TensorFlow 2.0 in ActionTensorFlow 1.x Deep Learning CookbookMachine Learning with TensorFlow 1.xMachine Learning with TensorFlow, Second EditionTensorFlow 2 Pocket PrimerProgramming with TensorFlowTensorFlow Machine Learning ProjectsHands-On Neural Networks with TensorFlow 2.0TensorFlow for Deep LearningTensor Flow Pocket PrimerNatural Language Processing with TensorFlowTensorFlow: Powerful Predictive Analytics with TensorFlowHands-On Convolutional Neural Networks with TensorFlowTensorFlow 2.0 Computer Vision CookbookIntelligent Mobile Projects with TensorFlowLearning TensorFlow.jsDeep Learning with TensorFlow 2 and KerasLearning TensorFlowTensorFlow 2 Pocket ReferenceMachine Learning Using TensorFlow CookbookTensorFlow 2.0 Quick Start GuideTensorFlow Machine Learning CookbookLearn TensorFlow 2.0Learn TensorFlow in 24 HoursHands-On Computer Vision with TensorFlow 2Mastering Computer Vision with TensorFlow 2.xPro Deep Learning with TensorFlowHands-On Machine Learning with TensorFlow.jsTensorFlow for Deep LearningTinyMLLearning TensorFlow.jsDeep Learning with TensorFlow 2 and Keras Second EditionDeep Learning with TensorFlowMastering TensorFlow 1.xAdopting TensorFlow for Real-World AITensorFlow For DummiesArtificial Intelligence with PythonHands-On Machine Learning with Scikit-Learn, Keras, and TensorFlowLearn TensorFlow EnterpriseThe TensorFlow Workshop

구글 TensorFlow 소개

第１章 Ａｎｄｒｏｉｄアプリケーション開発の基本（Ａｎｄｒｏｉｄとは；開発に必要なもの ほか） 第２章 開発環境の構築（Ａｎｄｒｏｉｄアプリケーション開発に必要なツール；セットアップ手順 ほか） 第３章 開発ツールの使い方（ＨｅｌｌｏＷｏｒｌｄ；エミュレータの起動 ほか） 第４章 Ｖｉｅｗの基本（Ｖｉｅｗとは；ＩｍａｇｅＶｉｅｗ ほか） 第５章 画面遷移（画面遷移の方法；Ａｃｔｉｖｉｔｙの状態とライフサイクル ほか） 付録 実習解答プログラム例

楽々!Android Studioはじめの一歩

Smart Airport concept is the future of Airport operation and it may dramatically change the industry towards modern technology adaptation. This study mainly focuses on the Smart airport applications in to passenger terminal process. Literature scattered on different sources have been summarised to explain the features of smart airport with practical cases in global context. Empirical indications on implementation of the smart airport applications discussed based on practical implications of airport operations. Special attention given to the cases of the best performing major airports in Asia, Middle East & Europe. This study contribute to the field of academia and industry by identifying the advantages of smart airport implementation under the key areas of Aviation Security, Passenger Convenience, Operational Efficiency and Optimizing Limited resources. Further implantation methodology under passenger, baggage handling & regulatory controls have been discussed in this paper. Finally key challenges in implementation of the smart airport concept has been addressed and concludes with empirical justifications highlighting future research priorities on implementing the concept.

/pdf/smart-airport-a-review-on-future-of-the-airport-operation-59xlvuinhs.pdf

Smart Airport: A Review on Future of the Airport Operation

Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology.

https://www.mdpi.com/1424-8220/21/6/2146/pdf

Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review.

Technological and educational challenges towards pandemic-resilient aviation.

Passengers' luggage is being reported missing in large amounts yearly at airports around the world due to several reasons. It causes tension to passengers at the destination airports and incurs excessive insurance costs for the airlines. An airport luggage protection system is presented in this paper to serve as a solution to this problem. The presented system ensures that passengers will exit the airport with only their own luggage. The system is based mainly on the RFID (radio frequency identification) technology. RFID tags, placed on both the passenger's ticket and luggage, will contain electronically stored encrypted information captured by scanning luggage tags with a barcode reader. In addition, a camera is used to capture the image of passenger's ticket and software tools developed for image processing are utilized to obtain data for a reference database. A prototype system has been developed and successfully tested.

