TECH OFFERS

This software is a cutting-edge artificial intelligence-based management solution, designed to transform the landscape of professional sports through advanced performance analytics and optimization. This versatile technology is a comprehensive system comprising three innovative modules: MONITOR, EVALUATE, and COACH, each tailored to address the pivotal challenges in sports management—athlete well-being, resource optimization, and tactical decision-making. The software is poised for application across a broad spectrum of sports, promising to equip professional teams with the tools necessary for sustaining peak performance, ensuring athlete health, and securing a competitive advantage. We are actively seeking partnerships with sports teams, technology firms, and academic institutions to further develop and commercialize this groundbreaking solution. AI-SPOT not only signifies a leap forward in sports management technology but also offers a scalable model for future advancements in athlete performance optimization. It introduces a paradigm shift in sports analytics by integrating advanced artificial intelligence to offer unprecedented insights into athlete management, performance optimization, and strategic decision-making. This technology sets a new benchmark over existing solutions with its innovative approach to athlete load monitoring, injury prediction, and match performance analysis, addressing the multifaceted needs of professional sports teams. At its core, the software is engineered to mitigate the prevalent risks of injuries and athlete burnout, employing machine learning to analyze and balance internal and external loads for optimal athlete performance and longevity. Its user-friendly interface allows for real-time adjustments and predictions, ensuring athletes can perform at their peak while minimizing the risk of injury. The EVALUATE Module elevates resource and manpower management by utilizing a rich dataset of historical and projected performance metrics, facilitating strategic decisions that align with the team’s objectives. Through customizable evaluation protocols and cross-validation techniques, AI-SPOT delivers precise and actionable insights. In the throes of competition, the COACH Module shines by providing real-time visual analytics and strategic recommendations based on AI-generated data, enabling coaches to make informed decisions on the fly. This module’s use of Voronoi tessellation for visualizing game dynamics offers unparalleled insights into athlete and team performance. Its robust and versatile technology, validated by diverse datasets from various sources, showcases its wide-ranging applicability beyond traditional sports analytics. Its adaptability and predictive accuracy present a significant opportunity for innovation across multiple sectors. The primary and extended application areas include: Professional Sports Teams: The cornerstone application, where AI-SPOT enhances performance analysis, injury prevention, and tactical decision-making, directly contributing to the success and longevity of athletes. Fitness and Rehabilitation Centers: Utilizing AI-SPOT's predictive analytics for personalized training programs and injury recovery processes, thereby improving client outcomes and service quality. Military Training and Performance Optimization: Applied within the Singapore Armed Forces and similar institutions worldwide, AI-SPOT can optimize soldier training, monitor load to prevent overexertion, and enhance overall combat readiness. Entertainment and Performing Arts Management: In artist management, AI-SPOT can analyze performance stress and optimize schedules to prevent burnout, ensuring peak performance during tours and productions. Educational Institutions and Sports Academies: To develop young athletes, AI-SPOT can provide insights into optimal training loads, performance tracking, and injury risk assessments, fostering a healthier approach to sports education. Sports Medicine and Research: Offering a data-driven foundation for studies on athlete performance, injury prevention, and rehabilitation methods. Wearable Technology Integration: Development of products that integrate AI-SPOT's analytics with wearable devices, providing real-time feedback and insights to athletes and coaches. The solution stands out in the competitive landscape of sports analytics with its integration of advanced machine learning algorithms, offering unparalleled accuracy in injury prediction and performance assessment. This technology not only bridges the gaps found in previous research but also introduces a holistic approach to athlete management and tactical decision-making. Here are the distinct benefits and advantages that define AI-SPOT's unique value proposition: Innovative Aspects: Predictive Analytics for Injury Prevention: AI-SPOT leverages cutting-edge AI to predict the risk of injuries with high precision, allowing teams to implement proactive