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Electronic Systems and Internet of Things Engineering

Dr Bronson Philippa

Agriculture is becoming increasingly technological in order to be environmentally sustainable while ensuring high productivity. This research develops practical technology for agriculture. One area of focus is smart irrigation technology for sugarcane, to enable more efficient and sustainable irrigation practices. Another area is near infrared spectroscopy to assess quality of horticultural products.

Prof Wei Xiang, Dr Eric Wang, Dr Bronson Philippa

  • Studies on physical-layer IoT techniques: Low-power and long-range IoT communications, massive machine-to-machine (M2M) communications, low-latency and ultra-reliable IoT communications, and multiple-access schemes for connecting massive numbers of IoT smart devices simultaneously.
  • Studies on application-layer IoT techniques: Big IoT data analytics; Networks of embedded devices (smart things), protocols, and architecture; Advances in wireless sensor networks (WSN); Solutions to improve IoT big data networks; Networks of embedded devices: privacy and security.
  • Practical applications of IoT networks: IoT for remote monitoring of healthcare, smart agriculture, smart city applications (smart water networking, smart grid, etc.), IoT for environmental monitoring.

Dr Bronson Philippa

This research works to improve organic solar cells and organic light emitting diodes (OLEDs). The major focus is using simulations to better understand charge transport and exciton dynamics. Simulations provide detailed insight, such as how material properties impact upon the overall device performance, and how device structures can be designed to improve efficiency.

Prof Wei Xiang, Dr Eric Wang, Dr Bronson Philippa

Studies on next-generation wireless communications techniques such as 5G mobile communications, massive MIMO, spatial modulation, new channel coding schemes such as Fountain codes, Polar codes, etc. Studies on multimedia communications focusing on error-resilient video compression and streaming; Deep learning based computer vision applications, e.g., region-of-interest (ROI) image processing, saliency object detection, machine learning and neural networks based image and video analytics, etc.