The dairy farming industry faces significant challenges in managing livestock and optimizing farm operations. A major hurdle is accurate cow monitoring, complicating precise record-keeping of identification, health status, milk yield, and food biosecurity. Current data collection and analysis rely on manual methods, hindering timely tracking and data-driven decision making. Lack of real-time data and insights leads to potential wastage and negatively impacts cow welfare and productivity. Addressing these challenges demands an innovative solution, and IoT gateway represents a promising approach for such advancements in the industry. NEXCOM’s NDiS B560S, a slim, embedded fanless computer offers a comprehensive smart farming solution. It integrates with electronic and visual cow tags to enable accurate monitoring, automated data collection, nutrition analysis, and optimized resource allocation.   Powered by the Intel® Core™ i5-8500T processor, the NDiS B560S embedded fanless computer provides seamless connectivity, effectively enhancing cow health and performance through accurate identification and tracking of individual cows. The system facilitates real-time data access on breeding records, health metrics, and milk production, arming operators with vital information to make informed decisions on nutrition, reproduction strategies, and necessary veterinary interventions for smart farming.   The IoT gateway also accomodates temperature and humidity sensors via M.2 2230 Key E, enabling anticipatory monitoring of environmental conditions, thus mitigating risks associated with heat stress and other potential adverse effects on cow health. With integrating LAN and Wi-Fi capabilities, along with an intuitive touch HMI interface, it permits farm operators to remotely oversee and control various farm aspects. Operators can access real-time data from RFID tags, receive immediate alerts, and make informed decisions from anywhere, significantly boosting efficiency and flexibility.   Leveraging the innovative NDiS B560S embedded fanless computer and cow tags, dairy farms can revolutionize operations, authenticate livestock data, and eliminate human errors associated with traditional paper records. This advanced traceability solution improves food safety, regulatory efficiency, resource allocation, and more.   Diagram     Key Features for Application Needs   Support 8/9th Gen Intel® Core™ i3/i5/i7 LGA socket type embedded processor, up to 35W Intel® H310 Intel® integrated UHD 630 graphic engine Support 2 independent 4K2K 60Hz display output Compact and slim design (H: 39mm) Support 1 x 2.5” SATA HDD 2 x HDMI 2.0, 4 x USB 3.0, 2 x USB 2.0, 2 x GbE LAN, 4 x COM, 1 x Line-out, 1 x Mic-in Support M.2 Key B/E/M Fanless design

The Background The highway electronic toll collection (ETC) is an important part of modern transportation and can effectively improve vehicle traffic efficiency. The advanced ETC system, combined with real-time vehicle identification and blacklist monitoring functions, assists the police in tracking down vehicle theft and other illegal activities. The solution combines powerful edge computing, high-speed data processing and reliable device connectivity to ensure seamless operation under harsh highway conditions.   Solution Overview The ETC system was built using Neu-X302-Q as the main computing device and NDiS B561-PoE to control the ETC gate and also capture the images through PoE camera. These devices collaborated to deliver robust, real-time data analysis, enabling effective toll management and monitoring.   Neu-X302-Q The Neu-X302-Q served as the main computing platform for processing vehicle data, running blacklist comparisons, and triggering alerts for unauthorized vehicles. Its fanless design, Intel® 8th/9th Core™ processor, and high I/O expandability made it ideal for 24/7 operation in harsh roadside environments. The device handled large-scale data communication and ensured minimal latency, critical for real-time ETC operations.   NDiS B561-PoE The NDiS B561-PoE, powered by Intel® 12th Gen Intel® Core™ processor, not only to controls the ETC gate but also captures images of vehicles passing though ETC lane and immediately communicates with the Neu-X302-Q to process the data. Its advanced graphics support allowed seamless real-time visualization, and the PoE functionality significantly reduced wiring complexity by delivering both data and power through a single cable. Its rugged design ensured uninterrupted operations in extreme conditions.   Overall, the deployment of Neu-X302-Q and NDiS B561-PoE revolutionized the highway ETC system, enabling accurate, high-speed toll processing and offering better control over vehicles that attempt to evade tolls or engage in illegal activities. The Neu-X302-Q and NDiS B561-PoE industrial-grade computing devices can transform highway toll collection, paving the way for smarter and safer road infrastructures.   Application Diagram    

Along the bustling banks of the Thames in London, sleek digital totems now stand as silent guides for curious visitors. These modern sentinels display real-time boat schedules, weather updates, and a wealth of local information, transforming the riverside experience. At the heart of this smart city's evolution lies NEXCOM's powerful Neu-X102-N50, the driving force behind these innovative information hubs.   These innovative information totems are revolutionizing visitor experiences in waterfront destinations citywide. While their exteriors may vary to suit local aesthetics, their core remains constant: NEXCOM's powerful edge computing system, the Neu-X102-N50.   At the heart of these totems lies impressive technology tailored for outdoor applications. The Neu-X102-N50 boasts an Intel Alder Lake-N N50 processor and up to 16GB of RAM, ensuring smooth performance even in challenging environments. Its ability to operate in temperatures from -5°C to 50°C makes it suitable for diverse climates.   The Neu-X102-N50's technical prowess extends beyond its processor. With support for up to two HDMI ports for playing vivid content, it can deliver eye-catching visuals to attract and inform visitors. Its M.2 & mPCIe slots allow for expandable storage, LTE & Wi-Fi 6 capability, ensuring ample space for rich content and lightning-fast wireless connectivity. These features enable the totems to serve as comprehensive information hubs, capable of handling high-traffic areas with ease in the smart city.   Tourists interact with vibrant 32-inch touchscreen displays, accessing a wealth of information beyond just schedules and weather. Local attractions, dining recommendations, and even real-time air quality data are at their fingertips. The edge computing system's dual 2.5GbE LAN ports and 4G LTE connectivity ensure that this information is always current and readily available. Through a USB light sensor and COM port, the totem can automatically adjust its brightness, ensuring all information remains readable in varying light conditions while contributing to the system's energy efficiency, aligning with modern urban sustainability goals.   For totem operators, remote management capability is key. They can update content and perform system maintenance through LAN or LTE, significantly reducing operational costs and ensuring efficient management.   These edge computing systems improve the visitor experience and provide valuable data insights for urban planning and tourism management. Through cameras connected via USB 3.2 high-bandwidth ports, the Neu-X102-N50 ensures smooth capture and transmission of data, enabling real-time monitoring and analysis of visitor flows. This advanced capability allows city planners and tourism officials to make informed decisions, optimize resource allocation, and enhance overall urban mobility, while maintaining a seamless and enjoyable experience for tourists and locals alike.   The Neu-X102-N50 represents a significant step forward in smart city technology, blending seamlessly into urban landscapes while providing an essential service to tourists and locals alike. As more areas of the city adopt this technology, we can expect to see a transformation in how people interact with and navigate waterfront destinations, ushering in a new era of informed and engaged urban tourism.   Application Diagram  

The bustling metropolis of Bangkok, Thailand, stands as a beacon of cultural richness and economic vitality. However, similar to other emerging cities in South East Asia, its growth has led to an unprecedented challenge – traffic congestion. There were traffic lights at about 500 locations in Bangkok. In many areas they were still controlled by timers and did not adjust to actual traffic conditions.   Thailand government launched “Thailand 4.0 project” to Integrate AI into current traffic management systems for cities to optimize road conditions and enhance urban transportation planning.   Solution AIEdge-X®500’s LAN ports are connected to CCTV traffic cameras installed at intersections to record and perform license plate recognition to catch traffic violators who exceeds speed limit, cross red line and motorists who parked their vehicles in no-parking areas.   On top of it, City Administration and Office of Transport, Traffic Policy and Planning developed a traffic management model and use AI to estimate traffic congestion in each hour, analyze bottlenecks and come up with solutions in real time. For example, by adjusting traffic lights in line with traffic volume.   NEXCOM AIEdge-X®500 seamlessly integrates into the traffic signal box, showcasing its remarkable performance even in the challenging conditions of high temperatures and humidity that are characteristic of the subtropical climate. The device operates efficiently within a temperature spectrum of 0°C to 45°C and a humidity range of 10% to 90%.   Powered by Intel® 8th/9th Gen. Core™ processor, the AIEdge-X®500 integrates maximum graphic processing potential with support for large storage and peripheral and internal devices to effectively meet industrial AI requirements, from image processing/optimization to machine/deep learning and machine vision.   This AIEdge-X®500 Edge AI computing solutions are expected to be deployed to 100 other locations in next 2 to 3 years to facilitate traffic management in Bangkok and reduce traffic violations in the city.   Application Diagram  

NEXCOM Servers Provide Edge Video AI Analytics and Processing Intel® Edge Video Infrastructure reference design provides smart city edge video processing running on NEXCOM NSA 7160R; tests of the solution show it meets Intel® EVI performance metrics for AI and storage performance. Intel® Network Builders Community partner, NEXCOM has tested the Intel EVI 2.0 software (that is included in the Intel EVI reference design) on its NSA 7160R, a powerful three-in-one server equipped with dual 4th Gen Intel® Xeon® Scalable processors for high performance video processingand AI inference, high-bandwidth LAN modules, and a high-capacity NVMe storage module. The test results used Intel EVI 2.0 test protocols to examine the throughput of NSA 7160R across four workloads that are important for the performance of computer vision applications: Image/Video Storage and Retrieval AI Inferencing (Image/Video) Feature Matching Clustering As the tests in this paper will show, the NEXCOM NSA 7160R with the Intel EVI reference design creates a system that is capable of efficiently processing edge video server workloads. The solution brief was created by Intel® Corporation. To read the full story, please download the PDF. 

