Collaborating Authors


Smart 5G Patrol Robots Deployed To Fight Coronavirus


If a high temperature or the absence of a mask is detected, the robots send an alert to the relevant authorities. All data can be transmitted to a centralised control center for real-time situational response and decision making. Moreover, although these robots are self-driving machines, they can also be controlled remotely, thereby saving manpower by reducing patrolling responsibilities and preventing cross-infection. These next-generation 5G patrol robots have already been spotted at airports and shopping malls in the cities of Guangzhou, Shanghai, Xi'an and Guiyang.

Machine learning for tomographic imaging – Physics World


The field of artificial intelligence and machine learning, particularly the subcategory of deep learning, has experienced massive growth in recent years, with applications ranging from speech recognition to material inspection, healthcare to gaming, to name but a few. One area that's being transformed by machine learning is tomographic imaging – in which a series of data projections (such as X-ray radiographs, for example) are reconstructed into a three-dimensional image. A newly published book, Machine Learning for Tomographic Imaging, presents a detailed overview of the emerging discipline of deep-learning-based tomographic imaging. The book arose from discussions among four colleagues with a long-standing interest in advanced medical image reconstruction: Ge Wang from Rensselaer Polytechnic Institute, Yi Zhang of Sichuan University, Xiaojing Ye from Georgia State University and Xuanqin Mou from Xi'an Jiaotong University. "Deep tomographic reconstruction is a new area, and the development of this area has been rapid over the past years," explains Wang.

Realization of spatial sparseness by deep ReLU nets with massive data Machine Learning

--The great success of deep learning poses urgent challenges for understanding its working mechanism and rationality. The depth, structure, and massive size of the data are recognized to be three key ingredients for deep learning. In this paper, we aim at rigorous verification of the importance of massive data in embodying the out-performance of deep learning. T o approximate and learn spatially sparse and smooth functions, we establish a novel sampling theorem in learning theory to show the necessity of massive data. We then prove that implementing the classical empirical risk minimization on some deep nets facilitates in realization of the optimal learning rates derived in the sampling theorem. This perhaps explains why deep learning performs so well in the era of big data. With the rapid development of data mining and knowledge discovery, data of massive size are collected in various disciplines [50], including medical diagnosis, financial market analysis, computer vision, natural language processing, time series forecasting, and search engines. These massive data bring additional opportunities to discover subtle data features which cannot be reflected by data of small size while creating a crucial challenge on machine learning to develop learning schemes to realize benefits by exploring the use of massive data. Although numerous learning schemes such as distributed learning [26], localized learning [32] and sub-sampling [14] have been proposed to handle massive data, all these schemes focused on the tractability rather than the benefit of massiveness. Therefore, it remains open to explore the benefits brought from massive data and to develop feasible learning strategies for realizing these benefits. Deep learning [18], characterized by training deep neural networks (deep nets for short) to extract data features by using rich computational resources such as computational power of modern graphical processor units (GPUs) and custom processors, has made remarkable success in computer vision [23], speech recognition [24] and game theory [40], practically showing its power in tackling massive data. C.K. Chui is also associated with the Department of Statistics, Stanford University, CA 94305, USA. Shao-Bo Lin is with the Center of Intelligent Decision-making and Machine Learning, School of Management, Xi'an Jiaotong University, Xi'an, China.

Artificial intelligence is watching China's students but how well can it really see?


Almost every second of Betty Li's school life is monitored. The 22-year-old student at a university in northwestern China must get through face scanners to enter her dormitory and register attendance, while cameras above the blackboards in her classrooms keep an eye on the students' attentiveness. Like many other educational institutions across the country, the university in Xian, Shaanxi province, deployed AI-powered gates and facial recognition cameras several years ago as a part of the "smart campuses" campaign promoted by the Ministry of Education. Some schools are even exploring ways to use artificial intelligence to analyse the behaviour of teachers and students. The universities are at the forefront of a national effort to lead the world in emerging technologies and move China's economy up the value chain.

Robust and Efficient Fuzzy C-Means Clustering Constrained on Flexible Sparsity Machine Learning

--Clustering is an effective technique in data mining to group a set of objects in terms of some attributes. Theoretical analyses and extensive experiments on several public datasets demonstrate the effectiveness and rationality of our proposed REFCMFS method. S a fundamental problem in machine learning, clustering is widely used for many fields, such as the network data (including Protein-Protein Interaction Networks [1], Road Networks [2], Geo-Social Network [3]), medical diagnosis [4], biological data analysis [5], environmental chemistry [6] and so on. K-Means clustering is one of the most popular techniques because of its simplicity and effectiveness, which randomly initializes the cluster centroids, assigns each sample to its nearest cluster and then updates cluster centroid itera-tively to cluster a dataset into some subsets. Over the past years, many modified versions of K-Means algorithms have been proposed, such as K-Means based Consensus clustering [7], Optimized Cartesian K-Means [8], Group K-Means [9] and so on. Jinglin Xu and Junwei Han were with the School of Automation, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China. Feiping Nie is with School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China. Xuelong Li is with School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China.

