The Trend 5G technology has been launched at an astounding pace and is continuously accelerating in its development, enhancing the functionality and performance of FWA (Fixed Wireless Access) application. Initially, FWA was a means to replace economically unviable wired networks for last-mile connectivity in rural and remote areas. Empowered by 5G and benefiting from increased bandwidth, complete connectivity, and rapid, flexible deployment, FWA has branched out further into various vertical markets. This recent advancement in 5G FWA technology has set up an arena for players from all over the world to compete for substantial business opportunities.   According to a June 2023 report by Ericsson, it is projected that by 2028, there will be over two hundred million 5G FWA users, constituting 17% of fixed network connections. The report also notes that there are already over 100 telecommunications companies worldwide offering 5G FWA application services. In the context of global efforts to bridge the digital divide, 5G FWA has become a crucial component in achieving nationwide broadband connectivity.   Currently, the primary application of 5G FWA is in public network scenarios where wireless transmission is used to reach the last mile. However, with the completion of the 3GPP Release 17 standardization, 5G applications are becoming more comprehensive. In addition to the fundamental functions of 5G, such as eMBB (Enhanced Mobile Broadband) in both FR1 and FR2 frequency ranges, URLLC (Ultra-Reliable Low Latency Communication), and mMTC (massive Machine Type Communication), advanced features like 5G network slicing, 5G TSN (Time-Sensitive Networking), 5G security, and NTN (non-terrestrial networks) enable 5G FWA technology to be used as 5G private network in various settings. These settings include smart factories, smart manufacturing, smart cities, and intelligent transportation (5G-V2X), etc.   The Challenge The widespread adoption of 5G FWA across various sectors and situations underscores the importance of comprehending the unique requirements of each application in order to identify the most suitable equipment.   For service providers currently evaluating different options, it's advisable to take into account the following factors: the reliability of equipment for managing traffic, meeting critical low-latency demands, the necessity for mobility and outdoor wide-area connectivity, and a comprehensive, future-proof solution that caters to both present and future requirements.   For different field applications, 5G FWA can essentially be categorized into four attribute grades: Consumer Grade, Enterprise Grade, Industrial Grade, and Telecom Grade. Different grades of 5G FWA focus on different features and functions, allowing various usage scenarios to better showcase the advantages of 5G FWA. The following TABLE I illustrates the characteristics of different grades of 5G FWA.   TABLE I5G FWA GRADES AND THEIR ATTRIBUTES AttributeGrade Bandwidth Performance Computing (AI) Latency Reliability Slicing Security PoE LAN IP Code Consumer ★ ★★ ★ ★★★ ★ ★ ★ ★ ★ - Enterprise ★★ ★★★ ★★★★★ ★★★ ★★★★★ ★★ ★★★★★ ★★★★★ ★★ - Industrial ★★★★★★ ★★★ ★★★★★ ★ ★★★ ★★★ ★★★ ★★★★★ ★★★ IP5xIP6x Telecom ★★★★★ ★★★ ★★★★★ ★★★ ★★★ ★★★ ★★★ ★★ ★★★ IP6x Requirements: Low ★/Middle ★★/High ★★★   Consumer Grade Deployment location: homes, suburban areas, islands Deployment type: indoor Purpose: 5G wireless transmission to replace wired transmission Benefits: increased bandwidth, fast deployment, reduced cost of laying wires Network environment: private and public Applications: MHN (mobile hotspot network), AP (access point)   Enterprise Grade Deployment location: office, bank, shopping mall, campus Deployment type: indoor Purpose: optimized user experiences and services Benefits: increased bandwidth, high performance, latency, and stability Network environment: private and public Applications: WIPS, SASE, MHN   Industrial Grade Deployment location: factory, smart cities, healthcare, sports event video streaming Deployment type: indoor, semi-outdoor and outdoor Purpose: optimized network bandwidth and performance, ultra-low latency, Quality of Service (QoS Benefits: stability and increased security Network environment: private Applications: Network Slicing, PoE Control, Firewall, IoT Gateway   Telecom Grade Deployment location: utility pole, smart traffic lights and control Deployment type: indoor, semi-outdoor and outdoor Purpose: consistent and stable network performance Benefits: stability and increased security Network environment: indoor, semi-outdoor and outdoor Applications: 5G Network Slicing, Network-in-a-box, 5G-V2X   Solution Realizing the downsides of too many alternatives on the market and customers’ confusion, NEXCOM provides clarity by tailoring its products to cater to diverse application grades and settings for 5G FWA applications, suitable for deployment in both private and public networks. NEXCOM's range of 5G FWA appliances includes a selection of desktop units and 1U servers, categorized according to CPU performance and offering various wireless and wired connectivity options.   