Background One of Taiwan’s largest steel corporation. The main products include plates, bars, electrical steel coils, and hot-dip galvanized coils. The company ships out steel coils that weigh tens of tons each. These 'heavy' loads are transported, processed, and shipped by the heavy-duty lifting device called the 'EOT Crane.' By moving horizontally and vertically in the factory area, it can lift and transport steel coils to designated positions, managing the schedule and transporting thousands of tons of steel coils in and out of the factory and warehouse.   Challenge Operating the EOT crane used to require manual labor. Operators would enter the driver's seat, hanging from the ceiling, to control the lifting and transport of steel coils. The environment was high-temperature and risky. Additionally, human workers had limitations in terms of working hours, needing at least four hours of rest. Once the EOT crane malfunctions, it can lead to a complete halt in the entire production line, resulting in losses of up to millions of dollars. Manual inspections required regular maintenance. The onsite environment was extremely harsh. Frequent on-site visits for measurements posed safety risks.   Solution Machine Vision Analyzer: NISE 3910E     To overcome these challenges, the steel company developed an automated EOT crane system using unmanned operation. The system consists of PoE cameras, a 3D scanner, an industrial computer for machine vision (AI/ML algorithms) and communication, a PLC, and an HMI. The NISE 3910E, installed in EOT cranes, offers robust protection against a range of environmental challenges. Notably, it is equipped to handle wide temperature variations, shocks, and vibrations, making it particularly suitable for the demanding and critical operating environment of a steel manufacturer's warehouse. With a COM port isolation design, NISE 3910E enhances system reliability by preventing surges in harsh environments. To create an unmanned EOT crane, the cranes needed machine vision. The steel company aimed to convert the visual information operators see into logical control commands through an IPC, which would then be interpreted and executed by the crane itself. NISE 3910E, powered by 12th/13th Gen Intel® Core™ i processors, integrates high-resolution cameras and 3D scanners via PoE LAN ports as image inputs for machine vision. It maximizes graphic processing performance through GPU cards on PCIe slots to effectively meet industrial AI requirements, from image processing/optimization to machine/deep learning and machine vision/object recognition. Additionally, NEXCOM offers a comprehensive software service called OT-X, a powerful new embedded IoT OS that can run on x86 platforms as an OT and IoT integrated gateway, supporting OPC UA. Industrial computers can be effortlessly transformed into software components. It enables intuitive Docker image deployment from cloud to edge and effortlessly extends microservices for OT and AI applications. The high-resolution cameras were installed on the unmanned EOT crane to capture two-dimensional images of the trailer platform, while the 3D scanner reconstructed vertical coordinates on these images. This development significantly improved computer vision technology. These images were then transmitted to an HMI used by the crane operator. The operator would indicate the storage position for the steel coils on the screen, and the control computer would automatically convert this storage position into coordinates and send them to the EOT crane control system for lifting. In addition to using a machine vision system to accurately position steel coil storage slots, the company also developed various intelligent logistics-related technologies. These included optimizing the lifting schedule, calculating the best lifting path using AI, and establishing a queue optimization system for unmanned warehouses. This system could accurately predict operation times, allowing drivers to arrive at the warehouse at the appropriate times for loading and unloading tasks. Furthermore, by analyzing the processes of hundreds of thousands of steel coils entering and leaving the warehouse, the steel company created an optimized storage slot prediction feature in their warehouse management system. This ensured that each steel coil could be delivered to the customer using the fewest lifting operations and the shortest distance. During lifting operations, the steel company designed a versatile smart lifting clamp that could identify the coil's identity, detect the coil's center, and accurately lift and transport the steel coil. Additionally, the clamp featured an active safety protection mechanism. Utilizing deep learning technology, it could detect personnel movement within the EOT crane's operating range, automatically identify and avoid obstacles.   