scholarly journals Implementation of conventional communication system in deep learning

2017 ◽  
Vol 7 (1.1) ◽  
pp. 696
Author(s):  
Satyanarayana P ◽  
Charishma Devi. V ◽  
Sowjanya P ◽  
Satish Babu ◽  
N Syam Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Communication Network ◽  
Deep Learning ◽  
Communication System ◽  
Communication Systems ◽  
Physical Layer ◽  
Learning Capacity ◽  
Constrained Learning ◽  
Complex Channel

Machine learning (ML) has been broadly connected to the upper layers of communication systems for different purposes, for example, arrangement of cognitive radio and communication network. Nevertheless, its application to the physical layer is hindered by complex channel conditions and constrained learning capacity of regular ML algorithms. Deep learning (DL) has been as of late connected for some fields, for example, computer vision and normal dialect preparing, given its expressive limit and advantageous enhancement ability. This paper describes about a novel use of DL for the physical layer. By deciphering a communication system as an auto encoder, we build up an essential better approach to consider communication system outline as a conclusion to-end reproduction undertaking that tries to together enhance transmitter and receiver in a solitary procedure. This DL based technique demonstrates promising execution change than traditional communication system.  

scholarly journals Image classification using Deep learning

2018 ◽  
Vol 7 (2.7) ◽  
pp. 614 ◽  
Author(s):  
M Manoj krishna ◽  
M Neelima ◽  
M Harshali ◽  
M Venu Gopala Rao
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Classical Problem

The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning. We use AlexNet architecture with convolutional neural networks for this purpose. Four test images are selected from the ImageNet database for the classification purpose. We cropped the images for various portion areas and conducted experiments. The results show the effectiveness of deep learning based image classification using AlexNet.  

scholarly journals An Introduction to IoT Based Posture Detection and Underlying Sensor Technology

2020 ◽  
Author(s):  
Jahnvi Gupta ◽  
Nitin Gupta ◽  
Mukesh Kumar ◽  
Ritwik Duggal
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Patient Monitoring ◽  
Elderly Care ◽  
Medical Science ◽  
Sensor Technology ◽  
Human Posture ◽  
Posture Recognition ◽  
Selection For

Analysis of human posture has many applications in the field of sports and medical science including patient monitoring, lifestyle analysis, elderly care etc. Many of the works in this area have been based on computer vision techniques. These are limited in providing real-time solution. Thus, Internet of Things (IoT) based solution are being planned and used for the human posture recognition and detection. The data collected from sensors is then passed to machine learning or deep learning algorithms to find different patterns. In this chapter an introduction to IoT based posture detection is provided with an introduction to underlying sensor technology, which can help in selection for appropriate sensors for the posture detection.<br>

scholarly journals Future OFDM-based Communication Systems Towards 6G and Beyond: Machine Learning Approaches

2021 ◽  
Vol 1 (1) ◽  
pp. 19-25
Author(s):  
Filbert H. Juwono ◽  
Regina Reine
Keyword(s):  
Machine Learning ◽  
Communication Network ◽  
Data Processing ◽  
Fading Channels ◽  
Communication Systems ◽  
Multipath Fading ◽  
Spectrum Efficiency ◽  
Learning Approaches ◽  
Multipath Fading Channels ◽  
Rate Transmission

The vision towards 6G and beyond communication systems demands higher rate transmission, massive amount of data processing, and low latency communication. Orthogonal Frequency Division Modulation (OFDM) has been adopted in the current 5G networks and has become one of the potential candidates for the future communication systems. Although OFDM offers many benefits including high spectrum efficiency and high robustness against the multipath fading channels, it has major challenges such as frequency offset and high Peak to Power Ratio (PAPR). In 5G communication network, there is a significant increase in the number of sensors and other low-power devices where users or devices may create large amount of connection and dynamic data processing. In order to deal with the increasingly complex communication network, Machine Learning (ML) has been increasingly utilised to create intelligent and more efficient communication network. This paper discusses challenges and the impacts of embedding ML in OFDM-based communication systems.

scholarly journals Redefining Wireless Communication for 6G:Signal Processing Meets Deep Learning with Deep Unfolding

2021 ◽  
Author(s):  
Anu Jagannath ◽  
Jithin Jagannath
Keyword(s):  
Signal Processing ◽  
Deep Learning ◽  
Wireless Communication ◽  
Communication Systems ◽  
Domain Knowledge ◽  
Physical Layer ◽  
Learning Approaches ◽  
Communication Architecture ◽  
Recent Emergence ◽  
Rate Improvement

The year 2019 witnessed the rollout of the 5G standard, which promises to offer significant data rate improvement over 4G. While 5G is still in its infancy, there has been an increased shift in the research community for communication technologies beyond 5G. The recent emergence of machine learning approaches for enhancing wireless communications and empowering them with much-desired intelligence holds immense potential for redefining wireless communication for 6G. The evolving communication systems will be bottlenecked in terms of latency, throughput, and reliability by the underlying signal processing at the physical layer. In this position paper, we motivate the need to redesign iterative signal processing algorithms by leveraging deep unfolding techniques to fulfill the physical layer requirements for 6G networks. To this end, we begin by presenting the service requirements and the key challenges posed by the envisioned 6G communication architecture. We outline the deficiencies of the traditional algorithmic principles and data-hungry deep learning (DL) approaches in the context of 6G networks. Specifically, deep unfolded signal processing is presented by sketching the interplay between domain knowledge and DL. The deep unfolded approaches reviewed in this article are positioned explicitly in the context of the requirements imposed by the next generation of cellular networks. Finally, this article motivates open research challenges to truly realize hardware-efficient edge intelligence for future 6G networks.<br>

scholarly journals Penerapan Computer Vision Menggunakan Metode Deep Learning pada Perspektif Generasi Ulul Albab

