東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Artificial Intelligence Techniques f...

Alshammri, Ghalib.

Linked to FindBook

Google Book

Amazon

博客來

Artificial Intelligence Techniques for Diffusion-based Bio-molecular Nano Communication Networks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial Intelligence Techniques for Diffusion-based Bio-molecular Nano Communication Networks./
Author:	Alshammri, Ghalib.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	265 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-12, Section: A.
Contained By:	Dissertations Abstracts International80-12A.
Subject:	Bioengineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13881702
ISBN:	9781392240724

Artificial Intelligence Techniques for Diffusion-based Bio-molecular Nano Communication Networks.
Alshammri, Ghalib.

Artificial Intelligence Techniques for Diffusion-based Bio-molecular Nano Communication Networks. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 265 p.

Source: Dissertations Abstracts International, Volume: 80-12, Section: A.

Thesis (Ph.D.)--Stevens Institute of Technology, 2019.

This item must not be added to any third party search indexes.

Molecular communications (MC) is an emerging field that is sought to serve in many vital future Nano applications such as drug delivery and nanomedicine. In this dissertation we tackle two challenging problems in MC System and network design, namely, data detection at the link level and optimal relay design at the network level.Threshold-based detection and cooperative (relay) transmission techniques have been developed for wireless sensor networks (WSNs) in recent years to improve network transmission rate, delay and bit error rate (BER) performance, while not as much research has taken place for Molecular Communication based systems. The special characteristics and constraints of bio-molecular nano communication network, such as dynamic environmental conditions and various nanonetwork topologies, makes it necessary to revisit the techniques used for threshold-based detection and cooperative relay transmission.In this thesis, we propose detection approaches that consider and rake into account the various MC channel parameters (e.g., radius of the propagating molecules, viscosity, drift velocity, and the temperature of the fluid environment). We also consider and propose novel solutions for the challenging problem of inter-symbol interference (ISI).In this research we provide two key contributions: We propose and investigate high performance receiver designs using novel threshold-based detection techniques for On-OFF-Keying in diffusion-based MC systems. Techniques we contribute include:(1) Molecular Memory-Assisted Threshold-Based Detection Technique. (2) Fuzzy Rule-Based Classification System. (3) NFIS: Neuro-Fuzzy Inference System using Polynomial Approximation. (4) Low-Complexity Machine Learning Detection Technique. (5) ARFIS: Adaptive-Receiver-Based Fuzzy Inference System.These techniques achieve very low incomes bit error rates compared to traditional approaches.We propose and investigate a long-range nanoscale MC network system based on intelligent multi-agent decision support mechanisms that aggregate the sensed data and simplifies the complexity of the relay schemes, where the intermediate agents do not decode the received/related information. Our proposed techniques provide a novel cooperative decision system that utilizes intelligent agents in combination with pulse energy ratio and sense-and-release technique. We evaluate the performance of our proposed techniques various system parameters, such as diffusion coefficients and transmission distance.

ISBN: 9781392240724Subjects--Topical Terms:

657580
Bioengineering.

Artificial Intelligence Techniques for Diffusion-based Bio-molecular Nano Communication Networks.
LDR:03730nmm a2200349 4500 001 2209453
005 20191104073439.5
008 201008s2019 ||||||||||||||||| ||eng d
020 $a 9781392240724
035 $a (MiAaPQ)AAI13881702
035 $a (MiAaPQ)stevens:10605
035 $a AAI13881702
040 $a MiAaPQ $c MiAaPQ
100 1 $a Alshammri, Ghalib. $3 3436545
245 1 0 $a Artificial Intelligence Techniques for Diffusion-based Bio-molecular Nano Communication Networks.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 265 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-12, Section: A.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Lawrence, Victor;Chandramouli, Rajarathnam.
502 $a Thesis (Ph.D.)--Stevens Institute of Technology, 2019.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a Molecular communications (MC) is an emerging field that is sought to serve in many vital future Nano applications such as drug delivery and nanomedicine. In this dissertation we tackle two challenging problems in MC System and network design, namely, data detection at the link level and optimal relay design at the network level.Threshold-based detection and cooperative (relay) transmission techniques have been developed for wireless sensor networks (WSNs) in recent years to improve network transmission rate, delay and bit error rate (BER) performance, while not as much research has taken place for Molecular Communication based systems. The special characteristics and constraints of bio-molecular nano communication network, such as dynamic environmental conditions and various nanonetwork topologies, makes it necessary to revisit the techniques used for threshold-based detection and cooperative relay transmission.In this thesis, we propose detection approaches that consider and rake into account the various MC channel parameters (e.g., radius of the propagating molecules, viscosity, drift velocity, and the temperature of the fluid environment). We also consider and propose novel solutions for the challenging problem of inter-symbol interference (ISI).In this research we provide two key contributions: We propose and investigate high performance receiver designs using novel threshold-based detection techniques for On-OFF-Keying in diffusion-based MC systems. Techniques we contribute include:(1) Molecular Memory-Assisted Threshold-Based Detection Technique. (2) Fuzzy Rule-Based Classification System. (3) NFIS: Neuro-Fuzzy Inference System using Polynomial Approximation. (4) Low-Complexity Machine Learning Detection Technique. (5) ARFIS: Adaptive-Receiver-Based Fuzzy Inference System.These techniques achieve very low incomes bit error rates compared to traditional approaches.We propose and investigate a long-range nanoscale MC network system based on intelligent multi-agent decision support mechanisms that aggregate the sensed data and simplifies the complexity of the relay schemes, where the intermediate agents do not decode the received/related information. Our proposed techniques provide a novel cooperative decision system that utilizes intelligent agents in combination with pulse energy ratio and sense-and-release technique. We evaluate the performance of our proposed techniques various system parameters, such as diffusion coefficients and transmission distance.
590 $a School code: 0733.
650 4 $a Bioengineering. $3 657580
650 4 $a Communication. $3 524709
650 4 $a Nanotechnology. $3 526235
690 $a 0202
690 $a 0459
690 $a 0652
710 2 $a Stevens Institute of Technology. $b Computer Engineering. $3 2049827
773 0 $t Dissertations Abstracts International $g 80-12A.
790 $a 0733
791 $a Ph.D.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13881702