorithms, the learning speed is also a critical issue. Thus, the challenge on hardware implementations of learning algorithms lies on achieving a high processing speed with the consideration of limited hardware resource.
In this talk, the parallel architecture for implementing learning algorithms is introduced by using analog computing techniques. Several analog circuitries are designed to carry out the complex functions such as Gaussian function and Euclidean distance. These computations in the learning algorithms can be done in real time within the compact chip area. On the basis of analog computational circuitries, a generally applied architecture in fully parallel is developed to implement some machine learning algorithms. Since the chaos of analog signals is used for learning instead of clock-based numerical iterations, the learning operation is accomplished autonomously and self- converges with a high speed.
Obviously, the analog computing is not competitive to the digital fashions over accuracy and programmability. However, it offers us another option to build IoT hardware in an efficient, fast, but reasonably inaccurate way.