TECH OFFERS

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. The common practice of urban farming is to discard the spent nutrient solution after a few cycles of plant growth. A targeted hydroponics nutrient solution reduces the frequency needed to periodically adjust the nutrient. The technology provider has formulated the nutrient formulation that has considered the rate of nutrient uptake by the desired plant, the nutrient’s ratio and its availability in the solution. The formulation is specific to individual crop types and ensures the best growth for each crop type, e.g. lettuce or kale.  Indoor hydroponics farmers  Fertiliser / chemical production company may wish to market this as solution to farmers or to produce as off-the-shelf products for mass market consumer Agriscience company may package this as a solution to downstream clients   Increases yield (up to 20%) Reduces the manpower needed to monitor the nutrients intake of the plant More resource-efficient with lesser nutrient adjustment Reduces common problems stemming from imbalanced nutrients Sustainable, eco-friendly solution that can potentially lead to zero-wastage Better nutrition for the consumer nutrients solution, plant growth, crop optimisation, plant nutrients, nutrients recipes, nutrients formulations, kale, caixin, mizuna, kailan, mustard, lettuce Life Sciences, Agriculture & Aquaculture, Waste Management & Recycling, Food & Agriculture Waste Management, Sustainability, Food Security

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. This technology offer is a compact, precise, battery-powered grouting robot that runs on Robot Operating System (ROS2) that has the following features: Grout canister with specialised nozzles to dispense and grout directly into tile gaps Grouting mechanism enables grout both centre and corner lines Self-cleaning mechanism (sponge belt) to clean up any excess grouting  SLAM techniques to automatically map out a room in real-time via two-dimensional (2D) Light Detection and Ranging (LIDAR) Recognise grout lines via a downward-facing camera with sub-millimeter precision, additionally, computer vision based detection of doors, steps, pipes and holes not visible to 2D LIDAR Grouting can be performed in any given space, including irregularly shaped rooms/corridors. Human intervention is only required to assist the robot to traverse between floors within the construction site, to re-fill the grout canister, change grout colour (if the next room has a different grout colour), and to manually fill up tile gaps that are obstructed behind pipes/objects that the robot cannot physically get to (though such cases are limited) The battery life on the robot lasts for an operating duration of 5 hours, across a floor space of 60sqm. This technology can be deployed to automate the tile grouting process for the flooring/tiling industry while the technology stack that the robot operates on can also be applied for other applications within the construction industry, such as: Tile laying Floor cleaning Quality assurance/quality checks Autonomous data collection (when outfitted with a range of sensors e.g. noise, temperature) Compared to machines/tools which are already available to aid the tile-grouting process, but still require human operation/intervention, this tile-grouting robot is autonomous and automates this laborious task. This purpose-built robot operates at a significantly lower bill of material cost when compared with non-purpose built arm effectors from large robotic manufacturers which cost more than USD$50,000 (excluding other essential sensors/components specific to a particular application) The technology owner would like to work with construction companies and tiling companies for test-bedding opportunities. Additionally, construction-related companies such as grout manufacturers, material supplies, and tool manufacturers are also of interest for potential R&D collaboration and co-development opportunities. Tile Grouting, Construction Automation, Robotics, SLAM, Robot Operating System Infocomm, Video/Image Analysis & Computer Vision, Robotics & Automation

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.    The features and specifications of this rapid screening technology include: Spectroscopic methods to collect unique spectral measurements from samples based on their chemical compositions Heavy metal classification between cadmium and lead Generation of datasets from spectral measurements to create predictive model to identify heavy metal presence and predict concentration levels Predictive model is trained on spectral measurements for increased accuracy 95% accuracy in heavy metal detection, with trace concentration detection of as low as 4ppm This technology is further customisable to include other classes of heavy metals e.g. mercury, and to include other food types e.g. seafood, meats etc. Detection and measurement of heavy metal species in food/feed powder products such as: Insect powders Animal feeds Milk powders Protein supplement powders Plant-based nutritional supplements Rapid detection of heavy metal species with minimal sample preparation Screening of large amounts of food or feed powder samples within a short period of time Model performance and prediction results are comparable to industry accepted method to measure heavy metal content  Rapid screening, heavy metals, food powders, food safety Infocomm, Video/Image Analysis & Computer Vision, Big Data, Data Analytics, Data Mining & Data Visualisation, Foods, Quality & Safety, Processes

