TECH OFFERS

Machine Learning (ML) and Deep Learning (DL) have been the primary growth driver of Artificial Intelligence (AI) and has seen widespread adoption in areas such as Computer Vision, Speech Processing, Natural Language Processing, and Graph Search, among many others. It is also well-known that AI both needs and produces large amounts of data. However, traditional data repositories have not scaled effectively to handle the large amounts of vector representations that are common in AI applications - in such cases, searching for similarities across high-dimensional vectors is inefficient. To address such limitations, vector databases have been developed to address the limitations of traditional hash-based searches and search scalability, enabling similarity searches across large datasets. This technology offer is a unified Online Analytical Processing (OLAP) data platform that supports approximate vector search, enabling efficient searching over billion-scale structured data and vector data. The data engine simplifies the process of building enterprise-level AI applications such as search and recommendation systems, video analytics, text-based searches, and chatbots while accelerating the development of production-ready systems. Developers no longer need to deal with complicated scripts to query vector data as low latency, high-performance structured data, and vector data searches are made possible via vector data indexing methods and the use of extended Structured Query Language (SQL) syntax.

Different plant species have different nutrient requirements. The current practice of urban farming uses a generic hydroponic nutrient solution that is suitable to most plant types, and a crude sensing system that only measures total ion content in the solution. This approach often results in nutrients deficiency and/or overloading and hence requires consistently monitoring. Overloading of nutrients not only increases the input costs, it also results in greater quantities of contamination in effluent to be disposed after harvest.  A targeted hydroponic nutrient solution reduces the need to periodically adjust the nutrient. The technology provider has studied and formulated different nutrient recipes that had shown improved yield compared to commercial products. This ensures the best growth for each crop type. It also reduces common problem stemming from imbalanced nutrient, e.g. leaf chlorosis due to nutrient deficiency. All these translate to a better yield and a more marketable produce for the farmers. Formulations developed include Mizuna, Kale, Lettuce, Mustard, Kalian, and Caixin. The technology provider is seeking for licensing partners from the agriculture industry.

Tile grouting is the process of filling up gaps between tiles, after individual tiles have been laid onto cement screed and is a critical part of virtually every construction project. Yet, it remains a highly laborious process, and is considered one of the most physically demanding tasks as it often results in injuries to tilers' knees and back. This, in turn, leads to quality issues when grouting is not performed correctly. The construction labour shortage in Singapore, especially in the tiling/construction industry has likewise catalysed the demand for automation of such jobs - especially since such tasks are deemed to be less desirable to a younger generation of workers. This technology offer is a tile grouting robot powered by Computer Vision (CV) and Simultaneous Localisation and Mapping (SLAM) techniques, running on Robot Operating System (ROS2) to boost construction productivity and reduce the occurrence of workplace injury. The robot is able to boost productivity by at least 5 times and this results in an amortisation time of roughly 24 months.

The presence of heavy metals in food or feed powders involves contamination of the food chain and potential harm to public health, as such, rapid detection is a time-critical issue. The uncertainty about food safety caused by the possible presence of heavy metals is of concern to consumers and regulatory authorities and this is typically addressed by increasing the testing frequency of food or feed samples. However, existing testing methods are often time-consuming and require highly skilled laboratory personnel to perform the testing. This technology employs spectroscopic imaging methods and machine learning techiniques to rapidly detect heavy metals in food or feed samples. The machine learning model can perform a multi-class differentiation of the various heavy metals based on spectroscopic measurements. It is also able to predict the concentration of heavy metals present in food or feed powders using spectroscopic measurements. Minimal sample preparation is required for this method, allowing for the rapid screening of food or feed powder samples. The technology owner is interested in collaboration with companies working with food powders, with an interest in heavy metal content within food powders.   

This technology offer is a Ultrahigh Frequency (UHF) Radio-Frequency Identification (RFID) tag antenna for use on metal structures. 2 versions are available: A compact dual-band version with folded strip structure, with a total size of only 20 mm × 30 mm × 1.5 mm. This tag can be well used in different RFID systems, which work at different UHF bands, such as European and American frequencies. The reading patterns of this tag are with different directions in two bands. A single band version with a total size of only 10 mm × 30 mm × 1.5 mm. This tag can be well used in planar as well as conformal platforms, such as metallic cylinders and bearings. Automated factories should be interested in these tags, and they can use the miniaturized tags with RFID technology to intelligently detect whether the machinery and equipment are running normally.

