Technical studies and research documenting innovative architectures and algorithms in our projects
How do we measure AI agent performance objectively? A practical methodology for building a fair benchmark comparing ZeroClaw and OpenClaw on realistic tasks, and analyzing the differences that actually matter when picking a daily-driver agent.
Comprehensive guide to how 5G impacts IoT: evaluating eMBB, URLLC, mMTC, RedCap, and legacy LPWANs across industrial, smart city, and mission-critical deployments.
A technical exploration of Facial Expression Recognition (FER): FACS action units, landmark tracking, deep CNNs, Vision Transformers, and addressing real-world bias.
The definitive engineering pillar on the Internet of Things: layered architecture, protocol comparisons (MQTT, CoAP, Matter), edge vs cloud processing, and lifecycle security.
A practical engineering guide comparing UART, I2C, SPI, and RS-232/RS-485: wiring efficiency, clocking, signal integrity, and hardware decision matrices.
Comprehensive technical deep dive into image classification: convolution operations, ResNet backbones, Vision Transformers (ViT), multimodal CLIP, and metric evaluation.
A technical breakdown of 5G New Radio: flexible numerology, OFDM waveforms, 3D beamforming, Massive MIMO channel state estimation, and Open RAN split 7.2x fronthaul.
Algorithmic deep dive into on-demand shared ride-pooling: combinatorial request matching, dynamic vehicle routing (DARP), and balancing passenger detour times.
A Digital Twin is a comprehensive, data-driven virtual representation of real-world entities or processes, synchronized at purpose-driven fidelity for monitoring, predictive analysis, and decision support.
Affective Computing bridges artificial intelligence, psychology, and HCI to measure expressive signals across facial movements, vocal tonality, text, and physiological telemetry without over-claiming mind reading.
Architectural walkthrough on leveraging computer vision facial landmarks as contextual probabilistic signals in recommender systems, handling privacy bounds and algorithmic bias.
Model Context Protocol (MCP) is the open industry standard unifying how generative AI agents and models interface with external tools, APIs, and databases via standardized transports.
In-depth technical breakdown of next token prediction: from historical N-grams and LSTMs to causal self-attention Transformers, decoding heuristics, and Arabic morphology tokenization.