2021 ◽  
Vol 7 (2) ◽  
pp. 98-107
Author(s):  
Imamul Arifin ◽  
Reydiko Fakhran Haidi ◽  
Muhammad Dzalhaqi
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Library Research

Machine learning merupakan salah satu penerapan kecerdasan buatan. Penggunaan machine learning pada computer vision erat berkaitan dengan deep learning yang mana para ilmuwan komputer mendapatkan inspirasi mengenai teknologi deep learning dari alam sekitar. Tujuan penelitian pada naskah ini adalah Mengetahui dan memahami teknologi deep learning beserta contoh sederhana dalam pemrosesan objek gambar dan Mengetahui dan memahami teknologi kecerdasan buatan dalam perspektif generasi ulul albab sehingga bisa memberikan manfaat secara menyeluruh bagi dunia. Penelitian yang dilakukan pada karya tulis ini merupakan jenis penelitian kualitatif dengan metode studi pustaka (library research) menggunakan berbagai buku dan literatur bacaan lainnya seperti jurnal dan website khusus sehingga menghasilkan informasi dari topik yang diteliti. Teknologi kecerdasan buatan akan selalu berkembang dan menuju arah yang semakin canggih, tetapi teknologi juga mempunyai dampak negatif. Generasi Ulul Albab harus bisa berjuang untuk memberikan dampak positif bagi masyarakat karena sejatinya generasi ulul albab adalah harapan kemajuan peradaban islam di berbagai sektor ilmu pengetahuan dan teknologi.

scholarly journals An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet

2021 ◽  
Vol 11 (5) ◽  
pp. 2196
Author(s):  
Andrea Tundis ◽  
Gaurav Mukherjee ◽  
Max Mühlhäuser
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Communication Systems ◽  
The Internet ◽  
Learning Models ◽  
Specific Content ◽  
Character Identification ◽  
Code Language ◽  
Mixed Code

Internet-based communication systems have become an increasing tool for spreading misinformation and propaganda. Even though there exist mechanisms that are able to track unwarranted information and messages, users made up different ways to avoid their scrutiny and detection. An example is represented by the mixed-code language, that is text written in an unconventional form by combining different languages, symbols, scripts and shapes. It aims to make more difficult the detection of specific content, due to its custom and ever changing appearance, by using special characters to substitute for alphabet letters. Indeed, such substitute combinations of symbols, which tries to resemble the shape of the intended alphabet’s letter, makes it still intuitively readable to humans, however nonsensical to machines. In this context, the paper explores the possibility of identifying propaganda in such mixed-code texts over the Internet, centred on a machine learning based approach. In particular, an algorithm in combination with a deep learning models for character identification is proposed in order to detect and analyse whether an element contains propaganda related content. The overall approach is presented, the results gathered from its experimentation are discussed and the achieved performances are compared with the related works.

scholarly journals Redefining Wireless Communication for 6G:Signal Processing Meets Deep Learning with Deep Unfolding

2021 ◽  
Author(s):  
Anu Jagannath ◽  
Jithin Jagannath
Keyword(s):  
Signal Processing ◽  
Deep Learning ◽  
Wireless Communication ◽  
Communication Systems ◽  
Domain Knowledge ◽  
Physical Layer ◽  
Learning Approaches ◽  
Communication Architecture ◽  
Recent Emergence ◽  
Rate Improvement

The year 2019 witnessed the rollout of the 5G standard, which promises to offer significant data rate improvement over 4G. While 5G is still in its infancy, there has been an increased shift in the research community for communication technologies beyond 5G. The recent emergence of machine learning approaches for enhancing wireless communications and empowering them with much-desired intelligence holds immense potential for redefining wireless communication for 6G. The evolving communication systems will be bottlenecked in terms of latency, throughput, and reliability by the underlying signal processing at the physical layer. In this position paper, we motivate the need to redesign iterative signal processing algorithms by leveraging deep unfolding techniques to fulfill the physical layer requirements for 6G networks. To this end, we begin by presenting the service requirements and the key challenges posed by the envisioned 6G communication architecture. We outline the deficiencies of the traditional algorithmic principles and data-hungry deep learning (DL) approaches in the context of 6G networks. Specifically, deep unfolded signal processing is presented by sketching the interplay between domain knowledge and DL. The deep unfolded approaches reviewed in this article are positioned explicitly in the context of the requirements imposed by the next generation of cellular networks. Finally, this article motivates open research challenges to truly realize hardware-efficient edge intelligence for future 6G networks.<br>

Sign in / Sign up

Export Citation Format

Share Document