TECH OFFERS

Only 20% of actual coffee is extracted from beans to produce coffee in its beverage form, leaving the remaining 80% (six million tons annually) deemed as spent coffee grounds (SCG) to be disposed or used in landfills or as non-food product components to make fertilisers, furniture, deodorisers or skin care products. A technology was created to counteract SCG wastage and valorise it for human consumption. This particular invention comprises of methodologies to create two types of ingredients using leftover SCG - oil-grind and water-grind processed SCG. A simple, reproducible method of conching is employed to convert leftover SCG into smooth pastes, where specific conching parameters help refine the SCG to an acceptable particle size, eliminating grittiness in numerous valorised products similar to SCG. The product utilises common ingredients like oil and water to conche SCG with improved taste and textural properties. The shelf stability and nutritional composition (including caffeine) of the ingredients were also validated to ensure the food possessed good sensorial properties and are scale up ready. This technology increases SCG’s potential use as a versatile ingredient in different food applications. The technology provider is seeking off-takers from food manufacturers, food services industry, companies interested to valorise side streams to turn SCG into edible compounds. Technology Features: Uses reproducible method of conching into a functional ingredient with high insoluble dietary fibre (13g/100g) content. Fibre content is higher than instant coffee powder (<1g/100g) and coffee flavourings (0g/100g) and Lower caffeine levels (133mg/100g) compared to regular coffee (3600mg/100g) and is similar to decaffeinated beverages Sodium (<3mg/100g) and sugar free (<0.1g/100g)  Additive free (clean label) Specifications: SCG with particle size ranging between 4.82µm D(v,0.1) to 39.3 µm D[4,3] Moisture content 58.6% The technology was validated by incorporating SCG ingredients into a range of common food products such as beverages and ice cream (water-grind SCG), spreads and chocolate (oil-grind SCG) to help relevant food industries gain a deeper understanding of SCG valorisation, for a greater adoption among food manufacturers to create products using SCG. Can be developed into Ready-to-Drink (RTD) beverages, coffee ice cream, coffee spreads and confectionary (e.g. chocolates and cakes) Companies specialising in upcycling sidestreams and sustainability can explore this technology   Coffee consumption in Singapore increased by 4.8% in the last seven years with 105000, 60kg bags of coffee consumed in 2023 and the market is growing. There is a global push to reduce food side streams and Singapore's Zero Waste Masterplan on the treatment of such side streams by commercial and industrial generators, which aligns with the proposition of this technology. Similar technologies such as these may not be as cost effective. The technology uses basic ingredients such as water and oil and is easily reproducible. It does not involve high CAPEX investment or vigorous training processes that disrupts production process. Conching machines are commercially available, and the licensee can choose to purchase the equipment based on their production scale requirements. The conching process is easy to pick up. In addition, replacing coffee flavouring agents with SCG, customers can benefit from the natural and functional coffee flavour and caffeine SCG imparts into all the food applications. The product is rich in insoluble fibre which can help to regulate blood cholesterol and glucose levels. Caffeine is known to stimulate the Central Nervous System (CNS) in the body, which can improve cognitive abilities (e.g. alertness, reaction time). Coffee, Spent Coffee Grounds (SCG), valorisation, water-grind SCG, oil-grind SCG, scale-up, accelerated shelf-life evaluation, food safety and quality, food industries, technology adoption Foods, Ingredients, Processes, Waste Management & Recycling, Food & Agriculture Waste Management

Facial recognition systems or tools identifying and measuring facial features in a video image have gained traction in recent years for applications such as smart surveillance. Such systems which may either be cloud or edge-based, often require real-time computations done in order to meet operational requirements. Edge-computing while providing benefits such as lower latencies and data privacy, are challenged by computational constraints of edge devices. Addressing this challenge, a Singapore company has developed a Neuromorphic AI-based solution that enables facial recognition by combining pre-processed real-time video data with recognition capabilities. The low-power edge AI solution allows computing to be carried out at the camera location with adaptive learning capabilities. Neuromorphic computing, which mimics the neural structure of the human brain, represents a novel approach in artificial intelligence and offers significant benefits for facial recognition technologies, including lower power consumption, faster processing speeds, and improved learning capabilities. The tech owner is seeking