This technology offer is an integrated, CMOS-compatible, compact device that provides dispersion compensation of dispersion in optical fibers. Dispersion impairments is a well-known problem in the transmission of high-speed data over fiber, that limits both the fiber reaches, or poses lower limits on the power required. The technology developed allows a seamless, very low loss method for compensation of fiber dispersion, providing high magnitudes of dispersion for countering dispersion in optical fibers. Without dispersion compensation, signals are susceptible to degradation from optical fiber dispersion, with the extent of degradation worsening with longer fiber reaches. Without proper dispersion compensation, transmitted data will experience high Bit Error Rates (BER) at the receiver. This technology solves this important problem and increases the fiber reaches which may be served.  

Electron-beam lithography (EBL) is a form of maskless lithography tool that is widely used in nanofabrication due to its rapid-prototyping and high-resolution patterning capabilities. However, the patterning process employing EBL typically suffers from low throughput due to the patterning process of scanning across all designated pixels by a scanning focused electron beam. This technology introduces the use of plasma treatment to increase the patterning throughput for closed polygon structures in EBL process without affecting the resolution of the patterned structures, allowing the patterning efficiency of EBL to be enhanced by 50 times and above. This technology is potentially useful for manufacturers of beam lithography tool such as electron beam lithography (EBL) and focused ion beam lithography (FIB) to develop next generation lithography system for high volume fabrication equipment. In addition, this technology could also be useful in semiconductor and nanophotonic industry for rapid prototyping of photomask and photoreticle in the fabrication process which could help improve the manufacturing cost of photomask fabrication.

Due to social stigma surrounding Sexually Transmitted Diseases (STD), cost barriers, and lack of awareness, a large proportion of men suffering from male health issues avoid accessing in-person medical care, instead, many men turn to online sources such as search engines or discussion forums to seek help. This further compounds the problem as this failure to seek medical attention leads to delays in diagnosis and subsequent treatment, while misinformation and misguidance from the general public can be dangerous and life-threatening. This technology aims to address the gap between crowd-sourced diagnosis and primary healthcare practitioners through a fully anonymous, AI-driven mobile application screening tool that covers 90% of genital pathologies e.g. bumps and lesions, and certain disease and viruses, such as syphilis, herpes, Human Papillomavirus (HPV). This technology offer is Artificial Intelligence (AI) enabled mobile application that utilises a Convolutional Neural Network (ConvNet or CNN) deep learning algorithm equipped with custom-built network layers to screen for male health issues, e.g. genital warts, sexually transmitted diseases (STD) etc. Data augmentation techniques and synthetic data generation methods have been used to vary the dataset and increase and sample size for realistic model training and testing. The highly accurate model has an accuracy of 60-90% for most cases of STI/STDs.

The latest Singapore National Population Health Survey has reported a concerning diabetes trend. From 2019-2020, 9.5% of the adults had diabetes, slightly dropping to 8.5% from 2021-2022. About 1 in 12 (8.5%) of residents aged 18 to 74 were diagnosed, with an age-standardised prevalence of 6.8% after accounting for population ageing. Among the diabetes patients, close to 1 in every 5 (18.8%) had undiagnosed diabetes, and 61.3% did not meet glucose control targets. Prediabetes is also prevalent, with 35% progressing to type 2 diabetes within eight years without lifestyle changes. Untreated Type 2 diabetes can lead to severe health issues. Tackling this challenge requires a holistic approach, focusing on awareness, early diagnosis, and lifestyle adjustments for diabetes and prediabetes. Recognising the need for innovation to address this, the technology owner develops a cost-effective and non-invasive AI-powered solution, Blood Glucose Evaluation And Monitoring (BGEM), that detects glucose dysregulation in individuals to monitor and evaluate diabetic risks. BGEM allows users to track their blood glucose levels regularly, identify any adverse trends and patterns, and adopt early intervention and lifestyle changes to prevent or delay the onset of diabetes. Clinically validated in 2022, with a research paper published in October 2023, the technology is open for licensing to senior care/home care providers, telehealth platforms, health wearables companies, and more.