scholarly journals 3D Neural Spheroids: Surface‐Functionalized Self‐Standing Microdevices Exhibit Predictive Localization and Seamless Integration in 3D Neural Spheroids (Adv. Biosys. 11/2020)

2020 ◽  
Vol 4 (11) ◽  
pp. 2070113
Author(s):  
Aziliz Lecomte ◽  
Lidia Giantomasi ◽  
Silvia Rancati ◽  
Fabio Boi ◽  
Gian Nicola Angotzi ◽  
...  
Keyword(s):  
Seamless Integration

A REVIEW ON THE BENEFITS OF HORTICULTURAL THERAPY FOR RETIREMENT COMMUNITY IN MALAYSIA

2017 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Amirul Amin Ismail ◽  
Ismail Samsuddin ◽  
Azman Zainonabidin ◽  
Harlina Mohd Ali
Keyword(s):  
Well Being ◽  
The Elderly ◽  
Retirement Community ◽  
Analysis Method ◽  
Horticultural Therapy ◽  
Seamless Integration ◽  
Ageing Society ◽  
After Effects ◽  
Malaysian Population ◽  
The Impact

By the year 2030, Malaysian population will experience the after effects of the rapid growth of ageing society. This paper investigates the impact of seamless integration of horticultural activity in the new residential typology of retirement community. It is believed that horticultural therapy is not only beneficial for physical and psychological but also promotes socialisation opportunities among the elderly. Comparative analysis method on selected precedent studies has been carried out and analysed in accordance with Malaysian context. Initial findings indicate that a retirement community with horticultural activity gives therapy for healthier well-being. This therapeutic activity can be apositive change in elderly lifestyle and essential towards the establishment of retirement community in Malaysia. 

A Seven-Layered Model Architecture, Network Model, Protocol Stack, Security, Application, Issues and Challenges in Internet of Vehicle

2020 ◽  
Vol 14 ◽  
Author(s):  
Intyaz Alam ◽  
Sushil Kumar ◽  
Pankaj Kumar Kashyap
Keyword(s):  
Internet Of Things ◽  
Network Model ◽  
Transportation Systems ◽  
Vehicle Speed ◽  
Protocol Stack ◽  
Seamless Integration ◽  
Communication Architecture ◽  
Smart Transportation ◽  
Smart Healthcare ◽  
Essential Components

Background: Recently, Internet of Things (IoT) has brought various changes in the existing research field by including new areas such as smart transportation, smart home facilities, smart healthcare, etc. In smart transportation systems, vehicles contain different components to access information related to passengers, drivers, vehicle speed, and many more. This information can be accessed by connecting vehicles with Internet of Things leading to new fields of research known as Internet of Vehicles. The setup of Internet of Vehicle (IoV) consists of many sensors to establish a connection with several other sensors belonging to different environments by exploiting different technologies. The communication of the sensors faces a lot of challenging issues. Some of the critical challenges are to maintain security in information exchanges among the vehicles, inequality in sensors, quality of internet connection, and storage capacity. Objective: To overcome the challenging issues, we have designed a new framework consisting of seven-layered architecture, including the security layered, which provides seamless integration by communicating the devices present in the IoV environment. Further, a network model consisting of four components such as Cloud, Fog, Connection, and Clients has been designed. Finally, the protocol stack which describes the protocol used in each layer of the proposed seven-layered IoV architecture has been shown. Methods: In this proposed architecture, the representation and the functionalities of each layer and types of security have been defined. Case studies of this seven-layer IoV architecture have also been performed to illustrate the operation of each layer in real-time. The details of the network model including all the elements inside each component, have also been shown. Results: We have discussed some of the existing communication architecture and listed a few challenges and issues occurring in present scenarios. Considering these issues, which is presently occurring in the existing communication architecture. We have developed the seven-layered IoV architecture and the network model with four essential components known as the cloud, fog, connection, and clients. Conclusion: This proposed architecture provides a secure IoV environment and provides life safety. Hence, safety and security will help to reduce the cybercrimes occurring in the network and provides good coordination and communication of the vehicles in the network.

scholarly journals Integration of 3D Printed Flexible Pressure Sensors into Physical Interfaces for Wearable Robots

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2157
Author(s):  
Kevin Langlois ◽  
Ellen Roels ◽  
Gabriël Van De Velde ◽  
Cláudia Espadinha ◽  
Christopher Van Vlerken ◽  
...  
Keyword(s):  
Pressure Distribution ◽  
Human Subjects ◽  
Pressure Sensors ◽  
Poor Performance ◽  
Capacitive Pressure Sensor ◽  
Upper Arm ◽  
Seamless Integration ◽  
Dynamic Task ◽  
Wearable Robots ◽  
3D Printed