Benefit and Result 1. Minimizing Labor Operations and Management:Unmanned EOT cranes can operate continuously for 24 hours without interruption. This automation even functions overnight when all employees have left. With backend management systems, the system can perform automatic stacking and inventory management the night before. 2. Optimizing Operation:After achieving full automation in lifting operations, the steel company integrated their unmanned EOT crane system with logistics and information flows, reducing the need for additional manual operations and management. For example, by connecting the EOT crane to logistics information, they could determine the coils required for the following day's shipment. The system would automatically rearrange the coils the previous night, positioning them near the loading bay. This shortened preparation time for the next day's shipment.Alternatively, when a delivery driver checked in outside the warehouse, they could swipe an ID card. Once the driver's identity and vehicle number were confirmed, the warehouse management system received the mission details and automatically initiated the lifting task for the designated steel coils. 3. Reducing Downtime and Maintenance Costs: The unmanned EOT crane system continuously monitors the condition of critical crane components such as motors, gears, and brakes. By analyzing data from these components, AI can assess their health and performance, enabling maintenance teams to intervene at the right time and prevent catastrophic failures. AI can also identify abnormal crane behavior by establishing baseline patterns and flagging deviations. If the crane's performance deviates from the norm, AI can issue alerts, allowing maintenance teams to investigate and address potential issues before they escalate into more significant problems. Since its deployment in 2018, the system has completed over 60,000 trips and lifted more than 300,000 steel coils. Following the success of the first system, the steel company introduced a second system in the same warehouse in November of last year. This warehouse can accommodate around 20,000 metric tons of steel coils, and the two unmanned EOT cranes achieve full warehouse automation for steel coil lifting. The steel company not only utilized this system internally but also exported it to steel plants in China. In 2019, they sold 12 systems, and during the COVID-19 pandemic last year, they provided remote assistance to customers for system calibration and implementation.  

  Background Hong Kong's railway system epitomizes efficiency and connectivity in urban transit. It serves as the city's arterial network, efficiently carrying millions of commuters daily. Renowned for its punctuality and reliability, the railway system stands as a model for urban transportation globally. Connecting diverse districts, blending residential, commercial, and cultural landscapes, its importance as an infrastructural backbone is undeniable. With this tremendous responsibility, ensuring the safety of railway infrastructures and operations is of utmost importance.   Challenge All types of transportation infrastructure, including highways, railways and subways, have one thing in common: they face critical challenges in operationalizing data across safety, maintenance efficiency and always-on security. Maximizing safety while minimizing maintenance costs is a massive challenge for any transit authority. In particular, let’s delve into operational safety. Manual inspections can take up to 10 times longer than machine inspections; it is both labour intensive and compromises accuracy. Inclement weather conditions, such as wind, rain and darkness, can further complicate manual inspections. Extreme precipitation events have become more frequent in Hong Kong. The hourly rainfall record was broken several times in the last few decades. In consequence, the inspections often fail to monitor the safety of operating tracks. For instance, vegetation intrusion or railway tracks shift can lead to derailments. Hundreds of incidents occur annually due to undetected infrastructure defects, vegetation and landslides. Unplanned disruptions result in an average of 300 hours of operations lost per year across these sectors. Traditional camera infrastructure and IP cameras often lack depth perception for accurate monitoring. Satellite imagery doesn’t have enough precision and is highly dependent on weather and satellite availability. Aerial monitoring is costly, with airspace regulation issues and the requirement for experienced pilots.   