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. For the dual-band UHF RFID tag antenna, the main innovation is that its size is very compact. Compared to previous compact tags, this technology has the smallest dimensions of 0.06 λ × 0.09 λ × 0.0045 λ, where λ is the wavelength of free space at 915 MHz. In addition, very different from other products, the reading patterns of this tag are different in two bands. This design is able to provide a sufficiently far identification distance ( > 7 meters in European RFID band, and > 5 meters in American RFID band) at such extremely small size, proving that this tag has very high radiation efficiency. For the single band UHF RFID tag antenna, the main innovation is that its size is greatly reduce by three new strategies. Compared to previous compact tags, this technology has the smallest dimensions of 0.03 λ × 0.09 λ × 0.0045 λ, where λ is the wavelength of free space at 915 MHz. This design is able to provide a sufficiently far identification distance ( > 5 meters) with such extremely small size, proving that this tag has very high radiation efficiency. The primary application area of this technology is industrial intelligent RFID multifunctional detection system. The anti-metal tag of this system can use this technology because this single tag can cover two UHF RFID bands simultaneously. This technology also can be well used in intelligent driving RFID positioning system in different countries. Some products about logistics RFID management, luggage RFID management, containers RFID management, and shelves RFID management can be developed at different regions. The rapid development of the Internet of Things (IOTs) has spawned a variety of new technologies and product applications. The application trends of anti-metal tags are expected to be more extensive and more suitable for multi-scenario applications in RFID market. The technology can offer some business opportunities in smart RFID logistics and smart agriculture. It has very broad prospects for the rich sensing applications of the IOTs in the near future. This design can save a lot of installation space for the rest of the industrial equipment, especially for the dual band tag which works at 2 different frequencies, with its multifunctional radiation beams. Due to its highly efficient radiation, it can reduce the input power of the transmit components of its RFID system. Other risk factors due to the continuous heating state of the system can be further reduced.  The technology owner is keen to license this technology to RFID application technology companies, including for logistics management, access control and equipment management, etc. RFID, UHF, RFID for Metal Electronics, Sensors & Instrumentation, Embedded Systems

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.   The technology has the following specifications: Dispersion magnitude is scalable through appropriate design, depending on the fiber lengths that need to be compensated for. Operating wavelength is tailorable. Tunability may be introduced through thermo-optic control. Dispersion compensation is applicable for both intensity modulated direct detection modulation formats and coherent modulation formats. This device is CMOS-compatible, low loss and operates in transmission mode. May be seamlessly integrated with photonic integrated circuits. The technology owner has experimentally demonstrated that the technology works. High-speed characterization using 30 Gb/s NRZ and 30 Gbaud/s PAM4 data showed a restoration of the eye diagram that deteriorated after propagating through 2km of optical fiber. BER characterization showed a 1.3dB improvement in power penalty out of a 1.8dB degradation at the error-free (BER = 10-12) level. Scalable dispersion has also been experimentally proven. The transmission of high speed data over optical fiber is well known to be impaired by dispersion in the optical fiber. This technology provides a very low loss solution to dispersion compensation and has been shown to restore the eye diagram and improve the bit error rate of high speed data. Potential applications would be pre- or post- dispersion compensation of optical fiber dispersion. Transceivers which serve long fiber reaches and/or utilize high speed data could incorporate this technology in the transceiver chip (either transmitter or receiver), to provide an integrated, low loss, CMOS-compatible means of high quality dispersion compensation that can be easily integrated with the rest of the transceiver chip. The silicon photonics transceiver market is projected to grow to S$6.4 billion by 2026 with a compound annual growth rate of 25%. Asia Pacific is expected to show the fastest growth with increasing adoption of high-speed data systems, supportive government initiatives, and fast-growing consumer demand. Increases transceiver reaches Provides either pre- or post- dispersion compensation, with very low loss. This would allow existing transceiver products to serve longer reaches, or support higher data rates. It would also open up new opportunities for new product lines which provide integrated dispersion compensation, reducing the complexity of deployment for network operators. Very low cost, requiring only integration of the device within the transceiver chip, negligible increase in power budget. The technology owner is looking for R&D collaboration, IP licensing/acquisition, testbedding opportunities with optical transceiver companies, data center hardware companies, telecommunications companies or silicon photonics companies. Electronics, Semiconductors, Memory & Storage