strategies to safeguard athletes. Comprehensive Performance Assessment: Beyond traditional metrics, AI-SPOT analyzes both tangible and intangible factors affecting performance, providing a 360-degree view of an athlete's contribution. Real-time Tactical Decision Support: The COACH Module empowers coaches with actionable insights during games, enhancing strategic decisions with data-driven confidence. Key Advantages for Users: Enhanced Athlete Longevity and Well-being: By accurately predicting injury risks and optimizing training loads, AI-SPOT contributes to the health and career longevity of athletes. Strategic Resource Allocation: The EVALUATE Module aids in the intelligent deployment of resources, ensuring that teams can maximize their performance potential efficiently. Competitive Edge in High-stakes Competitions: Real-time insights and tactical analytics provide teams with a strategic advantage, turning data into a powerful tool for winning.   Infocomm, Artificial Intelligence

Lactate monitoring has become an essential tool for optimizing athletic performance by providing real-time insights into the body’s metabolic response during exercise. However, traditional lactate testing methods are invasive and often require laboratory equipment, limiting their practicality for continuous monitoring in everyday training scenarios. This new sweat-based wearable technology offers a non-invasive, real-time solution for continuous lactate detection, solving this challenge for athletes and coaches. By enabling real-time tracking of lactate levels through sweat, this technology helps fine-tune training intensity, prevent overtraining, and improve overall performance. Athletes and coaches can use this data to adjust training regimens, optimize recovery strategies, and refine race-day tactics, pushing performance limits while minimizing risks. The technology owner is seeking collaboration and licensing opportunities with:  Health and wellness product manufacturers,  Sports-related industry partners, including sports training facilities and equipment manufacturers.  This wearable solution addresses the critical needs of the sports and wellness industries, offering an innovative tool for enhancing athletic performance and supporting more precise, data-driven training programs. The sweat-based lactate measurement wearable device brings innovative, non-invasive technology to both the sports and wellness markets, addressing the growing demand for data-driven, personalized fitness tools. By integrating seamlessly into everyday activities and workouts, this technology offers a practical solution for tracking athletic performance. Non-invasive sweat-based patch – Designed for ease of use, the patch can be applied to the skin without causing discomfort.  Hygienic design – The part that collects sweat is disposable, ensuring cleanliness, while the main sensing unit is reusable. Low sweat volume requirement – Unlike traditional testing methods that require significant physical exertion, this patch functions effectively with minimal sweat, allowing it to be used during daily activities (low physical exertion).  Real-time monitoring – The device provides immediate feedback on lactate levels, enabling athletes to adjust their performance strategies on the go.  Personalized data – By collecting and analyzing sweat lactate levels, users can receive tailored insights for optimizing training intensity, recovery, and overall performance. The sweat-based lactate detection wearable device offers a versatile solution for both sports and healthcare markets by providing personalized, real-time data. Key applications include: Athletic Performance – Professional athletes can track lactate levels during training and competitions, enabling precise adjustments to optimize performance and recovery. General Fitness – Health-conscious consumers can use the device to monitor metabolic responses during workouts, improving fitness routines and overall wellness. Sports Equipment Integration – Sports equipment companies can integrate this wearable into their products, combining IoT technology with gear to offer personalized exercise plans and enhance consumer insights. Medical Application - AMI Diagnosis (Potential) – In healthcare, the device could support early detection of acute myocardial infarction (AMI) in emergency settings, offering a non-invasive and rapid diagnostic tool. Bio-Marker Detection (Potential) – With 8 detection channels, the device has potential for broader biomarker monitoring, expanding its use in both sports and wellness. This technology supports the growing trend of integrating IoT capabilities into sports products and further potential into health and wellness. By leveraging this solution, sports equipment companies/ wellness solution providers can enhance their product lines with wearable devices that provide valuable consumer insights. This enables these companies to analyze exercise patterns and