partners such as camera system manufacturers and system integrators to co-develop or testbed the technology for video surveillance applications. Technical specifications* of the Facial Recognition system Able to achieve 96% accuracy under optimal lighting conditions, and at allowable head rotation of ±15 degrees System can be interfaced with 8, 16 and 32 channels of security video channels Face Detection Detection of multiple faces in a photo, as well as on thermal images Detection speed – (webcam resolution ±15° in plane head rotation): 0.0081 seconds Facial Feature Detection Detection of 70 facial feature points Detection time – (excluding face detection stage): 0.00027 seconds (3733 FPS) Eye Center Detection Detection time (eye centres only, not including face detection stage): 0.00027 seconds (3752 FPS) Gender Recognition (different genders, not including face and facial feature detection stages): 0.0039 seconds Facial Expression Recognition Recognizes if the subject smiles and if the eyes are open or closed. Expression recognition time (not including face and facial feature detection stages): 0.0043 seconds. *Preliminary specfications as measured on AMD Ryzen 5 1600X processor with 12 threads Potential applications include (but not limited to): Video surveillance Security access control and biometrics On-the-fly registration and ID checks via webcam (e.g., Healthcare, Finance) Mobile, remote applications without consistent network connectivity The global video analytics market is anticipated to grow at a CAGR of 23.4% to gain $20.3 billion by 2027, according to a report by MarketsandMarkets. The edge-based segment is expected to grow at a higher CAGR during the forecast period. In the edge-based architecture, video analytics is embedded into the camera and the video there itself. Advancements in deep learning and its integration with the edge system are expected to drive its adoption in the coming years. High speed parallel processing – able to process complex tasks at high speed Adaptive learning – able to carry out anomaly detection and learning on-the-fly Training with minimal dataset – system is able to start working with minimal sample dataset Low-power edge AI - No GPU required Infocomm, Security & Privacy, Video/Image Analysis & Computer Vision, Video/Image Processing

The manufacturing of electronic devices often involves multiple steps, where metal wiring is a fundamental component providing electrical connections and resin resist stripping solution is used to remove photoresist layers after the patterning and etching processes. Both steps are critical in the production of various electronic components, including printed circuit boards (PCBs), semiconductors, and microelectronic devices. Advances in such processes continue to push the boundaries of what's possible in electronics manufacturing, enabling the production of devices with higher performance, smaller size, and lower cost. Research and development efforts to seek safer, more efficient, and environmentally friendly methods are ongoing challenges. The technology owner has developed a proprietary chemical solution for metal wiring, especially targeting copper (Cu) wiring, although metals such as aluminium and gold are also used due to their excellent electrical conductivity. This unique chemical solution can ensure high-quality and precious shape control of wiring formation. This is also complemented by an advanced resin resist stripping solution, which not only exfoliates the photoresist, but also preserves the base metal from scraping / damage. On top of this, this solution enables controlled roughing of metal surfaces to meet specific requirements. The effectiveness of both solutions has been validated in the mass production of liquid crystal panels. The technology owner is seeking R&D collaboration with industrial partners interested in adopting these solutions into their electronic device manufacturing lines. Key features of the metal wiring chemical solution include: Precious shape control of metal wiring using unique electronic polarity of organic acids High-quality and high-stability control of the wiring shape Applicable for laminated metals and specific layer etching High design flexibility to meet application needs Seamlessly integration with existing metal wiring processes Key features of the resin resist stripping solution include: Achieve fine pattern formation using nucleophilic substitution ability of amine High stripping power suitable for hardbake resist Compatible stripping force to control base metal damage Reduce damage rates and improve product reliability Customisable to meet different requirements These innovative solutions could revolutionise the manufacturing processes of various electronic substrates and components. Potential applications include (but are not limited to): Liquid crystal display (LCD) panel production Semiconductor manufacturing Manufacturing of flexible electronics And beyond: medical device and microelectronic device manufacturing Optimise the manufacturing process of electronic substrates Fully customisable for specific substrates and requirements Easily adaptable into existing manufacturing lines Proven effectiveness in the mass production of liquid crystal display panels Metal Etching Solution, Resist Peeling Solution,, semiconductor manufacturing, electronic equipment manufacturing Electronics, Semiconductors, Display, Manufacturing, Chemical Processes

