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Showing 7,057–7,080 of 20,289 skills
- Event Driven Fly Inspired Motion DetectionEvent-driven framework for fly-inspired visual motion detection using event cameras and biologically structured neural computationVotes: 0GitHub stars: 3
- Efficient Robust Snn Semg Fatigue DetectionA skill for implementing efficient and robust spiking neural networks for surface electromyography (sEMG) based muscle fatigue detection, based on arXiv:2607.11065Votes: 0GitHub stars: 3
- E S2feat Semantic Guided Spiking Local FeatureE-S2Feat for event camera local features using SNNs.Votes: 0GitHub stars: 3
- Dual Timescale Memory Spiking Neuron Astrocyte NetworkDual-timescale memory in spiking neuron-astrocyte networks for efficient navigation. Combines STDP (long-term) with astrocytic calcium transients (short-term) to create Topological-Context Memory. Applicable to neuromorphic robotics, edge AI, and efficient exploration. Activation: astrocyte memory, neuron-astrocyte network, dual-timescale memory, topological context memory, snan navigation, spiking astrocyte, memristive navigationVotes: 0GitHub stars: 3
- Convolution Delay Recurrent SnnCombining convolutional recurrent connections with delay learning (DelRec extension) in spiking neural networks. Achieves 99% recurrent parameter savings, 52x faster inference while retaining accuracy. Evaluated on audio classification tasks.Votes: 0GitHub stars: 3
- Clockless Fpga Neuromorphic ScalingScalable neuromorphic computing via clockless (asynchronous) FPGA-based Boolean spiking neurons. Use when: designing scalable neuromorphic architectures, implementing autonomous time-continuous spiking dynamics on commercial FPGAs, building energy-efficient Boolean neural processors without custom ASIC, or studying emergent spiking behavior from asynchronous digital circuits. Covers excitatory/inhibitory synaptic weight configuration, audio classification benchmarks, and scaling strategies fo...Votes: 0GitHub stars: 3
- Clockless Asynchronous Neuromorphic ComputingClockless asynchronous neuromorphic computing methodology — scalable spiking neural networks on FPGAs without dedicated analog hardware. Use when designing energy-efficient neuromorphic systems, implementing Boolean spiking neurons on FPGAs, or bridging analog neuromorphic gaps with reconfigurable digital chips. Also triggers: clockless SNN, FPGA neuromorphic, asynchronous spiking, autonomous spiking dynamics, reconfigurable neuromorphic chip.Votes: 0GitHub stars: 3
- Clane Neuromorphic Continual LearningCLANE - 在神经形态硬件（Intel Loihi 2）上从事件相机实现动作的持续学习。首个端到端部署的神经形态持续学习系统，结合脉冲 2D CNN 和 CLP-SNN 学习头，通过 Temporal Aggregation Layer 和 Normalization Layer 处理动作序列。实现 100x 能量降低和 16x 延迟减少。Activation: neuromorphic continual learning, event camera, Loihi 2, spiking CNN, CLP-SNN, action recognition, on-device learning, energy-efficient AI, edge deployment, 神经形态持续学习, 事件相机, 能量高效 AI.Votes: 0GitHub stars: 3
- As Fedbridge Heterogeneous Ann Snn Federated LearningAS-FedBridge methodology for heterogeneous ANN-SNN federated learning with pseudo-spike bridge distillation. Enables mixed ANN-SNN client training by projecting continuous signals into spike-compatible space to overcome representational misalignment. Use when implementing federated learning systems with both traditional ANNs and energy-efficient Spiking Neural Networks on edge devices.Votes: 0GitHub stars: 3
- As Fedbridge Ann Snn Federated LearningAS-FedBridge framework for heterogeneous ANN-SNN federated learning. Creates lightweight Bridge with Pseudo-Spike Interface to align continuous ANN activations with discrete SNN spikes. Use when implementing mixed ANN-SNN federated learning systems, addressing representational misalignment in edge AI, or optimizing resource-efficient collaborative learning across heterogeneous neural network architectures.Votes: 0GitHub stars: 3
- Spikepeft Parameter Efficient Snn FinetuningSpikePEFT framework for parameter-efficient adaptation of spiking neural networks on point cloud data. Uses Intrinsic Dynamics Tuning (IDT) and Silent-State Disambiguation Adaptation (SSDA) to achieve high accuracy while updating only ~5% of parameters.Votes: 0GitHub stars: 3
- Systems Engineering Apr14 2026Systems engineering research synthesis from April 14, 2026 arXiv papers. 8 papers covering: multikernel serverless OS, datacenter digital twins, proactive K8s autoscaling with DQN, CPS hardware testing, multi-robot rigidity control, Koopman irregular sampling, physics-informed SSM, and LLM-based formal verification.Votes: 0GitHub stars: 3
