TECH OFFERS

The proliferation of e-commerce, ride-hailing and food-delivery services have fueled the need for more accurate and reliable estimation of delivery times. The current common estimation of delivery time is based on Estimated Time of Arrival (ETA) which relies on route distance that is calculated between the origin and the desired destination. It only considers the duration from pick up to drop off, and does not consider the additional time needed for preparing and offloading the goods. This technology offer is a Machine Learning (ML) model that is able to calculate the stop duration (job completion duration), which together with the ETA, provides the Estimated Time of Completion (ETC). This ML model is for Singapore use only. For the Machine Learning (ML) model to calculate the stop duration, users will need to key in the input parameters such as building name, block number, road name, postal code, day of week, day of month and time. The system will predict the stop duration (job completion duration) in minutes. ML model enables prediction of ETC based on historical data and a small set of input parameters Highly correlated with ETA which can be easily obtained from API services such as Google or Onemap Takes in consideration of temporal data including hours of the day and day of the week. Can be integrated with existing web/mobile based solutions. Apart from being a productive tool for route planning systems, the software can also be used in various situations such as: Customer Services/Call Centres Fleet Management  Loading Bay Assignment This model can be used to improve the existing route planning systems as it provides additional job completion duration prediction on top of estimated ETA.  Low cost - the model is easy to implement and incorporate into other applications. Simple to use - only a small set of input parameters are required. Able to predict the stop duration (job completion duration) with reasonable accuracy. The technology owner is keen to out-license this technology to collaborators in the field of last mile-logistics, or collaborators who are providers of software for last-mile logistics. This ML model is for Singapore use only. Last Mile Delivery, Estimated Time of Completion (ETC) Infocomm, Artificial Intelligence, Logistics, Delivery & Distribution, Value-Added Services

There are 30,000 occupational drivers in Singapore, out of which 13,500 are 45 years old and above. The risk of acquiring cardiovascular disease increases with age and is potentially exacerbated by low physical activity and high emotional stress levels, which are two typical characteristics of occupational drivers arising from their work environment. Low level of physical activity and high stress levels have been shown to have significant relationship with heart rate variability, one of the indicators of cardiovascular disease. This technology is developed to help drivers to monitor their stress level, provide them with instantaneous feedback and the necessary alerts for a timely intervention. This technology offer presents a cross-platform AI system that estimates the stress levels continuously in real time, and can be easily integrated with commercially available photoplethysmography (PPG) wearables, e.g., a PPG wristwatch. In addition, this technology can be adapted for the monitoring of workplace stress with the aim of improving overall mental well-being. The current practice of using electrocardiogram (ECG) to measure the electrical activities of the heart is mainly found in the clinical settings and not easily accessible by the general population due to high costs. The need for an accurate placement of ECG electrodes does not allow the continuous monitoring of the heart condition in a non-clinical setting. It is not easy for individuals to continuously monitor their stress levels at the workplace in daily life, especially for drivers. This technology offer enables continuous, real-time monitoring and is not limited to clinical settings. It is cost-effective without the need to purchase medical grade ECG devices. The solution can be easily customised and adapted to existing and future PPG wearables. It is portable and feasible for all-day monitoring of stress levels and heart conditions in diverse workplace environments. Essentially, it is an algorithm that extracts proprietary features to predict stress levels, with an unique approach, from heart beat signals customised to a given context in the workplace. This facilitates the creation of contextualised stress profiles for specified workplace situations ranging from physical combat conditions to air-conditioned office sedentary conditions. The technology owner is interested to out-license this technology offer. Potential application areas are: Public or private hospitals, clinics, community care centers. Individual drivers and any workers who might be exposed to workplace related stress. Sports sectors including sports training, research and competition. Medical devices, equipment suppliers and manufacturers. Potential products: Existing or future wearables with PPG, e.g., wristwatch, headset, t-shirt, etc. Hardware and platform independent, easy to be incorporated into existing and future wearables. Portable and feasible for 24/7 stress monitoring. Affordable and cost-saving without the need to purchase medical grade ECG devices Real-time monitoring and timely feedback customised for users stress monitoring, ai algorithm, ai system, heart monitoring, workplace stress, wearable technology Infocomm, Artificial Intelligence, Internet of Things, Wireless Technology, Sustainability, Sustainable Living

