TECH OFFERS

When planning surgeries, doctors and medical engineers need to create 3D surgical plans pre-operation, and their only way to model internal body parts is to rely on Computerized Tomography (CT) images. For patients living with implanted metal artifacts, the artifacts will lead to an interference on image generation and visualization of anatomical structures thereby resulting in visual errors of the images. Current available CT image generating tools has its limitations in processing images with visual noise such that it greatly reduces the visibility of hard and soft bone surfaces. This leaves medical engineers with an extended period of manual image correction and uncertainty, resulting in higher risk of unsuccessful surgeries due to inaccurate surgical modelling. The process of bone segmentation usually takes several hours as Cone Beam Computed Tomography (CBCTs) need to be corrected manually.  To overcome these challenges, the company has developed an algorithm to create automated 3D models that is cost-efficient and timely. The technology is able to deliver precise anatomical identity of both hard and soft bone surface and is compatible with all segmentation and planner software. This technology is clinically proven for Maxillofacial and Orthodontics 3D surgical planning (bone grafting and implantation) and can be integrated into systems of CBCT machine and Medical 3D printer. 3D models are created within 5 minutes Reduce manual CT correction by 90% 86-95% accuracy in clinical trials Targeting oral CBCT anatomical region Simple and fast user interface (after registration, CBCT recordings can be uploaded, afterwhich user can download the 3D models) Offers engineering assistance for implant and bone replacement surgery planning (in complex accident-traumatic cases) Orthodontics and maxillofacial surgeries. The technology can be developed for all CT types (including animal CTs). The software can currently be used as a web service or be integrated into CBCT machines. Dental imaging market is projected to reach USD 4.1 billion by 2025 from USD 2.6 billion in 2020. Faster, cheaper and more accurate surgical planning for Selective Laser Sintered Implant, 3D printed Surgical Navigation Tool and 3D Bone Block. Competitors create their 3D models from CBCT records by 50 minutes manual work. This technology is able to create the same quality 3D models from the same CBCT records by 5 minutes without human work. Compared to existing CBCT segmentation deep-learning software that performs segmentation of bone structures according to predetermined geometries (different bone parts are registered in advance), this tehcnology method automatically classify pixels belonging to bone structures with acceptable precision. Bone surfaces are accurately segmented, and the planned implant is of the right size and fits properly to reduce surgery risks and re-construction of surgery. CBCT (cone-beam computer tomograpy), CBCT Segmentation, 3D reconstruction, Maxillofacial, Orthodontic, Dentistry, Surgical Planning, Bone replacement and Implantation, Bone segmentation, Medical Software, Medical CAD/CAM Healthcare, Telehealth, Medical Software & Imaging

Monitoring of contaminants in fluids often require capital-intensive machinery and sampling comes at a hefty price tag. With the advent of tightening regulations across various industries including environmental and food industries, there is a need for a more cost-effective and efficient method to meet the growing demands and regulatory requirements in the market. Molecular Imprinted Polymers or MIPs are one such sensor technology that can potentially address this challenge. MIPs are synthetic materials that are designed to recognize and selectively bind to specific molecules, similar to the way antibodies recognize and bind to antigens. MIPs can be engineered to bind to a wide range of analytes, including organic and inorganic molecules, peptides, proteins, and even whole cells. The unique feature of MIPs is that they possess high selectivity and sensitivity for the target molecules, making them ideal candidates for designing high-performance sensors. This technology relates to a cost-effective online monitoring system using MIPs technology to detect trace levels of chemical and biological contaminants on-site in the fluid phase with low interference, high accuracy, and sensitivity. The automated real-time monitoring system requires little supervision and can be easily operated. The robust sensor is designed for long-term operation and requires minimum maintenance without compromising the reproducibility and integrity of the data. This technology allows monitoring can be applied in industries such as agriculture, food, chemical processes, environment monitoring and waste management. The technology provider is seeking partners that are interested in co-development, R&D collaborations or licensing. This technology is primarily based on the mass change and energy dissipation from the analyte adsorptions and interactions on the sensor chip, which gives a piezoelectric effect and delivers real-time, high sensitivity, and high selectivity data. The entire sampling and analysing process is automated. Key features include: Shortened analysis time  (<10 mins) compared to conventional sensors (30 - 45 mins) High accuracy, and sensitivity (ppb level detection) Real-time and online monitoring Label-free, non-toxic, and environmentally friendly sensing process Regenerable sensor chips Modular designs Automated system Heavy metal detection Pesticide residue detection Endotoxin detection Wastewater treatment and resource recovery Water quality monitoring in water bodies The manufacturing process and water monitoring regulations are becoming increasingly stringent. The global water quality monitoring market has a CAGR of 6.5% from 2020 to 2027, showing the potential commercial gains from such sensors. As more and more substances are required to be monitored, users can find convenience and cost savings from having a sensor that is able to detect multiple target molecules.  Proprietary algorithm to overcome interferences  Cost-effective (per sample basis: 5 SGD  vs. 15- 25 SGD sensor, MIPS, monitoring, water Foods, Quality & Safety, Environment, Clean Air & Water, Sensor, Network, Monitoring & Quality Control Systems

The potential of green hydrogen to plug the intermittency of solar and wind whilst burning like natural gas and serving as feedstock in industrial chemical processes has attracted the interest of industry, governments and investors. From oil and gas players, utilities, industries from steel to fertilisers and more, green hydrogen is regarded as the best bet for harmonising the intermittency of renewables.   Green hydrogen is produced through water electrolysis, a process that separates water into hydrogen and oxygen, using electricity generated from renewable sources. Today, it accounts for just 0.1% of global hydrogen production according to the World Economic Forum. The main disadvantage of green hydrogen production via water electrolysis is (1) its high energy consumption of more than 50 kWh per kg and the need for large land areas and (2) the competition of usage for water it creates.   The proposed hydrogen production technology is based on the decomposition of methane (CH4) molecule in oxygen-free environment by low energy microwave plasma. Unlike electrolysis, this process does not produce CO2 as it decomposes CH4 directly into gaseous hydrogen and solid carbon, both are industrially valuable products. Compared to water electrolysis, this process saves up to 5 times the energy required to produce hydrogen from methane, at competitive costs. The process can be installed on-site, at the end of the gas infrastructure, reducing the need to invest in a new H2 infrastructure. The fact that, coupled with biomethane, the technology is CO2 negative, representing an indirect air capture solution is another major advantage.   The technology owner is seeking OEM partners in Singapore (1) to co-develop complete solutions integrating the proposed technology for specific applications or (2) integrate the technology into industrial demonstration sites. Energy efficient: Methane decomposition performs at similar energy efficiency to steam reforming and typically uses 5 times less electricity than an electrolyser, and requires a fraction of the space/land. Low cost: Producing green hydrogen at the cost of grey hydrogen. On-site on-demand: Compact, modular and stackable solution deliver a range of energy capacity, from 200 kg to several tonnes per day. The modules may be assembled to reach multi-megawatt scale. Transport and storage: No need for storage or transport by allowing existing gas infrastructures to deliver hydrogen on-demand. Daily output: One module produces up to 200 kg of 98% purity hydrogen per day. Feedstocks: Bio-methane and natural gas. Sustainability: With natural gas feedstock, hydrogen produced is CO2-free. With bio-methane feedstock, it has a negative carbon balance, in addition to avoiding methane emissions. Hydrogen co-product: Co-produced solid carbon can be used in existing market (e.g. tires, ink) but also in new markets (e.g. building materials, agriculture, etc). Decarbonisation of industrial processes. Hydrogen for chemical industry. Cities, buildings and data centres. Energy generation and powerplant. Clean transport. Hydrogen refuelling station. Solid carbon usage, e.g. tires, ink, building materials, etc. The global green hydrogen market size was valued at US$3.2B in 2021 and is expected to expand at a compound annual growth rate (CAGR) of 39.5% from 2022 to 2030 (Grand View Research, 2022). Plasma decomposition of methane, resulted in up to 5 times lesser energy consumption than electrolysis through a carbon dioxide-free process. Green hydrogen, CO2-free process, CO2 negative process Sustainability, Low Carbon Economy

