Q: What programming languages and frameworks does VYROX use?

Our technology stack includes: Languages - Python 3.10+ (AI/ML, backend), C/C++17/20 (embedded, performance), Rust (security-critical systems), Go (microservices), TypeScript (web), Swift/Kotlin (mobile), CUDA C++ (GPU kernels), SQL (databases). Frameworks - PyTorch 2.x and TensorFlow 2.x (deep learning), ONNX Runtime (inference), OpenCV 4.x (computer vision), FreeRTOS and Zephyr (RTOS), FastAPI (APIs), React/Vue.js (frontend). DevOps - Docker, Kubernetes, GitLab CI/CD, Terraform, Prometheus/Grafana. We maintain strict coding standards, comprehensive testing, and thorough documentation across all projects.

Q: What hardware platforms does VYROX research support?

We develop on: Edge AI - NVIDIA Jetson AGX Orin (275 TOPS), Jetson Xavier NX, Jetson Nano, Intel NUC with OpenVINO, Google Coral TPU; IoT MCUs - ESP32-S3/C3 (Wi-Fi/BLE), STM32F4/H7/U5 (industrial), Nordic nRF52/53 (BLE/Thread), Silicon Labs EFR32 (ZigBee/Matter); Prototyping - Raspberry Pi 4/5, Arduino platforms; Industrial - PLCs, industrial PCs, rugged embedded systems. Our hardware lab includes oscilloscopes, logic analyzers, spectrum analyzers, and EMC pre-compliance equipment for thorough development and testing.

Q: How does VYROX ensure security in IoT systems?

Security is built into every layer: Device Security - secure boot with hardware root of trust, TPM 2.0/ATECC608 secure elements, encrypted firmware storage, secure OTA updates with code signing; Communication Security - TLS 1.3/DTLS for all connections, AES-256-GCM encryption, certificate-based mutual authentication, PKI infrastructure; Architecture - zero-trust design (never trust, always verify), network segmentation, microsegmentation; Operations - regular penetration testing, vulnerability scanning, SIEM integration, 24/7 monitoring; Compliance - IEC 62443 industrial security standard, NIST cybersecurity framework alignment, PDPA compliance for Malaysian deployments. We have never had a critical security breach in production systems.

Q: What certifications does VYROX hold?

VYROX International holds: MSC Status (2016) - MDEC recognition for innovative technology R&D; ISO 9001:2015 (2021) - Quality Management System for R&D processes; ISO 27001:2022 (2023) - Information Security Management System; CIDB G7 (2020) - Grade 7 construction industry certification for large projects; Matter Certification (2024) - CSA certification for Matter protocol compliance; NVIDIA Partner (2022) - Certified expertise in Jetson and GPU computing. Our engineers also hold individual certifications including CISSP, CEH, AWS/Azure/GCP certifications, and protocol-specific certifications from Silicon Labs and Nordic.

Question 1

What is AUTOSERVA?

Accepted Answer

AUTOSERVA is an MSC Status award-winning deep technology framework developed by VYROX International since 2008. It represents our comprehensive research and development efforts in Artificial Intelligence, Internet of Things, Computer Vision, Edge Computing, Robotics, and High-Performance Computing. The framework enables the development of intelligent building automation, smart city infrastructure, and industrial IoT solutions. AUTOSERVA encompasses proprietary algorithms, protocols, and hardware designs that have been deployed in over 10,000 devices across Southeast Asia.

Question 2

What are the core research areas at VYROX?

Accepted Answer

VYROX's R&D spans ten core technology areas: (1) AI & Machine Learning - deep learning, neural networks, model optimization; (2) Computer Vision - object detection, facial recognition, video analytics; (3) IoT Protocols - ZigBee, Z-Wave, Matter, mesh networking; (4) Edge Computing - distributed processing, edge AI inference; (5) Embedded Systems - RTOS, firmware, PCB design; (6) High-Performance Computing - GPU clusters, distributed training; (7) Natural Language Processing - speech recognition, conversational AI; (8) Cybersecurity - encryption, zero-trust architecture; (9) Robotics - autonomous navigation, SLAM; (10) Data Engineering - real-time analytics, data pipelines. Each area has dedicated research teams with PhD-level expertise.

Question 3

What AI technologies does VYROX research?