One use of AI, including deep learning, is in prediction tasks, such as image scene understanding and medical image diagnosis. As deep learning models are complex, heatmaps are often used to help explain the AI’s prediction by highlighting pixels that were salient to the prediction. While existing heatmaps are effective on clean images, real-world images are frequently degraded or ‘biased’-such as camera blur or colour distortion under low light. Images may also be deliberately blurred for privacy reasons. As the level of image clarity decreases, the performance of the heatmaps decreases. These heatmap explanations of degraded images therefore deviate from both reality and user expectations.  This novel technology-Debiased-CAM-describes a method of training a convolutional neural network (CNN) to produce accurate and relatable heatmaps for degraded images. By pinpointing relevant targets on the images that align with user expectations, Debiased-CAMs increase transparency and user trust in the AI’s predictions. Debiased-CAMs are effective in helping users identify relevant targets even on images affected by different clarity levels and multiple issues such as camera blur, poor lighting conditions and colour distortion. The AI’s prediction also becomes more accurate. As the model is trained using self-supervised learning, no additional data is needed to train it.  The training for Debiased-CAM is generalisable, and thus applicable to other types of degraded or corrupted data and other prediction tasks such as image captioning and human activity recognition. Used to train a convolutional neural network (CNN) to produce accurate and relatable heatmaps for degraded images. By pinpointing relevant targets on the images that align with user expectations, Debiased-CAMs increase transparency and user trust in the AI’s predictions. It also increases the ability of meeting regulatory standards to deploy CNN models in the following applications, where explainable AI is required. Healthcare, eg. Radiology Autonomous Vehicles   Produces accurate, robust and interpretable heatmaps for degraded images Works on images with multiple degradation levels and types such as blurring and improper white balance Agnostic to degradation level, so that enhancement can be applied even when the level is unknown Perceived by users to be more truthful and helpful as compared to current heatmaps distorted due to image degradation Method of training can be applied to other degradation types and prediction tasks Explainable AI Infocomm, Video/Image Analysis & Computer Vision, Artificial Intelligence

Current methods of monitoring construction safety and productivity are tedious, costly and prone to human errors. Resulting in operations being non-compliant, dangerous and inefficient which leads to project delays, cost overruns and even reputational damage. This technology offers an enhanced safety and productivity tracking solution in the construction industry by leveraging on video analytics to detect safety hazards and high-risk scenarios as well as productivity insights. It provides actionable insights in the form of alerts, charts and reports to enable safety officers and project managers to make better-informed decisions for their operations. This technology is hardware agnostic and is compatible with any IP camera or network video recorder to retrieve and analyze the video feed in real-time and provide alerts that can be sent to various messaging platforms. A server is deployed to provide the full spectrum of services such as running the software, triggering alerts, as well as the dashboard. This technology is enabled by the large construction datasets that powers object detection and tracking. The current range of detection includes scenarios such as barricade removal, workers working at height or under lifted load, safe distancing, and presence of workers in high-risk zones, PPE and more. Besides the detection of high-risk scenarios, this technology can also track productivity insights such as construction floor progress or precast lifting times. The deployment period for an existing use-case will take within 2 to 4 weeks and newer use-cases will vary from 2 to 6 weeks depending on the complexity of detection. This technology has viable use-cases in providing automated safety monitoring and alerting to other industries such as manufacturing, maritime, oil and gas as well as new use-cases for the construction industry. Medium to large construction projects are often delayed and experience cost overruns, which can be significantly improved through significant productivity gains, cost savings and early risk identification just by enabling end users to have a better understanding of their operations wherever they are which would make this a very attractive solution. Main value propositions are: Significantly improve safety hazard detection and compliance with automatic 24/7 monitoring Increase in productivity by reducing manual site inspections of up to 50% Early identification of risks to plan for mitigation Reduce human errors and ensure consistency   Infocomm, Video/Image Analysis & Computer Vision, Big Data, Data Analytics, Data Mining & Data Visualisation, Artificial Intelligence

