Abstract:Robotics is a focus of the science and technology development in the 21st century. Robots are employed in more and more fields, with the rapid progress of their intelligence level. Since China has become the largest market of robots in the world, we should seize the opportunity, get ready to the challenge and accelerate the development of China’s robot industry.

Abstract:In cloud computing, in order to achieve resource sharing, virtual machines (VMs) of different tenants might be scheduled to run on the same physical machine, namely VMs co-residency, which would bring many new security issues. Therefore, security threats due to VMs co-residency, including resources interference, covert or side channel, denial of service and virtual machine load monitoring were reviewed in this paper. Besides, existing detection methods of co-residency were introduced, four kinds of defense about co-residency were summarized and further trends were also pointed out.

Abstract:Data delivery provided data transmission services for various applications over Vehicular Ad Hoc Networks (VANETs). In particular, it was an indispensable component in the vehicle active safety systems, urban life services, and emergency rescue applications. However, it faced such challenges as brought by the rapid changing of network topology, frequent network partitions, and the limited wireless channel capacity. The paper analyzed the characteristics of VANETs, pointed out the main influencing factors in the VANET data delivery, and then reviewed the existing technologies, including the basic implementation approaches and the optimization strategies for improving the capabilities of data delivery.

Abstract:With the development of wearable technology and Internet of Things industry, the field of wildlife tracking was undergoing innovative progress. In this paper, the development of tracking technology was reviewed comprehensively, wildlife tracking system based on satellite positioning and mobile communication technology was designed and realized. According to those signals such as location, acceleration and signal intensity, which was collected by tracking devices installed on animals, preliminary research of system energy saving mechanism and animal behavior recognition were carried out. In addition, the empirical test and system improvement were discussed. This proposed method for monitoring wildlife short-time movement and behavior has great significance for species ecology research.

Abstract:Electrocardiogram(ECG) classification is a complex pattern recognition problem. At present, most of the ECG classification methods based on different machine learning model had achieved a high classification accuracy, but the learning efficiency was low. Therefore, a fast ECG learning algorithm was necessary. In this paper, a method of extreme learning machine was presented, which mapped the original feature space into Hilbert space with different kernel functions and made the ECG date in high dimensional space linearly separable. At last, the experimental verification was carried on MIT-BIH standard library. The results show that the proposed method has higher accuracy and faster learning speed than existing methods, which may be a potential tool for detection and analysis of clinical dynamic electrocardiogram and personalized real-time ECG monitoring.

Abstract:In order to improve the performance of joint reconstruction of multi-sensor acceleration data from different wearable devices, a novel approach to jointly reconstruct based on distributed compressed sensing (DCS) algorithm was proposed. The basic idea was that the raw data was firstly compressed through encoding, and the encoded data was sent to remote terminal. Then, with the spatiotemporal correlation of data from sensors, the joint reconstruction method based on Block Sparse Bayesian Learning (BSBL) was applied to decode the compressed data at remote terminal. At last, the wearable data from University of California-Berkeley database was analized. Experiments show that the proposed approach can gain better performance than the traditional joint reconstruction algorithms such as TMSBL and tMFOCUSS, and decode the compressed data accurately. The proposed technique may be helpful for telemedicine application.

Abstract:Electroencephalogram(EEG) contains a large amount of brain function state information, and has been widely applied in many fields, such as cranial nerve disease diagnosis, brain-computer interface, and sleep stage and anesthetic depth monitoring. EEG is a weak bio-electric signal, whose amplitude is just microvolt and frequency is lower than 150 Hz. Since EEG is easily interfered by signals, like electro-oculogram and electrocardiogram, effective extraction of EEG is key to EEG analysis. An EEG acquisition system based on STM32 was designed to realize effective acquisition of EEG. In this system, EEG signals were sensed by three lead biological electrodes on the forehead, then amplified through multistage amplification in the front analog circuit and filtered by passive filter or active filters. Meanwhile, electrical level rising circuit and electrode connection state detection circuit were also added in. After that, EEG signals were converted to digital value by 12 bit analog-to-digital converter, and then transferred to upper computer via bluetooth module to realize effective extraction and transmission, providing the basis for further processing and analysis. The effectiveness of this EEG acquisition system was verified by comparing output with other similar products.

Abstract:An electrophysiological glasses based on independent chipset was proposed. It consisted of signal acquisition module, signal preprocessing module, microcontroller module and signal analysis module. Due to the high input impedance, active electrode, instead of conductive paste, was used as signal acquisition module, which could improve system availability. The signal preprocessing module took advantage of electrophysiological analog front-end to filter and amplify electrophysiological signal, thus removed interference and enhanced the signal to noise ratio. Cortex-M0 processor of stmicroelectronics was adopted as the microcontroller module for analog-to-digital conversion, analog front-end setup, and controlling of bluetooth module transceiver. The signal analysis module could realize digital filtering, feature extraction, and heart rate monitoring for electrophysiological signal. Based on this, experiments and signal analysis about neck electrocardiogram and brain electrical interaction were carried out.

Abstract:Aimed at the fall issues of the aged, a wearable pre-impact fall early warning and protection system which consists of an early fall alarm, protection airbags, a remote monitoring platform and a guardian’s smart phone application was developed. When fall happens, the early fall alarm triggerd airbag system, then airbags in belt inflated as soon as possible to reduce the damage to the aged. After the aged fall down, the early fall alarm rang and sent message to guardian for help. Meanwhile, the kinesiology data during falling time was sent to remote monitoring platform for storage, and falling location was showed in the electronic map. Finally, the reliability of this early fall alarm and protection system was verified. The results indecate that this system could detect falls accurately, accomplish a series functions such as early fall warning and alarm, airbag inflation, data transmission and storage and real-time location, which may be beneficial to reduce direct damage and shorten rescue time.

Abstract:Traditional operating platform of electrocardiogram (ECG) acquisition system was based on a wired device, which was not compatible with existing personal communication terminals like mobile phones, and portable computers. Moreover, it had many disadvantages such as poor maneuverability, high price, complexity and instability for continuous service. To solve those problems, this work took the wearable ECG acquisition system as the design theme. STM32 was provided for collecting and processing ECG signal for remote monitoring to access different users simultaneously. Long-term stability of ECG data real-time acquisition and monitoring were realized in some place like hospital based on the Internet, which could provide assistance and support for health care system.

Abstract:In order to explore the correlation of lower-limb muscle of children with diplegia cerebral palsy, and analyze the influence of spasticity on lower-limb muscle relevance of those children, surface electromyography (SEMG) was used as the source of information. In experiment, both SEMG acquisition and muscle tension test of lower limbs gastrocnemius and anterior tibial muscle of twelve cerebral palsy children were carried out. Then, signals were filtered and root-mean-square disposed by Acqknowledge software, and statistical analysis about those obtained data was carried out using SPSS 19.0. The results show that spasticity bate the relevance of lowerlimb muscle of children with diplegia cerebral palsy. This research may provide a reference for basic research and rehabilitation training of cerebral palsy children.