Accepted Answer

Our AI research includes: Deep Neural Networks (DNN) with custom architectures, Convolutional Neural Networks (CNN) including ResNet, EfficientNet, and Vision Transformers for image processing, Recurrent Neural Networks (RNN/LSTM/GRU) for time-series prediction, Transformer architectures for NLP tasks, Reinforcement Learning for optimization problems, Federated Learning for privacy-preserving distributed training, Transfer Learning and few-shot learning for efficient model adaptation, Neural Architecture Search (NAS) for automated model design, and Model Compression techniques (quantization to INT8/INT4, pruning, knowledge distillation) for edge deployment. We train models on our 8x NVIDIA A100 GPU cluster and deploy optimized versions to edge devices.

Question 4

What IoT protocols does VYROX work with?

Accepted Answer

We have deep expertise in: ZigBee 3.0 - full protocol stack implementation for mesh networks up to 65,000 nodes; Z-Wave Plus and Z-Wave Long Range - 800/900 MHz implementations; Matter Protocol (Project CHIP) - we are CSA-certified with full Matter support; Thread Networking - IPv6-based mesh for low-power devices; LoRaWAN - long-range (15km+) networks for outdoor IoT; Bluetooth Mesh - BLE 5.0+ for building automation; Wi-Fi HaLow (802.11ah) - sub-GHz Wi-Fi for IoT; Custom RF protocols - proprietary 433/868/915 MHz solutions. Our multi-protocol gateways can bridge between all these standards, enabling true device interoperability.

Question 5

What is VYROX's approach to Edge Computing?

Accepted Answer

Our edge computing architecture processes data locally to reduce latency to under 5ms for critical decisions, decrease bandwidth usage by 70%, enable offline operation, and preserve data privacy. We develop: Edge AI Inference Engines optimized for NVIDIA Jetson, Intel OpenVINO, and Google Coral; Fog Computing hierarchies with intelligent data aggregation; Container orchestration using K3s for edge Kubernetes; Edge-Cloud synchronization with delta sync and conflict resolution; TinyML solutions running on microcontrollers with as little as 256KB RAM. Our edge devices can run sophisticated ML models (object detection, anomaly detection) while maintaining real-time performance.

Question 6

What computer vision capabilities has VYROX developed?

Accepted Answer

Our Computer Vision Lab has developed: Object Detection using YOLOv8/v9, RT-DETR achieving 95%+ mAP; Facial Recognition with ArcFace achieving 99.7% accuracy at <100ms inference; Instance and Semantic Segmentation for scene understanding; Pose Estimation for occupancy and behavior analysis; License Plate Recognition supporting Malaysian, Singaporean, and regional plate formats; Multi-Object Tracking using DeepSORT and ByteTrack; Video Analytics processing 1000+ camera streams simultaneously; Anomaly Detection for security applications. All models are optimized for edge deployment on NVIDIA Jetson and can run at 30fps on embedded hardware.

Question 7

What programming languages and frameworks does VYROX use?

Accepted Answer

Our technology stack includes: Languages - Python 3.10+ (AI/ML, backend), C/C++17/20 (embedded, performance), Rust (security-critical systems), Go (microservices), TypeScript (web), Swift/Kotlin (mobile), CUDA C++ (GPU kernels), SQL (databases). Frameworks - PyTorch 2.x and TensorFlow 2.x (deep learning), ONNX Runtime (inference), OpenCV 4.x (computer vision), FreeRTOS and Zephyr (RTOS), FastAPI (APIs), React/Vue.js (frontend). DevOps - Docker, Kubernetes, GitLab CI/CD, Terraform, Prometheus/Grafana. We maintain strict coding standards, comprehensive testing, and thorough documentation across all projects.

Question 8

What hardware platforms does VYROX research support?

Accepted Answer

We develop on: Edge AI - NVIDIA Jetson AGX Orin (275 TOPS), Jetson Xavier NX, Jetson Nano, Intel NUC with OpenVINO, Google Coral TPU; IoT MCUs - ESP32-S3/C3 (Wi-Fi/BLE), STM32F4/H7/U5 (industrial), Nordic nRF52/53 (BLE/Thread), Silicon Labs EFR32 (ZigBee/Matter); Prototyping - Raspberry Pi 4/5, Arduino platforms; Industrial - PLCs, industrial PCs, rugged embedded systems. Our hardware lab includes oscilloscopes, logic analyzers, spectrum analyzers, and EMC pre-compliance equipment for thorough development and testing.

Question 9

How does VYROX ensure security in IoT systems?

Accepted Answer

Security is built into every layer: Device Security - secure boot with hardware root of trust, TPM 2.0/ATECC608 secure elements, encrypted firmware storage, secure OTA updates with code signing; Communication Security - TLS 1.3/DTLS for all connections, AES-256-GCM encryption, certificate-based mutual authentication, PKI infrastructure; Architecture - zero-trust design (never trust, always verify), network segmentation, microsegmentation; Operations - regular penetration testing, vulnerability scanning, SIEM integration, 24/7 monitoring; Compliance - IEC 62443 industrial security standard, NIST cybersecurity framework alignment, PDPA compliance for Malaysian deployments. We have never had a critical security breach in production systems.