NEXCOM's desktop uCPEs are designed with both RISC and x86 architectures and are available either as a complete solution package with network OS or as white-box options for companies with own software research and development resources.   The entry-level appliance in the desktop 5G FWA lineup is the Arm-based uCPE - DTA 1376. This device is equipped with an NXP ® Layerscape® 4 cores processor that incorporates DPAA (data path acceleration architecture) to deliver a comprehensive set of networking accelerations, effectively integrating all facets of packet processing. DTA 1376 features seven 1GbE copper ports for Ethernet connectivity and offers optional support for 5G FR1 and Wi-Fi connectivity.   The mainstream appliance in the desktop 5G FWA lineup is Intel-based uCPE – DTA 1164W Series. Powered by Intel Atom® C3436L 4 core CPU and featuring a maximum of 16 GB of DDR4 ECC memory, M.2 SATA 2242 Key M 8GB SSD, it supports six 1GbE RJ45 copper ports, two 1Gb ports , Wi-Fi 6 and PoE, capable of providingup to 30W (802.11at) with a 72W 54V PoE power adaptor.   The Intel-based uCPE – DFA 1163 Series stands out as the highest-performing unit among the 5G FWA desktop uCPE lineup. It is equipped with an Intel Atom® C3558R/C3758R processor, boasting 4 or 8 cores respectively. This professional uCPE integrates a 10GbE SFP+ fiber LAN port for upstream data transmission to back-end Ethernet switches and onward to central servers. It also features copper ports with varying link speeds, including two 2.5GbE RJ45 ports and eight 1GbE Ethernet switch ports, enabling Ethernet services for IoT devices, such as VLAN and QoS. In terms of wireless connectivity, the DFA 1163M/Q SKUs stand out FWA product line with its support not only for Wi-Fi and 5G FR1 but also for 5G FR2 (mmWave).   The industrial grade DIN rail for 5G FWA applications - ISA 141 – is designed for deployments in relatively harsh environments. Powered by Intel’s quad-core Atom® processor, it is a compact, fanless appliance equipped with three 1GbE copper ports for network connectivity with one fiber combo port. The compact DIN rail design allows ISA 141 to be easily embedded in existing network infrastructure; while the out-of-band (OOB) management function enables IT personnel to maintain the devices remotely, guaranteeing consistent, high-performance operation. Its exceptional feature set includes dual Wi-Fi and dual 5G for concurrent connectivity and wireless load balancing, ensuring highly adaptable and advanced wireless connectivity.   The performance of each 5G FWA uCPE was tested through the Transmission Control Protocol (TCP) standard. The tests were performed at the NEXCOM office through Amari Callbox, 3GPP compliant eNB/gNB and EPC/5GC. The topology is shown in Figure 1.   Figure 1. 5G FR1 NSA/SA Test Topology   In 5G FR2 NSA mode, NEXCOM uCPE boxes underwent testing with a 3CC configuration, whereas 5G FR1 SA and NSA utilized the maximum Callbox capacity of 4CC. In this context, 3CC and 4CC denote the number of aggregated carriers employed for testing, dictated by test equipment configuration and network requirements. The outcomes are integral to understanding the uCPEs' performance under realistic and demanding conditions.   The test primarily emphasized download capabilities, allocating an average of 70% of Amari Callbox resources for this purpose. Meanwhile, approximately 20% were reserved for upstream tasks, and the remaining 10% were allocated for other functions. The achieved results for each 5G FWA uCPE were standardized and are presented in Mbps in TABLE II.   TABLE II5G FWA PRODUCT PORTFOLIO, UPLINK AND DOWNLINK SPEED TEST RESULTS and GRADE MAPPING     In the 5G FR1 testing, the four DUTs utilize 5G modules sourced from diverse manufacturers. While the 5G FR2 NSA DUTs leverage two specific 5G modules: the X55 and X62. The X55 module provides compatibility with 3GPP Release 15, while the X62 module - an entry-level solution - supports 3GPP Release 16 with an exceptional cost-performance ratio. For a more in-depth understanding of each uCPE box's testing configuration and results, kindly request further information from NEXCOM representatives.   Overall tests prove that each of the tested appliances is ready for 5G FWA deployments in both SA (Stand Alone) and NSA (Non Stand Alone), i.e. public and private network environments.   Conclusion 5G FWA uCPE applications are boundless: from enabling real-time data processing for smart cities to ensuring mission-critical communications in industrial settings, and from revolutionizing healthcare with telemedicine solutions to providing seamless connectivity in remote areas. The impact of 5G FWA uCPE ensures reliable, low-latency, and high-bandwidth connections, and penetrates across diverse sectors, driving innovation and progress.   NEXCOM provides a diverse 5G FWA uCPE range tailored for various sectors and use cases. Each appliance comes with predefined features and expandable space, allowing customers to select additional options for a customized uCPE that suits their requirements. To make it simple, NEXCOM’s 5G FWA uCPE is also integrated with a light-weighted network OS for easy setting & control, enabling customers to concentrate on their applications without worrying about complex networking configurations.