offer personalized exercise/ wellness plans, contributing to the broader digital transformation of the sports and wellness industry. Non-invasive, real-time lactate monitoring for personalized performance insights. No blood sample required. Continuous data helps athletes optimize training and recovery. Easy-to-use, minimal sweat requirement, suitable for both athletes and general consumers. Integrates with sports equipment, enhancing IoT-enabled, data-driven fitness products. Potential medical application for early detection of acute myocardial infarction (AMI). Broader biomarker detection capability, expanding future use cases in sports and wellness. This increases the potential of the wearable patch to provide other forms of personalized health data.   Lactate Monitoring, Wellness, Sports, Non-invasive, Sweat-Based Sensor, Personalized Personal Care, Wellness & Spa, Healthcare, Diagnostics, Chemicals, Analysis, Infocomm, Healthcare ICT

The DeepGI system is a cutting-edge AI tool designed to help detecting images captured during endoscopic examinations. Utilizing deep learning models, it can detect abnormalities in both the colon and stomach with over 90% accuracy. This real- time alert system assists medical professionals in identifying polyps with the potential to develop into cancer (neoplastic) and those without such risk (hyperplastic) in the colon. Additionally, it can pinpoint areas in the stomach where precancerous conditions, known as Gastric Intestinal Metaplasia (GIM), may be present. DeepGI is a vendor-unlocked system, compatible with most endoscopic cameras. Technical consist of DeepGI AI software algorithm installed in the medical grade computer include capture card, GPU Technical Features Deep Learning Model: Utilizes a deep learning model trained on a large dataset of endoscopic images. Real-Time Analysis: Capable of processing endoscopic images in real-time during procedures. Multiple Abnormality Detection: Can detect various gastrointestinal abnormalities, including polyps, GIM, and other precancerous conditions. Classification: Can classify detected abnormalities into different categories, such as neoplastic and hyperplastic polyps. Localization: Can pinpoint the exact location of abnormalities within the endoscopic image. Technical Specifications Image Input: Accepts endoscopic images in various formats (e.g., JPEG, PNG, TIFF). Image Preprocessing: Employs image preprocessing techniques (e.g., normalization, augmentation) to enhance image quality and improve model performance. Model Architecture: Specifies the architecture of the deep learning model, including the number of layers, filters, and activation functions. Training Dataset: Details the size and composition of the dataset used to train the model. Evaluation Metrics: Defines the metrics used to evaluate the model's performance, such as accuracy, sensitivity, specificity, and precision. Integration Capabilities: Can be integrated with various endoscopic camera software. User Interface: Provides a user-friendly interface for endoscopists to interact with the system. The ideal collaboration partners in the value chain: medical institution, device manufactures, DeepGI researcher, deep-tech company with marketing channel. The DeepGI technology can be deployed in the healthcare industry specifically within the fields of gastroenterology and oncology. Potential Applications: Early detection of gastrointestinal abnormalities: DeepGI can be used to identify precancerous conditions like GIM and polyps at an early stage, enabling timely intervention and potentially improving patient outcomes. Improved accuracy in polyp classification: The technology can help differentiate between neoplastic and hyperplastic polyps, aiding in treatment decisions. Enhanced endoscopic procedures: DeepGI can assist endoscopists by providing real-time guidance during procedures, reducing the risk of missing abnormalities. Research and development: DeepGI can be used for research purposes to study the development of gastrointestinal diseases and evaluate the effectiveness of new treatments. Potential Products: Standalone DeepGI system: A standalone medical device that can be integrated into existing endoscopy suites. DeepGI as a software add-on: A software module that can be integrated into existing endoscopy systems. Cloud-based DeepGI platform: A platform that allows healthcare providers to access DeepGI as a cloud-based service. Mobile app for healthcare providers: A mobile app that enables endoscopists to access and analyze images captured during procedures. In summary, the DeepGI technology has the potential to revolutionize the field of gastroenterology by improving the early detection, diagnosis, and treatment of gastrointestinal abnormalities. DeepGI represents a significant advancement over current state-of-the-art methods in gastrointestinal endoscopy due to several key factors: Increased Accuracy and Sensitivity: DeepGI