Automatic epilepsy detection is of great significance for the diagnosis and treatment of patients. Most detection methods are based on patient-specific models and have achieved good results. However, in practice, new patients do not have their own previous EEG data and therefore cannot be initially diagnosed. If the EEG data of other patients can be used to achieve cross-patient detection, and cross-patient and patient-specific experiments can be combined at the same time, this method will be more widely used. In this work, an EEG classification model based on a self-organizing fuzzy logic (SOF) classifier is proposed for both cross-patient and patient-specific seizure detection. After preprocessing, the features of the original EEG signal are extracted and sent to the SOF classifier. This classification model is free from predefined parameters or a prior assumption regarding the EEG data generation model and only stores the key meta-parameters in memory. Therefore, it is very suitable for large-scale EEG signals in cross-patient detection. Selecting different granularity and classification distance in two different experiments after post-processing will achieve the best results. Experiments were conducted using a long-term continuous scalp EEG database and the [Formula: see text]-mean of cross-patient and patient-specific detection reached 83.35% and 92.04%, respectively. A comparison with other methods shows that there is greater performance and generalizability with this method.

The location of participants in Location-based CrowdSensing (LCS) represents important information for task completion. Tasks in areas with high concentration of participants (AHCP) can be completed quickly, whereas task completion is difficult in areas with sparse participants (ASP). Incentive mechanisms are necessary to motivate participants to move toward ASP. Previous studies have faced two main problems. First, most incentive mechanisms assume that participant motivation is not affected by external factors. Second, when participants fail to complete tasks, only the cost of the participant is considered the loss. However, reference effect from behavioral economics proves that participants are influenced by both internal and external factors. Furthermore, loss aversion studies have shown that participant evaluations of loss are more severe than simple costs. Therefore we propose an incentive mechanism based on behavioral economics (IBE) consisting of two schemes for participant selection (IBE-PS) and payment decisions (IBE-PD). Based on reference effect, IBE-PS is proposed to control the task selection and pricing of participants. Based on loss aversion, IBE-PD is proposed to encourage participants to complete tasks in ASP many times. Theoretical analysis and simulation results demonstrate that IBE can improve the task completion rate, the participant utility, and the platform welfare.

With the development of Online to Offline (O2O) model and the rapid growth of service types and numbers, service matching algorithms have become the key in connecting users and services. The traditional service matching algorithms lack consideration for the limited resources of O2O services, leading to the Matthew effect more seriously. In this context, how to alleviate the Matthew effect through the optimization of matching algorithms has become an urgent problem in this field. Based on this, this paper proposes an adaptive optimization algorithm of O2O service matching to achieve the balance of supply and demand by optimizing supply, thus alleviating the Matthew effect. In addition, a computational experiment system is constructed to verify the effect of different matching algorithms on alleviating the Matthew effect. The result shows that our proposed algorithm can provide new means and ideas for alleviating the Matthew effect.

As we all know, the accuracy of GPS become worse indoors due to the satellite signal attenuation. However, people spend most of his or her time in the indoor environment and the location-based service is needed in the indoor environment. Hence, many indoor positioning techniques have been researched to provide location-based service for visitors in public buildings such as museums, galleries, etc. However, the distinguish of human's moving patterns and initial position detection are two difficult problems in Pedestrian Dead Reckoning (PDR) system. Therefore, A smartphone-based indoor positioning system (called Improved Pedestrian Dead Reckoning, IPDR) using fuzzy theory and WLAN mapping algorithm is presented to solve these problems. In our proposed system, we propose a method called fuzzy step length detection which can distinguish five different human's moving patterns and handle various ways of holding the phone. The WLAN mapping algorithm could calculate the initial position. Calibrated position can be obtained from the IPDR position and the mapping position from WLAN map acquired in advance. The performance of our IPDR system is verified via a set of simulations and the result is positive.

Non-geostationary (NGEO) satellite networks have a series of advantages over terrestrial networks. However, traditional routing algorithms such as the Dijkstra's Shortest Path (DSP) algorithm always lead to some Inter-Satellite Links (ISLs) heavily loaded. To guarantee a better distribution of traffic among satellites, this paper proposes a Fuzzy Satellite Congestion Indicator (FSCI) to estimate congestion status among neighboring satellites. Indeed, a satellite notifies its neighboring satellites of its FSCI. When it is about to get congested, it requests its neighboring satellites to decrease their data forwarding rates by sending them a self status notification signaling message. In response, the neighboring satellites search for less congested paths according to Fuzzy Route Determination. The routing strategy discussed above is Fuzzy Satellite Routing(FSR). This routing algorithm avoids both congestion and packet drops at the satellite. It also ensures a better traffic distribution over the entire satellite constellation. The mechanism of multiple traffic classes is also discussed in FSR. The good performance of FSR, in terms of short end-to-end delay, higher throughput, and lower packet drops, is verified via a set of simulations using the Network Simulator 2 (NS2).

Recently, due to the low accuracy of GPS in indoor environment, many indoor positioning techniques have been researched to provide location-based service for visitors in public buildings such as museums, galleries, etc. But many indoor positioning techniques cannot distinguish human's moving patterns and external infrastructures are needed to improve accuracy. In this paper, a Fuzzy Pedestrian Dead Reckoning (FPDR) system with fuzzy magnetic map matching algorithm is presented. This system only utilizes inertial sensors of smartphone. The fuzzy stride length algorithm in FPDR could handle five different human moving patterns and various ways of holding the phone. Also, walking distances, heading direction and floor of building can be obtained in real time. When the user stops to appreciate showpieces in museums, relevant magnetic data will be collected. The fuzzy magnetic map matching algorithm could calculate the final location with the position from FPDR and the calibration position from magnetic map acquired in advance. The performance of FPDR system is verified via a set of simulations and the error rate is lower than other typical indoor location methods.

The mesh topology structure constituted by inter- satellite links(ISLs) along with longitude and latitude, as a feature of most LEO satellite constellations, has not been fully used. Additionally, most LEO networks design its routing protocol depending on path distance, which is only proportional to the propagation delay without consideration of the queue delay. In this paper, a low-complexity routing algorithm (LCRA) based on load balancing is proposed, which can obtain the best path by distributed computation with the location information of the current node and destination. There is no iteration process in the computation, thus saving the computational cost. Additionally, each node informs its neighbouring nodes of its congestion information so that packets can choose the next hop dynamically according to the status of links, so as to shorten the mean queue delay and reduce the packet loss rate. Results assessed by NS2 presents the superiority of LCRA in terms of end-to-end delay, throughput and packet loss rate in comparison with other routing algorithms.

All cable modems used in the CATV network use the same upstream and downstream frequencies to establish bidirectional communication with their central system. The upstream frequency band usually suffers from ingress impulse noises that cause severe interference to upstream communication. We have developed a method to detect, at the central system, the places where these noises get into the coaxial cable, using signals/radio waves in the atmosphere in the downstream frequency range such as mobile phones, which are not used in the CATV infrastructure. We also developed a new tap-off system, in which the central system can turn on and off high pass filters connected to output connections in a tap to disable the upstream frequency band on each coaxial subscriber's line, when the line carries excess noises. With this detection system of ingress noise and the new tap-off system, the vulnerability of the security and the stability of the upstream channels in CATV can be improved.