The Trend The world goes digital. This statement is no news anymore but a way of life. We are seeing big data generated every second online in exponential volume and speed. According to the forecast, just in a few years from now the total volume of information will be more than doubled: from 75 zettabytes (ZB) in 2021 to 175ZB in 2025[1].   Gadgets of personal use (cell phones, laptops, PCs) are hitting a record high for their storage and memory capacity, together with more cloud services available on the market. The same growing demand has been seen in the commercial sector as well, as evidenced by hybrid clouds of different scales which are being built by enterprises and institutional organizations large and small, either on their own or commissioned by service providers.   Data continuously evolve with technology. Pure analog has given way to digital signals decades ago. To transport its sheer volume nowadays is in itself a formidable task, and critical data must be shielded with another layer of security during transport. Cyber security, therefore, becomes an indispensable part before data reach the final destination, even more so nowadays when daily activities go online.   The Challenge How to integrate a new solution into an existing legacy network infrastructure has always been a big headache for IT professionals. A painless upgrade is ideal but not always realistic. More often than not, partial downtime is necessary. As a result, organizations have only one question on their plate: whether they are ready to move further aligned with the latest tendencies or to step aside.   For those who want to stay rock-solid, it is important to find the appliance that can enable more effective and secure network management. By effectiveness here, fast transfer, analysis, store a bigger quantity of data are meant. And with proper network management tools, enhanced network security and accessibility can be provided.   NEXCOM Solution NEXCOM proudly introduces a new appliance to enhance its cyber security product line – NSA 5190. It is a new generation 1U rackmount appliance with the newest Intel® Core™ processor and the latest PCIe 4.0 interface. NSA 5190 is a modular, flexible network solution, which will ideally fit into SD-WAN, web monitoring, load balancing, and network virtualization deployments.   12th Gen Intel® Core™ processor (former code-named, Alder Lake S) brings additional computing power to proceed with bigger volumes and heavier workloads. It became possible due to a combination of performance- and efficient-cores in a single CPU, or P cores and E cores respectively[2]. The hybrid architecture achieves higher performance with less power consumption. The CPU also offers large caches to store data so that requests for data can be carried out faster.   Another important capability to highlight is the Intel® 600 series chipset that brings additional expansion options and value-added features. Several examples include, integrated MAC, Intel® Rapid Storage Technology, Intel® Trusted Execution Technology, and more.   Intel® Rapid Storage Technology provides enhanced data protection and expandability. Regardless of the system operating with one or multiple hard drives, users can experience the benefits of both enhanced performance and lower power consumption. Moreover, under the condition that more than one drive is used, additional protection against data loss in the event of hard drive failure is available.   Besides new capabilities brought by the processor, when compared with previous generation appliances of the same product line there is a key advantage in memory speed and capacity. NSA 5190 supports four DDR4 2666/3200 DIMM, with a maximum memory of 128GB, which is twice its predecessor.   NSA 5190 also features an upgrade in the LAN connector interface from PCIe 3.0 to PCIe 4.0. The greatest advantage of PCIe 4 over PCIe 3 is in its speed, it doubles the per-lane bandwidth to 2 gigabytes per second and is backward and forward compatible. By adopting dedicated LAN modules, NSA 5190 proves itself as a highly configurable networking appliance.   Finally yet importantly, flexibility. With decades of RD experience, NEXCOM mastered designing scalable multifunctional appliances for different application scenarios. NSA 5190 is not an exception. The mainboard is designed with an edge connector for an add-on card. The choice of card to be installed depends on customers’ requirements; it could be either FPGA, AI, or smart NIC card. Each provides its additional capabilities and serves its purpose.   Conclusion The evolvement of technologies brings new possibilities yet new challenges, and NEXCOM’s newly released 1U rackmount - NSA 5190 - is ready for both. Its futureproof design, with significantly increased memory capacity, data transfer speeds, and a set of optional features, makes NSA 5190 a perfect appliance for various use cases in businesses of all scales. NSA 5190 can manage heavy workloads without wearing out the CPU and is able to proceed with big data volumes in a shorter time.     NSA 5190 1U Rackmount Appliance with 12th Gen Intel® Core™ Processor, 2 x 1GbE RJ45 ports, and 4 x LAN Module Slots   12th Gen Intel® Core™ processor PCH: R680E 4 x DDR4 2666/3200 non-ECC/ECC UDIMM, up to 128GB 1 x M.2 2280 Key M (SATA) 1 x TPM module 1 x PCIe4 x4 connector for low profile riser card 2 x 1GbE RJ45 ports 4 x LAN module slots  

