TOKYO, March 11, 2021 /PRNewswire/ — University of Electro-Communications publishes the March 2021 issue of UEC e-Bulletin
The March 2021 issue of the UEC e-Bulletin includes a video of UEC Associate Professor Yuichi Sei, describing his Web Internet of Things for analyzing data while protecting privacy.
The research highlights are, ‘A candidate excitonic insulator under pressure,’ Kazuyuki Matsubayashi; and ‘Improving the counting capability of Internet-of-Things systems,’ Yuichi Sei.
The Topics section describes an “Innovative outlook for treating lazy eyes: Integrating polarized spectacles and smart tablets offers promising long-term treatment for children’s amblyopia”.
The News and Events features the Ambassador of the Republic of Rwanda to Japan, H.E. Mr. Rwamucyo visits UEC on 17 February 2021.
March 2021 issue of UEC e-Bulletin
Solid state physics: A candidate excitonic insulator under pressure
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.
In an excitonic insulator, a low number of normally mobile electrons spontaneously bind (to so-called electron holes, having a positive charge) and become immobile. They were predicted to exist in the 1960s, and have been looked for in experiments ever since. Recently, encouraging possible signatures of the elusive excitonic insulating state have been observed in a layered material containing tantalum, nickel and selenium, with the chemical formula Ta2NiSe5. Now, Kazuyuki Matsubayashi from the University of Electro-Communications, Tokyo, Japan, and colleagues have probed the properties of this material under pressure. Their results help to close in on settling the question whether Ta2NiSe5 is an excitonic insulator or not — and excitingly, they show that it probably is.
First, the researchers measured the electrical resistivity of a Ta2NiSe5 crystal along the three main (crystallographic) directions at ambient pressure while varying the temperature. For all three directions, the resistivity dropped with increasing temperature, with an anomalous ‘kink’ at around TC = 53 °C, the alleged temperature marking the transition to the excitonic insulating state.
The scientists then measured the temperature dependence of the resistivities with increasing applied pressure. Up to around 3 GPa, they obtained the same qualitative picture as before. But for higher pressures, resistivities first increased with increasing temperature, after which an anomalous decrease set in at around T* = -100 °C.
The results of Matsubayashi and colleagues led to two insights. First, the anomalies at Tc and T* probably have the same origin, suggesting that also at high pressure, the insulating excitonic state can develop. Second, by looking at the temperature dependence of the resistivity ratios, it becomes clear that dimensionality aspects play an important role in the formation of excitons — within certain layers of atoms, the conductivity is significantly different from that in the perpendicular direction. Rightly so, the researchers concluded that “these results deserve further high-pressure study to complete the phase diagram on Ta2NiSe5”
Caption: Pressure dependence of (a) normalized resistivity ρ(P)/ρ(0 GPa) and (b) anisotropy ratio at room temperature. ρb/ρa and ρc/ρa denote in-plane and inter-plane anisotropy, respectively. Solid lines are a guide to the eyes.
H. Arima et al, Resistive anisotropy of candidate excitonic insulator Ta2NiSe5 under pressure,
J. Phys.: Conf. Ser. 1609 012001, (2020)
Machine learning: Improving the counting capability of Internet-of-Things systems
Devices being able to automatically recognize objects, animals or people are becoming more and more widespread. Such automatic recognition usually involves sensors or cameras that are part of an Internet of Things (IoT) system, which connects to a neural network. Examining and classifying an object is then done based on machine-learning techniques.
Quite often, the required information behind automatic recognitions is a count — for example, the number of deer caught on a camera trap. Normally, the accuracy of such a count would depend on the accuracy of the machine learning model’s recognition ability. For the camera trap example: if deer are recognized with low accuracy, the total deer count would also be of low accuracy. But now, Yuichi Sei and Akihiko Ohsuga from the University of Electro-Communications, Tokyo, Japan, have shown that it is possible to get good total count accuracy even with low recognition accuracy.