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. This technology offer comprises the following features: Vision-based AI algorithm which analyses a picture and detects/identifies STDs Built-in AI explainability - visualised as heatmaps that highlight the occurrence of recognised abnormalities/pathologies Synthetic data generation pipeline for data augmentation, data bias correction iOS and Android mobile applications with anonymised data acquisition Web-based integration via a suite of REST APIs is also available for developer use. To handle the issue of data privacy, the technology complies with Health Insurance Portability and Accountability Act (HIPAA) requirements and it does not associate any names, emails, or phone numbers with the collected images i.e. screening results are provided directly to the original source of the data. This technology can be applied for use in the following areas: Pre-screening prior to seeking medical attention at a primary care facility Self-serviced recovery monitoring (post-treatment) Preventive/predictive healthcare Telemedicine/remote health monitoring Additionally, the technology is also applicable for the detection and identification of certain visible dermatological conditions and oncological conditions. First in the world application of AI techniques to assist in male STD screening Highly accurate models  Low-latency screening - results returned within 3 seconds Built-in AI explainability presented through heatmaps which increases the confidence level of the end-user The technology owner is keen to work with universities, research institutes, medical institutions, clinics and digital healthcare providers to testbed the technology and provide additional data that will improve the accuracy of the CNN model.   Sexually Transmitted Disease, Convolutional Neural Network, Dermatology, Oncology Infocomm, Video/Image Analysis & Computer Vision, Artificial Intelligence, Healthcare, Telehealth, Medical Software & Imaging

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. The BGEM technology is an end-to-end managed AI platform that leverages Photoplethysmography (PPG) enabled wearable sensors to monitor various heart rate variability (HRV) features associated with blood glucose fluctuation. The solution comprises the following features: Optimised and validated AI algorithm Mobile Demo App Including UI/UX design guideline User-friendly visualisations SaaS Scalability Security API Integration The BGEM technology offers a cost-effective, non-invasive approach to predicting an individual's diabetes risk. The applications include: Population Health Perspective: The technology leverages the high growth rate of smart wearables and hearables, presenting an opportunity to identify undiagnosed diabetes individuals within the population. Preventive Health Monitoring: With the ability to monitor blood glucose changes regularly at minimal cost, the technology empowers high-risk users to adopt a healthier lifestyle and, therefore, prevent or delay the onset of diabetes. Diabetes around the world in 2021: 537 million adults (20-79 years) are living with diabetes, 1 in 10. This number is predicted to rise to 643 million by 2030 and 783 million by 2045. Over 3 in 4 adults with diabetes live in low- and middle-income countries. Diabetes is responsible for 6.7 million deaths in 2021 - 1 every 5 seconds. Diabetes caused at least USD 966 billion dollars in health expenditure – a 316% increase over the last 15 years. 541 million adults have Impaired Glucose Tolerance (IGT), which places them at high risk of type 2 diabetes. Overview of the wearable technology market: The market is projected to expand at a compound annual growth rate (CAGR) of approximately 12.5% between 2023 to 2030. Estimated to be worth USD 55.5 billion in 2022, with a projected revenue of USD 142.4 billion by 2030. Current blood glucose monitoring technologies either require finger pricking for blood extraction or the insertion of sensors into the skin and discomfort through wearing patches for extended periods. Instead, the technology uses external sensors and algorithms to detect and predict diabetes risk. No object needs to be inserted into the user's body or continuously worn throughout the day, resulting in minimal pain and discomfort. Additionally, the only equipment required for testing is the wearable device. No additional disposable equipment needles or test strips are needed, which makes blood glucose monitoring much more convenient and cost-effective than other "State-of-the-Art" solutions. The Unique Value Proposition of BGEM include: Market-ready: It is a market-ready non-invasive diabetes risk detection and prediction AI solution that leverages consumer-grade wearables to detect blood glucose dysregulation. Performance: Demonstrates outstanding prediction and detection capabilities. Cloud-based: Operates on a cloud-based platform for seamless integration. Third-party compatibility: Easily implemented with third-party devices and apps. Sustainability: Reduction in bio-medical waste such as needles, test strips etc User-friendly: Non-invasive, convenient and allows frequent measurement. Non-invasive measurement, blood glucose, diabetes mellitus, preventive healthcare, AI, ML, Wearables, PPG, Blood Glucose Monitoring, Diabetes Monitoring, Diabetes Evaluation, Non-Invasive Diabetes Monitoring, Diabetic Risk Assessment Infocomm, Artificial Intelligence, Healthcare, Diagnostics

In Singapore, more than 30,000kg of okara are generated from soya milk and tofu production. Due to the high amount of insoluble dietary fiber and a unique, poignant smell of okara, it is often discarded as a waste product. Despite okara's low palatability, it is rich in nutrients. Therefore, the technology owner has developed a cost-effective formulation to include okara in feed for abalone. The formulation can potentially be adapted and customised for other aquatic species. The technology owner is seeking potential partners to license and commercialise the technology. The technology allows for an alternative nutrient source for animal feed allowing for the sustainability of food supply and reduction of food waste. The formulation consists of a cost-effective plant-based functional ingredient, lowering the costs of feed for aquaculture farms. The nutritional composition can be tailored for different species to increase growth rates and survivability. Okara is used as a cost-effective feed for high-value abalone, a commonly cultured species of mollusc. Okara-based feed results in the beautiful purple colouration of the shell and increased growth and survivability of abalone. In comparison, the okara-based feed costs ~30% less than commercial feed used in the industry. There is potential for okara to be included in feed for other aquatic species such as shrimp and fish. The success of this method will valorise okara, transferring them into a nutrient-dense aquatic feed while promoting a more environmentally sustainable food production chain.       okara, aquatic feed Foods, Ingredients