The demand for thinner, lighter, and more durable glass in consumer electronics and other high-tech applications is constantly increasing. Thinner and lighter glass not only enables the design of sleeker devices but also enhances the tactile responsiveness of touchscreens, contributing to the overall performance and durability of the product. However, conventional glass thinning solutions face challenges, particularly in dealing with surface imperfections, which can impact the quality and efficiency of glass manufacturing. During the thinning process, surface imperfections like scratches and dimples become more pronounced, significantly affecting the optical clarity and overall quality of the glass, leading to increased product rejection rates and manufacturing costs. To address such challenges, the technology owner has developed a proprietary chemical solution specifically for glass slimming or thinning, aiming at effective dimple suppression while maintaining the desired thickness. This advanced solution enables the uniform melting or removal of material from the glass surface, preventing the spread of small scratches and the formation of dimples on the glass surface. This chemical solution ensures the production of super-thin and high-quality glass that aligns with the dynamic demands of the evolving industry. Its application proves beneficial in industries where the precision and quality of glass are paramount. The technology owner is seeking R&D collaboration with industrial partners interested in adopting this chemical solution in the processing or manufacturing of glass related components. This solution enables the uniform melting or removal of material from the glass surface, effectively mitigating the spread of small scratches and preventing dimple formation. Key features of this technology include: Enhanced glass performance: dimple suppression contributes to the uniformity of glass, ensuring optical clarity, tactile responsiveness and aesthetic appearance Improved product durability: the presence of dimples leads to stress concentration. Suppressing dimples helps maintain the strength and durability of glass product Cost-effectiveness: this chemical solution can be seamlessly integrated into existing  glass processing or manufacturing lines, making it viable for mass production This chemical solution can be applied to processing / manufacturing of various thinner and lighter glass components. Potential applications include (but are not limited to): Advanced display: liquid crystal display (LCD) in various electronic devices like TVs, monitors, smartphones, etc. Optics and photonics: high performance lenses and optical components for cameras and microscope Medical devices: lab-on-a-chip devices, microfluidic devices, biosensors, diagnostic chips, etc. Flexible electronics: rollable / foldable displays, flexible solar cells, and wearable devices, etc. Improve the overall quality of glass Reduce rejection rates and manufacturing costs Customisable to meet different requirements Adaptable to existing glass processing facilities Glass Processing, dimple suppression, Electronics, Optics, Medical Devices Manufacturing, Surface Finishing & Modification, Chemical Processes, Chemicals, Organic

A new approach to a standards-based communications protocol for one-to-many Internet-of-Things devices. Being based on a standard, adopters are free from vendor lock-in. The protocol is particularly useful for IoT devices fitted with low-power displays (e.g., electronic labels). The technology is ideal for supermarkets (electronic price labels), pick-to-light systems, inventory management, and can easily be extended to any system where a one-to-many sensor network is critical.  Using a standards-based approach is also more environmentally friendly because users no longer need to dispose of their devices should they choose another communications service provider that uses the standard. An open-source software framework is also available that provides a cloud-based interface for devices using this communications protocol.   New approach using an existing 2.4GHz, wireless, standard protocol that is widely available. The technology extends the standard to an intermittent advertising framework for ultra-low power consumption on devices. The protocol is suitable for sending data (e.g., images and pricing) and commands (e.g., turn on an LED), as well as receiving data (e.g., temperature, sensor data, battery status), with up to 32,000 devices per access point. Devices can be connected to and updated within as little as 1.5 seconds. Open-source software framework immediately available for ease of implementation. Retail sector: Electronic shelf labels (ESLs) showing data and/or images, including e.g., price and product information, stock levels, and/or inventory management data. Standards-based communications ensures greater freedom-to-operate for retailers, with ESL displays ranging from 1.5” to >10” and multiple colour options (black-and-white to multiple colours). Inventory management/warehousing: Pick-to-light systems with displays showing inventory and updatable stock information. Hospitals/healthcare: Low-power information displays that can be used to show critical data (e.g., patient name, allergies, conditions), potentially replacing manually created slot-in cards. Sensor network: One-to-many standards-based platform for IoT sensor deployment (e.g., temperature sensors in cold-storage facility, especially if fitted with displays). A significant opportunity exists for this standards-based communication protocol to be used in supermarket electronic shelf labels (ESLs). While a cumulative total of 788 million ESLs have been installed worldwide, the potential market is upwards of tens of billions, and one of the major reasons for slower uptake of ESLs has been market fragmentation primarily driven by the proprietary protocols offered by ESL vendors. By shifting to a standards-based communications protocol, retailers will no longer be locked-in, and can benefit from the broader ecosystem offered by a standards-based platform. Regarding using the new protocol for IoT generally, many IoT customers have held back in adopting IoT technologies for fear of a particular technology becoming obsolete or are holding out on the promise of something better in the future. Consequently, many IoT customers partner with multiple vendors to diversify their supply of devices and ensure they are not trapped into a single vendor. While there are standards readily available for IoT already, this new implementation significantly broadens the scope by allowing for display integration as well as a higher-speed and lower-energy framework for IoT. For electronic shelf labels, the present ‘state-of-the-art’ is entirely proprietary. This technology’s unique value proposition stems from the implementation of a standards-based solution for ESLs that: increases Interoperability: One of the major benefits of adopting a standards-based platform is that products and solutions from one vendor can work with those from another. Improves flexibility and reduces vendor lock-in: Retailers who adopt a standards-based solution can switch to a different vendor if they become dissatisfied with their current solution provider. Benefits from economies of scale: Standards-based enables greater product choice, increased competition, a larger selection of products, a reduced burden of entry, faster time to market, and collaboration from a wide number of players across the value chain. For IoT devices, this technology further improves on ‘state-of-the-art’ protocols by increasing IoT device flexibility, improving device communications speed all the while reducing power consumption and extending battery life. Standards-based communications protocol, IoT devices Electronics, Display, Infocomm, Internet of Things, Wireless Technology, Logistics, Inventory Management

In today's fast-paced business environment, the efficiency of B2B sales prospecting is paramount. Traditional methods often fall short, plagued by challenges like low conversion rates, low deliverability, and high operational costs. A Singapore-based AI startup has introduced a groundbreaking solution designed to automate and enhance B2B sales prospecting through advanced AI and Machine Learning (ML) algorithms. Their technology addresses the critical pain points in sales prospecting by automating the process with high-performance emails, thus ensuring higher deliverability and higher conversion rates at significantly reduced costs. They are part of Singapore’s inaugural Generative AI Sandbox, a collaborative effort between Enterprise Singapore and the Infocomm Media Development Authority (IMDA). Advanced Sales Profiling: Central to the technology is a sophisticated sales profiling system that scrutinizes over 50 communication parameters to identify the most impactful engagement strategies for each prospect. This system probes the subtleties of sales communication, refining aspects such as email timing, message sequencing, and the strategic inclusion of elements like case studies. This detailed approach ensures personalized interactions for every prospect, significantly enhancing conversion potential. Proprietary ML Algorithms: The elimination of guesswork in sales prospecting is driven by the Startup's proprietary ML algorithms. These algorithms dissect an extensive set of communication parameters to tailor every facet of the email campaign, crafting each message to resonate with its recipient. From selecting the perfect opener to determining the exact call-to-action, the technology guarantees emails are primed for maximum engagement and response. High-Performance Automated Emails: The Startup elevates the automation of sales email creation and distribution to new heights. Users have reported a 40% surge in sales conversions at costs 90% lower than traditional sales development representative (SDR) tactics. The platform's automation extends to the ongoing refinement of email campaigns via smart feedback loops, requiring