scholarly journals Event Camera Simulator Improvements via Characterized Parameters

2021 ◽  
Vol 15 ◽  
Author(s):  
Damien Joubert ◽  
Alexandre Marcireau ◽  
Nic Ralph ◽  
Andrew Jolley ◽  
André van Schaik ◽  
...  
Keyword(s):  
Processing Speed ◽  
Energy Efficient ◽  
State Of The Art ◽  
Wide Spectrum ◽  
Vision Sensor ◽  
Dynamic Vision ◽  
Complex Scenes ◽  
Efficient Data ◽  
Event Camera ◽  
Noise Models

It has been more than two decades since the first neuromorphic Dynamic Vision Sensor (DVS) sensor was invented, and many subsequent prototypes have been built with a wide spectrum of applications in mind. Competing against state-of-the-art neural networks in terms of accuracy is difficult, although there are clear opportunities to outperform conventional approaches in terms of power consumption and processing speed. As neuromorphic sensors generate sparse data at the focal plane itself, they are inherently energy-efficient, data-driven, and fast. In this work, we present an extended DVS pixel simulator for neuromorphic benchmarks which simplifies the latency and the noise models. In addition, to more closely model the behaviour of a real pixel, the readout circuitry is modelled, as this can strongly affect the time precision of events in complex scenes. Using a dynamic variant of the MNIST dataset as a benchmarking task, we use this simulator to explore how the latency of the sensor allows it to outperform conventional sensors in terms of sensing speed.

scholarly journals Artificial Intelligence Techniques for Cognitive Sensing in Future IoT: State-of-the-Art, Potentials, and Challenges

2020 ◽  
Vol 9 (2) ◽  
pp. 21 ◽  
Author(s):  
Martins O. Osifeko ◽  
Gerhard P. Hancke ◽  
Adnan M. Abu-Mahfouz
Keyword(s):  
Artificial Intelligence ◽  
Data Collection ◽  
Energy Efficient ◽  
State Of The Art ◽  
Future Internet ◽  
Future Research ◽  
Research Directions ◽  
Efficient Data ◽  
Future Research Directions ◽  
Efficient Data Collection

Smart, secure and energy-efficient data collection (DC) processes are key to the realization of the full potentials of future Internet of Things (FIoT)-based systems. Currently, challenges in this domain have motivated research efforts towards providing cognitive solutions for IoT usage. One such solution, termed cognitive sensing (CS) describes the use of smart sensors to intelligently perceive inputs from the environment. Further, CS has been proposed for use in FIoT in order to facilitate smart, secure and energy-efficient data collection processes. In this article, we provide a survey of different Artificial Intelligence (AI)-based techniques used over the last decade to provide cognitive sensing solutions for different FIoT applications. We present some state-of-the-art approaches, potentials, and challenges of AI techniques for the identified solutions. This survey contributes to a better understanding of AI techniques deployed for cognitive sensing in FIoT as well as future research directions in this regard.

scholarly journals In-Memory-Computing Realization with a Photodiode/Memristor Based Vision Sensor

Materials ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5223
Author(s):  
Nikolaos Vasileiadis ◽  
Vasileios Ntinas ◽  
Georgios Ch. Sirakoulis ◽  
Panagiotis Dimitrakis
Keyword(s):  
Energy Efficient ◽  
State Of The Art ◽  
Behavioral Model ◽  
Resistance Switching ◽  
Hardware Accelerators ◽  
Vision Sensor ◽  
Memristive Device ◽  
Vision Sensors ◽  
Switching Dynamics ◽  
Novel Design

State-of-the-art IoT technologies request novel design solutions in edge computing, resulting in even more portable and energy-efficient hardware for in-the-field processing tasks. Vision sensors, processors, and hardware accelerators are among the most demanding IoT applications. Resistance switching (RS) two-terminal devices are suitable for resistive RAMs (RRAM), a promising technology to realize storage class memories. Furthermore, due to their memristive nature, RRAMs are appropriate candidates for in-memory computing architectures. Recently, we demonstrated a CMOS compatible silicon nitride (SiNx) MIS RS device with memristive properties. In this paper, a report on a new photodiode-based vision sensor architecture with in-memory computing capability, relying on memristive device, is disclosed. In this context, the resistance switching dynamics of our memristive device were measured and a data-fitted behavioral model was extracted. SPICE simulations were made highlighting the in-memory computing capabilities of the proposed photodiode-one memristor pixel vision sensor. Finally, an integration and manufacturing perspective was discussed.

