15 citations · 32 across the 20 of their papers we have counts for
23 papers
Approximating Analytically-Intractable Likelihood Densities with Deterministic Arithmetic for Optimal Particle Filtering
Orestis Kaparounakis, Yunqi Zhang, Phillip Stanley-Marbell
Particle filtering algorithms have enabled practical solutions to problems in autonomous robotics (self-driving cars, UAVs, warehouse robots), target tracking, and econometrics, wi…
Digital Methods to Quantify Sensor Output Uncertainty in Real Time
Orestis Kaparounakis, Phillip Stanley-Marbell
Modern data-driven applications that make real-time decisions increasingly depend on advanced sensors which use pre-stored calibration data. In such applications, accurate characte…
Sensitivity Analysis of the Laser Power Control System to Measurement Noise in SLS 3D Printers
Hamid Toshani, Janith Petangoda, Chatura Samarakoon +1
Uniform temperature distribution in Selective Laser Sintering (SLS) is essential for producing durable 3D prints. Achieving uniformity requires a laser power control system that mi…
Simulated Eyeblink Artifact Removal with ICA: Effect of Measurement Uncertainty
Jennie Couchman, Orestis Kaparounakis, Chatura Samarakoon +1
Independent Component Analysis (ICA) is commonly-used in electroencephalogram (EEG) signal processing to remove non-cerebral artifacts from cerebral data. Despite the ubiquity of I…
Electron-Tunnelling-Noise Programmable Random Variate Accelerator for Monte Carlo Sampling
James T. Meech, Vasileios Tsoutsouras, Phillip Stanley-Marbell
This article presents an electron tunneling noise programmable random variate accelerator for accelerating the sampling stage of Monte Carlo simulations. We used the LiteX framewor…
The Data Conversion Bottleneck in Analog Computing Accelerators
James T. Meech, Vasileios Tsoutsouras, Phillip Stanley-Marbell
Most modern computing tasks have digital electronic input and output data. Due to these constraints imposed by real-world use cases of computer systems, any analog computing accele…
GFET Lab: A Graphene Field-Effect Transistor TCAD Tool
Nathaniel J. Tye, Abdul Wadood Tadbier, Stephan Hofmann +1
Graphene field-effect transistors (GFETs) are experimental devices which are increasingly seeing commercial and research applications. Simulation and modelling forms an important s…
Bridging the Band Gap: What Device Physicists Need to Know About Machine Learning
Nathaniel Tye, Stephan Hofmann, Phillip Stanley-Marbell
This article surveys the landscape of semiconductor materials and devices research for the acceleration of machine learning (ML) algorithms. We observe a disconnect between the sem…
Machine Learning for Sensor Transducer Conversion Routines
Thomas Newton, James T. Meech, Phillip Stanley-Marbell
Sensors with digital outputs require software conversion routines to transform the unitless analogue-to-digital converter samples to physical quantities with correct units. These c…
An Algorithm for Sensor Data Uncertainty Quantification
James T. Meech, Phillip Stanley-Marbell
This article presents an algorithm for reducing measurement uncertainty of one physical quantity when given oversampled measurements of two physical quantities with correlated nois…
Probabilistic Value-Deviation-Bounded Source-Dependent Bit-Level Channel Adaptation for Approximate Communication
Bilgesu Arif Bilgin, Phillip Stanley-Marbell
Computing systems that can tolerate effects of errors in their communicated data values can trade this tolerance for improved resource efficiency. Many important applications of co…
Content-Aware Automated Parameter Tuning for Approximate Color Transforms
Chatura Samarakoon, Gehan Amaratunga, Phillip Stanley-Marbell
There are numerous approximate color transforms reported in the literature that aim to reduce display power consumption by imperceptibly changing the color content of displayed ima…
Inferring Human Observer Spectral Sensitivities from Video Game Data
Chatura Samarakoon, Gehan Amaratunga, Phillip Stanley-Marbell
With the use of primaries which have increasingly narrow bandwidths in modern displays, observer metameric breakdown is becoming a significant factor. This can lead to discrepancie…
Automated Physics-Derived Code Generation for Sensor Fusion and State Estimation
Orestis Kaparounakis, Vasileios Tsoutsouras, Dimitrios Soudris +1
We present a new method for automatically generating the implementation of state-estimation algorithms from a machine-readable specification of the physics of a sensing system and…
A System for Generating Non-Uniform Random Variates using Graphene Field-Effect Transistors
Nathaniel Joseph Tye, James Timothy Meech, Bilgesu Arif Bilgin +1
We introduce a new method for hardware non-uniform random number generation based on the transfer characteristics of graphene field-effect transistors (GFETs) which requires as few…
Synthesizing Compact Hardware for Accelerating Inference from Physical Signals in Sensors
Vasileios Tsoutsouras, Max Vigdorchik, Phillip Stanley-Marbell
We present dimensional circuit synthesis, a new method for generating digital logic circuits that improve the efficiency of training and inference of machine learning models from s…
Payload-Mass-Aware Trajectory Planning on Multi-User Autonomous Unmanned Aerial Vehicles
Vasileios Tsoutsouras, Joseph Story, Phillip Stanley-Marbell
Future unmanned aerial vehicles (drones) will be shared by multiple users and will have to operate in conditions where their fully-autonomous function is required. Calculation of a…
Efficient Programmable Random Variate Generation Accelerator from Sensor Noise
James Timothy Meech, Phillip Stanley-Marbell
We introduce a method for non-uniform random number generation based on sampling a physical process in a controlled environment. We demonstrate one proof-of-concept implementation…
Newton: A Language for Describing Physics
Jonathan Lim, Phillip Stanley-Marbell
This article introduces Newton, a specification language for notating the analytic form, units of measure, and sensor signal properties for physical-object-specific invariants and…
Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms
Phillip Stanley-Marbell, Armin Alaghi, Michael Carbin +13
When a computational task tolerates a relaxation of its specification or when an algorithm tolerates the effects of noise in its execution, hardware, programming languages, and sys…
A Hardware Platform for Efficient Multi-Modal Sensing with Adaptive Approximation
Phillip Stanley-Marbell, Martin Rinard
We present Warp, a hardware platform to support research in approximate computing, sensor energy optimization, and energy-scavenged systems. Warp incorporates 11 state-of-the-art s…
Probabilistic Value-Deviation-Bounded Integer Codes for Approximate Communication
Phillip Stanley-Marbell, Paul Hurley
When computing systems can tolerate the effects of errors or erasures in their communicated data values, they can trade this tolerance for improved resource efficiency. One method…
Incremental Color Quantization for Color-Vision-Deficient Observers Using Mobile Gaming Data
Jose Cambronero, Phillip Stanley-Marbell, Martin Rinard
The sizes of compressed images depend on their spatial resolution (number of pixels) and on their color resolution (number of color quantization levels). We introduce DaltonQuant,…