Energy efficiency has been a hot research topic for many years and many routing algorithms have been proposed to improve energy efficiency and to prolong lifetime for wireless sensor networks (WSNs). Since nodes close to the sink usually need to consume more energy to forward data of its neighbours to sink, they will exhaust energy more quickly. These nodes are called hot spot nodes and we call this phenomenon hot spot problem. In this paper, an Enhanced Power Efficient Gathering in Sensor Information Systems (EPEGASIS) algorithm is proposed to alleviate the hot spots problem from four aspects. Firstly, optimal communication distance is determined to reduce the energy consumption during transmission. Then threshold value is set to protect the dying nodes and mobile sink technology is used to balance the energy consumption among nodes. Next, the node can adjust its communication range according to its distance to the sink node. Finally, extensive experiments have been performed to show that our proposed EPEGASIS performs better in terms of lifetime, energy consumption, and network latency.

Fall detection is an important public healthcare problem. Timely detection could enable instant delivery of medical service to the injured. A popular non-intrusive solution for fall detection is based on videos obtained through ambient camera, and the corresponding methods usually require a large dataset to train a classifier and are inclined to be influenced by the image quality. However, it is hard to collect fall data and instead simulated falls are recorded to construct the training dataset, which is restricted to limited quantity. To address these problems, a three-dimensional convolutional neural network (3D CNN) based method for fall detection is developed which only uses video kinematic data to train an automatic feature extractor and could circumvent the requirement for large fall dataset of deep learning solution. 2D CNN could only encode spatial information, and the employed 3D convolution could extract motion feature from temporal sequence, which is important for fall detection. To further locate the region of interest in each frame, a LSTM (Long Short-Term Memory) based spatial visual attention scheme is incorporated. Sports dataset Sports-1M with no fall examples is employed to train the 3D CNN, which is then combined with LSTM to train a classifier with fall dataset. Experiments have verified the proposed scheme on fall detection benchmark with high accuracy as 100%. Superior performance has also been obtained on other activity databases.

Abstract-In disruption-tolerant networks (DTNs), network topology constantly changes and end-to-end paths can hardly be sustained. However, social network properties are observed in many DTNs and tend to be stable over time. To utilize the social network properties to facilitate packet forwarding, we present LocalCom, a community-based epidemic forwarding scheme that efficiently detects the community structure using limited local information and improves the forwarding efficiency based on the community structure. We define similarity metrics according to nodes' encounter history to depict the neighboring relationship between each pair of nodes. A distributed algorithm, which only utilizes local information, is then applied to detect communities and the formed communities have strong intra-community connections. We also present two schemes to first select and then prune gateways that connect communities to control redundancy and facilitate efficient inter-community packet forwarding. Extensive real-trace-driven simulation results are presented to support the effectiveness of our scheme.

Brain-computer interfaces (BCIs) are used to translate brain activity signals into control signals for external devices. Currently, it is difficult for BCI systems to provide the multiple independent control signals necessary for the multi-degree continuous control of a wheelchair. In this paper, we address this challenge by introducing a hybrid BCI that uses the motor imagery-based mu rhythm and the P300 potential to control a brain-actuated simulated or real wheelchair. The objective of the hybrid BCI is to provide a greater number of commands with increased accuracy to the BCI user. Our paradigm allows the user to control the direction (left or right turn) of the simulated or real wheelchair using left- or right-hand imagery. Furthermore, a hybrid manner can be used to control speed. To decelerate, the user imagines foot movement while ignoring the flashing buttons on the graphical user interface (GUI). If the user wishes to accelerate, then he/she pays attention to a specific flashing button without performing any motor imagery. Two experiments were conducted to assess the BCI control; both a simulated wheelchair in a virtual environment and a real wheelchair were tested. Subjects steered both the simulated and real wheelchairs effectively by controlling the direction and speed with our hybrid BCI system. Data analysis validated the use of our hybrid BCI system to control the direction and speed of a wheelchair.

In this paper, we study two important attacks in wireless sensor networks: 1) the fabricated report with false votes attack and 2) the false votes on real reports attack. Most of the existing works address the first attack while leaving an easy way for the attackers to launch the second. We draw our motivation from this and propose a probabilistic voting-based filtering scheme (PVFS) to deal with both of these attacks simultaneously. With the general en-route filtering scheme as the underlying model, PVFS combines cluster-based organization, probabilistic key assignment, and voting methods to curtail these attacks. Through both analysis and simulation, we demonstrate that PVFS can achieve strong protection against both the aforementioned attacks while maintaining a sufficiently high filtering power.

Twelve open problems and one conjecture are posed in this exposition. Among them, eight open problems and the conjecture are on calculation of the exact value of the Hausdorff measure of self-similar sets, and four open problems are on topological entropy. We give a brief survey of the recent research results related to these open problems and conjecture.

A strategy for the synthesis of quinazolinones via acceptorless coupling of o-aminobenzamides with methanol has been accomplished in the presence of the metal-ligand bifunctional catalyst [Cp*Ir(2,2'-bpyO)(H2O)]. Notably, this research exhibited the potential of transition-metal-catalyzed activation of methanol as a C1 source for the construction of heterocycles.

The nature of insight has been the interdisciplinary focus of scientific inquiry for over 100 years. Behavioral studies and biographical data suggest that insight, as a form of creative cognition, consists of at least four separate but intercorrelated stages as described by Wallas (1926). Yet no quantitative evidence was available for insight- or insight-stage-specific brain mechanisms that generalize across various insight tasks. The present work attempted, for one, to present an integrated and comprehensive description of the neural networks underlying insight and, for another, to identify dynamic brain mechanisms related to the four hypothetical stages of insight. To this end, we performed two quantitative meta-analyses: one for all available studies that used neuroimaging techniques to investigate insight, and the other for the phasic brain activation of insight drawn from task characteristics, using the activation likelihood estimation (ALE) approach. One key finding was evidence of an integrated network of insight-activated regions, including the right medial frontal gyrus, the left inferior frontal gyrus, the left amygdala and the right hippocampus. Importantly, various brain areas were variably recruited during the four stages. Based on the ALE results, the general and stage-specific neural correlates of insight were determined and potential implications are discussed.