A monolithic frequency synthesizer with wide tuning range, low phase noise and spurs was realized in 0.13,$mu$m CMOS technology. It consists of an analog PLL, a harmonic-rejection mixer and injection-locked frequency doublers to cover the whole 6--18,GHz frequency range. To achieve a low phase noise performance, a sub-sampling PLL with non-dividers was employed. The synthesizer can achieve phase noise $-$113.7,dBc/Hz@100,kHz in the best case and the reference spur is below $-$60,dBc. The core of the synthesizer consumes about 110,mA*1.2,V.

An earthquake is a destructive natural disaster, which cannot be predicted accurately and causes devastating damage and losses. In fact, many of the damages can be prevented if people know what to do during and after earthquakes. Earthquake education is the most important method to raise public awareness and mitigate the damage caused by earthquakes. Generally, earthquake education consists of conducting traditional earthquake drills in schools or communities and experiencing an earthquake through the use of an earthquake simulator. However, these approaches are unrealistic or expensive to apply, especially in underdeveloped areas where earthquakes occur frequently. In this paper, an earthquake drill simulation system based on virtual reality (VR) technology is proposed. A User is immersed in a 3D virtual earthquake environment through a head mounted display and is able to control the avatar in a virtual scene via Kinect to respond to the simulated earthquake environment generated by SIGVerse, a simulation platform. It is a cost effective solution and is easy to deploy. The design and implementation of this VR system is proposed and a dormitory earthquake simulation is conducted. Results show that powerful earthquakes can be simulated successfully and the VR technology can be applied in the earthquake drills.

Distributed compressive video sensing (DCVS) has received considerable attention due to its potential in source-limited communication, e.g., wireless video sensor networks (WVSNs). Multi-hypothesis (MH) prediction, which treats the target block as a linear combination of hypotheses, is a state-of-the-art technique in DCVS. The common approach is under the supposition that blocks that are dissimilar from the target block are given lower weights than blocks that are more similar. This assumption can yield acceptable reconstruction quality, but it is not suitable for scenarios with more details. In this paper, based on the joint sparsity model (JSM), the authors present a Tikhonov-regularized MH prediction scheme in which the most similar block provides the similar common portion and the others blocks provide respective unique portions, differing from the common supposition. Specifically, a new scheme for generating hypotheses and a Euclidean distance-based metric for the regularized term are proposed. Compared with several state-of-the-art algorithms, the authors show the effectiveness of the proposed scheme when there are a limited number of hypotheses.