The sustainable urban farming concept is growing rapidly, and Singapore is progressing well towards it.  The heating, ventilation, and air conditioning (HVAC) system accounts for more than 50% of the total energy used in an indoor agricultural farm, according to data on energy use. Technological advancements can help to address energy reduction and improve the productivity of indoor farms. Low energy-based concepts can be implemented by mainstream farm owners in Singapore to increase farm productivity and serve the increasing market demands directly.  This technology offer is a Low-Energy (Low-E) HVAC system for farming. It can cool, heat, dehumidify and ventilate any indoor space using up to 100% outdoor air exchange. It is able to achieve and maintain the optimum cooling, drying conditions, and sufficient level of carbon dioxide that are needed for farming with lower energy consumption. The operating cost of the Low-E HVAC fitted grow room is 35% to 37% lower than the conventional HVAC system for the same application. The technology owner is keen to do R&D collaboration and test-bedding with potential indoor agricultural farm owners.    The main features of this technology offer are:  35-37% energy reduction compared to conventional system 60% reduction of integrated airborne particle concentration of PM1.0 particulates Combined cooling, dehumidification and fresh air ventilation processes with up to 100% outdoor air exchange Unique Low-Energy (Low-E) HVAC system, eliminates the need to use separate equipment for each process  Using computational fluid dynamics (CFD) method to maintain optimum cooling, drying conditions, and sufficient level of carbon dioxide to resist growth of mould, mildew, and potentially hazardous organisms. Portable, modular, and scalable assembly for different sizes of application   The technology offer can be deployed in the following applications: Urban agriculture – farming and gardening Greenhouses/outdoor enclosed farms Enclosed incubation and isolation area  Medical / scientific laboratory for sample preparation and storage  The system is also scalable and customisable for bigger application areas.       This technology offer is a novel low-E HVAC system with:  100% outdoor air exchange to ensure the undisrupted supply of carbon dioxide and oxygen for plant growth and maturity  40% to 60% drying conditions within the grow room with lower energy consumption compared to the conventional HVAC system. Computational fluid dynamics (CFD) simulation method to ensure uniformity of air distribution. Capable of achieving 35 to 37% lower electricity compared to the conventional HVAC system Portable, modular and flexible setup for both indoor and outdoor growing and can be adjusted even during operation The technology owner is keen to do R&D collaboration and test-bedding with potential indoor agricultural farm owners.  low energy hvac, urban farming, greenhouse, climate control, low operating cost Environment, Clean Air & Water, Mechanical Systems, Green Building, Heating, Ventilation & Air-conditioning, Indoor Environment Quality

The rise in health consciousness has accelerated the development of sports wearable devices. Currently, most common sports wearables are physiological indicators for monitoring vital signs (e.g., heart rate, blood pressure, SpO2, etc.) and metabolites (e.g., glucose, pH, lactic acid, etc.). However, these devices cannot quantitatively analyse the force-generating process. The existing kinematical indicators monitoring posture and motion also have limitations, such as poor wearing comfort, low sensitivity, and weak capacity for real-time data analysis. The technology is an ultra-thin microfiber strain sensor that has superior elasticity, durability, and sensitivity. Using this proprietary technology, the technology owner has developed a comfortable fabric wearable to monitor muscle activities during sports and rehabilitation. By incorporating machine learning algorithms, more than 15 data metrics are being analysed in real-time to accurately characterise sports performance, optimise training standards, and prevent fatigue or injury. This technology is available for licensing and R&D collaborations with partners in the sports, fitness, healthcare, and rehabilitation areas, e.g., sportswear and smart wearable companies, gyms, healthcare providers, sports training institutes, etc. The technology owner has developed a full technology suite for sports monitoring, consisting of the following modules: 1. Wearable Band: Fabric band woven with a microfiber sensor capable of tracking motions, forces, and pressure Lightweight and comfortable band with similar dimensions to a smartwatch (< 35g) Highly stretchable sensor to be stretched to more than 200% of its original length Wireless transmission unit to provide real-time Bluetooth data transmission to the mobile app Utilises a rechargeable battery capable of lasting more than 7 hours upon fully charging 2. Mobile User App: Ready App for Android and Windows PC Home screen with multiple functions: Select the type of training: workout, power, time, etc. Track the history of previous workouts Sensor calibration to ensure accurate tracking and analytics 3. Cloud Server (Al / ML): Derive more than 15 data metrics, e.g., muscle expansion/contraction, speed, power, range of motion, workout consistency, fatigue level, muscle