leverages deep learning algorithms, which are particularly adept at analyzing complex medical images like endoscopic scans. This enables it to identify subtle abnormalities that may be missed by human experts or traditional computer-aided diagnosis systems. Real-Time Detection: DeepGI is designed to process endoscopic images in real-time, providing immediate feedback to the endoscopist during the procedure. This allows for timely adjustments and reduces the risk of overlooking critical findings. Enhanced Classification: DeepGI can accurately differentiate between benign and malignant polyps, even those with subtle morphological features. This aids in determining the appropriate course of action for patients. Vendor-Agnostic Compatibility: DeepGI is compatible with a wide range of endoscopic cameras from different manufacturers, making it a versatile tool for various healthcare settings. Potential for Reduced Biopsies: By providing more accurate and confident diagnoses, DeepGI can potentially reduce the need for unnecessary biopsies, minimizing patient discomfort and healthcare costs. DeepGI's UVP lies in its ability to improve the accuracy, efficiency, and effectiveness of gastrointestinal endoscopy procedures. By providing real-time, accurate, and comprehensive analysis of endoscopic images, DeepGI offers a significant advantage over traditional methods. This can lead to earlier detection of abnormalities, more appropriate treatment decisions, and improved patient outcomes. Healthcare, Medical Devices

Oxygen enrichment membrane technology is emerging as a promising, cost-effective, and energy-efficient method for producing oxygen-enriched gas (OEG) with oxygen purities of 30-45%. Traditional oxygen production methods, such as cryogenic distillation and pressure swing adsorption, are often costly, energy-intensive, and complex, making them less suitable for applications requiring moderate oxygen enrichment. This innovative technology addresses these challenges through a thin-film composite (TFC) hollow fiber membrane that incorporates a novel use of polydimethylsiloxane (PDMS) as a selective layer on a polyethersulfone (PES) substrate. The PDMS selective layer is applied using a flow coating technique, which is both simple and scalable, allowing for consistent production of high-performance membranes. The technology was upscaled to commercial-sized membrane modules producing 15-53 Nm³/h of OEG with oxygen purities between 31-38%. The membrane system operates at ambient temperatures and pressures, offering significant energy savings and reduced operational costs compared to traditional methods. The benefits of this technology are substantial, including improved cost-effectiveness, enhanced energy efficiency, and flexibility in scalability, making it suitable for a wide range of industrial applications.  The technology owner is seeking collaboration with membrane manufacturers to further scale up this innovative technology, and with end-users who have a demand for oxygen-enriched gas with 30-40% O₂ purity. Two-Piece Module Design: Features a two-piece configuration with central coupling, enhancing compatibility with the TFC membrane and PDMS coating for a uniform, defect-free selective layer. Simplified Maintenance: Allows replacement of only the affected half of the module, reducing maintenance costs. Prototype System: Comprises 20 modules in a containerized skid with an air compressor, wet air receiver, refrigerated air dryer, and scaffolds. Operational Efficiency: Operates at 5 bar, producing OEG at 15-53 Nm³/h with 31-38% oxygen purity. Integration with OEG Gasifier: Replaces part of the liquid oxygen in municipal solid waste gasification, achieving 34.5-45.2 Nm³/h flow rate and over 20% liquid oxygen replacement in a 7-day test. With the ability to generate oxygen-enriched gas (OEG) with oxygen purity levels between 30 to 45% at a low working pressure of 5 bar, the TFC hollow fiber membrane technology offers versatile commercial applications across various industries: Healthcare Sector: Suitable for medical uses that require oxygen purity levels of 30 to 40%, such as oxygen therapy and respiratory support. Wellness Industry: Applicable in nitrox diving, oxygen bars, and training rooms, where controlled oxygen environments can enhance user experience and performance. Combustion Manufacturing Sectors: Ideal for furnace combustion, wastewater incineration, and petrochemical processes that benefit from oxygen-enriched air with 25 to 35% oxygen purity, leading to improved combustion efficiency and reduced emissions. Aquaculture Industry: Used for aeration in recirculating aquaculture systems (RAS), enhancing oxygen levels in water to support healthier and more productive aquatic environments. Additionally, the technology produces a pressurized nitrogen-enriched retentate stream of nitrogen purity greater than 85%. This nitrogen-enriched gas stream can be utilized in: Chemical and Oil & Gas Industries: Employed as an inert purge gas to prevent combustion and oxidation reactions during various processes. Food and Refinery Industries: Used as a blanketing gas to protect sensitive products from oxidation, moisture, or contamination, ensuring product quality and safety.  