The Trend In an era defined by rapid technological advancement and digital transformation, the landscape of cybersecurity is undergoing fundamental change. As cyber threats increase, enterprises face mounting challenges in defending their assets against an ever-expanding array of attacks. High-profile data breaches, coupled with a global shortage of skilled cybersecurity professionals, underscore the urgent need for innovative solutions capable of safeguarding sensitive data and critical infrastructure. Against this backdrop, the convergence of artificial intelligence (AI) and cybersecurity emerges, promising to revolutionize the way to detect, respond to, and mitigate cyber threats.   The surge in requests for implementing AI algorithms into cybersecurity is driven by several compelling trends. From the constant attacks of advanced cyber threats to the pressing need for regulatory compliance, IT personnel worldwide are seeking intelligent and adaptive security solutions capable of keeping pace with the evolving threat landscape. Furthermore, the integration of AI into security operations empowers organizations to automate routine tasks and achieve greater operational efficiency.     The Challenge As companies start their journey of implementing AI cybersecurity hardware, they encounter countless struggles that demand innovative solutions and strategic approaches. The primary obstacle is the complexity of integrating AI hardware into existing IT infrastructure seamlessly. IT professionals must navigate compatibility issues, interoperability concerns, and the need for seamless integration with established security systems. Additionally, the resource-intensive nature of AI cybersecurity requires careful consideration of computational resources, memory allocation, and storage capacity to ensure optimal performance and scalability.   Moreover, the sensitive nature of data processed by AI cybersecurity hardware underscores the critical importance of privacy and security. IT professionals face the tough task of safeguarding sensitive data against breaches, unauthorized access, and compliance violations while harnessing the power of AI for threat detection and mitigation. Balancing the need for robust data protection measures with using data effectively for AI-driven insights is a delicate challenge, requiring the implementation of rigorous encryption and access control techniques.   NEXCOM Solution NEXCOM offers a solution to empower organizations to explore the potential of AI-driven cybersecurity to fortify network defense, protect digital assets, and secure a safer future in the digital age.   NEXCOM's NSA 7160R-based cybersecurity solution addresses the multifaceted challenges in implementing AI hardware in cybersecurity operations. Leveraging a modular design and sharing the same form factor with the previous generation of its product family, NEXCOM's solution mitigates integration complexity by seamlessly integrating with existing IT infrastructure, minimizing compatibility issues.   Furthermore, NSA 7160R is designed with scalability in mind, enabling companies to navigate resource constraints effectively by dynamically allocating computational resources, optimizing memory usage, and scaling storage capacity to meet evolving operational demands. Customers can choose different DDR5 speeds based on their budget and requirements. A flexible configuration of LAN modules enables up to 2.6TB Ethernet connectivity per system or allows up to 128GB of additional storage through storage adaptors.   By prioritizing performance optimization, NEXCOM's solution enables enterprises to achieve superior detection accuracy, response times, and scalability, delivering actionable insights and proactive threat mitigation capabilities to safeguard against emerging cyber threats effectively. NSA 7160R supports the latest dual 5th Gen Intel® Xeon® Scalable processors and is backward compatible with 4th Gen Intel®Xeon® Scalable processors, allowing customers to scale up both in CPU core count and processor generation.   