Indoor positioning systems (IPSs) have gained attention as outdoor navigation becomes prevalent in everyday life. Research is being actively conducted on how indoor smartphone navigation can be accomplished and improved using received signal strength indication (RSSI) and machine learning (ML). IPSs have more use cases that need further exploration, and we aim to explore using IPSs for the indoor navigation of an autonomous robot. We collected a dataset and trained models to test on a robot. We also developed an A* path-planning algorithm so that our robot could navigate itself using predicted directions. After testing different network structures, our robot was able to successfully navigate corners around 50 percent of the time. The findings of this paper indicate that using IPSs for autonomous robots is a promising area of future research.

We study the problem of reconstructing low-rank matrices from their noisy observations. We formulate the problem in the Bayesian framework, which allows us to exploit structural properties of matrices in addition to low-rankedness, such as sparsity. We propose an efficient approximate message passing algorithm, derived from the belief propagation algorithm, to perform the Bayesian inference for matrix reconstruction. We have also successfully applied the proposed algorithm to a clustering problem, by reformulating it as a low-rank matrix reconstruction problem with an additional structural property. Numerical experiments show that the proposed algorithm outperforms Lloyd's K-means algorithm.

Medical data mainly includes various types of biomedical signals and medical images, which can be used by professional doctors to make judgments on patients' health conditions. However, the interpretation of medical data requires a lot of human cost and there may be misjudgments, so many scholars use neural networks and deep learning to classify and study medical data, which can improve the efficiency and accuracy of doctors and detect diseases early for early diagnosis, etc. Therefore, it has a wide range of application prospects. However, traditional neural networks have disadvantages such as high energy consumption and high latency (slow computation speed). This paper presents recent research on signal classification and disease diagnosis based on a third-generation neural network, the spiking neuron network, using medical data including EEG signals, ECG signals, EMG signals and MRI images. The advantages and disadvantages of pulsed neural networks compared with traditional networks are summarized and its development orientation in the future is prospected.

Deep learning of artificial neural networks (ANNs) is creating highly functional processes that are, unfortunately, nearly as hard to interpret as their biological counterparts. Identification of functional modules in natural brains plays an important role in cognitive and neuroscience alike, and can be carried out using a wide range of technologies such as fMRI, EEG/ERP, MEG, or calcium imaging. However, we do not have such robust methods at our disposal when it comes to understanding functional modules in artificial neural networks. Ideally, understanding which parts of an artificial neural network perform what function might help us to address a number of vexing problems in ANN research, such as catastrophic forgetting and overfitting. Furthermore, revealing a network's modularity could improve our trust in them by making these black boxes more transparent. Here, we introduce a new information-theoretic concept that proves useful in understanding and analyzing a network's functional modularity: the relay information $I_R$. The relay information measures how much information groups of neurons that participate in a particular function (modules) relay from inputs to outputs. Combined with a greedy search algorithm, relay information can be used to identify computational modules in neural networks. We also show that the functionality of modules correlates with the amount of relay information they carry.

It was particularly exciting to have directly imaged exoplanets for the first time in 2008 (Kalas et al., 2008) as it gave signals directly from these exoplanets and thus confirmed their existence. Since then, many research groups have spent considerable efforts to improve the techniques of high-contrast imaging in order to detect more exoplanets (Tamura, 2009; Enya & Abe, 2010; Kuzuhara et al., 2013; Dou et al., 2015; Dou & Ren, 2016). In addition, new high-contrast imaging instruments were developed for eight-meter class telescopes such as the Gemini Planet Imager (GPI) (Macintosh et al., 2006) for Gemini South, the Subaru Coronagraphic Extreme Adaptive Optics (SCExAO) (Jovanovic et al., 2015) for Subaru Telescope, and the Spectro-Polarimetic High contrast imager for Exoplanet Research (SPHERE) (Beuzit et al., 2019) for Very Large Telescope (VLT). Moreover, a new camera was designed for SCExAO to further advance the performance of high contrast imaging (Walter et al., 2020). It is notable that these instruments often bring very interesting related results (Mayama et al., 2006; Itoh et al., 2008). To detect exoplanets through the method of direct imaging, the highcontrast imaging employs the technique of angular differential imaging (ADI) (Marois et al., 2006) and produces many frames with different parallactic angles, i.e.

