Threat Modeling

Systematically identify assets, entry points, trust boundaries, and likely adversary capabilities. Techniques: STRIDE, DREAD, and attack-surface analysis. Threat models inform mitigation priorities and logging requirements.

Zero Trust Architecture

Zero trust enforces never trust, always verify with strong identity, device posture checks, microsegmentation, policy-based access, continuous authentication, and encrypted transport (mTLS).

Detection & Telemetry

Collect high-fidelity telemetry: endpoint EDR events, network flows (NetFlow/IPFIX), process trees, DNS logs, and cloud audit trails. SIEM/XDR correlates events with detection rules and ML-based anomaly detection. Playbooks and SOAR automate triage.

Incident Response

IR lifecycle: preparation, identification, containment, eradication, recovery, and post-incident review. Forensic readiness (WORM logs, chain-of-custody) and tabletop exercises are essential. Purple teaming aligns red/blue to improve detection.

References

1. NIST SP 800-series, MITRE ATT&CK framework, SANS incident response materials.

CI/CD Pipelines

Pipelines automate build, test, security scanning, and deployment. Key stages: source → build → unit/integration tests → container/image build → vulnerability scanning → deploy → smoke tests → promote. Artifacts must be immutable and signed.

Infrastructure as Code & GitOps

IaC (Terraform, CloudFormation) codifies infra. GitOps operates on the principle that the desired state is stored in Git and controllers reconcile cluster state (Flux, ArgoCD). Benefits include auditability, rollbacks, and declarative drift detection.

Observability & SRE

Observability comprises metrics, logs, and traces. SRE defines SLOs/SLIs, error budgets, and incident response. Practices include canary analysis, chaos engineering, and automated remediation.

Release Strategies

• Blue/Green: run two environments and switch traffic.
• Canary: gradually shift traffic and monitor metrics.
• Feature flags: toggle functionality without redeploying.

References

1. Fowler et al., CI/CD best practices; HashiCorp/Terraform docs; CNCF / GitOps resources.

Image Representations & Preprocessing

Images are tensors (H×W×C). Preprocessing: normalization, resizing, augmentation (flip, crop, color jitter). Feature extractors learn hierarchical representations from edges to semantic concepts.

Convolutional Neural Networks

Convolutions, pooling/strided convs, residual connections (ResNet), and depthwise separable convolutions (MobileNet) form the backbone of vision models. Transfer learning with pretrained backbones is standard.

Detection & Segmentation

Detectors: two-stage (Faster R-CNN) vs single-shot (YOLO/SSD). Segmentation: semantic (FCN, DeepLab), instance (Mask R-CNN), and panoptic segmentation (unified). Key trade-offs: speed vs accuracy, anchor-based vs anchor-free paradigms.

Evaluation Metrics

Classification: accuracy, F1; detection: mAP (mean Average Precision) at IoU thresholds; segmentation: IoU / Dice. Consider calibration and per-class analysis for imbalanced datasets.

Deployment

Edge inference uses quantization, pruning, and hardware accelerators (VPU, NPU). Cloud inference supports large models and batching. Real-time video analytics requires pipeline optimizations and batching strategies to meet FPS and latency targets.

References

1. He et al., “Deep Residual Learning for Image Recognition” (ResNet)
2. Ren et al., “Faster R-CNN”
3. Lin et al., “Focal Loss for Dense Object Detection” (RetinaNet)

Foundational Components

Tokenization: Byte-Pair Encoding (BPE), WordPiece, and SentencePiece produce subword units balancing vocabulary size and OOV handling.

Embeddings: Context-free (word2vec, GloVe) vs. contextual embeddings (ELMo, BERT) where vector representations vary with sentence context.

Transformer Architecture

Self-attention computes pairwise token interactions with scaled dot-product attention. Transformers stack encoder/decoder layers, using multi-head attention and feed-forward blocks with layer normalization and residual connections. Pretraining objectives include masked language modeling and next-token prediction.

Training Paradigms

Pretrain on large corpora (self-supervised) and fine-tune on downstream tasks. Transfer learning dominates—foundation models are adapted to classification, QA, summarization, and generation.