minimal initial setup. Dynamic Email Generation: Beyond static messaging, the Startup's technology dynamically produces each email based on live interactions, ensuring follow-up messages are influenced by how recipients interact with preceding communications. This leads to highly personalized and efficacious email sequences. Exceptional Deliverability and Spam Filter Evasion: The Startup achieves over 99% email deliverability, ensuring messages reach their intended audiences. The method of crafting distinct emails for each campaign greatly lowers the chance of triggering spam filters, thus enhancing engagement opportunities. The integration of advanced sales profiling into the Startup's offerings reaffirms a commitment to providing customized, effective sales prospecting solutions. By meticulously analyzing and enhancing communication based on a broad spectrum of parameters, the technology ensures clients can engage each prospect most effectively, greatly increasing sales conversion rates while lowering costs. The Startup streamlines the sales prospecting process, offering businesses a direct path to finding more leads and converting more clients. By automating and optimizing email campaigns with AI and ML technologies, their solution ensures: Increased Lead Generation: Quickly identify and engage potential leads with minimal effort, maximizing outreach. Higher Conversion Rates: Tailor every communication to the prospect's needs and behaviors, significantly improving the likelihood of conversion. Efficiency and Cost Savings: Reduce the resources and time spent on manual prospecting, allowing teams to focus on closing deals. The AI solution stands out by offering a suite of benefits that directly address the challenges faced in B2B sales prospecting: Simplicity, Scalability, and Automation: Designed for straightforward operation, the Startup demands merely 15 minutes for setup before its AI, honed by top-tier sales experts, assumes control to refine email campaigns through continuous feedback. This simplicity and scalability cater to businesses of any size with ease. Fully Automated System: Requires just 15 minutes to set up, after which their AI, trained by world-class sales experts, takes over. Significant Performance Gains: Clients report a 40% higher sales conversion at 90% reduced costs compared to conventional strategies. Customizable and Scalable: Their solution is designed to fit seamlessly into existing workflows, offering APIs for rapid integration and full customizability. Artificial Intelligence Infocomm, Artificial Intelligence

Artificial Intelligence/Machine Learning (AI/ML) performance is predicated on training with good quality data. However, such data is often difficult to acquire due to ethical concerns, logistic problems, high cost, data bias, and inherent poor data quality. Privacy restrictions and data regulations further compound the problem of data acquisition, restricting many organisations long-term access to valuable historical data. Ultimately, this creates the problem of incomplete or biased data which degrade the overall performance of trained AI/ML models.   This technology offer is a controlled synthetic data generation with differential privacy capability for structured (tabular) data. Its synthetic data engine utilizes conditional GANs (cGANs) coupled with optional differential privacy to synthesize data with similar properties as real data without the associated privacy risks. The core technology is a synthetic data engine that learns the distribution of the input data and selects the column to generate based on this distribution. Gaussian noise is further added to the gradients to protect the privacy of the data. The technology can generate data quickly: 10,000 rows, 8 columns in 8 minutes (evaluated on Nvidia GTX1080) and is mainly intended to generate synthetic datasets to address data scarcity, data privacy, and data augmentation. This generative process involves the following features: Conditional Generative Adversarial Networks (cGANS) generate synthetic data that mimic real data Sensitive data is obfuscated with statistical noise and randomization Definable privacy levels allowing adjustability between utility and data privacy (Differential privacy allows Machine Learning models to be trained on synthetic tabular data and achieve similar results as models trained on real data) Quality Assurance (QA) component generates reports to aid the assessment of data quality and risk metrics APIs for rapid integration, with full customisability This technology can be used for the following types of structured data: non-time series time series multi-tables free-text fields It can be applied in the following use-cases: Data Augmentation Increase the size of your datasets without wasting time to procure new data Data Extrapolation Extrapolate known data to generate unavailable or unknown data points Bias Correction De-bias or equalize the distribution of datasets Targeted Generation Generate rich data, including infrequent scenarios This synthetic data generation with differential privacy technology provides accessible privacy by design - adding privacy-preserving techniques