Sensing pressure at the physical interface between the robot and the human has important implications for wearable robots. On the one hand, monitoring pressure distribution can give valuable benefits on the aspects of comfortability and safety of such devices. Additionally, on the other hand, they can be used as a rich sensory input to high level interaction controllers. However, a problem is that the commercial availability of this technology is mostly limited to either low-cost solutions with poor performance or expensive options, limiting the possibilities for iterative designs. As an alternative, in this manuscript we present a three-dimensional (3D) printed flexible capacitive pressure sensor that allows seamless integration for wearable robotic applications. The sensors are manufactured using additive manufacturing techniques, which provides benefits in terms of versatility of design and implementation. In this study, a characterization of the 3D printed sensors in a test-bench is presented after which the sensors are integrated in an upper arm interface. A human-in-the-loop calibration of the sensors is then shown, allowing to estimate the external force and pressure distribution that is acting on the upper arm of seven human subjects while performing a dynamic task. The validation of the method is achieved by means of a collaborative robot for precise force interaction measurements. The results indicate that the proposed sensors are a potential solution for further implementation in human–robot interfaces.

scholarly journals The INUS Platform: A Modular Solution for Object Detection and Tracking from UAVs and Terrestrial Surveillance Assets

Computation ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 12
Author(s):  
Evangelos Maltezos ◽  
Athanasios Douklias ◽  
Aris Dadoukis ◽  
Fay Misichroni ◽  
Lazaros Karagiannidis ◽  
...  
Keyword(s):  
Decision Making ◽  
Object Detection ◽  
Situational Awareness ◽  
Machine Learning Techniques ◽  
Measurement Instruments ◽  
Time Data ◽  
Modular Architecture ◽  
Seamless Integration ◽  
Object Detection And Tracking ◽  
Detection And Tracking

Situational awareness is a critical aspect of the decision-making process in emergency response and civil protection and requires the availability of up-to-date information on the current situation. In this context, the related research should not only encompass developing innovative single solutions for (real-time) data collection, but also on the aspect of transforming data into information so that the latter can be considered as a basis for action and decision making. Unmanned systems (UxV) as data acquisition platforms and autonomous or semi-autonomous measurement instruments have become attractive for many applications in emergency operations. This paper proposes a multipurpose situational awareness platform by exploiting advanced on-board processing capabilities and efficient computer vision, image processing, and machine learning techniques. The main pillars of the proposed platform are: (1) a modular architecture that exploits unmanned aerial vehicle (UAV) and terrestrial assets; (2) deployment of on-board data capturing and processing; (3) provision of geolocalized object detection and tracking events; and (4) a user-friendly operational interface for standalone deployment and seamless integration with external systems. Experimental results are provided using RGB and thermal video datasets and applying novel object detection and tracking algorithms. The results show the utility and the potential of the proposed platform, and future directions for extension and optimization are presented.

scholarly journals Adaptive quantum state tomography with neural networks

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yihui Quek ◽  
Stanislav Fort ◽  
Hui Khoon Ng
Keyword(s):  
Machine Learning ◽  
Quantum State ◽  
The State ◽  
Learning To Learn ◽  
Seamless Integration ◽  
Quantum State Tomography ◽  
Flexible Machine ◽  
Meta Learning ◽  
Long Time ◽  
State Tomography

AbstractCurrent algorithms for quantum state tomography (QST) are costly both on the experimental front, requiring measurement of many copies of the state, and on the classical computational front, needing a long time to analyze the gathered data. Here, we introduce neural adaptive quantum state tomography (NAQT), a fast, flexible machine-learning-based algorithm for QST that adapts measurements and provides orders of magnitude faster processing while retaining state-of-the-art reconstruction accuracy. As in other adaptive QST schemes, measurement adaptation makes use of the information gathered from previous measured copies of the state to perform a targeted sensing of the next copy, maximizing the information gathered from that next copy. Our NAQT approach allows for a rapid and seamless integration of measurement adaptation and statistical inference, using a neural-network replacement of the standard Bayes’ update, to obtain the best estimate of the state. Our algorithm, which falls into the machine learning subfield of “meta-learning” (in effect “learning to learn” about quantum states), does not require any ansatz about the form of the state to be estimated. Despite this generality, it can be retrained within hours on a single laptop for a two-qubit situation, which suggests a feasible time-cost when extended to larger systems and potential speed-ups if provided with additional structure, such as a state ansatz.

scholarly journals A Comprehensive Open-Source Simulation Framework for LiFi Communication

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2485
Author(s):  
Shakir Ullah ◽  
Saeed Ur Rehman ◽  
Peter Han Joo Chong
Keyword(s):  
Open Source ◽  
Hybrid System ◽  
Resource Sharing ