TECHINNOVATION TECH OFFERS

Due to the rising demand for industrial automation, autonomous robotic technology is gaining traction across various industries optimising processes and increasing operational efficiency. For these autonomous robots to execute their task with precision, they are required to be constantly aware of their surroundings to response adequately and quickly. Technologies developed from off-the-shelf components (e.g. camera-based perception) faces integration issues while industries with weak or no connectivity to the Global Navigation Satellite System (GNSS) are unable to utilise them. Developed by a Singapore-based startup, the proposed technology herein encompasses a modularised sensor hardware and edge AI software within a compact form factor, addressing fundamental 3D vision problems such as localisation positioning and obstacle detection. This solution can be easily customised, integrated and deployed in GPS-denied or GPD-obstructed areas while maintaining accuracy and reliability in navigation, tracking and monitoring of fleet, autonomous vehicles, etc. The technology solution has engaged with numerous proof-of-concept (POC) projects, including one notable engagement within the defence industry for a drone integration. The technology owner is looking for collaborative partners, such as manufacturers and system integrators within the automation space, who wishes to further enhance their robot’s perception capabilities.

As global temperatures rise, governments are setting eco-friendly building standards to address concerns about energy consumption and carbon emissions. Improving energy efficiency in buildings, especially in hot climates where cooling demands increase energy use, has become a major challenge. This has driven the need for sustainable and energy-efficient building materials. Aerogels are among the most promising insulation materials due to their large specific surface area (500-1200 m²/g), high porosity (80-99.8%), and ultra-low density (around 0.003 g/cm³). They are amorphous, chemically inert, non-flammable, and exhibit extremely low thermal conductivity (0.01-0.03 W/(m·K)). Silica aerogel (SA) is particularly notable for having the lowest thermal conductivity, making it ideal for building insulation. The technology owner has developed an advanced insulation coating that incorporating in-house fabricated silica aerogel (SA) powders to enhance both thermal and acoustic insulation in buildings. This technology also works with silica aerogel powders purchased externally. Incorporating 20 vol% SA into paint and plaster formulations can reduce surface temperatures by up to 12°C and chamber temperatures by up to 3.3°C, helping to lower air conditioning use and save energy. The coating also improves acoustic insulation, offering a dual benefit. By meeting the growing demand for greener building solutions, this technology offers a competitive edge in reducing energy consumption and improving overall comfort and building performance.   The technology owner is seeking industrial partners for test-bedding and is also open to licensing opportunities for commercialization, especially with construction companies, building material manufacturers, and developers focused on sustainable and energy-efficient construction.

In the construction sector, manual inspections have traditionally been the primary method for detecting water seepage surface defects, a mandatory requirement for construction projects. However, these inspections often suffer from the inherent subjectivity of human judgment, leading to potential inconsistencies and inaccuracies. To overcome these limitations, a handheld water seepage detection system was developed and rigorously tested in collaboration with the Building and Construction Authority (BCA). This innovative system is designed as a portable, intelligent alternative to traditional methods, aiming to enhance the objectivity and reliability of water seepage detection. The system utilizes advanced Long-Wave Infrared (LWIR) thermal sensing technology to accurately detect temperature variations indicative of water seepage. Unlike manual inspections, which can be prone to error, this system offers precise differentiation between genuine water seepage defects and common artifacts found on construction sites, such as glue and paint. By minimizing false alarms, it provides a more dependable and efficient approach to identifying and addressing water-related issues. This advancement not only improves the accuracy of inspections but also ensures that potential water damage is detected early, reducing the risk of costly repairs and enhancing the overall integrity of construction projects.    

Conventional aircrafts have been a preferred mode for fast middle mile delivery within logistics and supply chain. With the increasing traction of drone technology, the usage of drone as a potential alternative mode of transport within the supply chain is greatly considered, given its comparable performance and more sustainable. Current conventional aircraft requires a runway for landing and take-off, limiting their use-case within space constraint areas. For drones, its slow operational speed and short operation range greatly limits it widespread industrial adoption. The technology owner developed a UAV solution which enables faster operational speed (up to four times) and longer flight duration (over 2 hours) compared to incumbent multicopter drones of the same class. This UAV solution comprises of tilt-electric ducted fans, a hybrid energy source and optimised design to harnesses the advantages from both drone and conventional aircraft technologies. Due to these capabilities, the technology solution enables high-risk and high-difficulty missions to be carried out, with the safety of the operator in mind, such as wide-area surveillance and reconnaissance, transportation to remote regions and shore-to-ship deliveries. The technology owner is looking for collaboration partners seeking to further explore drone technologies or integrate drones into existing workflow processes which require longer operational range and faster speed that current traditional multicopter drone is unable to accommodate.