Solutions Advanced computing and AI are the path forward, covering tasks from image processing and optimization to machine and deep learning, as well as machine vision and object recognition for safety applications, including collision avoidance. NEXCOM, an NVIDIA NPN partner and Metropolis partner, in partnership with Kodifly (https://www.kodifly.com/), an NVIDIA Inception partner, a Hong Kong based spatial intelligence company, deployed the Intelligent Railway Infrastructure System (IRIS), assisting Hong Kong Railways in efficient and safe infrastructure management. NEXCOM’s product, ATC 3750-A6CR, a robust edge AI railway computer featuring the NVIDIA® Jetson AGX Orin™ system-on-module, connects high-resolution cameras and LiDAR sensors by PoE LAN ports as the image and Point Cloud Data (PCD) inputs for machine vision.     Additionally, NEXCOM offers a comprehensive software service called NEXCOM Accelerator Linux (NAL), integrating the NVIDIA JetPack™ 5.1.1 software development kit, Ubuntu 20.04, an onboard MCU library, and custom-made peripheral I/O functionality drivers. It provides developers with efficient control of the hardware and NVIDIA® Jetson™ system-on-modules through APIs, sample code, and I/O utility, facilitating a seamless solution to accelerate customers’ APP developments. The stable software architecture of NAL made it easy for software vendors to extract train information and develop programmes, allowing their software to successfully interface with the ATC 3750 and meet the requirements of this project.   IRIS, the infrastructure monitoring system, is powered by SpatialSense, a LiDAR and Camera scanner that can be mounted on any moving vehicle. The SpatialSense and processing device are installed on the train, surveying the railway infrastructure, capturing precise details, and creating a digital twin. The software analyses the railway circuit and helps to identify potential concerns, such as intrusions, obstacles or the growth of trees that can cause disruption of service on the tracks. The exact location of potentially hazardous instances is pinpointed directly on the 3D map in an intuitive web application. Staff from the control centre can view the alert in real time and immediately rectify the risk, such as by removing trees or repairing rails. This solution can also be used by railway operators to schedule maintenance and troubleshoot problems, helping to prevent accidents.   4 Steps of how Intelligent Railway Infrastructure System (IRIS) works     Moreover, this mobile computer facilitates real-time information monitoring by integrating LiDARs, IP cameras or mmWave radar. It provides essential functionalities such as driving and braking control, collision protection, people counting, traffic light detection, railway ballast bed monitoring, railway station monitoring and anonymized monitoring of drivers’ physiological states. These features aim to enhance safety in rolling stock applications.   The railway SKUs for ATC 3750-A6CR, along with the optional power isolation kit VTK PWA series, provide complete power protection. They hold the EN 50155 certification and are equipped with an M12 X-coded connector to prevent wire detachment. In such a complex environment that’s exposed to natural elements, a ruggedized solution is required. Equipped with passive cooling and an optional fan kit for hybrid cooling, the ATC 3750-A6CR can effectively dissipate heat over a wide temperature range in harsh environments. These features enable high-speed data processing, efficient operations and excellent connectivity, facilitating real-time obstacle detection for collision avoidance. NEXCOM plays a crucial role in unfenced environments by detecting and eliminating blind spots, where unexpected obstacles like animals or plants may enter, minimizing accidents, damages, deaths and delays while maintaining transportation volume and revenue.   Benefit and Result   Improved Inspection Efficiency By employing the AI solution, the public transportation network can reduce the impact of uncontrollable factors such as weather and labour shortages, significantly lowering costs and improving inspection efficiency.   Improved Prediction Accuracy Compared to traditional optical sensors, LiDAR can accurately detect the distance, location and shape of objects, collecting data from a wider range by emitting lasers in all directions. In this case, it is crucial to effectively and accurately identify the location of trees and whether surrounding objects will obstruct the train’s path.   Reduced Maintenance Costs According to the reports by the public transportation network and the Federal Railroad Administration, Hong Kong will spend $65 billion on railway asset maintenance and renewal in the next five years. By switching from breakdown maintenance to preventive maintenance, the AI monitoring solution enables substantial savings.   The smart transportation solution, built using NEXCOM’s ATC 3750-A6CR and Intelligent Railway Infrastructure System (IRIS), is a game-changing platform revolutionizing the transportation industry. The platform provides efficient, cost-effective and safe solutions, and has been widely adopted across numerous vertical markets. It contributes to new standards for innovation and technological advancement in the transportation sector.

