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eess.SP updates on arXiv.org

ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Federated Learning with Hypergradient-based Online Update of Aggregation Weights Soft Graph Diffusion Transformer for MIMO Detection SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting Sequential Inference for Gaussian Processes: A Signal Processing Perspective Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation Adaptive Transform Coding for Semantic Compression EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures Sparse Graph Learning from Sparse Data via Fiedler Number Maximization A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals EVT-Based Generative AI for Tail-Aware Channel Estimation Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring Speech Enhancement Based on Drifting Models Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids Explainable AI in Speaker Recognition -- Making Latent Representations Understandable Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
On-CMOS High-Throughput Multi-Modal Amperometric DNA Anal...
Hamed M. Jafari, Xilin Liu, Roman Genov · 2022-07-30 · via eess.SP updates on arXiv.org

Accurate temperature regulation is critical for amperometric DNA analysis to achieve high fidelity, reliability, and throughput. In this work, a 9x6 cell array of mixed-signal CMOS distributed temperature regulators for on-CMOS multi-modal amperometric DNA analysis is presented. Three DNA analysis methods are supported, including constant potential amperometry (CPA), cyclic voltammetry (CV), and impedance spectroscopy (IS). In-cell heating and temperature sensing elements are implemented in standard CMOS technology without post-processing. Using proportional-integral-derivative (PID) control, the local temperature can be regulated to within +/-0.5C of any desired value between 20C and 90C. The two computationally intensive operations in the PID algorithm, multiplication, and subtraction, are performed by an in-cell dual-slope multiplying ADC in the mixed-signal domain, resulting in a small area and low power consumption. Over 95% of the circuit blocks are synergistically shared among the four operating modes, including CPA, CV, IS, and the proposed temperature regulation mode. A 3mmx3mm CMOS prototype fabricated in a 0.13um CMOS technology has been fully experimentally characterized. Each channel occupies an area of 0.06mm2 and consumes 42uW from a 1.2V supply. The proposed distributed temperature regulation design and the mixed-signal PID implementation can be applied to a wide range of sensory and other applications.