before, during or after AI training, together with the following benefits: Synthetic data does not require further data sanitization, providing a safe data sandbox environment Reduces the need to pay for additional datasets by generating missing data or de-biasing existing datasets Overcomes the challenges of data acquisition by enriching real data with synthetic data through controlled generation Synthetically generated data become your data assets, with potential for monetization as new revenue streams Protect real data by combining made up data points to make it harder to distinguish what is real even if data is compromised Indefinite retention time without associated compliance risks and full accessibility to rich statistical data to provide a boost to AI/ML model resilience and performance The technology owner is looking to collaborate with technology partners in the field of AI/ML to co-develop new products/services, and for collaborators to test-bed in pilot projects. data, generation, privacy preserving, definable privacy, machine learning, synthetic data Infocomm, Security & Privacy, Data Processing

The exponential growth of electronic waste (E-waste) generation is proliferating due to the ever-increasing demand for electrical and electronic equipment (EEE) driven by industrial revolution and development. The COVID-19 crisis has further accelerated the shift towards digital transformation, contributing to an upsurge in E-waste generation. To-date, the industrial practices of extracting palladium (Pd) from electronic waste and mining ores rely on hydrometallurgy techniques using highly corrosive acids, typically aqua regia at elevated temperature. The process poses severe hazards to workers and lead to environmental pollution. Aqua regia’s capability to dissolve many various metals results in low selectivity for Pd. Despite ongoing efforts to develop alternative methods, these methods often prove impractical for industrial adoption. The technology provider has developed a proprietary lixiviant capable of extracting palladium up to 4,000 ppm at saturation with high extraction efficiency and selectivity within 12 hours. This lixiviant is facile, cost-effective, and significantly less corrosive and hazardous compared to current industrial practices. Substituting fuming aqua regia with this lixiviant could enhance the protection of workers and environmental safety. Importantly, the proposed technology is highly compatible with existing hydrometallurgy processes, eliminating the need for companies to change their current infrastructure. An E-waste industry partner has successfully conducted a pilot-scale (5-Litre scale) evaluation, validating the effectiveness and applicability of the lixiviant on their Pd-coated samples. The technology provider is actively seeking industry partners interested in test-bedding and licensing of this technology. Low cyanide concentration (< 50 ppm) stabilized in alkaline solution Optimal operating temperature of 90°C High selectivity (> 86%) and high extraction rate (> 86%) of palladium Cost-effective at ≤ USD 2.12/L extracting up to 4,000 ppm palladium at saturation within 12 hours Easy adoption and high compatibility with existing industrial hydrometallurgy systems Improve workplace safety and health which better protects workers and the environment Electronic wastes, such as Pd-coated connectors, Pd-coated wire bonding, etc. Pd-coated industrial wastes Recovered palladium can be further refined for resale and reuse In recent years, many countries have mandated environmental responsibilities to electronic manufacturers to establish producer recycling programs and ban E-waste disposal into landfills. E-waste contains precious metals, such as palladium, gold and silver that are highly sought-after by E-waste recycling companies due to their scarcity, high value and demand, and are actively traded as commodities over the last decades. The extraction of precious metals from E-waste is not only commercially attractive but also aligns with Corporate Social Responsibility and Environmental, Social, and Governance goals for resource recovery and environmental protection. The global E-waste market size was valued at USD 52.6 billion in 2022 and is expected to expand at a compound annual growth rate (CAGR) of 12.1% from 2022 to 2032, to reach USD 160.2 billion (Market.us, 2023). The proposed technology features a proprietary lixiviant capable of extracting palladium up to 4,000 ppm at saturation with a high extraction efficiency (≥ 89%) and high purity (≥ 92%). This cost-effective lixiviant is significantly less hazardous as compared to current industrial practices, thus better protecting workplace safety and health. Notably, the technology is compatible with existing hydrometallurgy processes and has been successfully verified at pilot-scale (5-Litre) in collaborating with an industry partner. Hydrometallurgy, palladium recovery, palladium extraction, palladium recycling, precious metal recovery, precious metal extraction, precious metal recycling, electronic waste (E-waste) recycling, electronic waste treatment Chemicals, Catalysts, Waste Management & Recycling, Industrial Waste Management