The researchers’ trick lies in using a so-called confusion matrix, a table of percentages expressing how well the IoT system can distinguish recorded objects. For example, if the objects are deer, cats and dogs, the matrix contains information on how many times a deer is correctly recognized as such, or falsely as a cat, or a dog, and so on for all possible cases.
The confusion matrix comes into play after the IoT has been ‘trained’. The latter means that from a large set of example images, of which the IoT is ‘told’ what is on it (for example a deer), the IoT’s machine-learning model develops a procedure for deciding what is on a previously unseen image. Applying such a trained IoT is referred to as a baseline.
Sei and Ohsuga developed a way to compensate, as effectively as possible, estimated overall counts obtained in a baseline run for errors in individual object recognitions. They applied Bayes’ theorem, a mathematical formula giving the probability of an outcome taking into consideration conditional probabilities that are relevant to the outcome in question. In the case of IoT object recognition, these conditional probabilities are related to the numbers in the confusion matrix.
The scientists applied their method using images of objects from a few image databases (for training, generating the confusion matrix, and testing) in six tests. On average, they found that the estimation errors were reduced by 64 % compared to the baseline runs.
The work of Sei and Ohsuga is innovative. Whereas previous studies have aimed to improve the classification accuracy for each individual observation, the present study aimed to improve the accuracy of total counts instead.
Caption: Objective of the research.
Yuichi Sei and Akihiko Ohsuga, “Count Estimation with A Low-Accuracy Machine Learning Model”,
Journal: IEEE Internet of Things Journal (early access)
Digital Object Identifier (DOI): 10.1109/JIOT.2020.3038273
Researcher Video Profiles
Yuichi Sei Associate Professor, Department of Informatics, Graduate School of Informatics and Engineering, University of Electro-Communications.
Web Internet of Things for analyzing data while protecting privacy
Associate Professor Yuichi Sei’s research is focused on analyzing data while protecting privacy. The background is to resolve privacy issues that arise due an increasing number of businesses using personal data that is collected from individuals, including voice, viewing, health, location, and train ticket gate data.
Innovative outlook for treating lazy eyes
Integrating polarized spectacles and smart tablets offers promising long term treatment for children’s amblyopia
Amblyopia also known as “lazy eye” is an eye disorder that results in poor vision of one eye. It is prevalent in children and young adults with 3% of newborn babies affected across all races. The most common treatment is covering the healthy eye with a “patch” and thereby forcing the other affected one to work harder to improve neurological connections with the brain.
However, eye-patching can lead to both psychological stress and physical skin rashes. So it is challenging to encourage children to wear an eye-patch for the long periods of time that is required for effective treatment.
News and Events
Ambassador of the Republic of Rwanda to Japan, H.E. Mr. Rwamucyo visits UEC
On February 17, 2021, His Excellency Mr. Ernest RWAMUCYO, Ambassador of the Republic of Rwanda visited the University of Electro-Communications (UEC).
About the University of Electro-Communications
The University of Electro-Communications (UEC) in Tokyo is a small, luminous university at the forefront of pure and applied sciences, engineering, and technology research. Its roots go back to the Technical Institute for Wireless Commutations, which was established in 1918 by the Wireless Association to train so-called wireless engineers in maritime communications in response to the Titanic disaster in 1912. In 1949, the UEC was established as a national university by the Japanese Ministry of Education and moved in 1957 from Meguro to its current Chofu campus Tokyo.
With approximately 4,000 students and 350 faculty members, UEC is regarded as a small university, but with expertise in wireless communications, laser science, robotics, informatics, and material science, to name just a few areas of research.
The UEC was selected for the Ministry of Education, Culture, Sports, Science and Technology (MEXT) Program for Promoting the Enhancement of Research Universities as a result of its strengths in three main areas: optics and photonics research, where we are number one for the number of joint publications with foreign researchers; wireless communications, which reflects our roots; and materials-based research, particularly on fuel cells.
SOURCE University of Electro-Communications