There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Networked control systems (NCSs) are spatially distributed systems for which the communication between sensors, actuators, and controllers is supported by a shared communication network. We review several recent results on estimation, analysis, and controller synthesis for NCSs. The results surveyed address channel limitations in terms of packet-rates, sampling, network delay, and packet dropouts. The results are presented in a tutorial fashion, comparing alternative methodologies

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A Survey of Recent Results in Networked Control Systems

National Institute of Standards and Technology における超伝導研究及び生活

European Resuscitation Council Guidelines for Resuscitation.

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

In light of the slow deployment of IP Multicast technology on the global Internet and the explosive popularity of peer-to-peer (P2P) file-sharing applications, there has been a flurry of research activities investigating the feasibility of implementing multicasting capability at the application layer, referred to as Application Layer Multicasting (ALM), and numerous algorithms and protocols have been proposed. This article aims to provide researchers in the field with an understanding of ALM protocols by identifying significant characteristics, from both application requirements and networking points of view, and by using those characteristics as a basis for organizing the protocols into an integrated and well-structured format. Current trends and directions for further research are also presented. This article surveys the literature over the period 1995?2005 on different application layer multicasting approaches.

A survey of application-layer multicast protocols

Due to continuing and rapid advances of both hardware and software technologies in camera and computing systems, we continue to have access to cheaper, faster, higher quality, and smaller cameras and computing units. As a result, vision based methods consisting of image processing and computational intelligence can be implemented more easily and affordably than ever using a camera and its associated operations units. Among their various applications, such systems are also being used more and more by researchers and practitioners as generic instruments to measure and monitor physical phenomena. In this article, we take a look at this rising trend and how cameras and vision are being used for instrumentation and measurement, and we also cast a glance at the metrological gauntlet thrown down by vision-based instruments.

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Camera as the instrument: the rising trend of vision based measurement

In this paper, we present two video datasets of drivers with various facial characteristics, to be used for designing and testing algorithms and models for yawning detection. For collecting these videos, male and female candidates were asked to sit in the driver's seat of a car. The videos are taken in real and varying illumination conditions. In the first dataset, the camera is installed under the front mirror of the car. Each participant has three or four videos and each video contains different mouth conditions such as normal, talking/singing, and yawning. In the second dataset, the camera is installed on the dash in front of the driver, and each participant has one video with the above-mentioned different mouth conditions all in the same video. The car was parked for both datasets to keep the environment safe for the participants. As a benchmark, we also present the results of our own yawning detection method, and show that we can achieve a much higher accuracy in the scenario with the camera installed on the dash in front of the driver.

/pdf/yawdd-a-yawning-detection-dataset-8307vtqftp.pdf

YawDD: a yawning detection dataset

As people across the globe are becoming more interested in watching their weight, eating more healthy, and avoiding obesity, a system that can measure calories and nutrition in every day meals can be very useful. In this paper, we propose a food calorie and nutrition measurement system that can help patients and dietitians to measure and manage daily food intake. Our system is built on food image processing and uses nutritional fact tables. Recently, there has been an increase in the usage of personal mobile technology such as smartphones or tablets, which users carry with them practically all the time. Via a special calibration technique, our system uses the built-in camera of such mobile devices and records a photo of the food before and after eating it to measure the consumption of calorie and nutrient components. Our results show that the accuracy of our system is acceptable and it will greatly improve and facilitate current manual calorie measurement techniques.

/pdf/measuring-calorie-and-nutrition-from-food-image-1nst8hsnpl.pdf

Measuring Calorie and Nutrition From Food Image

Fatigue and drowsiness of drivers are amongst the significant causes of road accidents. In this paper, we discuss a method for detecting drivers' drowsiness and subsequently alerting them. The aim is to reduce the number of accidents due to drivers fatigue and hence increase the transportation safety. Many special body and face gestures are used as sign of driver fatigue, including yawning, eye tiredness and eye movement, which indicate that the driver is no longer in a proper driving condition. Here, we propose a method of yawning detection based on the changes in the mouth geometric features.

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Shervin Shirmohammadi

Papers

A survey of application-layer multicast protocols

Camera as the instrument: the rising trend of vision based measurement

YawDD: a yawning detection dataset

Measuring Calorie and Nutrition From Food Image

Driver drowsiness monitoring based on yawning detection