stability, etc. Machine learning algorithms to evaluate the user’s health profile and provide recommendations The potential applications include but are not limited to: Sportswear (sports apparel, smart socks, footwear) Wearable devices (smart watches, smart glasses) Training equipment (gym armbands, intelligent coaching systems) Training institutes (athlete training, sports schools, military) Lightweight and comfortable Washable sensor allows for regular laundering Superior sensing performance (fast and accurate response) In-depth data analysis to characterise sports performance Machine learning to provide intelligent recommendation This technology is available for licensing and R&D collaborations with partners in the sports, fitness, healthcare, and rehabilitation areas, e.g., sportswear and smart wearable companies, gyms, healthcare providers, sports training institutes, etc. Sports Monitoring, Fitness and Healthcare, Microfiber Strain Sensor Materials, Plastics & Elastomers, Electronics, Sensors & Instrumentation, Infocomm, Artificial Intelligence

Recently, the rising adoption of Internet of Things (IoT) devices and portable electronics has made electronic waste (e-waste) pollution worse, especially when small and low-power IoT devices are single-use only. As such, low-cost and environmentally friendly power sources are in high demand. The technology owner has developed an eco-friendly liquid-activated primary battery for single-use and disposable electronic devices. The battery can be activated by any aqueous liquid and is highly customisable to specific requirements (i.e., shape, size, voltage, power) of each application. This thin and flexible battery can be easily integrated into IoT devices, smart sensors, and medical devices, providing a sustainable energy solution for low-power and single-use applications. The technology owner is keen to do R&D collaboration and IP licensing to industrial partners who intend to use liquid-activated batteries to power the devices. The technology is a single-use and non-rechargeable battery that can be instantly activated by any aqueous liquid (e.g., water, fruit juice, soft drink, etc.) as well as all types of body fluids (e.g., blood, saliva, urine, sweat, bile, etc.). The features of this technology are: Customisable shape, size, and power (1.5 to 6.0 V at 4 to 50 mW) Ultra-thin and flexible (<1 mm in thickness) Lightweight (when dry) High energy density (less than 5 mm2 for low-power application: 1.5 V, 2 mAh) Indefinite pre-activation shelf-life (no self-discharge) Non-toxic and biocompatible (safe for human beings) Environmentally friendly (no disposal pollution) This inherently safe and non-toxic battery can be widely applied in MedTech applications, disposable IoT, smart sensors, and low-power electronics. The potential applications include but are not limited to: Medical devices: digital pills, ingestible sensors, smart bandages, wearable biosensors, in-vitro diagnostics (IVDs), body fluid testing, etc. Disposable IoT: Bluetooth Low Energy (BLE) chips, microprocessors, wireless sensors (pH, temperature, humidity), micromotors, LEDs, heaters, etc. Other low-power electronics: smart labels, electronic skin patches, cold chain monitoring, smart packaging, etc. The technology offers the following unique features: Highly customisable for different applications Thin and flexible (adaptable to various designs) Long shelf-life (can be sealed for a very long time) Biocompatible (can be safely consumed) Environmentally friendly and non-toxic The technology owner is keen to do R&D collaboration and IP licensing to industrial partners who intend to use liquid-activated batteries to power the devices.  Primary Battery, Environmentally Friendly, Non-Toxic, MedTech, Disposable IoT Energy, Battery & SuperCapacitor, Healthcare, Medical Devices, Infocomm, Internet of Things

Strength training is beneficial for a person's overall health and wellness. There is increasing demand for strength training used in rehabilitation aimed at restoring the day-to-day functionality of elderly persons. Currently, continual adjustment and improvement to the strength training and rehabilitation plan is carried out using feedback based on visual analysis. This maybe time consuming, and has to be based on the experience of the rehabilitation therapist.  This technology offer is a near-infrared spectroscopy (NIRS) technique used to detect the effectiveness of strength training. By using the technology, muscle oxygen consumption information can be acquired and mapped as a two-dimensional distribution without the need of direct skin contact. As such, it is possible to accurately evaluate the effectiveness of strength training on a site-by-site basis. In-vivo changes in oxygen concentration in muscles during strength training can be determined by detecting changes in oxyhemoglobin and deoxyhemoglobin. In this technology offer, these changes are presented by variations in amplitudes of refracted content of an incidental NIR light directed into the skin. This method of analysing the changes in intramuscular blood flow is effective for understanding the muscle condition during strength training, and hence can be used to determine the effectiveness of the training.  