Cadaveric bones are used to carry out medical training for surgeons and trainees. However, such bones are limited in supply, difficult to store, inconsistent in terms of quality and costly to use for repeated training. As such, it is necessary to create an alternative to cadaveric bones that is equally realistic while being more cost effective and easier to obtain. This technology can resolve the limitations of cadaveric bones by offering the formulation and processing method to produce a Fused Deposition Modelling (FDM) system-agnostic bone-like 3D printing filaments for surgical models printing. Printed anatomical bone models developed from this technology will have the look and feel of the real bone. The technology presents an affordable and readily available alternative that minimises the demand for cadaveric bones while still providing realistic training to medical professionals. The technology owner is seeking for collaborations with companies interested to scale-up the manufacture of the filaments and/or licensing of the technology.   The technology offers the formulation and processing method to produce bone-like filaments that allows users to 3D print realistic and relatively lower cost cadaver bone models for use in medical training of surgeons and trainees. These bone-like filaments have the following features: Compatible with all FDM 3D printers Achieves good mechanical properties  (~80% improvement in tensile strength) compared to the commercial bone-like filament. Printed bone models using this technology feel realistic to drill and cut and screws can be tightened nicely, just like real cadaveric bones. This technology is primarily targeted for use in the healthcare industry (hospitals, medical schools, and biomedical companies) to obtain realistic anatomical bone models for surgical training and workshops at a more affordable price compared to cadaveric bones. Surgeons can also create 3D printed bone structures to help them plan surgical procedures before operating on the patient to better understand the procedure. Significantly lowers the cost for realistic medical trainings and workshops as compared to using cadaveric bones Fabricated bone models are equal in quality with actual cadaveric bones cadeveric bones, cadever, cadevers, 3d printing, fdm, medical training, surgery, surgeon, medicine, bone models, additive manufacturing, fused deposition modelling, printed anatomical bone models, surgical models, printing, bone-like Materials, Composites, Manufacturing, Additive Manufacturing, Moulding, Sintering, Casting & Nanoimprinting, Life Sciences, Industrial Biotech Methods & Processes

This technology offer helps to address the problems of global warming, food security crisis and energy crisis. With the increase in human population and rapid urbanisation, the change in weather patterns and increase in food demand has been inevitable. One of the major concerns faced in Singapore, due to global warming, is the urban heat island effect. This occurs when urban areas in cities have a higher air, surface and soil temperature than rural areas. Initiatives for high-rise greenery has been put in place to help solve the problem. However, there has been problems with limited space and high maintenance cost for these greeneries. Rooftop hydroponics farming is a possible solution to offset the running costs of rooftop greeneries or even generate profits for rooftop greeneries as it produces fresh produce, while simultaneously reducing the urban heat island effect. The reduction in urban heat island is due to a combination of green and blue body acting as a thermal buffer and contributing to the building sustainability (due to reducing in cooling costs). This initiative addresses the constraints of limited land, as solar energy generators require large areas for photovoltaic panels to be laid. This technology offer aims to provide an integrated solution to this economic challenge for environmentally sustainable urban planning. This Technology Offer is a luminescent solar concentrator that enables both power generation by photovoltaic modules, as well as efficient urban rooftop farming. In rooftops where solar panels are used for power generation, real estate would be taken up by the solar panels, so farming cannot be done. If crops are placed under the solar cells to be grown, the growth rate will not be optimal due to the obstruction of sunlight by the solar panels. This technology features a luminescent solar concentrator (LSC) film with organic dye which converts wavelengths not used in photosynthesis (green and yellow) to the red wavelength used for photosynthesis. This film is placed under the matrix of solar cells, and can be used to optimise the growth rates of crops placed under the solar cell matrix, so that solar power generation and rooftop farming can co-exist together. Know-how is also available to optimise the solar cell arrangement to maximise both solar cell power output and plant growth. The existing configuration has solar cell coverage of 54.1% which is able to maximise the amount of energy that could be harvested by the solar cells, and yet still ensure adequate light passing through the spacings between the solar cells to maximise the growth rate of the plants placed under the solar cells. This complete system is known as Wavelength Selective Photovoltaic (WSPV) system. The wavelength-selective solar photovoltaic system technology is suited for the following: Urban food production with simultaneous solar power generation Controlled plants R&D Industries that are interested to improve crops growth   Customer benefits includes: Optimise crop’s yield, yet achieving solar power generation Space saving Energy saving We are seeking industry partners to scale up, and to out-license the technology. Energy, Solar, Life Sciences, Agriculture & Aquaculture, Sustainability, Food Security