A smart system to minimize mishandled luggage at airports

The topic of paralysis has gained a lot of interest among scientist over the last years. Therefore, many projects were made for patients suffering from paralysis, yet none has succeeded in achieving an effective way to give these patients the ability to control their paralyzed body parts. This paper proposes a robotic upper-limb exoskeleton guided using 3D object detection and recognition and controlled via Electroencephalogram (EEG) signals. The proposed system is dedicated to patients with complete monoplegic, hemiplegic and quadriplegic paralysis. The main objective of the system is to give these patients the ability to move their upper-limb, and control a wheelchair using their thoughts, which offers them independence, better life quality and assist them in leading active roles in the society. This system consists of four main components, namely, EEG module, infrared (IR) depth camera, 3D printed upper-limb exoskeleton and a motorized wheelchair. The former two are used as inputs to the system and the collaboration between them shows the uniqueness of the proposed approach. EEG signals are segmented and classified through Fuzzy Logic technique and the results are used for choosing the desired object for grabbing from the surface of a table. Movement to the desired object is executed based on the 3D coordinates obtained from the IR depth camera, while inertial measurement unit (IMU) sensor is placed on the arm as a feedback element to ensure accurate movement and proper safety measures. System prototype showed sufficient results for the proposed idea.

Intelligent Exoskeleton for Patients with Paralysis

Patients who are conscious and aware of their environment but are physically disabled are known to have Locked-in Syndrome. The causes for this medical condition include traffic accidents, drug addiction and brain clots. There are some available solutions nowadays to help them communicate but the down side is the requirement for physical training which can be both time and money consuming. The main objective of this project is to help these patients communicate and engage more effectively in their daily life. In this paper, an Electroencephalogram (EEG)-based communication system is developed to facilitate communication of these patients with their caretakers. The implementation is composed of both hardware and software. The hardware consists of a 14-channels EEG module and a tablet. The software parts are: processing algorithm, online database and an android application. The EEG module on the patients' scalps keeps reading brainwaves continuously. Meanwhile, the tablet in front of them displays six basic needs, namely, food, water, washroom, help, sleep and entrainment. When the patients focus on a specific need, it will be detected when it matches with a predefined reference in the processing algorithm. The processing is done using fuzzy logic pattern recognition based on eye movement and color detection. The database acts as a two-way communication link between the patients and their caretaker. As the message will be sent through it to the android application-which is installed in the caretakers' phones-in the form of a pop-up notification. Interchangeably, a response message can be sent by the caretakers to state they are on their way for instance. Besides, the tablet will generate a voice message to inform the people around the patients about their need. A prototype system has been developed and successfully tested.

EEG-based communication system for patients with locked-in syndrome using fuzzy logic

Paralysis is the loss of muscle functions in any part of the body at any age and can be temporary or permanent. Many times, temporary paralysis can be treated by physical therapy without using high potency drugs. In this paper, a smart and innovative system is proposed and developed to help bell's palsy patients with temporary facial paralysis, by automating the physiotherapy at the comfort of patients' home using a 3D printed design. The proposed prototype performs the facial physiotherapy, as prescribed by the physiotherapist using a smartphone application. Furthermore, a smart app is also proposed and presented in this paper to perform intelligent face mapping, data-logging and progression functions for the patients to visualize improvements in their health conditions.

Smart Prediction System for Facial Paralysis

Alzheimer's patients require special care and constant monitoring. This paper utilizes smart glasses equipped with Augmented Reality (AR) screen to perform the basic functions of a caregiver and provide the patients with features that increase the independence and reduce caregiving costs. It targets patients during the early and middle stages of the disease who still possess control over their mental and physical behaviours. The core features are detecting the location of misplaced objects and guiding the patient to their last seen location, identifying and displaying the names of friends and relatives on the AR display, monitoring if the patient has lost the way and accordingly displaying the way home on the AR display while simultaneously sending the patient's location to the caregiver as an SMS, detecting if the patient removed the glasses, predicting the cause of removal, then notifying the caregiver with the predicted cause and the patient's location.

Wessam Shehieb

Papers

A smart system to minimize mishandled luggage at airports

Intelligent Exoskeleton for Patients with Paralysis

EEG-based communication system for patients with locked-in syndrome using fuzzy logic

Smart Prediction System for Facial Paralysis

Smart Assistive Glasses for Alzheimer's Patients