TECH OFFERS

While interest and demand for blockchain-related technologies continue to gain popularity and spurs the exponential growth in adoption of blockchain in areas such as payment, documents and digital identities, not just in finance, but in industries such as logistics, supply chain as well. Many emerging areas that rely on blockchain as a core technology lack the manpower needed to sustain budding development, this lack of technical skillset required for blockchain development is the primary hurdle to successful blockchain application development - less than 1% of the tech workforce is skilled or competent in blockchain-related development. The characteristic of blockchain technology, which enables a permanent record of digital information such that it cannot be modified by any single entity renders it well suited as a digital ledger of online transactions. As such, blockchain is a core technology for many emerging areas such as Decentralised Finance (DeFi) and Decentralised Autonomous Organisation (DAO). This technology offer consists of a platform tool and a set of zero-configuration REST APIs that abstract away the complexity of blockchain technology and enables any developer to easily build blockchain-based applications or integrate blockchain functionality into their existing systems. Intended as a low-code platform, it addresses the skills gaps traditionally required for blockchain development and deployment and allows companies to realise their blockchain ideas, enhance business operations and expand solution offerings.  

The current clean meat technologies grow lab meat with conventional 2D cell culture. However, the conventional cell culture technique has an overall low yield of cells, as the cells are restricted to growth on surface areas.  A new 3D scaffolding method has been developed to overcome this problem with the use of microcarrier beads that provide cells with additional surface area to attach onto and proliferate. The microcarrier beads are suspended in the cell culture thus maximizing the 3D volume of the cell culture, leading to an increased yield. A microcarrier type has been identified to yield the highest number of porcine cells. The conditions of the cell culturing process have been optimised to improve the cell viability in a 3D environment Companies interested in cell-cultured meat development could consider using this method to grow cell-cultured meat at a larger scale with a potentially lower cost of production. The technology developer is seeking companies that are keen to scale up lab-grown meat applications. 

Many existing smart city solutions only show the impact of urban development, but few show the impact that urbanisation imposes on daily activities and long-term outcomes such as population obesity and job availability/accessibility. In short, such solutions show the activities e.g. large crowds are visiting the neighbourhood park, that are happening in real-time (what), the location (where), and the time that they occur (when), but do not have the ability to include data that makes it possible to explain the reason for such activities (why). In order to bring about any intervention or identify missed opportunities, understanding the reason behind such activity is vital. This technology utilises data on city infrastructure systems to help users understand how and where the built environment creates a set of physical constraints that influence what planned and unplanned activities are possible, and in turn how this influences long term outcomes including health and climate change. This technology imports, translates and combines datasets into spatialised models which are used to generate analytics outputs. These outputs include a comprehensive explanation of the way streets, pedestrian networks, public transport and land use interact with each other. In this manner, socio-economic and/or demographic datasets can be linked, enabling people and places to be combined in a single analytical model.

In this modern age of data, many systems and Internet-of-Things (IoT) information sources are independent and scattered, resulting in the increased complexity of processing heterogeneous data for visualisation purposes. Digital twins can help to mirror their physical, real-world equivalents in three-dimensional (3D) space to improve spatial perception and are ideally suited for high-risk environments that are physically inaccessible by humans. In such cases, IoT sensors are put in place to support real-time remote fault identification, operation, training, maintenance, and synchronise with various types of management dashboards to facilitate decision-making processes. This technology offer is a one-stop platform that empowers enterprises to create digital twins (or a one-to-one reproduction of physical real-world objects/building/machinery) - where next-generation spatial hardware e.g. Augmented Reality/Virtual Reality (AR/VR) headsets or smart glasses can be used to interact with contextual, real-time information in a fully rendered 3D environment that blends the digital information (sensor data and digital mapping) and physical (real-world) layers for a range of industrial applications including continuous monitoring and predictive maintenance.  The technology owner is keen to collaborate with companies in the Port, Manufacturing, and Property (facility management, building management, energy management, security management) industries for test-bedding of existing use-cases on a project basis, leading up to product R&D collaboration and eventually licensing.

Machine Learning (ML) is a sub-field of Artificial Intelligence (AI) where a machine is able to learn without being explicitly programmed. However, before a machine can effectively perform even the simplest AI tasks, e.g. differentiating between images containing an elephant or a tiger, it has to be trained on images containing both animals. To be useful in supervised learning, training data needs to be properly labelled or annotated by a human for the machine to extract the relevant features and produce an ML model that serves its intended purpose. This highlights the important role that data annotation plays in producing robust, accurate ML algorithms in video analytics, natural language processing, and audio processing. However, many organisations that want to embark on their supervised learning journey often face difficulties gaining access to high-quality labelled datasets, known as ground truth data, due to the abundance of low-quality, expensive and unstructured data. This technology offer is a mobile application-based data platform that enables companies to obtain high-quality annotated data. It de-centralises data collection and data annotation tasks into manageable bite-sized chunks for optimal annotation performance and crowd/out-sources the annotation task to a pool of data taggers via a mobile application. Labelling quality is established through a gamification system and a series of built-in verification procedures, including AI-assisted pre-filtering and collective human quality control.

Due to the high resource costs (data, computational power) associated with the creation of trained neural network models and the widespread application of deep learning in a plethora of sectors/industries, ranging from mobile apps to autonomous driving, trained models are often viewed as Intellectual Property (IP) of the entity that created them. Hence, it is increasingly critical for stakeholders to mark their ownership and protect their models against potential IP infringement. One way to claim ownership is through conventional digital watermarking, however, this technique is susceptible to model extraction attacks and while watermarking a model does not prevent theft, it enables legitimate owners to verify their ownership over stolen assets. This technology offer is a robust watermarking mechanism that protects the ownership of a high-performance neural network model to the entity that has invested resources to facilitate its training and performance tuning. It turns well-known defects of neural networks into a mechanism for verifiable proof of ownership, whenever required. In this instance, backdoors, which are inserted during a model's training phase to intentionally generate erroneous outputs, and adversarial samples (specifically structured perturbations that are entirely unobservable by the human eye), which are able to fool well-trained and high-performing models into misclassifying input data, are used.

This technology offer presents an autonomous materials handling system. The technology could reduce manual labor requirements and increase working efficiency. The solution consists of a Lidar Elevator Stand (LES) system, which will trigger the autonomous actuators (e.g., trolley puller, tray return robots) via the robot command center whenever no goods (e.g., trolley, tray, and stock) is detected in the designated area. Currently, the technology has been demonstrated in autonomous trolley return solutions. Generally, trolley replenishment requires deploying manual labour to monitor the available quantity at the trolley bay and replenishing it by physically operating an electric trolley puller to transport the new stack of trolleys. Therefore, the system was developed to solve the problem by triggering an autonomous trolley puller to replenish the new stack of trolleys whenever the trolley quantity is depleting. The system can be further customized and repositioned based on clients’ requirements.

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.

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.