In addressing the critical concerns of data privacy and security, NEXCOM's solution implements hardware-based robust encryption protocols, ensuring the confidentiality, integrity, and availability of sensitive information processed by AI. A series of various accelerators include Intel® Crypto Acceleration, Intel® QuickAssist Software Acceleration, Intel® Data Streaming Accelerator (DSA), Intel® Deep Learning Boost (Intel® DL Boost), Intel® Advanced Matrix Extensions (AMX), and more. [1]The set of accelerators may vary depending on selected processor SKU.   NSA 7160R empowers IT personnel to proceed with deployments confidently. To validate its efficacy in AI cybersecurity, NEXCOM conducted a series of tests comparing two configurations powered by dual 4th Gen Intel® Xeon® Scalable processor (DUT 1) and dual 5th Gen Intel® Xeon® Scalable processor (DUT 2). CPU SKUs’ chosen for the testing are correlated by performance and core count for fair and unbiased comparison. The rest of the configurations were of utmost equivalence. Detailed test configuration is shown in TABLE I.   For the tests, two open-source security AI models were chosen: MalConv and BERT-base-cased.     TABLE IDUT 1 AND DUT 2 TEST CONFIGURATIONS Item DUT1 DUT2 4th Gen Intel® Xeon®-based 5th Gen Intel® Xeon®-based CPU 2 x Intel® Xeon® Gold 6430 processors 2 x Intel® Xeon® Gold 6530 processors Memory 252GB16 (8+8) x 32G DDR5 4800 RDIMMs SSD 512GB1 x 2.5" SSD SATA III Storage 1.2TB4 x M.2 2280 PCIe4 ×4 4TB NVMe modules in slot 2 Ubuntu 22.04 Kernel v5.19     Test Results for MalConv AI Model MalConv (Malware Convolutional Neural Network) is an deep learning-based approach used in cybersecurity for the purpose of malware detection.   While traditional malware detection methods rely on signatures or behavior analysis, vulnerable to circumvention by polymorphic or unseen variants, MalConv utilizes convolutional neural networks (CNNs) to directly analyze executable file binary data. Trained on both malicious and benign files, MalConv learns to distinguish between them based on binary data patterns. This enables MalConv to detect polymorphic or unseen malware variants by identifying malicious characteristics within the binary code itself, bypassing reliance on signatures or behavior analysis.   Latency and throughput in the MalConvn AI model were tested on both DUTs. Latency and throughput in MalConv testing provide valuable insights into its performance, responsiveness, scalability, and efficiency in AI cybersecurity applications. Latency measurement helps determine the time taken by MalConv to analyze an input file and provide a classification (malicious or benign), while throughput measurement evaluates the ability of MalConv to process multiple files or data streams simultaneously within a given time frame.   The results of latency and throughput MalConv tests for different opt methods are shown in TABLE II.   TABLE IIMALCONV AI MODEL TEST RESULTS FOR LATENCY AND THROUGHPUT Framework Opt Method Model Platform Latency(ms) Throughput(samples/second)/(FPS) tensorflow 2.15.0 INC 2.2 Malconv.inc.int8.pb DUT 1 12.15 82.3 Malconv.inc.int8.pb DUT 2 11.18 89.47 onnxruntime 1.16.3 INC 2.2 Malconv.inc.int8.onnx DUT 1 16.55 60.43 Malconv.inc.int8.onnx DUT 2 14.47 69.1   Based on the achieved results we can conclude that 5th Gen Xeon based server shows better results in both opt methods and both test items (latency and throughput).   Lower latency is essential for real-time threat detection, enabling rapid response to security incidents. 5th Gen Xeon DUT shows 8% lower latency in tensorflow 2.15.0 framework by spending 0.97ms less than 4th Gen Xeon DUT. 5th Gen Xeon DUT shows 13% lower latency in onnxruntime 1.16.3 framework by spending 2.08ms less than 4th Gen Xeon DUT.   Figure 1. MalConv AI model test results for latency     Higher throughput indicates greater volume-handling capacity, which is essential for analyzing large datasets efficiently.   5th Gen Xeon DUT shows 9% higher throughput in tensorflow 2.15.0 framework by analyzing 7.17 more samples per second than 4th Gen Xeon DUT. 5th Gen Xeon DUT shows 14% higher throughput in onnxruntime 1.16.3 framework by analyzing 8.67 more samples per second than 4th Gen Xeon DUT.   Figure 2. MalConv AI model test results for throughput     Test Results for BERT-base-cased AI Model BERT (Bidirectional Encoder Representations from Transformers) is a powerful natural language processing model developed by Google. The "base" version refers to the smaller and computationally less expensive variant of BERT compared to its larger counterparts like BERT-large. The "cased" variant retains the original casing of the input text, preserving capitalization information.   