The technology owner is keen to out-license the technology to application developers from the physical training and rehabilitation industry.  This technology offer uses near-infrared spectroscopy (NIRS) to measure hemoglobin changes before and after training to detect effectiveness of physical training. The method:  uses the near-infrared region of the electromagnetic spectrum from 780nm to 2500nm.  does not need to have direct contact with the user's skin captures two-dimensional distribution of muscle oxygen consumption level detects surface scattering and internal scattering components uses precision shutter control technology This technology offer can be adopted in various industry such as: Physical education Training and rehabilitation  Medical and physiological diagnostics and research  This technology offer uses non-contact, near-infrared spectroscopy (NIRS) to measure muscle oxygen consumption in a targeted area of the muscle activity. It has a proprietary method used to trigger the electronic shutter to accurately extract the internal scattering of NIR light.  By displaying the measured oxygen consumption as a two-dimensional distribution, the operator can easily evaluate the effectiveness of muscle exercise over time. This method is efficient and removes the need for the operator to be experienced in visual evaluation of muscle condition; it is expected that this technology can be applied to various fields such as physical training and rehabilitation services. The technology owner is keen to out-license the technology to application developers from the physical training and rehabilitation industry.  physio, sensing technology, exercise, muscle Electronics, Sensors & Instrumentation, Lasers, Optics & Photonics

The solar energy industry is experiencing rapid growth and innovation, and machine learning is playing a key role in driving this trend. Solar energy plays a crucial role in the sustainability initiative providing a clean, renewable, and cost-effective source of power. The adoption of solar energy usage can help to address climate change, improve energy security, and provide access to electricity in remote areas. This growth is fueled by the increasing adoption of machine learning and artificial intelligence technologies, which are helping organisations in the solar energy industry to more accurately predict and optimise the performance of their solar panels. These models can effectively analyse images of solar panels to detect and diagnose issues such as microcracks, “snail trails”, broken glass, hot spots, dust build-up and other defects that may impact their performance. Building and deploying these models can be a complex process, requiring the use of multiple tools and a high level of technical expertise. This technology offer is a customisable end-to-end MLOps platform that is capable of streamlining the process and makes it easier for teams to build custom computer vision models specifically for solar energy monitoring and optimisation. With this platform, teams can quickly and easily convert their data into working models with enterprise-standard practices, ensuring the accuracy and reliability of their solar energy monitoring systems. The technology owner is keen to do R&D collaboration with organisations looking to improve and optimise the overall design and integration of solar energy systems.   The technology offer can help organisations improve the efficiency of solar panel systems by as much as 25%. It consists of the following features:  AI-Assisted Labeling - in-built annotating method with a mixture of contour analysis methods and deep-learning to label datasets with a few clicks per image with pixel-level accuracy. Image Augmentation - allows generation of synthetic variations of datasets directly in the platform to increase robustness. Multi Architecture and GPU Support - supports large data size that may require multiple GPUs to calculate gradients simultaneously.  Model Deployment & Active Learning - can be adopted in models built natively on the platform, on a fully managed GPU environment or edge deployment.  Works on 2D RGB Images (or converted from other spectrums) Supports polygon, bounding box, and mask labels Exportable to major annotation formats e.g. COCO JSON, LabelMe, PascalVOC, COCO MASK, CSV Width-Height, etc Supports model training with State of the Art models such as MaskRCNN, DeepLabV3 with "One-Click Train" feature Evaluation and Report Generation - to generate detailed evaluation result and statistical analysis of the model that can be included as part of the publication or technical specification sheet. The technology offer can be used for a variety of use cases in the solar energy industry, including: Building custom ML model to continuously monitor solar panels to identify and diagnose any issues affecting efficiency, such as power degradation, hotspots, and shading. Developing predictive maintenance models to proactively address