Question 10

What certifications does VYROX hold?

Accepted Answer

VYROX International holds: MSC Status (2016) - MDEC recognition for innovative technology R&D; ISO 9001:2015 (2021) - Quality Management System for R&D processes; ISO 27001:2022 (2023) - Information Security Management System; CIDB G7 (2020) - Grade 7 construction industry certification for large projects; Matter Certification (2024) - CSA certification for Matter protocol compliance; NVIDIA Partner (2022) - Certified expertise in Jetson and GPU computing. Our engineers also hold individual certifications including CISSP, CEH, AWS/Azure/GCP certifications, and protocol-specific certifications from Silicon Labs and Nordic.

Question 11

What is the research team composition at VYROX?

Accepted Answer

Our 10+ person R&D team includes: 15+ AI/ML Engineers (PhD/MSc from top universities, expertise in deep learning, computer vision, NLP); 12+ Embedded Systems Engineers (10+ years average experience, RTOS, PCB design, firmware); 8+ IoT Protocol Specialists (certified in ZigBee, Z-Wave, Matter development); 10+ Backend/Cloud Architects (AWS/Azure/GCP certified, distributed systems); 6+ Security Researchers (CISSP/CEH certified, penetration testing, cryptography); 8+ Data Scientists (MSc Statistics/Data Science, predictive modeling); 5+ Robotics Engineers (MSc/PhD, SLAM, autonomous navigation); 6+ QA Engineers (ISTQB certified, automated testing). We collaborate with researchers from University of Malaya, UTM, and international institutions.

Question 12

How can I collaborate with VYROX on research?

Accepted Answer

We welcome research collaborations through several mechanisms: Joint Research Projects - co-develop new technologies with shared IP and publication rights; Technology Licensing - license our patents and algorithms for your applications; Academic Partnerships - PhD student placements, joint publications, research grants; Custom R&D Engagements - contract research to solve your specific technical challenges; Proof of Concept Development - rapid prototyping to validate new ideas; Technology Transfer - bring our research to market through your channels. Contact our research team at enquiry@vyrox.com with your proposal. We respond to all serious inquiries within 48 hours.

Question 13

What is the difference between Edge AI and Cloud AI in AUTOSERVA?

Accepted Answer

AUTOSERVA uses a hybrid AI architecture: Edge AI runs on devices like NVIDIA Jetson (275 TOPS), ESP32, or Coral TPU. Benefits include: <5ms inference latency (vs 100-500ms cloud round-trip), 100% offline availability, data privacy (video never leaves premises), 70% bandwidth reduction. Typical edge models: object detection, facial recognition, anomaly detection. Cloud AI runs on our GPU cluster (8x A100, 640GB HBM2e). Benefits include: train large models (billions of parameters), unlimited compute scaling, complex analytics across all sites. We use Cloud for: model training, aggregated analytics, global dashboards, model updates. The AUTOSERVA platform automatically synchronizes edge models with cloud-trained versions, enabling continuous improvement while maintaining real-time edge performance.

Question 14

How does AUTOSERVA handle real-time data processing?

Accepted Answer

AUTOSERVA processes data at multiple layers: Device Layer - RTOS-based firmware provides microsecond-level response for control loops (e.g., motor control at 10kHz); Edge Layer - Stream processing on Jetson/industrial PCs handles video analytics at 30fps, sensor fusion at 100Hz, local ML inference at <50ms; Fog Layer - Apache Kafka processes 1M+ events/second with exactly-once semantics, complex event processing (CEP) for pattern detection; Cloud Layer - TimescaleDB stores time-series data (petabyte-scale), Apache Spark for batch analytics, real-time dashboards with <1s refresh. This architecture ensures critical decisions happen locally while enabling sophisticated analytics across all data.

Question 15

What neural network architectures does VYROX specialize in?

Accepted Answer

We specialize in architectures optimized for real-world deployment: CNNs - ResNet, EfficientNet, MobileNet, ConvNeXt for image classification; detection heads including YOLO, RT-DETR, Faster R-CNN. RNNs - LSTM, GRU, Temporal Convolutional Networks for time-series (energy prediction, anomaly detection). Transformers - Vision Transformers (ViT, Swin), BERT/RoBERTa for NLP, custom attention mechanisms for multi-modal data. Specialized - Autoencoders for anomaly detection, GANs for data augmentation, Siamese networks for verification, Graph Neural Networks for mesh topology optimization. We actively research Neural Architecture Search (NAS) to automatically design networks optimized for specific hardware constraints (Jetson, ESP32, Coral).

