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

After a skin injury or surgery, a scar may form as the wound heals. In this body's repair mechanism, the myofibroblast cells produce new collagens and they form an extracellular matrix (ECM) to repair a wound. Over time, most scars become flat and pale. However, in some abnormal cases, the body produces excessive collagens. The excessive ECM formation and deposition of these scar tissue will result in raised scars such as hypertrophic scar and keloid scar. These raised scars may leave lifelong marks on the skin. Although the raised scars are not dangerous or life-threatening, they create aesthetic concern, restrict physical movement and may also lead to itching, tenderness, pain or even depression and anxiety. The currently available scar removal products such as silicon patches and topical products may cause skin irritation, which has led researchers to look for safer and more effective solutions. The present technology is a series of phytonutrient-based remedial fluids, which can be used as a general topical agent or complemented with a nano sprayer for the management of raised scars. The product developed from this technology is a safe, non-invasive and convenient approach to suppress hypertrophic and keloid scars. The technology provider is looking for collaboration opportunities to co-develop skincare products incorporated with this series of plant-based remedial fluids for scar management, collaborators for conducting clinical studies to evaluate effects of the current prototypes as well as other partnership mode including IP licensing.  Hypertrophic and keloid scars are two different fibroproliferative disorders of the dermal tissue upon skin injury. The activation and local proliferation of myofibroblasts in the dermal layer is considered prerequisite for excessive ECM formation, including the overproduction of collagens for filling up the wounds. The phytonutrient-based remedial fluids consist of small-molecule flavanol glycosides extracted from ferns. Experimental results showed that these plant-based fluids could significantly suppress the activation and proliferation of skin myofibroblasts in patients with hypertrophic scars or keloids. These small molecules could also largely reduce the deposition of ECM. As a result, the formation of raised scar can be prevented. The prototypes of the phytonutrient-based remedial fluids have been developed and are currently undergoing product testing. They are designed to reduce raised scars and improve skin conditions. Over 50 volunteers with various types of scars and different skin conditions have tried the prototypes in the initial trial. Overall, positive feedback were received, including notable improvement in scar appearance, alleviation of itchy feeling and prevention of excessive scar tissues. This phytonutrient-based formulation can be used in scar treatment in the form of skincare products such as a general topical agent or nano-mist. They could help shrink, soften and flatten the raised scars, especially for hypertrophic scars and keloids. In addition, this technology can also be used for therapeutic purposes, such as inhibiting the development of fibrosis.  Since many scar treatment gel sheets or creams contain silicone, this natural, non-toxic product provides another option for the people who are allergic to silicone. Customers can be benefited from these herbal-based products with their non-irritating, refreshing texture. Personal Care, Cosmetics & Hair, Healthcare, Pharmaceuticals & Therapeutics