In AI cybersecurity, BERT-base-cased offers a versatile framework for natural language understanding in cybersecurity applications. This model can be utilized for various tasks such as threat intelligence analysis, email and message classification, malicious URL detection, incident response and threat hunting, and more.     During the tests static, dynamic and FP23 BERT-base-cased model latencies of each DUT were analyzed. The tests were conducted using 1 and 4 active cores to determine if there would be any improvement with increased core involvement. The results are shown in TABLE III.   Static model latency refers to the time it takes for the pre-trained Bert-base-cased model to process input data and make predictions without further adaptation. Dynamic model latency measures the time required for Bert-base-cased to adapt or fine-tune itself during runtime based on evolving threat conditions or changes in the operating environment. FP23 model latency represents the latency of Bert-base-cased when configured to maintain a specific false positive rate of 23%. Minimizing FP23 model latency allows security teams to respond more quickly to security incidents, reducing the time and resources required for investigation and mitigation.     TABLE IIIBERT-BASE-CASED AI MODEL TEST RESULTS FOR STATIC, DYNAMIC AND FP23 LATENCIES Framework Opt Method Core Used for Test Platform Static qatmodel Latency(ms) Dynamic qat model Latency(ms) FP32model Latency(ms) Pytorch 2.1.0 IPEX 2.1.100 1 Core DUT 1 97.5 472.46 862.99 DUT 2 86.28 327.53 726.27 4 Cores DUT 1 29.84 118.94 261.3 DUT 2 25.08 98.78 214.32     Based on the achieved results we can conclude that 5th Gen Xeon based server shows better results in all 3 test items (static, dynamic and FP23 BERT-base-cased model latencies) and both test setups for CPU resource allocations (1 and 4 cores).   Lower static model latency is desirable for real-time threat detection, enabling rapid analysis of text data such as security alerts, email content, or chat messages. Longer latency may introduce delays in processing, affecting the responsiveness of security operations and hindering timely threat mitigation efforts. 5th Gen Xeon DUT shows 12% lower latency in 1 core scenario by spending 11.22ms less than 4th Gen Xeon DUT. 5th Gen Xeon DUT shows 16% lower latency in 4 cores scenario by spending 4.76ms less than 4th Gen Xeon DUT.   Figure 3. BERT-base-cased AI Model Test Results for Static Latency     Lower dynamic model latency enables the model to respond more quickly to emerging threats and shifting attack patterns, enhancing its effectiveness in cybersecurity operations. 5th Gen Xeon DUT shows 31% lower latency in 1 core scenario by spending 144.93ms less than 4th Gen Xeon DUT. 5th Gen Xeon DUT shows 17% lower latency in 4 cores scenario by spending 20.16ms less than 4th Gen Xeon DUT.   Figure 4. BERT-base-cased AI Model Test Results for Dynamic Latency     Achieving lower FP23 model latency is essential for minimizing false positives while maintaining high detection accuracy. This ensures that security teams can focus their efforts on genuine threats without being inundated by false alarms. 5th Gen Xeon DUT shows 16% lower latency in 1 core scenario by spending 136.72ms less than 4th Gen Xeon DUT. 5th Gen Xeon DUT shows 18% lower latency in 4 cores scenario by spending 46.98ms less than 4th Gen Xeon DUT.   Figure 5. BERT-base-cased AI Model Test Results for FP23 Latency     Test Summary Both devices successfully executed AI security software, with the platform utilizing the 5th Gen Intel® Xeon® Scalable processor showcasing superior performance over the server employing the 4th Gen Intel® Xeon® Scalable processor. Both platforms demonstrated efficiency in latency and throughput for security-related tasks, and proved ready for AI cybersecurity.   Conclusion As the cybersecurity landscape continues to evolve, IT personnel must remain proactive in adapting to emerging threats and leveraging the latest advancements in AI technology. Integrating AI algorithms, such as MalConv and Bert-base-cased, into cybersecurity operations represents a significant advancement in the fight against cyber threats.   NEXCOM’s NSA 7160R servers offer enhanced threat detection, rapid response times, and improved operational efficiency, addressing the ever-evolving challenges faced by enterprises in safeguarding their digital assets. As both tested platforms demonstrate their significant contribution to addressing cybersecurity workloads, the decision on which platform to choose ultimately rests with the customer, who can select based on their specific requirements and the performance achieved.   Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries.

