Optimization of Power Efficient Spatial Division Multiplexed Submarine Cables Using Adaptive Transponders and Machine Learning

2022 ◽  
pp. 1-1
Author(s):  
Maria Vasilica Ionescu ◽  
Jeremie Renaudier ◽  
Amirhossein Ghazisaeidi ◽  
Arnaud Leroy ◽  
Olivier Courtois
Keyword(s):  
Machine Learning ◽  
Power Efficient ◽  
Submarine Cables

A Power-efficient Framework for Software-defined IoT Ecosystem using Machine Learning

2020 ◽  
Author(s):  
Faizur Rahman ◽  
Md. Shahriare Satu ◽  
Md. Ashaduzzaman ◽  
Md. Imran Khan ◽  
Shanto Roy
Keyword(s):  
Machine Learning ◽  
Power Efficient

scholarly journals Environmental Sound Recognition on Embedded Systems: From FPGAs to TPUs

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2622
Author(s):  
Jurgen Vandendriessche ◽  
Nick Wouters ◽  
Bruno da Silva ◽  
Mimoun Lamrini ◽  
Mohamed Yassin Chkouri ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Machine Learning Techniques ◽  
Sound Recognition ◽  
Learning Approaches ◽  
Environmental Sound ◽  
Embedded Devices ◽  
Power Efficient ◽  
Computationally Intensive ◽  
Environmental Sound Recognition

In recent years, Environmental Sound Recognition (ESR) has become a relevant capability for urban monitoring applications. The techniques for automated sound recognition often rely on machine learning approaches, which have increased in complexity in order to achieve higher accuracy. Nonetheless, such machine learning techniques often have to be deployed on resource and power-constrained embedded devices, which has become a challenge with the adoption of deep learning approaches based on Convolutional Neural Networks (CNNs). Field-Programmable Gate Arrays (FPGAs) are power efficient and highly suitable for computationally intensive algorithms like CNNs. By fully exploiting their parallel nature, they have the potential to accelerate the inference time as compared to other embedded devices. Similarly, dedicated architectures to accelerate Artificial Intelligence (AI) such as Tensor Processing Units (TPUs) promise to deliver high accuracy while achieving high performance. In this work, we evaluate existing tool flows to deploy CNN models on FPGAs as well as on TPU platforms. We propose and adjust several CNN-based sound classifiers to be embedded on such hardware accelerators. The results demonstrate the maturity of the existing tools and how FPGAs can be exploited to outperform TPUs.

scholarly journals A Novel OFDM Format and a Machine Learning Based Dimming Control for LiFi

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2103
Author(s):  
Itisha Nowrin ◽  
M. Rubaiyat Hossain Mondal ◽  
Rashed Islam ◽  
Joarder Kamruzzaman
Keyword(s):  
Machine Learning ◽  
Orthogonal Frequency Division Multiplexing ◽  
Polynomial Regression ◽  
Learning Algorithms ◽  
Pulse Amplitude ◽  
Machine Learning Algorithms ◽  
Power Efficient ◽  
Switching Algorithm ◽  
Optical Ofdm ◽  
Constellation Size

This paper proposes a new hybrid orthogonal frequency division multiplexing (OFDM) form termed as DC-biased pulse amplitude modulated optical OFDM (DPO-OFDM) by combining the ideas of the existing DC-biased optical OFDM (DCO-OFDM) and pulse amplitude modulated discrete multitone (PAM-DMT). The analysis indicates that the required DC-bias for DPO-OFDM-based light fidelity (LiFi) depends on the dimming level and the components of the DPO-OFDM. The bit error rate (BER) performance and dimming flexibility of the DPO-OFDM and existing OFDM schemes are evaluated using MATLAB tools. The results show that the proposed DPO-OFDM is power efficient and has a wide dimming range. Furthermore, a switching algorithm is introduced for LiFi, where the individual components of the hybrid OFDM are switched according to a target dimming level. Next, machine learning algorithms are used for the first time to find the appropriate proportions of the hybrid OFDM components. It is shown that polynomial regression of degree 4 can reliably predict the constellation size of the DCO-OFDM component of DPO-OFDM for a given constellation size of PAM-DMT. With the component switching and the machine learning algorithms, DPO-OFDM-based LiFi is power efficient at a wide dimming range.

Reliable and Power‐Efficient Machine Learning in Wearable Sensors

Fog Computing ◽  
2020 ◽  
pp. 385-409
Author(s):  
Parastoo Alinia ◽  
Hassan Ghasemzadeh
Keyword(s):  
Machine Learning ◽  
Wearable Sensors ◽  
Power Efficient ◽  
Efficient Machine

Crop Quality Prediction Using Ml And Neural Networks

2021 ◽  
Vol 10 (02) ◽  
pp. 07-11
Author(s):  
Kanakaveti Narasimha Dheeraj ◽  
Goutham. R. J ◽  
Arthi. L
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Random Forest ◽  
Machine Learning Techniques ◽  
Quality Prediction ◽  
Random Forest Regression ◽  
Power Efficient ◽  
Learning Techniques ◽  
Better Than

Agriculture is said to be the backbone of the economy. Farmers toil hard with different kinds of crops to make good and healthy food for the country. There are more existing systems but uses outdated machine-learning techniques based on RNN( Recurrent neural network) which makes the process slower and more time-consuming. Here We are proposing a new CNN(Convolutional neural network ) based system which is fast and gives accurate results within seconds. CNN is power-efficient and is more suitable for real-time implementation. In this project, we use CNN algorithms which is very much better than the RNN algorithms used in the existing system.More parameters will be taken for the consideration of prediction in the proposed system. And we use Random Forest Regression, Multiple Linear Regression

scholarly journals An Overview of Machine Learning-Based Energy-Efficient Routing Algorithms in Wireless Sensor Networks

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1539
Author(s):  
Qianao Ding ◽  
Rongbo Zhu ◽  
Hao Liu ◽  
Maode Ma
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Routing Algorithms ◽  
Wireless Sensor ◽  
Energy Efficient Routing ◽  
Power Efficient ◽  
Routing Schemes ◽  
Green Routing

Machine learning (ML) technology has shown its unique advantages in many fields and has excellent performance in many applications, such as image recognition, speech recognition, recommendation systems, and natural language processing. Recently, the applicability of ML in wireless sensor networks (WSNs) has attracted much attention. As resources are limited in WSNs, identifying how to improve resource utilization and achieve power-efficient load balancing is becoming a critical issue in WSNs. Traditional green routing algorithms aim to achieve this by reducing energy consumption and prolonging network lifetime through optimized routing schemes in WSNs. However, there are usually problems such as poor flexibility, a single consideration factor, and a reliance on accurate mathematical models. ML techniques can quickly adapt to environmental changes and integrate multiple factors for routing decisions, which provides new ideas for intelligent energy-efficient routing algorithms in WSNs. In this paper, we survey and propose a theoretical hypothetic model formulation of ML as an effective method for creating a power-efficient green routing model that can overcome the limitations of traditional green routing methods. In addition, the study also provides an overview of past, present, and future progress in green routing schemes in WSNs. The contents of this paper will appeal to a wide range of audiences interested in ML-based WSNs.

scholarly journals Design Optimisation of Power-Efficient Submarine Line through Machine Learning

2020 ◽  
Author(s):  
Maria Ionescu ◽  
Amirhossein Ghazisaeidi ◽  
Jérémie Renaudier ◽  
Pascal Pecci ◽  
Olivier Courtois
Keyword(s):  
Machine Learning ◽  
Design Optimisation ◽  
Power Efficient

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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