This Technology Offer is an Internet of Things ( IoT) based platform designed to assist the modern-day farmers in monitoring the entire farm seamlessly. It can be customized to suit each farm depending on the type of sensors, machine vision camera, cloud storage, etc., and is equipped with detailed data tracking and analytics to provide the most accurate growth process from start to finish. The software architecture used in this technology offer addresses a decentralized framework to provide the ability to exchange data between IoT devices autonomously without any centralized server. In recent years, the development of IoT applications has become increasingly complex. Thus, this technology addresses this problem by providing the ability to simplify the streaming of data to the IoT platforms over the web. This design can be customized for other applications. The software has a light-weight runtime, taking full advantage of its event driven, non-blocking model. This makes it ideal to run at the edge of the network on low-cost hardware such as Raspberry Pi as well as in the cloud. Real-time data can be easily imported and exported for sharing with others. Plug & Play solutions make it easy to connect various sensors. This system has good fault tolerance as each node runs a distinct component of the web server application software and identical copies of each other. Upon a node failure, the application software can be replaced by another good node easily. The failure and restoration processes of the hardware and software are highly dependent on the status of other components as well as the sequence of failure events. Data can be downloaded from each individual node or from the consolidated database in the cloud. This IoT platform architecture can be applied to the following segments of the market: Outdoor farming Indoor farming Laboratories/research/education institutions Home Hobbyist It can also be applied in the following segments of the market: Office Building, Home, Hotel: Fire alarm system Electrical system This technology is a low-cost functioning system, easy-to-install and is compatible with most standard Internet of Things (IoT) sensors, switches, and gateways. This Technology Offer allows modern-day farmers to monitor the entire farm seamlessly. It can be customized to suit each farm depending on the type of sensors, machine vision camera, cloud storage, etc., and is equipped with detailed data tracking and analytics to provide the most accurate growth process from start to finish. Urban Farming, IoT Farming, Indoor Farming, Outdoor Farming Electronics, Sensors & Instrumentation, Life Sciences, Agriculture & Aquaculture

The Dilution Air Processing Unit (DAPU) is an ideal solution for small and medium businesses to prepare themselves for the Covid-19 new normal by employing an enhanced air ventilation technique. The DAPU system allows the creation of zones (e.g. sickbays, waiting rooms, etc.) within workplaces with no recirculation of air. This prevents cross-contamination of unclean air in between the zones. This solution is suitable for hotels and other premises to be used for quarantine purposes. The DAPU consists of the following key features: Provides 100% fresh air supply with no recirculation Reduces airborne particles exposure by greater than 60% Achieves 25% energy efficiency in providing 100% fresh air supply as compared to conventional systems Uses fully portable modular approach Can be easily retrofitted to any existing air-conditioning system resulting in lower implementation cost The DAPU can also be deployed in any area without an existing air-conditioning system making it highly versatile. Key design advantages: A portable and modular design suitable for any area, even those without an existing Air conditioning system. Air Change Rate per Hour (ACH) of 40 for dilution, which means it is able to perform air change every 1.5 min for an entire room volume in contrast to the nominal 5 to 6 mins by conventional systems. 100% air change per room volume by fresh air in contrast to the nominal 25% to 30% using traditional mixed ventilation. The modular design feature makes it unique and versatile. The system is suitable for various operating modes bypassing intermediate devices. This allows for the adjustment of nominal operating conditions during post-pandemic situation. Key Performance advantages: Achieved 27.7% (target: 25+/-5%) energy efficiency improvements in building HVAC in comparison to conventional approach for achieving 100% fresh air supply. Actual measurements showed that there was a 30% reduction (target: 20%+/-5%) of cumulative concentration of airborne particles (greater than 0.3 µm and less than 1µm). This is in comparison to conventional room air conditioner without fresh air.  The use of the novel Bio antibody filter has reduced the airborne exposure of the occupants within the test chamber. There was a reduction of 45% of PM 1.0 particle concentration in the absence of fresh air.  With the availability of both 100% fresh air and the Bio antibody filter, airborne exposure of the occupants is further reduced by greater than 60%. DAPU is an innovative solution for enhanced ventilation and reduced transmission of airborne infectious diseases. Its aim is to offer an easily implementable and low-cost solution for 100% fresh air supply to buildings in curbing infections during a pandemic.  The system has 25% less energy consumption in comparison to conventional options and maintains the optimum humidity range at 45% to 55%. The modular design feature makes it unique and can be easily retrofitted to buildings. DAPU achieves 60% reduction of integrated airborne particle concentration in comparison to ordinary air-conditioners (with 100% recirculation).  DAPU could be an ideal solution when it comes to the creation of isolation zones within buildings to curb the spread of infectious diseases. This can play a vital role in safeguarding public health and ensuring global health security. Examples of practical applications are as follows: Isolation rooms in hospitals Sick Bays in campus or business places Swab stations Waiting rooms The DAPU technology demonstrates the innovative strategy in improving the capabilities of existing solutions and applying novel concepts to a very challenging situation such as pandemic control. The estimated market size for this technology could be largely due to its sustainable approach in meeting the demands for an expanding healthcare facility in the future. For pandemic control, DAPU can be operated indoor for the test chamber with 1 patient and 1 attending nurse/staff. This setup has a simple payback of less than 2 years. However, for high usage areas such as swab test stations in airports or conference venues, simple payback is expected to be less than a year. The business case for DAPU can only be properly articulated after this solution has been deployed in an actual situation and feedback obtained from stakeholders. This will ensure the integrity of the information provided and improvements to be made to ensure wider adoption of the system by other companies. dapu, air quality, Dilution Air Processing Unit Green Building, Heating, Ventilation & Air-conditioning, Sustainability, Sustainable Living