In healthcare, accurately assessing pain is critical, yet it remains a significant challenge, particularly for patients unable to effectively communicate their discomfort. This includes individuals with cognitive impairments, critical care patients, and pre-verbal children. Traditional pain assessment methods rely heavily on verbal communication or subjective observer-based pain assessment, which can lead to missed or poorly managed pain, resulting in either under-treatment or over-analgesia treatment. To address this issue, this advanced AI-based solution automates the process of estimating pain intensity by analyzing facial expressions captured in video data. The technology is a deep learning system consisting of facial landmarks (specific points on a person's face, such as the corners of the eyes, nose, mouth, and other prominent facial features) extraction, 3D normalization and Spatial-Temporal Attention Long Short-Term Memory (STA-LSTM) model. These facial landmarks provide critical information about facial expressions, allowing the system to accurately assess pain levels. By integrating this technology into clinical settings, healthcare providers can significantly enhance patient care, reduce the burden on staff, and improve overall pain management practices. This solution not only ensures timely and appropriate intervention but also protects patient privacy by using non-identifiable facial landmarks, making it a powerful tool for modern healthcare environments.

Cooking oil waste has become a significant environmental problem in recent years. Improper disposal of used cooking oil and fried food scraps can lead to pollution of water sources and the release of harmful greenhouse gases. When poured down into drains, it travels through sewage systems to rivers and oceans, disrupting ecosystems, clogging water treatment plant filters, and complicating water purification processes. Additionally, there are higher costs associated with waste disposal in volume-based plastic garbage bags which are also not environmentally friendly. This technology addresses the above pain points by offering a sustainable solution that recycles discarded fried food scraps into high valued biodiesel raw material, preventing water pollution and sewage pipe blockage when discarded without appropriate measures. This innovation addresses a critical market need by providing a greener alternative to conventional disposal methods, reducing waste disposal costs and the production of high valued biodiesel as an end point.  The technology owner is seeking collaborations with companies in the fields of waste management and biodiesel production for test-bedding and research & development projects aimed at recycling fried food scraps into biodiesel.

With the global trend of industrial automation, robotic arm technology is being developed and integrated to existing business workflow, increasing labour productivity and operational efficiency. However, current end effectors attached to robotic arm excels in automation tasks of handling homogeneous robust objects but fall short in handling irregular, fragile objects. Current robotic grippers (end effector) lack the finesse and limberness required, limiting their usage in particular areas such as agriculture, F&B, pharmaceutical and logistics. The technology solution developed is a smart soft robotic gripper as a modular end effector that addresses the challenge of handling objects with varying sizes, shapes, and weights, an issue unsolved by traditional robotic grippers. This innovative gripper tool mimics the flexibility and versatility of biological structures like tendrils, enabling an extra dimension of growth and twining capabilities. By wrapping itself securely around objects, the gripper manipulates items gently, minimizing the risk of crushing or damaging them. Its integrated sensors provide real-time contact feedback capabilities, enabling precise monitoring and adaptive control by the robotic system. The technology owner is seeking collaborative partners, such as system integrators or end-users, who are in need and keen to integrate a versatile, adaptive, and gentle handling solution that can operate efficiently in environments where object fragility or irregularity is a key concern.

In a more globalized and interconnected world, logistical management becomes more complex with more demand for real-time tracking of products within the supply chain. With global events potentially disrupting any section of the supply chain, companies within are now required to efficiently provide visibility of the orders while building up resiliency to ensure smooth operation of their services. The technology solution addresses the complexities of modern supply chains by providing a unified solution that integrates multiple products & tools for efficiently managing the logistics and warehouse. The platform uses AI and IoT to deliver real-time visibility, predictive analytics, and automated workflows. This innovation aims to improve efficiency, reduce costs, and enhance sustainability for enterprises within the logistics and warehouse sectors. The technology owner is looking for collaboration partners, who wish to leverage on their data and AI solutions to automate logistical workflow operations and enable data-driven decisions for supply chain optimisation.

Cancer is one of the most deadly diseases worldwide, requiring pathological testing to confirm the diagnosis. With recent advancements in digital pathology, there is a growing demand for AI-based digital pathology diagnostic products to manage and shorten diagnosis time for accurate and targeted treatments. Thyroid cancer is a relatively common type of cancer. The malignancy of thyroid nodules is determined through the pathological examination of cells obtained via Fine Needle Aspiration (FNA) or Core Needle Biopsy (CNB). However, due to the complexities of thyroid nodule pathology, over 30% of patients' biopsy samples are classified as indeterminate, meaning they cannot be definitively diagnosed as benign or malignant. Additional testing, such as Next-Generation Sequencing (NGS) molecular tests are required to provide further clarification. However, the high cost of testing limits patient accessibility. This technology is an AI-powered solution that analyze pathology images obtained through Core Needle Biopsy (CNB), providing diagnostic results for benign or malignant nodules. It offers interpretation and visualization of AI analysis results. This allows for accurate diagnosis of not only the benign(C2) and malignant(C6) cases traditionally identified by pathologists but also indeterminate(C3,C4,C5) cases that were previously challenging to diagnose. This approach ultimately reduces unnecessary surgeries, thereby improving the quality of life for patients. It offers a cost-effective alternative to expensive and less accessible NGS testing, delivering similar diagnostic accuracy without the need for additional tests. The technology owner is seeking partnerships with medical institutions specializing in oncology, including major hospitals and clinical laboratories, pharmceutical and biotechnology companies.