Limitations of Traditional Disaster Management Systems Throughout history, humanity has constantly faced the threat of natural disasters such as earthquakes, hurricanes, wildfires, and floods, which have the potential to cause extensive destruction, loss of life, and property damage. Traditional disaster management systems rely heavily on pre-defined rules, unvalidated statistical models, and human expertise and interaction, struggling to manage and process vast, diverse data streams and account for complex variables or unforeseen outcomes. There are several examples demonstrating how traditional solutions available up until now have fallen short. For example, while satellite images are able to provide a broad overview of an area, due to the insufficient frame rate of the high-speed camera, insufficient detail in the image resolution, and limited camera angles, they might lack the fine detail needed for certain tasks, such as detecting shallow landslides or assessing damage to individual buildings. Geotechnical approaches using borehole inclinometers are expensive, complex, and time-consuming. It is also impossible to conduct continuous monitoring, which is not in line with the principles of scale.   High-performance computing and IoT technologies are reducing disaster damage AI is changing the way disaster warnings are issued. AI, combined with IoT, edge computing, cameras, and sensors, is bringing about significant innovations in disaster prediction. By utilizing generative AI, deep learning, and machine learning algorithms to train on datasets from environmental sensors, environmental images, and disaster information, AI can learn about known disaster types and phenomena. Through trained models, it can identify potential disaster situations and signs that humans cannot directly find. In the early stages of disaster warning, it can identify the type, location, and time of potential disasters, and take proactive disaster prevention measures and actions to reduce the scope and impact of disasters.   Rugged edge AI computing platforms and IoT frameworks enable real-time AI disaster prediction and warning systems AI can extract features and set labels from historical disaster datasets (including real-time environmental sensor values, high-resolution camera image files, and disaster fact records) to train various disaster models and identify potential disaster situations that are difficult for humans or traditional models to identify through model inference. Edge AI computing platforms can collect data sources from on-site sensors and cameras in real time and use pre-trained models to infer and identify disaster precursors to meet the needs of complex AI visual applications while also shortening warning response times. Combined with IoT frameworks, disaster prediction systems can be flexibly deployed in distributed geographical locations. In addition, disaster prediction SaaS developed with cloud-native environments and containerization technologies makes it easier to deploy AI models, AI inference engines, and microservices to edge AI computing platforms, accelerating the auto-scaling of cloud-ground integrated applications. However, disaster management systems in outdoor environments face several significant challenges. Here are some of the most critical ones: Durability and Environmental Resilience: Outdoor equipment must be rugged enough to withstand harsh conditions, including extreme temperatures, rain, wind, dust, and even impacts from flying debris during disasters like wildfires, floods, or landslides. Power Autonomy and Instability: Reliable power is crucial, but access to outlets can be limited outdoors. Disaster zones might even experience widespread power outages. This necessitates the system to be self-sufficient with a power generator or solar panels, which have limitations on power storage and energy collection, respectively. Furthermore, the voltage fluctuations caused by unpredictable power sources, such as damaged electrical grids, temporary generators, or solar panels with variable output depending on sunlight, also can disrupt the system's operation. Robust Connectivity and Data Transmission: Outdoor environments may experience intermittent or limited network connectivity due to factors like terrain obstructions, weather conditions, or the sheer distance from communication infrastructure. This can lead to disruptions in data transmission and potential data loss, which can negatively impact the accuracy and timeliness of disaster management efforts.   System Architecture   NEXCOM's ATC 3750-IP7-6C is a rugged edge AI computing platform designed for harsh environments. In addition to its high-performance AI computing power, it also integrates wireless communication modules, a variety of wired communication interfaces, external environmental sensors, and high-speed cameras. Its tightly integrated mechanical design, high-airtight waterproof components, three-proof coating protection, vacuum airtightness, and submersion testing helps guarantee stable operation in harsh environments. Powered by the NVIDIA Jetson AGX Orin system-on-module that delivers up to 275 (INT8) TOPS of AI performance, the ATC 3750-IP7-6C edge AI computing platform comes with the containerized operating system NAL (NEXCOM Acceleration Linux). With the NVIDIA JetPack 6.0 upgrade, it also features new Jetson Platform Services, which add foundational and AI analytics services, generative AI capabilities, and multiple building blocks such as the Video Storage Toolkit (VST) and NVIDIA DeepStream software development kit. This simplify solution development for developers by eliminating the need for repetitive development on NVIDIA Jetson, empowering them to quickly assemble full-featured edge AI systems