Conventional diagnostic imaging of the skin involves the use of dermatoscopes. Dermatoscopes use skin surface microscopy to examine dermal and sub-dermal tissues to diagnose skin problems. However, these devices can be costly and provide a limited view of the immediate skin surface. This limitation meant that dermatoscopes have to be used in direct contact with the patient's skin. Because of this, they can only be used to image patients in the same physical location as the clinician conducting the examination. The overall result is that only a tiny portion of the global dermatology patient-base can be reached cost-effectively and efficiently. Telemedicine and telehealth network operations are rapidly developing ways to address patients broadly and at lower costs for them and their care providers. Yet, such tools neither deliver desmatoscope-like functionality nor improved it in way that it allows patients' skins to be examined and analysed during an online medical consultation with a general practitioner. In order to facilitate remote skin disease diagnosis, the use of software is required to acquire and share images in real-time and ideally, by the patients themselves. This software enables patients to take their medical sub-skin images with their mobile, tablet or laptop cameras, and securely share it with doctors. Crucially, dermatoscopy images can also be used with the technology to improve diagnostic accuracy. This technology is intended to position itself as a technology which when scaled-up, could allow for products that can enable optical biopsy and phototherapy.  The technology, Remote Diagnostic Imaging (RDI) is available in two modes: Real-Time (RT) and Store-and-Forward (SAF): RT provides remote real-time examination of the patient’s sub-skin by a clinician. SAF enables the patient to snap and forward the sub-skin images to the clinician for assessment. A non-clinician staff member at a clinic can also help the patient to take the images and forward them to a skin specialist. This remote imaging diagnostics is intended to facilitate time and cost savings for both doctors and patients.  The RDI service consists of proprietary software that works with any smart camera device such as mobile, tablet or laptop cameras. An individual at any remote location could take photographs of suspicious skin lesions, and then forward it to the doctor. While the functionality is similar to most image-sharing software, what sets the software apart from other algorithms is the presence of a sophisticated algorithm that acquires sub-skin features of the skin (in normal light) thereby noticing skin issues prior to manifestation on the skin surface. Cosmetic and Medical Dermatology - With 3rd party software, this skin imaging software platform has the potential to facilitate more accurate diagnosis and management of a range of skin diseases, such as psoriasis, acne, vitiligo and dermatitis, to more serious and potentially fatal conditions of melanoma. Optical Biopsy - Completing initial tissue analysis in a few minutes. Phototherapy - Producing the skin and tissue pectral data needed to deliver the correct type and dosage of opticat radiation. Podiatry (diabetes) – Diabetic patients can suffer from numbness in their feet, potentially leading to foot infections which if left untreated may necessitate amputation. The RDI and proposed multispectral imaging solutions can help podiatrists gain advance notice and clarity of potential issues. The key benefit being a better patient outcome.  X-ray (radiology) – X-ray radiation continues to be a major cause for health concern despite its significant benefit in patient diagnostics. It is also not known for being able to render soft tissue visual detail in the way that MRI and ultrasound does. Both RDI service and multispectral imaging embedded technology enable the use of pre-existing X-ray images to be used to acquire visualisation of more detail. The key benefit is reduced radiation dosage which results in less risk to the health of the patient.  In the near future, it is expected that an absorbed multispectral-based imaging app or device as well as the embeddable diagnostic platform will become an integral part of a smart diagnostics platform for remote clinical diagnosis. Dermatology clinics and hospitals can use these solutions for the remote diagnosis of any type of skin disease, track the progress of a patient condition after treatment, and better engage patients in the treatment process by empowering them to take proper preemptive care of their skin health. The technology is available as an embeddable algorithm, and as a secured cloud-based service that can be embedded to a website. The embeddable version of the technology is available in a licence or co-creation form. It can be embedded in OEM devices, equipment and machines. Some of the advantages of this innovative technology are as follows: Skincare consumers and patients can self-analyse and monitor their skin's health from the comfort and privacy of their home, while having their skin analysed visually. Information can be shared in real time with their skin specialist Provides an ability to visually 'see' the skin as light sees it and interacts with it on the surface and beneath opens up new diagnosis and treatment opportunities Synergenic tool for integration into the rapidly expanding telehealth platforms out in the market Potential to enable diagnosis of skin conditions as well as setup of pre-signal detection to alert potential skin conditions before they are visibly present. Enable storing of historical sub-skin imaging data through the SAF feature, this allows for image analysis overtime. melanoma, skin, imaging, diagnostic, analysis, microbe, microorganism, bacteria, virus, spectroscopy, smartphone, non-invasive, health, telemedicine, telehealth, dermatology, oncology, phototherapy, photoanalysis, radiology, biopsy, subskin, subsurface, embeddable, camera, microbiome, dermoscopy, cosmetic, optical Infocomm, Video/Image Analysis & Computer Vision, Personal Care, Cosmetics & Hair, Healthcare, Medical Devices, Healthcare ICT

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

Lists are an indispensable part of the online experience, often used to show many results, such as products, web pages, and food dishes. These items can be neatly sorted by a desired attribute like price, relevance, or healthiness. Listed items often have multiple attributes. However, instead of being able to sort multiple attributes simultaneously, consumers are currently limited to sorting only one attribute at a time. This makes searching for the desired item tedious and confusing. Imma Sort supports interpretable and multi-attribute sorting. Sorting for two or more attributes is possible. In contrast to existing search technology, Imma Sort trades off the smoothness of the sorted trend for the main attribute to increase ease of prediction for other attributes, by sorting them more approximately. Results for specific attributes can be made smoother by setting higher importance weights. Provides intuitively sorted results sorted by two or more attributes to improve decision-making and user experience Results can be customised by allocating higher weightage for selected attributes Enables users to perform multi-attribute sorting in any existing list interface without requiring sophisticated spreadsheets or data visualisations Can be integrated into search and recommendation systems across a wide range of applications Can also be incorporated into various search and recommendation systems for more effective search results. Examples of possible applications: Food dishes can be sorted by healthiness and tastiness Hotels can be sorted by price and distance Sorting by price and rating would generate results that generally trend in one direction for both attributes. This makes it easy for users to anticipate the values of multiple attributes as they move down the list, without having to construct a mental list for the secondary attribute. By decreasing users’ mental effort, this will improve decision-making and increase satisfaction. Multi-Attribute Sorting, e-commerce, algorithm Infocomm, eCommerce & ePayment, Enterprise & Productivity

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