Question 16

How does VYROX optimize AI models for embedded devices?

Accepted Answer

Our optimization pipeline achieves 5-10x speedup with <1% accuracy loss: Quantization - Post-training quantization to INT8 (4x smaller, 2-3x faster), Quantization-Aware Training for INT4 (8x smaller), mixed-precision (FP16/INT8) for optimal accuracy-speed tradeoff. Pruning - Structured pruning removes entire channels/layers, unstructured pruning for weight sparsity, iterative magnitude pruning with fine-tuning. Architecture - MobileNet, EfficientNet-Lite for mobile, custom architectures via NAS for specific hardware. Distillation - Train small student networks from large teacher models, feature distillation for object detection. Deployment - TensorRT for Jetson (2-5x speedup), TFLite for mobile/MCU, ONNX Runtime for cross-platform, OpenVINO for Intel. Example: Our facial recognition model runs at 99.7% accuracy, <100ms latency on Jetson Nano consuming only 5W.

Question 17

What is the Matter protocol and why does VYROX support it?

Accepted Answer

Matter is the unified smart home standard developed by the Connectivity Standards Alliance (CSA), backed by Apple, Google, Amazon, Samsung, and 500+ companies. Key features: IP-based (Wi-Fi, Thread, Ethernet) for reliable connectivity; local control without cloud dependency; unified device setup across ecosystems; strong security (CASE protocol, attestation); open-source reference implementation. VYROX achieved Matter certification in 2024 because: Universal compatibility - our devices work with Apple Home, Google Home, Amazon Alexa, Samsung SmartThings; Future-proof - Matter is the clear industry direction; Customer demand - enterprises want interoperability; Reduced complexity - single protocol instead of multiple integrations. Our Matter-certified devices include smart switches, sensors, gateways, and controllers.

Question 18

How does AUTOSERVA implement federated learning?

Accepted Answer

Federated learning enables AI improvement without centralizing sensitive data: Architecture - Each edge device (building/room) trains locally on its data; only model gradients (not raw data) are sent to the aggregation server; server combines updates using FedAvg or FedProx algorithms; updated global model is distributed back to edges. Privacy Guarantees - Raw sensor data, video, and occupancy patterns never leave the premises; differential privacy adds noise to gradients; secure aggregation prevents server from seeing individual updates. Use Cases - Personalized occupancy prediction (learns building-specific patterns); energy optimization (adapts to local equipment); anomaly detection (learns normal behavior without sharing). Results - We achieve 95%+ of centralized accuracy while keeping all data local, compliant with PDPA and GDPR requirements.

Question 19

What is VYROX's approach to sensor fusion?

Accepted Answer

Sensor fusion combines multiple data sources for robust understanding: Techniques - Kalman Filters (Extended, Unscented) for state estimation combining IMU, GPS, encoders; Particle Filters for non-linear, multi-modal distributions; Deep Learning fusion using attention mechanisms to weight sensor importance; Late fusion combining predictions from independent models. Applications - Indoor Localization: UWB + IMU + Visual Odometry achieves <10cm accuracy; Occupancy Detection: PIR + radar + camera + CO2 achieves 99%+ accuracy; Autonomous Navigation: LiDAR + camera + IMU for robust SLAM. Implementation - Sensor fusion runs on edge devices (Jetson) for real-time performance; redundancy ensures operation even with sensor failures; automatic calibration maintains accuracy over time. Our multi-modal approach significantly outperforms single-sensor solutions in challenging real-world conditions.

Question 20

How does VYROX ensure firmware security and prevent tampering?

Accepted Answer

Our firmware security implements defense-in-depth: Secure Boot Chain - Hardware root of trust (TPM 2.0, ATECC608); bootloader verifies firmware signature before execution; chain extends through all boot stages. Code Protection - Firmware encrypted at rest (AES-256); code signing with PKI certificates (RSA-4096); secure key storage in hardware security modules. Runtime Protection - Memory protection units (MPU) prevent code injection; stack canaries detect buffer overflows; watchdog timers detect hangs/tampering; runtime integrity checks of critical code. Secure Updates - OTA updates encrypted and signed; differential updates minimize bandwidth; automatic rollback on failed updates; version anti-rollback prevents downgrade attacks. Monitoring - Anomaly detection identifies unusual behavior; secure logging for forensics; remote attestation verifies device integrity. We conduct regular third-party penetration testing to validate these protections.

