We propose a real-time hand gesture interface based on combining a stereo pair of biologically inspired event-based dynamic vision sensor (DVS) silicon retinas with neuromorphic event-driven postprocessing. Compared with conventional vision or 3-D sensors, the use of DVSs, which output asynchronous and sparse events in response to motion, eliminates the need to extract movements from sequences of video frames, and allows significantly faster and more energy-efficient processing. In addition, the rate of input events depends on the observed movements, and thus provides an additional cue for solving the gesture spotting problem, i.e., finding the onsets and offsets of gestures. We propose a postprocessing framework based on spiking neural networks that can process the events received from the DVSs in real time, and provides an architecture for future implementation in neuromorphic hardware devices. The motion trajectories of moving hands are detected by spatiotemporally correlating the stereoscopically verged asynchronous events from the DVSs by using leaky integrate-and-fire (LIF) neurons. Adaptive thresholds of the LIF neurons achieve the segmentation of trajectories, which are then translated into discrete and finite feature vectors. The feature vectors are classified with hidden Markov models, using a separate Gaussian mixture model for spotting irrelevant transition gestures. The disparity information from stereovision is used to adapt LIF neuron parameters to achieve recognition invariant of the distance of the user to the sensor, and also helps to filter out movements in the background of the user. Exploiting the high dynamic range of DVSs, furthermore, allows gesture recognition over a 60-dB range of scene illuminance. The system achieves recognition rates well over 90% under a variety of variable conditions with static and dynamic backgrounds with naïve users.

This paper proposes an energy efficient antenna selection for large scale (LS) MIMO in green base station (BS). For the maximum energy efficiency (EE) operation of LS-MIMO, antenna selection is necessary, because the RF chains which are connected with antennas are expensive, and consumes a lot of power. However, the excessive amount of antennas in LS-MIMO hinder the efficient antenna selection. We determine optimum antenna number in various circumstances, and show that with LS-MIMO, simple random antenna selection can provide significant EE gain. Moreover, we also show if the EE optimum number of antenna is larger than a certain threshold, the random antenna selection is already very close to the optimum antenna selection. The proposed technique is verified by both mathematical analysis and numerical simulations.

In this paper we present a simple technique for real-time touchless hand gesture user interface (UI) for mobile devices based on a biologically inspired vision sensor, the dynamic vision sensor (DVS). The DVS can detect a moving object in a fast and cost effective way by outputting events asynchronously on edges of the object. The output events are spatiotemporally correlated by using novel event-driven processing algorithms based on leaky integrate-and-fire neurons to track a finger tip or to infer directions of hand swipe motions. The experimental results show that the proposed technique can achieve graphic UI capable finger tip tracking with milliseconds intervals and accurate hand swipe motion detection with negligible latency.

This demonstration shows a natural gesture interface for console entertainment devices using as input a stereo pair of dynamic vision sensors. The event-based processing of the sparse sensor output allows fluid interaction at a laptop processor load of less than 3%.

Analyzing the performance of finite buffered multistage networks has been considered a difficult task because of dynamic blocking effects due to finite sized buffers. With the multistage networks enhanced with combining capability, in which multiple requests directed to a shared location combine together to form a single request to be forwarded, the analysis becomes even more difficult due to the interactions between combining probability and queuing delay. Performance bounds for combining networks are known under two extreme assumptions: infinite combining queues and saturated finite combining queues. We analyze multistage combining networks with the consideration of blocking due to finite combining queues. Our analysis provides iterative solutions for combining probability, blocking probability, and queuing delay.>