China goes big on IoT with a constellation of 72 satellites - Tech Wire Asia


CHINA often goes all in when it comes to technology that it believes in. The country has invested significantly in autonomous vehicles, 5G, and artificial intelligence in recent times. The internet of things (IoT), however, didn't get all the attention it deserved -- but that's changing quickly. The Chinese Academy of Sciences (CAS) has just announced that it will be launching a constellation of 72 satellites to help the nation bolster its interests and ambitions in IoT over the next 3 years. According to Xinhua, China's national news agency, the program will be implemented by Beijing-based private satellite company "Commsat," which was funded by the Xi'an Institute of Optics and Precision Mechanics under the CAS.

Amazing drone footage of an £8billion Chinese high-speed railway

Daily Mail - Science & tech

In the time it takes some countries to slightly extend one train station platform, China can rustle up entire high-speed railways. And this stunning drone footage shows what incredible feats of engineering they can be. The clip shows 160mph trains running along the now completed 411-mile Xi'an to Chengdu high-speed line, which was started in October 2012. The clip shows 160mph trains running along the now completed 411-mile Xi'an to Chengdu high-speed line, which was started in October 2012 To connect Xi'an with Chengdu engineers had to tackle the fearsome Qinling Mountains that divide northern and southern China It's something to behold, with the £8billion (71bn yuan) line – finished in December 2017 - passing amid towering mountains and through huge tunnels. To connect the two cities engineers had to tackle the fearsome Qinling Mountains that divide northern and southern China and thread the track underneath numerous environmentally sensitive areas.

XJTLUIndoorLoc: A New Fingerprinting Database for Indoor Localization and Trajectory Estimation Based on Wi-Fi RSS and Geomagnetic Field Machine Learning

Abstract--In this paper, we present a new location fingerprinting database comprised of Wi-Fi received signal strength (RSS) and geomagnetic field intensity measured with multiple devices at a multi-floor building in Xi'an Jiatong-Liverpool University, Suzhou, China. We also provide preliminary results of localization and trajectory estimation based on convolutional neural network (CNN) and long short-term memory (LSTM) network with this database. For localization, we map RSS data for a reference point to an image-like, two-dimensional array and then apply CNN which is popular in image and video analysis and recognition. For trajectory estimation, we use a modified random way point model to efficiently generate continuous step traces imitating human walking and train a stacked twolayer LSTM network with the generated data to remember the changing pattern of geomagnetic field intensity against (x, y) coordinates. Experimental results demonstrate the usefulness of our new database and the feasibility of the CNN and LSTMbased localization and trajectory estimation with the database. Index Terms--Indoor localization, trajectory estimation, received signal strength, Wi-Fi fingerprinting, deep learning, CNN, LSTM, geomagnetic field. With the increasing demands for location-aware services and proliferation of smart phones with embedded highprecision sensors, indoor localization has attracted lots of attention from the research community. Global navigation satellite system (GNSS) like global positioning system (GPS), which provides accurate geo-spatial positioning, cannot be used indoors as the radio signals from satellites is easily blocked in an indoor environment.

China builds laser rifle that can remotely set fire to people's skin

The Independent - Tech

Chinese researchers are working on a new handheld laser weapon capable of burning skin and clothing from up to half a mile away, according to a report. The high-powered laser rifle will be used by anti-terrorism squads in the Chinese Armed Police, the South China Morning Post reports, with prototypes of the device already being tested at the Xian Institute of Optics and Precision Mechanics at the Chinese Academy of Sciences in Shaanxi province. The ZKZM assault rifle causes "instant carbonisation" of human tissue, according to the researchers behind it, and will "burn through clothes in a split second. " The unnamed researcher from the Chinese Academy of Sciences added: "If the fabric is flammable, the whole person will be set on fire." While the gun is not powerful enough to kill someone, the researcher said: "The pain will be beyond endurance."

China launches 'spy bird' drone to boost government surveillance

The Independent - Tech

Flocks of robotic birds are taking to the skies of China equipped with high-tech surveillance technology, according to a report. The so-called "spy bird" programme, first reported by the South China Morning Post, is already in operation in at least five provinces and provides another tendril in the country's already advanced surveillance network. The dove-like drones are being developed by researchers at Northwestern Polytechnical University in the Shaanxi province, who have previously worked on stealth fighter jets used by China's airforce. One of the researchers involved said the roll out of the technology was still in its early stages. "The scale is still small," said Yang Wenqing, an associate professor at the university's School of Aeronautics who worked on the programme.