NEXCOM today announced that its edge AI mobile computer for in-vehicle and rail — “IP67 AI Intelligent In-Vehicle/Railway Computer ATC 3750-IP7-8M” — and “In-vehicle/Railway Supercapacitor UPSVTK-SCAP” have won the 2025 Taiwan Excellence Award, demonstrating global leadership in automotive technology.   IP67-rated edge AI mobile computer for rail/in-vehicle: ATC 3750-IP7-8M The ATC 3750-IP7-8M, powered by the NVIDIA® Jetson AGX Orin™ system on module (SOM), delivers up to 275 TOPS and supports a wide range of autonomous machines and advanced in-vehicle applications such as advanced driver assistance systems (ADAS), automatic number plate recognition (ANPR), autonomous mobile robots (AMRs), machine learning (ML), intelligent transportation systems (ITS), and railway safety. Moreover, the product has obtained automotive E-mark and railway EN50155 certifications and achieves IP67 protection, making it one of the industry’s first high-end edge AI in-vehicle/railway computer integrating intelligent image recognition and AI video analytics technology.   Supercapacitor UPS: VTK-SCAP VTK-SCAP is an advanced uninterruptible power supply (UPS) developed by NEXCOM. Compared with traditional lithium-battery UPS, VTK-SCAP utilizes supercapacitors and operates in a wide temperature range of -35°C to 80°C, effectively addressing the extreme temperature variations in vehicle environments and meeting the needs of applications requiring delayed shutdown and backup data. It can be flexibly expanded with up to one master device and three secondary ones to support up to 200W computer systems, depending on the end customer's usage scenario and power requirements.VTK-SCAP has been certified by E13 mark and EN50155, making it suitable for in-vehicle and railway applications and providing clients with a flexible and comprehensive mobile computing solution. NEXCOM is committed to providing comprehensive AIoT digital transformation solutions. The company’s mobile computer series has repeatedly won national awards, demonstrating its global leadership in technology. NEXCOM will continue to invest in resources to assist global customers in providing superior intelligent in-vehicle solutions, working together towards a smart and sustainable future.   Learn more about NEXCOM’s mobile computing products:https://www.nexcom.com/Products/mobile-computing-solutions   Taiwan Excellence Award-Winning Products: IP67 AI Intelligent In-Vehicle/Railway Computer ATC 3750-IP7-8M In-vehicle/Railway Supercapacitor UPS VTK-SCAP

ロンドンの賑やかなテムズ川沿いには、洗練されたデジタルトーテムが静かな案内役として立ち並び、訪れる人々を迎えています。これらのモダンな案内板は、リアルタイムの船のスケジュールや天気情報、地域の詳細情報を表示し、リバーサイドでの体験一新しています。このスマートシティの進化の中心にあるのが、NEXCOMの強力なNeu-X102-N50 であり、これら革新的な情報ハブを支える原動力です。   これらの革新的な情報トーテムは、市内のウォーターフロントの観光地全体で、観光客の体験に革命をもたらしています。その外観は地域の美観に合わせて異なるものの、中核を担うのは、NEXCOMの強力なエッジコンピューティングシステム、Neu-X102-N50です。   これらのトーテムの中心には、屋外用途向けに特化した優れた技術が組み込まれています。Neu-X102-N50 は、Intel® Processor N50と最大16GB のRAMを搭載しており、厳しい環境でもスムーズなパフォーマンスを実現します。また、-5° C～50° Cまでの動作温度範囲に対応しており、多様な気候条件でも適応可能です。   Neu-X102-N50 の卓越した技術力は、プロセッサにとどまりません。最大2つのHDMIポートをサポートし、鮮やかなコンテンツを再生して、人目を引くビジュアルで観光客を引き付け、情報を伝えることができます。更に、M.2およびmPCIeスロットを備え、ストレージの拡張やLTEおよびWi-Fi 6 機能に対応し、リッチコンテンツの保存と超高速ワイヤレス接続を実現します。