Learning sensorimotor control with neuromorphic sensors: Toward hyperdimensional active perception

2019 ◽  
Vol 4 (30) ◽  
pp. eaaw6736 ◽  
Author(s):  
A. Mitrokhin ◽  
P. Sutor ◽  
C. Fermüller ◽  
Y. Aloimonos
Keyword(s):  
Deep Learning ◽  
Vision Sensor ◽  
Active Perception ◽  
Binary Vectors ◽  
Visual Component ◽  
Dynamic Vision ◽  
Action And Perception ◽  
Event Camera ◽  
The Moment ◽  
Inference Task

The hallmark of modern robotics is the ability to directly fuse the platform’s perception with its motoric ability—the concept often referred to as “active perception.” Nevertheless, we find that action and perception are often kept in separated spaces, which is a consequence of traditional vision being frame based and only existing in the moment and motion being a continuous entity. This bridge is crossed by the dynamic vision sensor (DVS), a neuromorphic camera that can see the motion. We propose a method of encoding actions and perceptions together into a single space that is meaningful, semantically informed, and consistent by using hyperdimensional binary vectors (HBVs). We used DVS for visual perception and showed that the visual component can be bound with the system velocity to enable dynamic world perception, which creates an opportunity for real-time navigation and obstacle avoidance. Actions performed by an agent are directly bound to the perceptions experienced to form its own “memory.” Furthermore, because HBVs can encode entire histories of actions and perceptions—from atomic to arbitrary sequences—as constant-sized vectors, autoassociative memory was combined with deep learning paradigms for controls. We demonstrate these properties on a quadcopter drone ego-motion inference task and the MVSEC (multivehicle stereo event camera) dataset.

A Macintosh Interface for the Cameca Camebax-Micro Electron Microprobe

1990 ◽  
Vol 48 (1) ◽  
pp. 438-439
Author(s):  
Carl E. Henderson
Keyword(s):  
Electron Microprobe ◽  
Processing Speed ◽  
Word Processing ◽  
State Of The Art ◽  
Computer Control ◽  
Third Party ◽  
Disk Storage ◽  
The Past ◽  
User Facility ◽  
Instrument Control

Over the past few years it has become apparent in our multi-user facility that the computer system and software supplied in 1985 with our CAMECA CAMEBAX-MICRO electron microprobe analyzer has the greatest potential for improvement and updating of any component of the instrument. While the standard CAMECA software running on a DEC PDP-11/23+ computer under the RSX-11M operating system can perform almost any task required of the instrument, the commands are not always intuitive and can be difficult to remember for the casual user (of which our laboratory has many). Given the widespread and growing use of other microcomputers (such as PC’s and Macintoshes) by users of the microprobe, the PDP has become the “oddball” and has also fallen behind the state-of-the-art in terms of processing speed and disk storage capabilities. Upgrade paths within products available from DEC are considered to be too expensive for the benefits received. After using a Macintosh for other tasks in the laboratory, such as instrument use and billing records, word processing, and graphics display, its unique and “friendly” user interface suggested an easier-to-use system for computer control of the electron microprobe automation. Specifically a Macintosh IIx was chosen for its capacity for third-party add-on cards used in instrument control.

scholarly journals Neuro-Inspired Computing with Spin-VCSELs

2021 ◽  
Vol 11 (9) ◽  
pp. 4232
Author(s):  
Krishan Harkhoe ◽  
Guy Verschaffelt ◽  
Guy Van der Sande
Keyword(s):  
Processing Speed ◽  
Delay Time ◽  
High Speed ◽  
State Of The Art ◽  
Polarization Modulation ◽  
Time Interval ◽  
Computing Technique ◽  
High Modulation ◽  
Short Time ◽  
Modulation Speed

Delay-based reservoir computing (RC), a neuromorphic computing technique, has gathered lots of interest, as it promises compact and high-speed RC implementations. To further boost the computing speeds, we introduce and study an RC setup based on spin-VCSELs, thereby exploiting the high polarization modulation speed inherent to these lasers. Based on numerical simulations, we benchmarked this setup against state-of-the-art delay-based RC systems and its parameter space was analyzed for optimal performance. The high modulation speed enabled us to have more virtual nodes in a shorter time interval. However, we found that at these short time scales, the delay time and feedback rate heavily influence the nonlinear dynamics. Therefore, and contrary to other laser-based RC systems, the delay time has to be optimized in order to obtain good RC performances. We achieved state-of-the-art performances on a benchmark timeseries prediction task. This spin-VCSEL-based RC system shows a ten-fold improvement in processing speed, which can further be enhanced in a straightforward way by increasing the birefringence of the VCSEL chip.

Energy Efficient Data Compression in Cloud based IoT

2021 ◽  
pp. 1-1
Author(s):  
Halah Mohammed Al-Kadhim ◽  
Hamed S. Al-Raweshidy
Keyword(s):  
Data Compression ◽  
Energy Efficient ◽  
Efficient Data
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