scholarly journals Stochastic Computing Emulation of Memristor Cellular Nonlinear Networks

Micromachines ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 67
Author(s):  
Oscar Camps ◽  
Mohamad Moner Al Chawa ◽  
Stavros G. Stavrinides ◽  
Rodrigo Picos

Cellular Nonlinear Networks (CNN) are a concept introduced in 1988 by Leon Chua and Lin Yang as a bio-inspired architecture capable of massively parallel computation. Since then, CNN have been enhanced by incorporating designs that incorporate memristors to profit from their processing and memory capabilities. In addition, Stochastic Computing (SC) can be used to optimize the quantity of required processing elements; thus it provides a lightweight approximate computing framework, quite accurate and effective, however. In this work, we propose utilization of SC in designing and implementing a memristor-based CNN. As a proof of the proposed concept, an example of application is presented. This application combines Matlab and a FPGA in order to create the CNN. The implemented CNN was then used to perform three different real-time applications on a 512 × 512 gray-scale and a 768 × 512 color image: storage of the image, edge detection, and image sharpening. It has to be pointed out that the same CNN was used for the three different tasks, with the sole change of some programmable parameters. Results show an excellent capability with significant accompanying advantages, such as the low number of needed elements further allowing for a low cost FPGA-based system implementation, something confirming the system’s capacity for real time operation.

Author(s):  
Oscar Camps ◽  
Mohamed-Moner al Chawa ◽  
Stavros G. Stavrinides ◽  
Rodrigo Picos

Cellular Nonlinear Networks (CNN) are a concept introduced in 1988 by Leon Chua and Lin Yang as a bio-inspired architecture, capable of massively parallel computation. Later on, CNN have been enhanced by incorporating designs that incorporate memristors to profit from their processing and memory capabilities. In addition, Stochastic Computing (SC) can be used to optimize the quantity of required processing elements; thus it provides a lightweight approximate computing framework, quite accurate and effective, though. In this work, we propose utilization of SC in designing and implementing a memristor-based CNN. As a proof of the proposed concept, an example of application is presented. This application combines Matlab and a FPGA in order to create the CNN. The implemented CNN has then been used to perform three different real-time applications on a 512x512 gray-scale and a 768x512 color image: storage of the image, edge detection, and image sharpening. It has to be pointed out that the same CNN has been used for the three different tasks, with the sole change of some programmable parameters. Results show an excellent capability with significant accompanying advantages, like the low number of needed elements further allowing for a low cost FPGA-based system implementation, something confirming the system’s ability for real time operation.


2019 ◽  
Vol 7 (1) ◽  
pp. 326-342 ◽  
Author(s):  
Titon Dutono ◽  
Zulmi Zakariyah ◽  
Tribudi Santoso ◽  
Denny Setiawan

Mostly  natural disasters in Java Island such as landslides are within the vicinity of not more than 200 Km from the district capital. Cellular communications require complex systems and rather vulnerable  to cope with disasters. NVIS mode is considered as a simple radio link during disaster mitigation initiation process. It needs a valid estimation to figure out the condition of the ionosphere. There are two purposes of this study, the first of which is an attempt to find out a fact the existences of authorized HF users who still work in the band of 3 MHz – 10 MHz.  The second is to integrate low cost HF radio communication, commonly available small single board computer hardware, and opensource software, to build a sounding system to evaluate the quality of NVIS channels. Prediction system such VOACAP give hourly prediction data, however it has an inherent limitation because of   nature the underlying databases is monthly average based, therefore, the estimation could not be made in a daily bases. However, a real-time channel evaluation (RTCE)  able to purify maximum observed frequency (MOF) estimation, and consequently, its able to select the best available frequency for short term  and real time operation. In this study, we used WSPR to perform a simple RTCE technique. Furthermore, we also reviewed the current regulatory status regarding  the availability of sub-10 MHz band for NVIS radio operation. The results show that discrepancies between simulation and measurement are occurred mainly because of sporadic data in the band of 60m and 80m. However, all of the measurement results and simulations almost have the same agreement regarding the quiet period between local midnight and local sunrise. The results of measurements show that 60m band is the most reliable NVIS channel between local sunrise and local midnight. Furthermore, 100 watts is a proper transmitter power to reach the required SNR for reliable voice communication. 


Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2138
Author(s):  
Jan Klimaszewski ◽  
Michał Władziński

Safety in human–machine cooperation is the current challenge in robotics. Safe human–robot interaction requires the development of sensors that detect human presence in the robot’s workspace. Detection of this presence should occur before the physical collision of the robot with the human. Human to robot proximity detection should be very fast, allowing machine elements deceleration to velocities safe for human–machine collision. The paper presents a new, low-cost design of distributed robotic skin, which allows real-time measurements of the human body parts proximity. The main advantages of the proposed solution are low cost of its implementation based on comb electrodes matrix and real-time operation due to fast and simple electronic design. The main contribution is the new idea of measuring the distance to human body parts by measuring the operating frequency of a rectangular signal generator, which depends on the capacity of the open capacitor. This capacitor is formed between the comb electrodes matrix and a reference plate located next to the matrix. The capacitance of the open capacitor changes if a human body part is in vicinity. The application of the developed device can be very wide. For example, in the field of cooperative robots, it can lead to the improvement of human–machine interfaces and increased safety of human–machine cooperation. The proposed construction can help to meet the increasing requirements for cooperative robots.


Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4697 ◽  
Author(s):  
Jan Klimaszewski ◽  
Daniel Janczak ◽  
Paweł Piorun

Tactile sensing is the current challenge in robotics and object manipulation by machines. The robot’s agile interaction with the environment requires pressure sensors to detect not only location and value, but also touch direction. The paper presents a new, two-layer construction of artificial robotic skin, which allows measuring the location, value, and direction of pressure from external force. The main advantages of the proposed solution are its low cost of implementation based on two FSR (Force Sensitive Resistor) matrices and real-time operation thanks to direction detection using fast matching algorithms. The main contribution is the idea of detecting the pressure direction by determining the shift between the pressure maps of the skin’s upper and lower layers. The pressure map of each layer is treated as an image and registered using a phase correlation (POC–Phase Only Correlation) method. The use of the developed device can be very wide. For example, in the field of cooperative robots, it can lead to the improvement of human machine interfaces and increased security of human–machine cooperation. The proposed construction can help meet the increasing requirements for robots in cooperation with humans, but also enable agile manipulation of objects from their surroundings.


2015 ◽  
Vol 24 (6) ◽  
pp. 1703-1711 ◽  
Author(s):  
Rosana Alves Dias ◽  
Filipe Serra Alves ◽  
Margaret Costa ◽  
Helder Fonseca ◽  
Jorge Cabral ◽  
...  

2018 ◽  
Author(s):  
J. I. Alvarez Claramunt ◽  
P. E. Bizzotto ◽  
F. Sapag ◽  
E. Ferrigno ◽  
J. L. Barros ◽  
...  

2017 ◽  
Vol 10 (2) ◽  
pp. 169-178 ◽  
Author(s):  
Shouhei Kidera ◽  
Luz Maria Neira ◽  
Barry D. Van Veen ◽  
Susan C. Hagness

Microwave ablation is widely recognized as a promising minimally invasive tool for treating cancer. Real-time monitoring of the dimensions of the ablation zone is indispensable for ensuring an effective and safe treatment. In this paper, we propose a microwave imaging algorithm for monitoring the evolution of the ablation zone. Our proposed algorithm determines the boundary of the ablation zone by exploiting the time difference of arrival (TDOA) between signals received before and during the ablation at external antennas surrounding the tissue, using the interstitial ablation antenna as the transmitter. A significant advantage of this method is that it requires few assumptions about the dielectric properties of the propagation media. Also the simplicity of the signal processing, wherein the TDOA is determined from a cross-correlation calculation, allows real-time monitoring and provides robust performance in the presence of noise. We investigate the performance of this approach for the application of breast tumor ablation. We use simulated array measurements obtained from finite-difference time-domain simulations of magnetic resonance imaging-derived numerical breast phantoms. The results demonstrate that our proposed method offers the potential to achieve millimeter-order accuracy and real-time operation in estimating the boundary of the ablation zone in heterogeneous and dispersive breast tissue.


Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6075
Author(s):  
Guilherme Fonseca Bassous ◽  
Rodrigo Flora Calili ◽  
Carlos Hall Barbosa

The rising adoption of renewable energy sources means we must turn our eyes to limitations in traditional energy systems. Intermittency, if left unaddressed, may lead to several power-quality and energy-efficiency issues. The objective of this work is to develop a working tool to support photovoltaic energy forecast models for real-time operation applications. The current paradigm of intra-hour solar-power forecasting is to use image-based approaches to predict the state of cloud composition for short time horizons. Since the objective of intra-minute forecasting is to address high-frequency intermittency, data must provide information on and surrounding these events. For that purpose, acquisition by exception was chosen as the guiding principle. The system performs power measurements at 1 Hz frequency, and whenever it detects variations over a certain threshold, it saves the data 10 s before and 4 s after the detection point. A multilayer perceptron neural network was used to determine its relevance to the forecasting problem. With a thorough selection of attributes and network structures, the results show very low error with R2 greater than 0.93 for both input variables tested with a time horizon of 60 s. In conclusion, the data provided by the acquisition system yielded relevant information for forecasts up to 60 s ahead.


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