The NAC-9014AE Aluminium Exit Button is a durable stainless steel push exit button designed for access control systems. Ideal for hollow doors, it provides reliable door release performance in commercial, residential, and industrial applications.
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The NAC-9002AE Aluminium Exit Button is a premium stainless steel push exit button designed for access control systems. Suitable for hollow doors, it provides secure, reliable, and convenient door release for commercial, residential, and industrial applications.
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The NAC-8004AR Access Card Reader is a reliable RFID access control reader with Wiegand output and reverse polarity protection, designed for secure door access in commercial, residential, industrial, and institutional applications.
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IP68 waterproof RFID access card reader with Wiegand 26/34 output and reverse polarity protection for indoor and outdoor access control applications.
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Advanced multi-biometric access controller with dynamic facial recognition, fingerprint authentication, ID card, password support, mask detection, and real-time event logging.
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Advanced facial recognition access controller with ARM processor, simultaneous five-person recognition, and mask detection technology for secure building access.
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Heavy-duty double door electromagnetic lock with 280Kg x 2 holding force and anodized aluminum finish for secure access control installations.
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ABS material door exit switch panel with DC 12V power supply, designed for reliable access control door release applications.
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Green emergency break glass button with durable ABS construction for safe and reliable emergency door release in access control systems.
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Professional 500 DPI fingerprint enrollment device with biometric template creation, identification, SDK support, and seamless software integration.
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Advanced facial recognition and fingerprint access controller with a 5-inch touch screen, ARM processor, and high-speed biometric authentication.
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Professional facial recognition and fingerprint access controller with 4.3-inch IPS touch screen, HD WDR camera, and 99.7% recognition accuracy.
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