Student LO Po Wen and student WANG Chaoqun won the First-Class Award of Information Technology at ‘Challenge Cup’ National Competition Hong Kong Regional Final in Hong Kong University Student Innovation and Entrepreneurship Competition 2016. Their project is called the Continuous Blood Pressure Monitoring System from Face Recognition. It aims at developing a novel physiological monitoring system based on optical recordings and face recognition technique in order to facilitate real time contactless monitoring over a long distance and enable physicians to monitor the real-time situation of multiple patients.

Congratulations to student Wu Xiaoxiao who received the Postgraduate Research Output Award 2014 at the 78th Congregation for the Conferment of Degrees!

Below is her sharing:
Over the last decade, transmit beamforming has been extensively studied and recently, widely applied as an efficient transmit scheme in multiple-input single-output (MISO) physical-layer multicast systems. Therein, beamforming based on semidefinite relaxation (SDR) has been shown to be promising, but its performance may degrade as the number of users increases. Our work proposes two innovative transceiver strategies, both of which subvert traditional fixed beamforming philosophy and can serve large-scale systems. Specifically, the first strategy, called stochastic beamforming (SBF), randomizes the beamformer in a per-symbol time-varying manner. We prove that their multicast achievable rate gaps with respect to the MISO multicast capacity must be no worse than 0.8314 bits/s/Hz, irrespective of any other factors such as the number of users. The second strategy combines transmit beamforming and the Alamouti space-time code, and it is shown to exhibit a better multicast rate scaling than SDR-based beamforming. Moreover, we further the work by combining SBF and the beamformed Alamouti scheme, wherein an improved constant rate gap of 0.39 bits/s/Hz is proven. These proposed beamforming schemes are tested under a channel-coded, many user setting, and yield significant SNR gains over many existing beamforming designs. Hence, they are practical and may reform beamforming systems with a low complexity payout.

ImageNet Large Scale Visual Recognition Challenge (ILSVRC) is one of the most important grand challenges in computer vision. There are more than 70 top computer vision groups participated in ILSVRC 2015. The CUHK team (CUvideo) including Prof. Wanli Ouyang, Prof. Hongsheng Li, Prof. Xiaogang Wang and five PhD students from EE department participated in ILSVRC 2015 and won the challenge of object detection from videos. CUvideo achieved the mean Averaged Precision (mAP) of 67.8%, while the team in the second place only obtained the mAP of 35.9%. The challenge required detecting 30 object categories from one thousand videos and CUvideo won 28 categories out of them. The CUHK team also ranked NO.2 in object detection in images (Microsoft was the winner). It required detection 200 object categories from 40,000 images. This was the second time for CUHK to participated in this ILSVRC. In ILSVRC2014, the CUHK team ranked the second in object detection in images, while Google was the Winner.

Lau Ming Wai Laura won the Best Paper Award in the special session (Women in Engineering) in TENCON 2015.

TENCON is a premier international technical conference of IEEE Region 10, also referred as the Asia Pacific Region, one of the largest regional organizations of the IEEE.

There are a number of submissions to the special session of Women in Engineering in TENCON this year. Based on the paper quality, only three candidatures were selected to give presentations in the special session for the Best Paper Award. The candidatures include an assistant professor at PESCE, Mandya, a professor at LBS College of Engineering, and Lau Ming Wai Laura (final-year undergraduate student in Electronic Engineering in CUHK).

Laura performed excellent in both presentation and Q&A parts and finally won the Best Paper Award.

Congratulations!