and manage edge AI applications. Through REST APIs, developers can easily access a variety of microservices, enabling the construction of unified cloud-to-edge vision AI applications. This functionality delivers the seamless replication of cloud-developed microservices and trained AI models to edge devices using IoT gateway and OTA functions. The alternative IoT OS for ATC 3750-IP7-6C is AIC OT-X. Bridging the gap between OT, IT, and IoT, AIC OT-X converges applications and microservices on edge devices. Industrial computers can be effortlessly transformed into software components. This powerful embedded IoT OS, compatible with x86 platforms, functions as an integrated OT and IoT gateway, supporting OPC UA. It empowers intuitive Docker image deployment from cloud to edge and effortlessly extends microservices for OT and AI applications. The API gateway acts as a central hub for monitoring software usage, providing valuable insights into the utilization of various software functions. This information can be used to optimize resource allocation and ensure the system operates at peak performance. VST and DeepStream SDK microservices streamline the management, analysis, and optimization of inference performance for data coming from cameras and sensors. Developers can create sophisticated disaster sign recognition applications utilizing multi-camera tracking and zero-shot detection techniques powered by generative AI from the cloud to the edge. One of the key advantages of the ATC 3750-IP7-6C is its ability to seamlessly integrate with a wide range of environmental sensors. With various I/O ports, including serial and digital I/O, as well as a CAN bus interface, the system can collect data from sensors deployed in the field, providing the necessary fuel for AI models tasked with detecting early warning signs of potential disasters. Through NAL's built-in hardware interfaces, developers can effortlessly access external sensors and peripheral devices using APIs. This intuitive approach simplifies the process of acquiring sensor data and controlling peripheral devices, allowing developers to focus on building innovative applications. The sensors are deployed throughout the disaster zone to collect environmental data like temperature, wind speed, air quality, water levels, or ground movement. It can also be connected to more peripherals such as GNSS, IP cameras, and IEEE 1588 signal receivers. Designed to withstand the harshest conditions, the ATC 3750-IP7-6C is built to operate in demanding outdoor environments; it is certified with the IP67 rating. With a wide operating temperature range (-20°C to 70°C), vibration and shock resistance that meets MIL-STD-810 standards, and a 9-36V DC-IN power input, this rugged-edge AI computing platform can be deployed in remote locations and continue functioning reliably, even in the face of extreme conditions. Environmental sensor data, geospatial imagery, and geographic information can be transmitted to data centers via wired and wireless connections. The ATC 3750-IP7-6C offers a comprehensive suite of communication options, including Gigabit Ethernet (with PoE+ support), Wi-Fi 5/6, cellular (LTE/5G), and GNSS capabilities. This ensures seamless data transmission and situational awareness, enabling effective coordination with other agencies or response teams. People use various AI algorithms to classify images of collected data based on whether they should be reviewed or acted upon — if so, an alert is sent out to a command center. Staff from the control center can view the alert in real time and immediately rectify the risk. The technology company continuously refines AI models, provides probabilistic forecasts, and enables real-time monitoring for early detection. With advanced techniques like deep learning, AI can more effectively model highly complex, non-linear systems like weather patterns or wildfire behavior, potentially leading to more reliable, timely warnings and deeper insights into underlying disaster risks.

Background Taiwan, a densely populated island nation, faces unique challenges in managing its traffic systems. According to the statistics report by the Ministry of Transportation and Communications, the country boasts a staggering 23 million vehicles, nearly one per person. This high vehicle density translates to a constant presence of cars and motorcycles on the streets, leading to potential safety concerns at intersections, such as: Red light violations. Illegal parking. Disregard for pedestrian safety. These challenges necessitate effective strategies to ensure the safety of all road users in Taiwan   Challenge While Taiwan has previously utilized traffic intersection cameras for law enforcement, these systems faced certain limitations: Low camera resolution: This led to poor image quality, particularly in low-light conditions, such as rainy days or nighttime, hindering accurate identification of violations Ineffective file compression: This resulted in increased file sizes, placing significant strain on processing power and impacting overall efficiency. Limited camera angles and rigidity in judgment standards: CAMShift (Continuously Adaptive Mean Shift), a color-based object detection algorithm, is commonly used in for issuing traffic violations, However, CAMShift has some limitations. One of the major drawbacks of the standard variant is that it cannot track the desired target when the background (or an object nearby) is of the same color. These factors led to inconsistency in capturing violations and potentially unfair judgments, prompting public complaints and requiring resource-intensive manual reviews. These limitations highlight the need for innovative solutions to improve traffic surveillance capabilities in Taiwan.   