These diverse applications highlight the technology's flexibility and potential to enhance operational efficiency, safety, and sustainability across multiple sectors. Cost-Competitive for Moderate O₂ Purity and Lower Flow Rates: Offers clear cost advantages for applications requiring OEG with 30-40% oxygen purity and flow rates below 1200 Nm³/h, making it ideal for retrofitting existing plant. Low Operating Pressure: Generates OEG at a lower pressure of just 5 bar, compared to 7-14 bar for existing technologies, enhancing safety and reducing operational costs. Easy Installation and Low Set-Up Costs: Simple to install with minimal upfront investment, reducing barriers to adoption. Quick Start-Up: Delivers oxygen-enriched gas of the required purity immediately upon start-up, improving operational efficiency and responsiveness. Modular and Flexible Design: The modular system allows customization to meet a wide range of OEG demands, providing flexibility in application across various industries. Low Maintenance and Easy Operation: Requires minimal maintenance, simplifying operations and reducing downtime. Portability: Can be designed as a portable system, enabling on-site oxygen generation for diverse applications. membrane, air separation, oxygen enriched gas, hollow fibres Chemicals, Polymers, Sustainability, Sustainable Living, Low Carbon Economy

Faced with the increasing levels of carbon dioxide, carbon capture, utilisation, and storage (CCUS) technologies have garnered significant attention. However, as most CCUS technologies rely heavily on various forms of monetary support from governments and faced numerous technical and scalability challenges, most of the CCUS facilities developed are unable to achieve financial profitability or even achieve a net reduction of carbon dioxide (CO2) emissions. The technology proposed herein relates to an electrochemical-based CO2 reduction reaction process, which can directly capture and convert CO2 to carbon nanotubes (CNTs), a high-value material that exhibits unique electrical and thermal properties suited for applications in various sectors, including electronics, energy storage, sensors and medical uses. In contrast to synthesis methods that involve complex reactions and expensive catalysts, the proposed method uses a molten salt chemistry that can convert CO2 to cathodic solid carbon nanotubes (CNTs) via the electrochemical process. Although high reaction temperature (about 760 degC) is required, this method is highly controllable and uses cost-effective pure iron catalyst, producing high quality CNTs at a relatively high production rate. Based on preliminary process modeling and technoeconomic analysis, this technology has the potential to be completely CO2-negative without re-emission, is more scalable, and profitable with high quality CNT materials. The technology owner is seeking to collaborate with industry partners and research institutions for joint R&D to advance the lab scale technology to pilot or event industrial production scale, as well as to explore applications for the CNTs produced. Upon further development, the system has the potential to be integrated with existing carbon capture systems to improve their financial viability and achieve carbon negative objective. The molten salt CO2 reduction reaction enables CO2 conversion into high value nanostructured CNTs, which captures carbon as a solid and stable material, complementing other processes that convert CO2 into combustible fuels. Provides a highly controllable production method, using cost-effective pure iron (Fe) as a catalyst and lithium carbonate (Li2CO3) based electrolyte. The electro-reduction reaction and CNTs produced exhibits good graphitization degree (0.24 ID/IG intensity ratio), high Faradaic efficiency (~80%), with a high production rate (~58 gCNTs gFe-1 h-1). Based on a preliminary process modeling and technoeconomic analysis, the system may potentially achieve a profitable CO2 utilisation, subject to further scale up and detailed studies. Energy Storage: The high-quality CNTs produced could be utilised in next-generation batteries and supercapacitors, enhancing energy storage capacity and charging speeds. Aerospace and Automotive: Lightweight, strong CNT composites could be developed for use in aircraft and vehicle manufacturing, improving fuel efficiency. Construction: CNT-reinforced materials (such as CNT-reinforced concrete) could lead to stronger, lighter building materials with improved durability and insulation properties. Environmental Remediation: The technology itself serves as a carbon capture solution, potentially deployable near industrial CO2 emission