The current oil and gas market situation and the growing demand for clean energy are driving companies to develop offshore infrastructures for oil and gas extraction and wind farms. Maintenance of underwater structures must be carried out by Remotely Operated Vehicles (ROVs), which can operate 24 hours a day, 7 days a week. However, the daily costs of such operations are high, and companies are looking for innovative solutions to reduce operation time and save costs. Currently, there are two types of ROVs in use: work-class and inspection ROVs. Work-class ROVs are large and heavy, weighing up to 8 tons, and are capable of performing a variety of tasks. They require vessels and cranes for transportation and deployment and can operate at depths of up to 8000m. They are equipped with specialized tools such as dredgers, robotic arms, LIDARs, and other vision-enhancing electronics. On the other hand, inspection ROVs are small and light, weighing up to 100kg, but have limited capabilities. They can only stream video and perform basic inspections, typically carrying only a camera and sonar. They are usually used at depths of up to 300m. A startup based in Poland has introduced a new class of ROV that combines the lightness of inspection ROVs with the capabilities of work-class ROVs. The ROV’s patented variable buoyancy hull technology gives it capabilities similar to those of a work-class ROV but with a smaller footprint. It can submerge without using vertical thrusters, making it very energy-efficient and reducing its carbon footprint. The unique variable buoyancy hull approach allows for precise control and intuitive operation, even in the presence of external factors such as sea currents. This technology is particularly useful for subsea missions close to the seabed, as the variable geometry hulls do not disturb sand and dust particles, allowing for better situational awareness for the operator. This innovative ROV can change their buoyancy to lift more (up to 1:1 payload-to-weight ratio), enabling it to carry various tools and equipment. The ML/AI-based control system automatically changes the buoyancy of the individual hulls, making it intuitive and easy for pilots to control and flattening the learning curve. The technology owner is seeking partnerships with the maritime, defense and other industries in the Southeast Asia region to testbed and tailor the ROV to meet specific application requirements. Physical Depth Rating [msw, meter sea water]: 300 Dimensions (LxWxH) [mm]: 1600x990x630 Weight in air [kg]: 200 including battery pack Dynamic payload [kg]: 100 Total payload [kg]: 200 Maneuverability and propulsion 4 horizontal brushless DC thrusters (different options available) 2 variable-buoyancy hulls No vertical thrusters Forward thrust [kgf]: 28 Lateral thrust [kgf]: 28 Maximum lift [kgf]: 100 Speed [kts]: >4 Video and lighting 6 independent cameras Video resolution: 4k on each camera Video transmission: h.265 12 LED Lights (2 per camera) Quick & easy camera mounting Included 3rd person POV camera arm for intuitive operation Pan and tilt platform (optional) ROV power Battery pack capacity [kWh]: 5.55 Operating time on one charge (typical inspection work) [hours]: 6 Time to swap battery pack [minutes]: 5 Battery pack charging time 0-100% [hours]: 3 Disposal of UXO (unexploded ordnance), both for defense and civilian industry, especially at offshore windfarm build sites. Inspection of subsea wind farms. Cost-effective and eco-friendly solution for tasks such as seabed mapping, undersea structure inspection, and ocean health monitoring. Cleaning and maintaining ships without dry dock, which leads to millions of dollars in savings. Advanced capabilities in inspection and infrastructure monitoring, particularly in coastal zones, harbors, and seaports. Inspection and maintenance of oil and gas infrastructure at depths of up to 300m. Inspection of ports and dams. Support operation in low-depth underwater mining. Inspection of wrecks. In 2020, worldwide ROV and AUV market was valued at about 2.9 billion dollars, and in 2021, it was over 3.6 billion dollars and rising. The war in Ukraine and the unstable situation with Russia’s oil and gas markets forced other countries to speed up offshore investments in green energy and oil and gas. The ROV’s usability is divided into inspection drones about 54%, work class about 34%, and the rest is for Autonomous Underwater Vehicles. This numbers shows that the drones can address more than 80% of the market and is a game-changing technology. Enhanced inspection class: Utilisation of sensors and tools previously available only to the bigger ROVs. Replacing divers: No human life at risk. Unlimited working hours. Rapid deployment: Ready to operate in 5 minutes 24/7 uninterrupted operations. Exceptional payload capabilities in compact form Variable buoyancy technology for precise control Modular design for easy integration of various sensing technologies Capable of anchoring on the sea-bottom for long-term monitoring Electric powered with a low carbon footprint. Environment, Clean Air & Water, Sensor, Network, Monitoring & Quality Control Systems