Priyesh Shukla
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View article: A Randomized Controlled Trial Comparing Laser Versus Open Surgical Approaches in the Management of Fistula-in-Ano at a Tertiary Care Center
A Randomized Controlled Trial Comparing Laser Versus Open Surgical Approaches in the Management of Fistula-in-Ano at a Tertiary Care Center Open
While laser surgery offers faster recovery and reduced postoperative morbidity, open surgery remains the more definitive treatment option due to its superior long-term outcomes. Surgical decision-making should be individualized based on fi…
View article: A Comparative Study of Harmonic Scalpel Versus Suture Ligation for Appendix Base Closure in Patients With Appendicitis Undergoing Laparoscopic Appendectomy: A Randomized Controlled Trial
A Comparative Study of Harmonic Scalpel Versus Suture Ligation for Appendix Base Closure in Patients With Appendicitis Undergoing Laparoscopic Appendectomy: A Randomized Controlled Trial Open
View article: A Prospective and Comparative Study of Laparoscopic Appendectomy and Open Appendectomy in the Surgical Treatment of Appendicitis
A Prospective and Comparative Study of Laparoscopic Appendectomy and Open Appendectomy in the Surgical Treatment of Appendicitis Open
View article: Highly parallel and ultra-low-power probabilistic reasoning with programmable gaussian-like memory transistors
Highly parallel and ultra-low-power probabilistic reasoning with programmable gaussian-like memory transistors Open
View article: Navigating the Unknown: Uncertainty-Aware Compute-in-Memory Autonomy of Edge Robotics
Navigating the Unknown: Uncertainty-Aware Compute-in-Memory Autonomy of Edge Robotics Open
This paper addresses the challenging problem of energy-efficient and uncertainty-aware pose estimation in insect-scale drones, which is crucial for tasks such as surveillance in constricted spaces and for enabling non-intrusive spatial int…
View article: Robust Monocular Localization of Drones by Adapting Domain Maps to Depth Prediction Inaccuracies
Robust Monocular Localization of Drones by Adapting Domain Maps to Depth Prediction Inaccuracies Open
We present a novel monocular localization framework by jointly training deep learning-based depth prediction and Bayesian filtering-based pose reasoning. The proposed cross-modal framework significantly outperforms deep learning-only predi…
View article: MC-CIM: Compute-in-Memory with Monte-Carlo Dropouts for Bayesian Edge Intelligence
MC-CIM: Compute-in-Memory with Monte-Carlo Dropouts for Bayesian Edge Intelligence Open
We propose MC-CIM, a compute-in-memory (CIM) framework for robust, yet low power, Bayesian edge intelligence. Deep neural networks (DNN) with deterministic weights cannot express their prediction uncertainties, thereby pose critical risks …
View article: MC-CIM: Compute-in-Memory with Monte-Carlo Dropouts for Bayesian Edge\n Intelligence
MC-CIM: Compute-in-Memory with Monte-Carlo Dropouts for Bayesian Edge\n Intelligence Open
We propose MC-CIM, a compute-in-memory (CIM) framework for robust, yet low\npower, Bayesian edge intelligence. Deep neural networks (DNN) with\ndeterministic weights cannot express their prediction uncertainties, thereby\npose critical ris…
View article: Ultralow-Power Localization of Insect-Scale Drones: Interplay of Probabilistic Filtering and Compute-in-Memory
Ultralow-Power Localization of Insect-Scale Drones: Interplay of Probabilistic Filtering and Compute-in-Memory Open
We propose a novel compute-in-memory (CIM)-based ultralow-power framework for probabilistic localization of insect-scale drones. Localization is a critical subroutine for path planning and rotor control in drones, where a drone is required…
View article: Probabilistic Localization of Insect-Scale Drones on Floating-Gate Inverter Arrays
Probabilistic Localization of Insect-Scale Drones on Floating-Gate Inverter Arrays Open
We propose a novel compute-in-memory (CIM)-based ultra-low-power framework for probabilistic localization of insect-scale drones. The conventional probabilistic localization approaches rely on the three-dimensional (3D) Gaussian Mixture Mo…
View article: Low Power Unsupervised Anomaly Detection by Nonparametric Modeling of Sensor Statistics
Low Power Unsupervised Anomaly Detection by Nonparametric Modeling of Sensor Statistics Open
This work presents AEGIS, a novel mixed-signal framework for real-time anomaly detection by examining sensor stream statistics. AEGIS utilizes Kernel Density Estimation (KDE)-based non-parametric density estimation to generate a real-time …
View article: Low Power Unsupervised Anomaly Detection by Non-Parametric Modeling of\n Sensor Statistics
Low Power Unsupervised Anomaly Detection by Non-Parametric Modeling of\n Sensor Statistics Open
This work presents AEGIS, a novel mixed-signal framework for real-time\nanomaly detection by examining sensor stream statistics. AEGIS utilizes Kernel\nDensity Estimation (KDE)-based non-parametric density estimation to generate a\nreal-ti…
View article: $MC^2RAM$: Markov Chain Monte Carlo Sampling in SRAM for Fast Bayesian Inference
$MC^2RAM$: Markov Chain Monte Carlo Sampling in SRAM for Fast Bayesian Inference Open
This work discusses the implementation of Markov Chain Monte Carlo (MCMC) sampling from an arbitrary Gaussian mixture model (GMM) within SRAM. We show a novel architecture of SRAM by embedding it with random number generators (RNGs), digit…
View article: $MC^2RAM$: Markov Chain Monte Carlo Sampling in SRAM for Fast Bayesian\n Inference
$MC^2RAM$: Markov Chain Monte Carlo Sampling in SRAM for Fast Bayesian\n Inference Open
This work discusses the implementation of Markov Chain Monte Carlo (MCMC)\nsampling from an arbitrary Gaussian mixture model (GMM) within SRAM. We show a\nnovel architecture of SRAM by embedding it with random number generators\n(RNGs), di…