sources. Textiles: CNTs could be incorporated into smart textiles for wearable technology applications. Water Purification: CNT-based filters could be developed for advanced water treatment systems. The carbon nanotube (CNT) market is projected to grow from USD 1.1 Billion in 2023 to USD 2.3 Billion by 2028, at a CAGR of 14.6% between 2023 and 2028. This proprietary electro-reduction process has the potential to achieve a net reduction of CO2 emissions without re-emission, offering an efficient and scalable CCUS solution, while producing high value CNTs material for various industrial uses. The process allows for CNTs to be produced with higher purity and quality than was previously possible from CO2. CCUS, CNTs Sustainability, Low Carbon Economy

Acknowledging the importance of high-quality data, this project aims to revolutionize data lifecycle management in the AI to improve data accessibility, collaboration, and commercialization. The solution enables (i) efficiently clean, process and extract valuable data assets from high volumes of mass data, and (ii) contribute and commercialize high-quality data assets without disclosing the actual data. DataS comprises three pillars: (1) GLASSDB serves as an end-user database, including add-in tools for data cleaning, visualization, security, aiding data owners in preparing data for future transactions. (2) Apache SINGA offers a powerful machine learning library to allow users to efficiently apply or develop AI models on their data. (3) Falcon enables privacy-preserving federated learning. It allows multiple parties to develop AI applications using joint data without compromising privacy. This technology uses a zero trust, three-layer design to ensure security and flexibility in data handling and AI development: Falcon Federated Learning: Enables secure collaboration without sharing data. Supports various models (deep neural networks, LLMs, SVMs) and frameworks (TensorFlow, PyTorch, etc.). Handles structured, semi-structured, and unstructured data. Apache SINGA: Scalable deep learning for healthcare applications. Supports data visualization, cleaning, extraction, and distributed training. ForkBase: On-premise data storage with version control. Features data obfuscation tools (pseudonymization, anonymization, synthesization) for enhanced privacy. This solution is ideal for industries needing advanced AI with stringent data protection, especially healthcare. AI requires good quality data and representative data, but privacy and security are the concern. we help you to unlock the power of data and collaboration, in a privacy-preserving and compliant way. Our solution works for Data exchange activities in any industry. Now we focus on financial, medical and legal data. We are the first solution that integrate data extraction, AI application and data collaboration in a single database. It helps our clients to commercialize their data asset easier, cheaper and more secure. Infocomm, Artificial Intelligence

Monitoring for product or part surface defects and anomalies such as cracks and chips throughout the manufacturing process is vital for product quality assurance and control. Traditional inspection or machine vision systems often struggle with complex and nonlinear defect patterns, leading to false positives and missed defects. Deep Learning AI-based detection methods, particularly those using deep neural networks (DNNs), typically require a sufficiently large amount of labelled training data to be effective. However, gathering and labelling such data can be time-consuming and costly, especially for rare or specialized defects. The technology owner has developed a cost-effective system solution leveraging on neuromorphic AI to utilise the principles of human cognitive memory with machine learning to detect surface defects and classifies them reliably and accurately. The system solution includes their patent-pending neuromorphic AI framework architecture with complementary hardware modules, patented lighting system and proprietary software platform. Through the use of neuromorphic edge-AI chip, the proprietary AI model requires smaller training dataset supported incremental learning capabilities, resulting in a high precision, high accuracy system overtime. As the system is camera agnostic and customisable, it enables easy integration and retrofitting to various industrial applications. There are currently a few ongoing POC projects with industrial partners for automating and enhancing quality checks within their manufacturing line. They are seeking industrial collaboration opportunities who are open to explore surface defect detection for quality assurance or monitoring applications. The technology system solution includes: Licensed neuromorphic AI chip with 5508 neurons for edge-AI computing Neuromorphic vision module using their patent pending neuromorphic AI framework architecture Patented programmable LED lighting system for accentuating