Question 21

What GPU infrastructure does VYROX use for AI training?

Accepted Answer

Our HPC facility features enterprise-grade GPU infrastructure: Primary Cluster - 8x NVIDIA A100 80GB GPUs (640GB aggregate HBM2e); 200Gbps InfiniBand HDR interconnect for NCCL; 2x AMD EPYC 7763 (128 cores, 2TB RAM); 100TB NVMe storage array (parallel file system). Capabilities - Train models up to 10B+ parameters; distributed training across all 8 GPUs; mixed-precision training (FP16/BF16); 24/7 operation with job scheduling (SLURM). Software Stack - PyTorch 2.x with torch.distributed; DeepSpeed for memory-efficient training; Weights & Biases for experiment tracking; MLflow for model registry. Performance - Typical training times: object detection model (8 hours), large NLP model (3-5 days); can process 10,000+ images/second for inference benchmarking. The cluster is primarily used for training models that are then optimized and deployed to edge devices.

Question 22

How does AUTOSERVA handle interoperability between different IoT protocols?

Accepted Answer

AUTOSERVA's multi-protocol architecture enables true device interoperability: Protocol Bridge Gateway - Hardware supports simultaneous ZigBee, Z-Wave, BLE, Wi-Fi, Thread radios; software translates between protocols in real-time; unified device abstraction layer presents consistent API. Translation Approach - Device capabilities mapped to common data model (inspired by Matter); state synchronization across protocols; command translation with protocol-specific optimizations. Example Scenarios - ZigBee sensor triggers Z-Wave light; Matter device controlled via legacy ZigBee remote; BLE beacon updates Wi-Fi dashboard. Supported Protocols - ZigBee 3.0, Z-Wave Plus, Z-Wave LR, BLE 5.0, Wi-Fi, Thread, Matter, LoRaWAN, custom RF. Legacy Support - Integration with older proprietary systems; protocol converters for RS-485, Modbus, BACnet. Our gateways manage 10,000+ devices across multiple protocols while maintaining sub-second response times.

Question 23

What research infrastructure does VYROX operate?

Accepted Answer

Our R&D facilities span 10,000+ sq ft across multiple specialized labs: AI Training Cluster - 8x A100 GPUs, InfiniBand interconnect, 100TB storage; Edge AI Lab - 20x Jetson AGX Orin, 50+ Jetson Nano/Xavier, thermal chambers (-20C to +70C); Embedded Systems Lab - 1GHz oscilloscopes, logic analyzers, spectrum analyzers (9kHz-6GHz), EMC pre-compliance, PCB prototyping; IoT Protocol Facility - RF chambers, protocol analyzers, 1000-device mesh test network, interference generators; Computer Vision Lab - multi-camera rigs, controlled lighting, LiDAR arrays, motion capture, high-speed cameras; Robotics Arena - 500 sqm floor, Vicon motion capture (sub-mm accuracy), drone flight cage. Total investment exceeds RM 10 million in equipment and facilities.

Question 24

What industries does AUTOSERVA serve?

Accepted Answer

AUTOSERVA technology is deployed across: Commercial Buildings - intelligent BMS for offices, malls, achieving 30-40% energy reduction; Hospitality - smart hotel systems, guest room automation, 25% energy savings; Industrial/Manufacturing - IIoT for predictive maintenance, 50% downtime reduction; Healthcare - patient monitoring, asset tracking, HIPAA-compliant systems; Education - smart campus for universities, classroom automation; Residential - premium smart homes, Matter-certified devices; Data Centers - environmental monitoring, energy optimization; Transportation - parking systems, traffic analytics. Our solutions manage 10,000+ devices across 500+ sites in Malaysia, Singapore, Thailand, and Indonesia.

Question 25

How does VYROX approach sustainability and energy efficiency?

Accepted Answer

Sustainability is embedded in our technology and operations: Energy Optimization AI - Our ML algorithms optimize HVAC, lighting, and equipment scheduling, typically achieving 30-40% energy reduction; predictive maintenance prevents energy-wasting equipment failures. Efficient Hardware - Edge devices designed for low power (<5W for AI inference); sleep modes reduce idle consumption by 90%; solar-powered sensor nodes for remote deployments. Data Center Efficiency - GPU cluster achieves PUE of 1.4; efficient cooling systems; renewable energy sourcing. Building Certifications - Our systems support Green Mark, LEED, and GBI certification for buildings. Measurement - Real-time energy dashboards, carbon footprint tracking, sustainability reporting. Customer Impact - Over 100 GWh energy savings across all deployments; equivalent to removing 20,000 cars from roads annually.

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