Vision-based Artificial Intelligence (AI) models require substantial time to train, fine-tune and deploy in production. After production, this process is still required when performance degrades and re-training on a new dataset becomes necessary; this maintenance process exists throughout the model's lifetime to ensure optimal performance. Rather than embarking on the time-consuming and painful process of collecting/acquiring data to train and tune the AI model, many organisations have turned to the use of pre-trained models to accelerate the AI model development process. This technology consists of a suite of pre-trained models that are intended to detect food, human behaviours, facial features and count people. These AI models are operable on video footage and static images obtained from cameras. Models are tuned and trained on various use-cases and are accessible via API calls or embedded within software as a Software Development Kit (SDK) library. These models can be deployed as AI as a Service on Microservices platform providing customer data protection with blockchain technology. With customer protection enhanced with blockchain technology, AI Model performance can further be enhanced to meet customer requirement.   The technology consists of a suite of pre-trained AI models that provide high accuracy (over 80%) and can be further customised to improve accuracy and adapted to different use-case scenarios. Models can be integrated in the following ways:  Installed library package embedded within software on-device/on-premise HTTP-based Application Programming Interface (API) calls with video/image data to cloud-installed library package The following are the features for various AI models: Abnormal Behaviour Recognition Continuous monitoring and detection of abnormal human behaviours e.g. fighting, loitering Event Detection Recognises a variety of subjects and events e.g. sports day, graduation, wedding, festival, Christmas, from video footage Optimised for lightweight compute capability (Intel OpenVino) Food (Fresh and Packaged) Recognition Real-time detection of fresh and packaged foods Detects abnormal fresh food or defective packaged food Classifies food types e.g. lotus, spinach, cucumber, radish etc. Optimised for low compute capability Privacy-Preserving Person Recognition Privacy preserved people detection, counting and human activity recognition Images are blurred to preserve private information that can lead to personal identification (irreversible) Optimised for lightweight edge computing Free (Empty) Space Recognition Semantic segmentation to identify empty spaces Customisable for any free-space detection scenario High accuracy in night scenes Safety Monitoring Object detection with prohibited and allowed zones (e.g. person or forklift) Detects and identifies safety risks associated with safety distances Enables audible alarm systems of abnormal situations Wellbeing and Safety Detection Automatically detects and classifies nudity images from images  Enables alerts to be delivered to parent/caregiver's device Customisable to detect new categories of inappropriate content This technology offer comprises a suite of AI models for the following applications: Abnormal Behaviour Recognition Public areas or areas where social order needs to be maintained e.g. food & beverage, entertainment establishments Event Detection Automatic creation and/or organisation of media content i.e. photo classification Automated adjustment of device hardware parameters e.g. audio, colour, brightness when displaying specific types of content e.g. sports Food (Fresh and Packaged) Recognition Food stock level detection, food inventory management Automatic detection of fresh/packaged goods within a constrained area Privacy-Preserving Person Recognition Privacy protection of visual information, in high traffic areas, without deterioration of video quality Free (Empty) Space Recognition Vehicle position localisation on roads Navigation (free-space localisation) in partial/fully self-driving automotive vehicles Identification of free storage spaces in the logistics industry Safety Monitoring Automated compliance checks Workplace safety analysis and tracking Wellbeing and Safety Detection  Parental control in browsers, smartphones or other image storage devices e.g. Network Attached Storage (NAS), Solid State Drives (SSD) AI Models were rigorously tested in the fields of different scenarios. The microservice platform where AI Model ingest the visual data streams offers a secure customer data protection and privacy using blockchain technology. Making this Microservice platform capable of tracking customer’s data usage and thus ensure privacy when AI model operating on the platform are simultaneously improved using unique customer data captured on customer’s premise. Accelerate AI development - eliminate the need for dataset creation, annotation, tuning and testing Customisable AI models - fine-tuned to environment and condition Operational support to continuously improve AI accuracy from newly collected data   event detection, abnormal human behaviour recognition, safety monitoring, food package detection, food freshness, nudity detection, empty space Infocomm, Video/Image Analysis & Computer Vision, Video/Image Processing, Artificial Intelligence