これらの機能により、トーテムは、高い利用者数を誇るエリアでも容易に対応できる、包括的な情報ハブとして機能します。   観光客は、鮮やかな32インチのタッチスクリーンディスプレイを通じて、スケジュールや天気情報以外にも豊富な情報にアクセスできます。地元の観光スポットやおすすめの飲食店、さらにはリアルタイムの大気質データまで、指先ひとつで確認できます。このエッジコンピューティングシステムは、デュアル2.5GbE LANポートと4G LTE接続を備えており、常に最新の情報を提供します。また、USB光センサとCOMポートを通じて、明るさを自動調整することができ、さまざまな光の条件下でも情報を読み取り易くするとともに、システムの省エネにも貢献しており、現代の都市の持続可能性の目標にも合致した設計が実現されています。   トーテムのオペレーターにとって、リモート管理機能は重要な要素です。LANやLTEを通じて、コンテンツの更新やシステムのメンテナンスを行うことができるため、運用コストを大幅に削減し、効率的な管理を行うことができます。   これらのエッジコンピューティングシステムは、観光客の体験を向上させるだけでなく、都市計画や観光管理に役立つ貴重なデータインサイトを提供します。Neu-X102-N50は、USB 3.2の帯域幅ポートに接続したカメラにより、データのスムーズな取り込みと伝送を可能にし、観光客の流れをリアルタイムでモニタリング・分析します。この高度な機能により、都市計画担当者や観光関係者は、リソース配分の最適化や都市のモビリティ向上のための情報に基づいた意思決定を行うことができ、観光客や地元住民の体験をシームレスかつ快適なものにします。   Neu-X102-N50は、都市の景観に自然に溶け込みながら、観光客や地元住民にとって不可欠なサービスを提供する、スマートシティ技術の大きな進歩を象徴しています。都市のより多くのエリアがこの技術を採用するにつれて、観光地との関わり方や、その行動に変革がもたらされ、訪れる人々が、情報をうまく活用しながら、より積極的に豊かな体験ができる、新時代の都市観光の到来が期待されます   ソリューションアーキテクチャ  

NEXCOM's ATC series product are build with NVIDIA® Jetson™ modules, NEXCOM offers a wide-ranging product portfolio that caters to various application needs, providing AI performance ranging from 20 TOPS to 275 TOPS. Whether it's enabling intelligent transportation, improving public safety, maximizing production efficiency, predicting abnormal asset condition or optimizing patent recognition, NEXCOM's diverse selection of Jetson™ modules with integrated industrial interfaces plays a pivotal role in deploying AI workload across multiple industries in field.

賑やかな大都市バンコク（タイ）は、豊かな文化と経済的活力の象徴である一方、東南アジアの他の新興都市と同様、その成長は交通渋滞というかつてない課題をもたらしました。約500か所に設置されていた信号機は、多くの地域で、まだタイマーで制御されており、実際の交通状況には対応していませんでした。   タイ政府は、道路状況を最適化し、都市交通計画を強化するために、現在の都市の交通管理システムにAIを統合する「タイ4.0プロジェクト」を開始しました。   NEXCOMのソリューション AIEdge-X®500 のLANポートは、交差点に設置されたCCTV（Closed-Circuit Television：防犯・監視）交通カメラに接続され、制限速度を超過したり、許可なく超えてはならない区画線を越えた交通違反者や、駐車禁止区域に車を駐車した違反者を捕まえるために、録画とナンバープレート認識を実行しています。   その上で、市行政と交通政策・計画局は、交通管理モデルを開発、AIを使って各時間帯の交通渋滞を推定し、例えば、交通量に合わせて信号機を調整するなど、ボトルネックを分析して、リアルタイムで解決策を打ち出しています。   NEXCOM の AIEdge-X®500は、0℃～45℃の温度範囲と10％～90％の湿度範囲で効率的に動作し、亜熱帯気候の特徴である高温多湿という厳しい条件下でも、その優れた性能を発揮、交通信号制御箱にシームレスに統合されています。   第8/9世代Intel ®Core™プロセッサ搭載のAIEdge-X®500は、大容量ストレージや周辺機器、内蔵デバイスをサポートした最大限のグラフィック処理能力の統合により、画像処理/ 最適化から機械/深層学習、マシンビジョンまで、産業用AI の要件を効果的に満たします。   AIEdge-X®500 エッジAIコンピューティング・ソリューションは、バンコクの交通管理を促進し、市内の交通違反を削減するために、今後2～3年で他の100カ所に展開される予定です。   ソリューションアーキテクチャ  

NEXCOM's VTK-SCAP: A Reliable Power Solution for In-Vehicle Computers VTK-SCAP is an uninterruptible power supply specifically designed for in-vehicle computers, providing up to 6 minutes of power when the vehicle encounters a power outage. Key Features  Extended Battery Life: Utilizes supercapacitors for a 10-year lifespan and up to 500,000 battery cycles. Temperature Resilience: Operates in extreme temperatures from -35°C to 80°C. Scalability: Can be expanded with multiple units to support larger power requirements. System Monitoring: Comes with software for monitoring battery health, charging/discharging, and power status. Flexible Installation

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. 

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.