Sleep apnea syndrome (SAS) is a sleep disorder characterized by the occurrence of pauses in breathing (apnea) during sleep. Such pauses can typically last for more than 10 seconds and are often followed by loud snoring. The brain interprets each breathing pause as danger — because of the decrease in oxygen supply — and sleep becomes shallow. As a result, a person suffering from SAS builds up a sleep debt, which may in turn lead to mental health issues like depression or dementia. In order to avoid medical complications, early detection of SAS is crucial. So-called non-contact detection methods are based on monitoring chest motion, e.g. by means of a sensor attached to the mattress sensor the person is sleeping on; from the recorded bio-vibration data, breathing frequencies and amplitudes can be derived. This type of method is not always effective. For example, when a person’s breathing is ‘forced’ (breathing accompanied by thoracic and abdomen movement, and in fact also a symptom of SAS), sleep apnea is difficult to detect. Now, Iko Nakari and Keiki Takadama from the University of Electro-Communications have developed a new method for processing overnight bio-vibration data that can detect SAS in a more universal way.
Self-driving car technology requires detectors capable of sensing a car’s environment, also in situations of limited visibility like bad weather conditions. Radar-based sensors have emerged as an essential component of driver assistance systems and self-driving vehicles, as they can robustly distinguish nearby pedestrians and other traffic-relevant objects. Apart from being applicable in bad weather, artificial recognition systems also need to be capable of dealing with so-called non-line-of-sight (NLOS) situations, when the line of sight between detector and object is obstructed. In traffic, NLOS situations occur when pedestrians are blocked from sight; for example, a child behind a parked car, about to run suddenly into the street. Now, Shouhei Kidera from the University of Electro-Communications and colleagues have developed a radar-based detection method for recognizing humans in NLOS situations. The scheme is based on reflection and diffraction signal analysis and machine-learning techniques.
One of the critical issues for the proliferation of Internet of Things (IoT) is the collection and computational analysis of wireless data from huge networks of smart sensors being implemented in applications that include smart transportation and cities. Recently, the technique of over-the-air computation (AirComp) has been proposed as an integrated approach for collection and processing of data from sensor networks transmitting Big-data simultaneously. Notably, although this method is efficient, it requires that all signals from nodes arrive simultaneously at the sink, aligned in signal magnitude so as to enable an unbiased estimation. However, for nodes far away from the sink with low channel gains, it is not possible to avoid misalignment in signal magnitude.
Insulators, by definition, do not conduct electrical current (in theory), and have a high electrical resistivity. Still, physicists distinguish between various types of insulators, differing in how the insulating states come about. The most common insulators are materials in which electrons cannot flow freely; too much energy would be required to ‘unbind’ them. Other types include Anderson insulators, in which electrons are ‘stuck’ because of quantum interference effects, and topological insulators, which are actually conducting at their surface. But one type of insulator, the so-called excitonic insulator, is particularly special — because it has never been unambiguously observed.
Reinforcement Learning (RL) is an effective way of designing model-free linear quadratic regulators (LQRs) for linear time-invariant networks with unknown state-space models. RL has wide ranging applications including industrial automation, self-driving automobiles, power grid systems, and even forecasting stock prices for financial markets.
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Advanced Wireless and Communication Research Center (AWCC), University of Electro-Communications, Tokyo.
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