Access plan recommendation is a query optimization approach that executes new queries using prior created query execution plans (QEPs). The query optimizer divides the query space into clusters in the mentioned method. However, traditional clustering algorithms take a significant amount of execution time for clustering such large datasets. The MapReduce distributed computing model provides efficient solutions for storing and processing vast quantities of data. Apache Spark and Apache Hadoop frameworks are used in the present investigation to cluster different sizes of query datasets in the MapReduce-based access plan recommendation method. The performance evaluation is performed based on execution time. The results of the experiments demonstrated the effectiveness of parallel query clustering in achieving high scalability. Furthermore, Apache Spark achieved better performance than Apache Hadoop, reaching an average speedup of 2x.

Kumamoto – Startup companies are acquiring a growing presence in the field of disaster prevention and reduction, leveraging their strength in technology and their ability to quickly develop goods and services responding to actual needs in afflicted areas. Wota Corp. released a portable recycled water treatment device in 2019. Called Wota Box, it is capable of making 98% of the water that is discharged after showers, handwashing and laundry reusable. With the quality of water managed by artificial intelligence technology, Wota Box makes potable water available when the supply of water is cut off. More than 20 local governments have introduced the device for use at times of disaster.

Multiple convolutional neural network (CNN) classifiers have been proposed for electroencephalogram (EEG) based brain-computer interfaces (BCIs). However, CNN models have been found vulnerable to universal adversarial perturbations (UAPs), which are small and example-independent, yet powerful enough to degrade the performance of a CNN model, when added to a benign example. This paper proposes a novel total loss minimization (TLM) approach to generate UAPs for EEG-based BCIs. Experimental results demonstrated the effectiveness of TLM on three popular CNN classifiers for both target and non-target attacks. We also verified the transferability of UAPs in EEG-based BCI systems. To our knowledge, this is the first study on UAPs of CNN classifiers in EEG-based BCIs. UAPs are easy to construct, and can attack BCIs in real-time, exposing a potentially critical security concern of BCIs.

Sony Corp. is considering spending around ¥100 billion ($900 million) to build a new semiconductor plant in southwestern Japan to meet the growing demand for image sensors used in smartphones, sources close to the matter said Tuesday. Sony, the world's largest maker of image sensors, is considering constructing the new plant adjacent to the site of its Isahaya plant in Nagasaki Prefecture, seeking to start operations sometime in the fiscal year ending March 2022, the sources said. The electronics giant holds about a 50 percent share of the global image sensor market. It also produces the sensors at plants in Kumamoto, Yamagata and Oita prefectures. The chip business is a key growth driver for Sony, as demand for image sensors, used in a wide range of products including self-driving vehicles, is expected to continue on an upward trend.

One of the highlights of this year's CEATEC (Combined Exhibition of Advanced Technologies) is Society 5.0 Town, an exhibition area where companies from diverse sectors such as retail, banking, construction, transport and local governments will showcase their technologies to connect people, information and things to enhance people's lives. Society 5.0 is a concept created in Japan and is defined as "a human-centered society that balances economic advancement with the resolution of social problems by a system that highly integrates cyberspace and physical space," according to the Cabinet Office website. The town's theme is in line with Japan's push to become a global leader in utilizing technology to create a community that maximizes energy efficiency and residents' convenience, as well as welfare. Dozens of predominantly Japanese companies will participate in Society 5.0 Town at CEATEC, to be held at Makuhari Messe in Chiba Prefecture from Oct. 15 to 18. Participants range from ANA Holdings Inc., Mitsubishi Estate Co. and Taisei Corp., among others. "We are a company about mobility. We want to solve mobility-related challenges. If you want to go somewhere, you have to go to an airport and fly. Avatar saves people from having to do that," said Akira Fukabori, director of the Avatar Division.