potential problems before they occur Analysing images of solar panels to detect cracked cells, microcracks, hot spots, dust build-up, broken glass, and other defects Optimising the placement and orientation of solar panels to maximize energy production Developing a monitoring system to detect when a junction box is faulty, providing alerts to maintenance teams to take action. Addressing the challenge of low power production efficiency caused by “Snail Trails” by automating the detection and remediation of micro-cracks   The technology offer helps a wide range of demographics in helping improve the efficiency of industrial application developers, deep-tech problem solvers, and researchers. It improves the development cycle by enhancing in-house capability to custom-build computer vision models that are robust and production-ready. Using this technology offer, the collaborators can enhance both speed and cost benefits when developing computer vision capabilities. Active learning methods can further increase model accuracy over time. The technology offer is designed to elevate the capabilities of AI companies in the Solar Panel industry by providing cutting-edge integration and advanced technology for image processing by streamlining data analysis, allowing AI algorithms to quickly process and analyse both IR (Infrared Spectrum) and Photovoltaic (PV) images with speed and accuracy. This enhances the accuracy of AI algorithms and reduces the risk of errors, leading to more effective maintenance and optimisation of solar panels. The advanced image processing capabilities of the platform drive innovation in the Solar Panel industry and allow AI companies to develop new and more advanced algorithms, resulting in improved performance, cost savings, and greater efficiency.  The technology owner is keen to do R&D collaboration with organisations looking to improve and optimise the overall design and integration of solar energy systems.  solar panel, energy management, predictive maintenance, machine learning, computer vision, image processing Infocomm, Video/Image Analysis & Computer Vision

Bioplastics have gained significant attention due to the environmental issues of fossil-based plastics and the realisation of limited petroleum resources. On the other side, industrial and agricultural organic wastes are produced in huge quantities worldwide, resulting in serious environmental and economic impacts. To solve the above problems, the technology owner has developed a 100% natural biotechnological process to convert industrial and agricultural organic waste into bioplastics. Bioplastics are fully biodegradable and biocompatible, with no harm to humans and environment. These bioplastics are applicable to industrial plastic processes and potentailly replace conventional plastics in short lifespan applications. The use of industrial and agricultural waste as cheaper sources not only makes the production process more economic but also helps in the management of organic waste, contributing to the goal of a circular economy. This technology is available for IP licensing and R&D collaboration with industrial partners who are interested in the sustainable production of bioplastics using organic waste. The bioplastics produced entirely from renewable resources can replace conventional fossil-based plastics in short lifespan applications. The features of this technology are as follows: Produced from industrial and agricultural organic waste 100% biodegradable in a natural environment (in 6 to 12 months) Excellent biocompatibility with no harm to humans and environment Improved mechanical properties by in-house modifications Customised formulations to meet different requirements (BioPE, BioPET, etc.) Up to 5 years lifespan (depending on the circumstances) Adaptable to existing plastic processes without additional equipment The bioplastics produced using this technology can potentially substitute almost all major plastics in single-use products and short lifespan applications. The potential applications are as follows: Rigid and flexible packaging: food containers, bottles, boxes, bags, films Disposable utensils: straws, chopsticks, cutleries Households: tableware, sanitary wares, sunglasses frames, stationary items Sports equipment: fishing tools, surfboards, helmets Medical applications: sutures, scaffolds, bone plates Other sectors: agricultural foils, device casings, machinery housings The technology offers the following unique features: 100% biodegradability in a nature environment Conversion of organic waste into bioplastics for a circular economy Eco-friendly alternative to conventional fossil-based plastics Applicable to existing plastic processes and production lines Scalable and cost-efficient production with organic waste as feedstock This technology is available for IP licensing and R&D collaboration with industrial partners who are interested in the sustainable production of bioplastics using organic waste.   Materials, Plastics & Elastomers, Waste Management & Recycling, Food & Agriculture Waste Management, Sustainability, Circular Economy