surface defects Proprietary image pre-processing AI algorithm and software library Proprietary software platform With the above components, the system solution has the capabilities to do the following: Edge-AI computing capabilities Identification of complex and nonlinear surface defect detection (such as chip and crack) even with high reflectivity and transparency Require smaller (10 to 20) dataset for training, hence reducing training time High accuracy rate of up to 95% Incremental learning capabilities to further improve accuracy and identification Quality Assurance (QA) Inspection: The technology solution is able to autonomous detect surface defects for products for QA checks in industries that require low tolerance in product quality, such as advanced manufacturing. With the system being adaptable to different lighting condition, the technology solution can be integrated along any production processes. Other Modes of QA Inspection: The technology system is adaptable to other inputs for QA inspections (e.g. x-ray imaging, acoustics) to accommodate for a larger variety of products for quality inspection. Equipment Condition Monitoring for Predictive Maintenance: The technology solution is able to adaptively learn operating condition status of equipment (e.g. anomaly vibration and acoustics, temperature) to execute predictive maintenance. Security and Surveillance Application: Facial-based and biometrics for security access control and surveillance ensures computing and storage is on the edge, providing security and tamper-proof. The technology system solution enables integration of an autonomous surface defect detection into any production line as a quality assurance solution. Being a customisable solution requiring only a much smaller dataset, the technology solution can easily be integrated with little downtime. By leveraging on their patent-pending neuromorphic AI framework architecture, patented lighting system and proprietary software platform, it enables the use of neuromorphic AI chips for edge-AI applications, provide incremental learning capabilities for enhanced accuracy and overcomes transparency and reflectivity issues in conventional machine vision. Surface Defect, Small Dataset, Neuromorphic AI Chip, Real-time Incremental Learning, Anomaly Detection Electronics, Sensors & Instrumentation, Infocomm, Video/Image Analysis & Computer Vision, Robotics & Automation

The demand for industrial automation and robotics has been steady increasing across various industries to automate manual work. By embracing autonomous robots, like Automatic Guided Vehicle (AGV) and Autonomous Mobile Robot (AMR), companies can reduce labour cost and enhance safety while increasing operational efficiency. However, this system can be costly, complex to integrate and scale into existing operations and require technical know-how for their operation. The technology owner has a technology solution focusing on AGV and AMR for industrial automation that aims to address existing adoption challenges. The technology solution provides a plug-and-play approach which comprises of a customisable AI-powered robot controller which can be used to build new industrial robots or to retrofit into existing deployed ones (e.g. AGV). This approach is cost-effective and simplifies integration into current operations. Using a Simultaneous Localisation and Mapping (SLAM) navigation system with ready-to-use software assessable via a mobile device, the technology solution is user-friendly and allows less experienced personnel to operate it easily. Their AI capabilities also enable autonomous decision-making while utilising data analytics for operational efficiency. The technology owner is looking for collaboration partners, such as industrial system integrators, who are keen to explore customisation of AGV and AMR solutions aimed to improve operation efficiency and reduce cost. The technology solution, a customisable AI-powered robot controller, enables the creation of AMR or enhancement of existing AGV via retrofitting. This robot controller includes capabilities such as: Being energy efficient and programmable main processor for customisation Ability to support machine learning and AI-capabilities, due to the integration of an accelerator, for autonomous decision-making Supports a variety of communication interfaces, including CAN bus, USB 3.0, RS-232, RS-485, while accepting digital inputs and digital outputs Having built-in Wi-Fi module and 4 ethernet interfaces User-friendly and accessible via a mobile device Pre-programmed with ready-to-use software for easy deployment, such as creating 2D maps, design robot pathways and managing robot states The technology solution is a versatile and customisable tool designed for various industrial applications including: Material handling application (e.g. warehouse, logistics centre, manufacturing facilities) for inventory management and safe transportation of fragile