Spent coffee grounds are one of the major food waste produced globally with several million tonnes being discarded annually. It has been reported that only 6% of the original coffee cherry can be used to make a cup of coffee and the remaining balance are inedible and has no value to the industry. As such, a large amount of residue is currently generated from the coffee industry and disposed of at incineration plants or landfills.   This technology features a cost-effective and scalable thermochemical process to transform spent coffee grounds into carbon-rich solid materials, known as hydrochar, as a form of low-cost solid adsorbents for industrial wastewater treatment. Thermochemical processes are well suited for wet biomass such as spent coffee grounds and utilises mild temperature profiles under relatively low pressures. The process also has the potential to convert other kinds of food waste, such as durian husks, coconut husks, fruit peels etc, into hydrochar.This presents a sustainable solution for creating a circular economy and minimising negative impact on the environment by converting non-edible and no value food waste into a value-added product for food and water industries. The technology relates to an innovative and custom-designed thermochemical reactor capable of converting the spent coffee grounds into solid adsorbents also known as hydrochar. Hydrochar particles produced have the following attributes which include a robust mesoporous framework, higher surface area, and functionalised removal of cations, anions and organic pollutants in wastewater. Up to 80% of the organics and chemical oxygen demand can be removed after passing through the hydrochar. After water treatment usage, hydrochar can be repurposed as a soil conditioner which helps in plant germination, closing the loop on food waste. The thermochemical reactor is also capable of converting other food wastes including durian husks, coconut husks, fruit peels, and other non-edible food waste. The technology can be adopted in the food and beverage industry that are looking to upcycle the non-edible and no value food waste into value-added products, such as solid adsorbents. The carbon-rich material, hydrochar, presents a sustainable alternative as the low-cost adsorbent that can attract interest from sectors that require treatment of reject and backwash water. These include industries from semiconductors, petrochemicals, wastewater treatment, desalination, and textiles. Offers a cost-effective process to produce higher value-added products from food waste, creating a circular economy Reduced disposal cost Revenue creation from waste Tailor-made design of thermochemical reactor to produce higher surface area and better efficiency of solid adsorbents from food waste Highly scalable hydrochar, wastewater treatment, sustainable, circular economy, adsorbents, spent coffe grounds, food waste, valorisation, thermochemical Environment, Clean Air & Water, Filter Membrane & Absorption Material, Chemicals, Organic, Waste Management & Recycling, Food & Agriculture Waste Management, Industrial Waste Management, Sustainability, Circular Economy