Solution     Solution Architecture   TNEXCOM’s TT 300-A3Q are installed in the traffic signal box in major crossroads in Taipei and Taichung City. Comparing to other high performance products, the NEXCOM TT 300-A3Q‘s compact size could seamlessly integrate into the limited space, demonstrating its remarkable performance even in the challenging conditions of high temperatures and humidity. The device operates efficiently within a wide temperature spectrum of -5°C to 55°C and a humidity range of 10% to 95%. With all I/Os at front, it is designed for easy maintenance and installation. The police officer uses TT 300-A3Q’s to connects CCTV cameras to record data and perform plate recognition to catch traffic violators who pass speed limit, cross red line, block pedestrian and parking violation. Powered by an Intel® 12th or 13th Gen Core™ processor, the TT 300-A3Q delivers maximum graphics processing power and supports connections to various storage devices, peripherals, and internal components. This empowers it to effectively handle industrial AI applications, encompassing tasks like image processing and optimization, machine learning, deep learning, and machine visionAI algorithms can analyze video footage and sensor data with high accuracy and high speed, reducing the chances of human error in identifying violations. This can lead to fairer enforcement and fewer wrongful citations. With higher resolution cameras, transmitting the increased data requires higher bandwidth and more stable interfaces like 5G and USB3.2 Gen2, which are supported by the TT 300-A3Q.   Benefits and Results In the future, City Government will expand the TT-300-A3Q edge AI computing system to other intersection. The main benefits are as below. 1. Enhance Law Enforcement Accuracy: In the past, traffic intersection violation identification was done by taking pictures with cameras in traditional way, such as CamShift algorithm. However, it has some limitations in tracking the desired target with the background of the same color. This would lead to misjudgments or repeated manual judgments. By implementing the NEXCOM TT 300-A3Q edge AI computing system, video footage and sensor data analysis can achieve high accuracy and speed, significantly reducing the likelihood of human error in identifying violations. Besides, it can also reduce labor costs and increase work efficiency in police office. 2. Optimize Traffic Flow: NEXCOM TT 300-A3Q edge AI Computing system can analyze real-time traffic data from various sources to optimize traffic flow. It can control traffic signals in real-time, adjusting signal timings based on actual traffic flow. This leads to reduced congestion, shorter travel times, and decreased fuel consumption. It also can suggest alternate routes in real-time, considering current traffic conditions. This minimizes delays and helps drivers avoid unexpected obstacles.  

As we encounter consistent yearly global population growth and unexpected pandemic outbreak, automated border control solutions are becoming more desired than ever. Nowadays, for travelers to enter the border of their destination, they can undergo an identity verification process by AI recognition, together with biometric verification via passport, facial, and fingerprints at eGates. NEXCOM is committed to implement the 12th Gen Intel® Core™ processor and Intel® 600 series chipset designed fanless visual edge computers into airports and ports. When installed, this computer can reduce labor costs and minimize contact while enhancing efficiency and security; leading to a fulfilling traveler’s experience.   When executed at customs, NEXCOM’s NDiS B561 fanless visual edge computer provides travelers with an intuitive and convenient border crossing process. Depending on the setting, operators may request up to three HDMI ports for three 4k2k independent displays and eight USB 3.2 for cameras, fingerprint scanner, passport scanner, and other peripherals. As this fanless visual edge computer connects to the border control system via WAN, it utilizes either LAN, Wi-Fi 6E, 4G, 5G, and AI recognition technologies to validate the traveler’s identity from the cameras and scanners.   Powered by the 12th Gen Intel® Core™ processor, Intel® 600 series chipset, and Intel® integrated UHD 770 graphics engine. The NDiS B561 handles challenging multimedia content and AI applications effortlessly via its high-speed computation capabilities. With practical I/Os, high-speed wireless technologies, AI recognition, and dependable technical support, it delivers an intuitive and reliable automated border control solution; significantly optimizing passenger flow.   Customer service has always been one of the utmost priorities for NEXCOM. Thus, the I/Os and chassis of NDiS B561 were customized to perfectly fit users’ application demands. Beyond I/Os, BIOS can also be customized to set and display users’ logos whenever this automated border control solution is booted up to promote brand recognition. NEXCOM’s technical support team is always on duty to provide and assist customers in completing comprehensive system validation. The NDiS B561 is a reliable and efficient high-performance visual edge computer that maximizes convenience and safety, providing a pleasant journey for everyone.   Application Diagram     Key Features for Application Needs Support 12th Gen Intel® Core™ i9/i7/i5/i3 LGA socket type embedded processor, up to 35W Intel® Q670E Intel® integrated UHD graphic engine driven by Xe architecture Support 3 independent 4K2K@60Hz display output. HDMI 2.1 resolution supports up to 8K@60Hz 1 x HDMI 2.1, 2 x HDMI 2.0 8 x USB 3.2, 4 x COM 1 x GbE LAN, 2 x 2.5G GbE LAN (PoE for B561-PoE) Support M.2 Key B/E/M Support extended temperature -20~60°C (B561 only) Fanless design