Evaluation Metrics & Safety

Metrics: accuracy, F1, BLEU/ROUGE (generation), perplexity (language modeling). Safety: toxicity detection, calibration, prompt robustness, and alignment considerations when deploying generative models.

Deployment

Serving strategies: server-side large model inference with batching and TPU/GPU acceleration; edge/quantized models for on-device inference. Retrieval-augmented generation (RAG) combines retrieval with generative models for up-to-date knowledge.

References

1. Vaswani et al., “Attention Is All You Need,” NeurIPS 2017.
2. Devlin et al., “BERT: Pre-training of Deep Bidirectional Transformers,” 2019.
3. Radford et al., OpenAI GPT series papers.

System Components

AR/VR systems combine optics (HMD lenses, pancake or Fresnel), displays (OLED/LCD/LCOS), low-latency graphics pipelines, inertial and optical tracking, audio rendering, and input devices (hand controllers, hand-tracking, eye-tracking).

Tracking & SLAM for AR

AR requires robust 6-DoF tracking. Visual–inertial odometry (VIO) fuses IMU measurements with camera frames. SLAM systems (feature-based or direct) build local maps; loop-closure reduces drift. Key metrics: pose latency, pose jitter, and tracking robustness under dynamic lighting.

Rendering Pipeline & Latency

Motion-to-photon latency is critical; techniques include asynchronous reprojection, late latching, foveated rendering with eye tracking, and multi-resolution shading (tiled rendering). For distributed AR, network latency and synchronization are additional constraints.

Interaction Models

Interactions range from device-based controllers to direct hand/gesture and gaze. Haptics and audio spatialization enhance presence. UX constraints: minimizing simulator sickness, maintaining stable world-locked anchors, and ensuring comfortable ergonomics.

Applications & Deployment

• Enterprise training and remote assistance (AR overlays).
• Industrial visualization, maintenance, and design review.
• Immersive entertainment, VR simulation, and social VR.
• Medical simulation and telesurgery assistance (requires deterministic low-latency networks).

References

1. SLAM and VIO literature; SIGGRAPH and IEEE VR proceedings for rendering and human factors.
2. ETSI/3GPP for MEC/low-latency networking enabling AR/VR at the edge.

SLAM and Mapping

SLAM constructs a map while simultaneously estimating robot pose. Approaches include EKF-SLAM, Graph-SLAM (pose graph optimization), and particle-filter based methods; modern systems use lidar/camera fusion and loop-closure detection using bag-of-words or learned embeddings.

Autonomy Stack & ROS

Robotic stacks include perception, state estimation, planning, control, and behavior layers. ROS/ROS2 provide middleware for messaging, componentization, and simulation (Gazebo, Ignition). Verification and safety require simulation-in-the-loop, formal methods for critical behaviors, and runtime monitors.

Kubernetes Control Plane

Key components: API server, etcd (cluster state), controller manager, scheduler, kubelet on nodes. The scheduler maps Pods to nodes based on resource requests, affinity/anti-affinity, and taints/tolerations.

Storage and Stateful Workloads

PersistentVolumes and CSI drivers expose block and file storage to pods. Patterns: StatefulSets for stable network IDs and ordered startup, Operators for lifecycle management of complex systems (databases, message queues).

Connectivity & Protocols

• MQTT: Lightweight pub/sub for telemetry over TCP/TLS.
• CoAP: Constrained REST over UDP for low-power devices.
• LoRaWAN: Low-power wide-area network for long-range sensor telemetry.
• Bluetooth Low Energy / Zigbee: Short-range mesh/local connectivity.

Data Pipelines & Edge Processing

Edge processing reduces bandwidth by filtering, aggregating, and running inference locally. Telemetry pipelines include message ingestion, stream processing, time-series databases, and feature stores for ML.

RAN and Spectrum

5G NR supports flexible numerologies, scalable OFDM, and beamforming. Spectrum is categorized as:

• Low-band: <1 GHz — wide coverage, lower capacity.
• Mid-band: ~1–6 GHz — balance of coverage and capacity.
• mmWave: >24 GHz — very high capacity, limited range, requires dense small cells and beam steering.

Edge & MEC

Multi-access Edge Computing (MEC) places compute and storage near the RAN to support low-latency services (AR/VR, gaming, V2X). Integration with 5GC enables local breakout and optimized routing.