CoolSo Technology and Tsinghua University are the grand prize winners of the inaugural EE Challenge 2023, organized by AspenCore, the publisher of EE Times Asia/Taiwan, EDN Asia/Taiwan and EE Times.

The challenge—with the theme “AIoT: Smart Life, Creative Future”—received nearly 100 entries from academia and startups, NGOs, social enterprises, and community groups. Awards included cash prizes that total $20,000.

CoolSo’s gesture control software relies on “muscle activity signal sensing technology” (mechanomyography, MG), which can be integrated with sensors in smartwatches and smart bracelets to deliver gesture-control functionality without altering any hardware design. Its applications extend beyond gaming to rehabilitation and sports, offering a feature like “double tap” for the Apple Watch. Moreover, it incorporates additional gestures for quicker response times, enhancing the user experience.

CoolSo intends to let users interact directly with digital content through hand movements, providing a user-friendly and immersive augmented reality (AR) interface for users of smart, wearable devices. According to CoolSo, its software solution can support multiple gestures in a more cost-effective, flexible, and easily integrable manner. Currently working with Qualcomm and with 25 customers and proof-of-concepts, CoolSo intends to further collaborate with customers to develop AR applications in gaming and sports.

m’AI Touch extends ‘social distancing’ to buttons

During the recent pandemic, people became concerned about using public panels and buttons. Current solutions like disinfectant film, voice recognition and sensor technology have limited preventive effects. Those solutions also come up against environmental factors.

Addressing this issue, students from the National Tsing Hua University designed “m’AI Touch,” a non-contact, anti-epidemic device—and won the grand prize for doing so.

This design, focusing on human-centric approaches, employs heterogeneous sensor design and time-series data fusion technology for high-quality, time-series sensing. It uses AI models and edge-computing devices for fast and accurate predictions, enabling non-contact operation. Future applications include food-ordering machines, vending machines and ATMs.

STRIKE 2.0 for scientific baseball training

Jingletek’s smart baseball training system, dubbed “STRIKE 2.0,” received the Smart Award at the EE Challenge. STRIKE, Jingletek’s flagship product already in its second generation, incorporates a chip and multiple precision sensors within the baseball. It detects pitching speed, rotation rate, rotation axis and other training data, transmits them to mobile phones via Bluetooth and offers coaches and players visual data to master their training conditions.

Jingletek executives said they plan to leverage the data collected by STRIKE to establish a scouting system, potentially exposing players using STRIKE to international professional leagues in Japan and the United States.

ICue intelligent sensor pad provides caregiving assistance

Humetrics’s “iCue Sensory Mat” is also recieved the Smart Award. By placing the iCue under a regular sleep mattress, it can detect real-time behavioral status, such as leaving the bed or prolonged lying. The iCue also conducts continuous and non-contact physiological monitoring, including respiration rate, respiration waveform and heart rate.

Humetrics leverages AI-incorporating data algorithms to generate personalized reports from the sensory pad data for caregivers or elderly family members. These reports aid organizations and family members in understanding the current physical and activity conditions of the elders, providing personalized health management and reducing the workload of care organizations.

Gochabar drives EV adoption

Gochabar, an EV-charging solutions system-integration service provider, clinched the EE Energy Efficiency Award in the EE Challenge. As a joint venture between eTreego, HOTAI and Yangde Group, Gochabar offers comprehensive services encompassing EV-charging equipment and field construction, user and grid system management, cash flow, energy creation and storage and system integration.

Incorporating patented technologies from IEK and Chevron, Gochabar’s flagship product Cloud Charging Management System manages EV charging, thereby providing site engineering and after-sales warranty services to ensure user peace of mind, safety, cost savings, energy conservation and user-friendliness.

VMFi’s “TranslationwindoW” enables real-time, two-way translation

VMFi’s AI-based, real-time translation system “TranslationwindoW” offers translation services in Chinese, English, Japanese and Korean. Displaying real-time subtitles on a transparent screen to allow face-to-face conversations while reading subtitles displayed on either side of the transparent screen, the system is being used by transport, airport, hotel and retail operators to help foreign customers overcome language barriers.

Clin improves energy efficiency, reduces carbon emissions

A team from the National Taiwan University of Science and Technology (NTUST) integrated cloud-monitored energy-storage systems in the cold storage tanks of fishing vessels. This integration optimized energy-storage systems through battery management, cloud monitoring, battery model inference and handling mechanisms, providing users with a webpage service to visualize cloud analysis results. The “Clin” project secured the EE Challenge Energy Efficiency Award.

NTUST’s team improved system performance and controllability through digital circuit design. Integration of cloud technology and energy-storage systems allowed remote monitoring and management. Compared with traditional diesel generators, this optimized system boasts higher energy efficiency and effectively reduces carbon emissions from fishing activities.

SL-STT MRAM overcomes high power consumption tech bottleneck

With advancements like AIoT and 5G, the demand for high-performance emerging memories, especially magnetoresistive memory (MRAM), has surged. NTU’s ESOE MRAM team introduced an MTJ with super lattice barriers (SL-MTJ) named SL-STT MRAM. This innovation won a Featured Wise Award.

SL-STT MRAM overcomes traditional STT-MRAM writing’s high power consumption and durability limitations. As an environmentally friendly, energy-saving, high-performance memory, it suits AI-driven high-performance computing and IoT edge computing applications.

AI enhances stoplight efficiency, reduces carbon emissions

To address the issue of unnecessary energy use when vehicles idle at traffic lights, students from Tamkang University integrated AI technology into traffic light systems. Their innovation, “We Make Roads Greener,” secured a Featured Wise Award for the Campus group.

Tamkang University’s system, utilizing YOLOv7 object detection and Deep-Q-Learning enhanced learning technology, monitors intersection traffic flow and detects in real time the number of motorcycles. It calculates optimal red light times, alleviating traffic issues caused by fixed red light durations.

By: DocMemory
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