- Scaling Kubernetes To 7500 NodesSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Ai Systems Engineering V ModelExtended V-Model methodology for developing AI-enabled complex systems. Addresses the challenges of integrating AI into classical systems engineering processes with iterative development, continuous V&V, data management, safety by design, and DevOps integration. Activation: V-model AI systems, complex systems engineering, cognitive cyber-physical systems, AI system development lifecycle.Votes: 0GitHub stars: 3
- Unified Control Theoretic Framework Saddle Point DynamicsThis paper studies equality-constrained minimization problems through the lens of feedback control. We introduce a unified control-theoretic framework by showing that a PID feedback law acting on the ... Activation: saddle-point dynamics, constrained optimization, primal-dual.Votes: 0GitHub stars: 3
- Self Adaptive Anomaly Detection Rl Human Feedback VehiclesOnline anomaly detection framework for connected vehicles integrating factorized deep Q-network with self-attention, statistical drift detectors, and human-in-the-loop retraining. F1 score 0.69 vs 0.11 for single detectors, with sustained adaptation after concept drift. Use when working with anomaly-detection, reinforcement-learning, connected-vehicles.Votes: 0GitHub stars: 3
- Robust Adaptive Backstepping Impedance ControlThis paper presents a Robust Adaptive Backstepping Impedance Control (RABIC) strategy for robots operating in contact-rich and uncertain environments. The proposed control strategy considers the compl... Activation: robotic control, impedance control, adaptive control.Votes: 0GitHub stars: 3
- Onnes Cryogenic Fault DiagnosisMulti-agent LLM physics-grounded digital twin simulator for cryogenic fault diagnosis in quantum computing infrastructure. Uses dilution refrigerator forward physics model with learned noise fingerprint + multi-agent LLM operations layer. Activation: cryogenic fault diagnosis, dilution refrigerator, quantum computing infrastructure, digital twin, multi-agent LLM, BlueFors, quantum hardware monitoringVotes: 0GitHub stars: 3
- Nl Cps Kubernetes ControlReinforcement Learning-Based Kubernetes Control Plane Placement in Multi-Region Clusters. Covers NL-CPS framework, multi-region cluster optimization, control plane node selection, and distributed system resilience. Activation: kubernetes control plane, multi-region cluster, control plane placement, NL-CPS, distributed systems engineering.Votes: 0GitHub stars: 3
- Nl Cps Kubernetes Control PlaneReinforcement Learning-Based Kubernetes Control Plane Placement in Multi-Region Clusters. Optimize control-plane node placement for reliability, scalability, and performance in heterogeneous environments.Votes: 0GitHub stars: 3
- Anytime Lidar Resolution Scaling Object DetectionAnytime computing method for LiDAR-based 3D object detection in cyber-physical systems. Multi-resolution inference with single DNN model, deadline-aware scheduler predicts execution time for all resolutions. Deployed in simulated autonomous driving with collision-free navigation. Use when working with anytime-computing, lidar-detection, input-resolution-scaling.Votes: 0GitHub stars: 3
- Anomaly Detection Dynamical SystemsLog-likelihood ratio based real-time anomaly detection for dynamical systems with theoretical characterization of error rates. Identifies system anomalies from multiple possible plant models for linear Gaussian systems. Use for: anomaly detection, fault diagnosis, dynamical system monitoring, log-likelihood ratio testing, industrial control applications. Activation: anomaly detection, log-likelihood ratio, dynamical systems, industrial control, fault detection.Votes: 0GitHub stars: 3
- Allostatic Control Systems Goal GovernanceFramework for designing control systems that govern not only goal pursuit but also goal appropriateness in changing environments. Implements two-timescale control with fast regulation loop and slow goal governance loop.Votes: 0GitHub stars: 3
- Self Adaptive Cps Anomaly DetectionSelf-adaptive anomaly detection for autonomous cyber-physical systems. Integrates RL-based detector selection, ensemble drift detection, and human-in-the-loop retraining with catastrophic forgetting prevention.Votes: 0GitHub stars: 3