or heavy loads Healthcare industry for autonomous delivery of medication and patent transportation Agricultural robots for improved precision for farming task Mobility for smart city initiatives and urban services Facilities management of inspection and maintenance The technology solution is a modular robot controller which incorporates AI-functions into AMR. The simple design enables creation of customised AMR or retrofitting into existing AGV for easy integration into existing operation processes. Its low power consumption also enables efficient performance of the robot, while enabling autonomous decision making and data analytics for operational optimisation. AGV, AMR, SLAM, AI, Industrial Mobile Robot Manufacturing, Assembly, Automation & Robotics, Infocomm, Robotics & Automation

Force sensing is widely used across various applications. In recent years, the demand for automation and labour-saving solutions has rapidly surged, driving the growth of markets for human-like robotic hand replacements. Additionally, extended reality (XR) and gaming controllers are striving to enhance the immersion through pressure sensing. As a result, there is an increasing need for force sensing technologies that closely mimic human sensation. The primary methods for force detection rely on capacitive and resistive sensors. These sensors measure force by detecting changes in capacitance or resistance values. However, they face challenges like low surface resolution and nonlinear responses to varying load ranges, leading to a perception that differs from natural human touch / operation. To address these challenges, the technology owner has developed an advanced capacitive force-sensing technology that utilizes micro-pillars (micro-protrusions) which are just tens of microns in height. This technology accurately detects changes in capacitance at low load ranges by leveraging micro-pillars. These micron-scale structures are formed using conductive rubber through an original micro-molding process. When configured in a grid layout, the technology ensures high linearity and surface resolution down to a single digital pitch, enabling force sensing that closely replicates human perception. The technology owner is keen to collaborate with industrial partners across various sectors, such as ICT manufacturers, robotics companies, and manufacturers of controller and electronic instruments, to explore potential applications. This capacitive force-sensing technology has the following key features: Force Sensing via Micro-pillars: utilizes micro-protrusions made from conductive rubber as electrodes Customisable: sensitivity characteristics, including linearity, load range, and capacitive response to force, can be customized to suit specific applications by adjusting the design and hardness of the micro-pillars High Sensitivity: able to detect low load range with high linearity by measuring the surface area change of electrode Excellent Durability: due to the use the elastic deformation of conductive rubber for force sensing Versatile: able to detect individual force inputs with a single sensor and pressure distribution with a grid layout module Features as a Force-Sensing Input Device: Compact Size: achieved even with the integration of a tactile feedback mechanism Integrated Solution: eliminate the need of combining an additional haptics device Features as a Pressure Distribution Sensor: High-Resolution and Sensitivity:  Provides detection capabilities similar to human touch / operation Precisely sense the shape and orientation of tiny objects Able to detect how an objects is behaving by tracking the pressure distribution change The technology can significantly enhance the user interface across a wide range of applications as an input device. Potential applications include, but are not limited to: Smartphones, Smart watches, XR systems, other information and communication technology (ICT) devices Game controllers Electronic musical instruments Digital cameras Use case as a sensor in both industrial and commercial products, such as: Human-like robotic hands Communication robots Robotic manipulators Characteristics of this device can be customized for various applications, particularly those involving human interaction or robotic pressure sensing Offers an intuitive and highly sensitive force-sensing solution with high output linearity and narrow-pitch detection An operation feeling with its linear output, closely matches human perception, making it ideal as an input device Ability to detect forces in low load ranges with high linearity and high surface resolution makes the sensing experience very close to human sensation Pressure distribution sensor, Force sensor, High sensitivity, Capacitive sensing, Linearity, Interface device, Input device Electronics, Sensors & Instrumentation, Infocomm, Human-Computer Interaction, Augmented Reality, Virtual Reality & Computer-Simulated Environments, Robotics & Automation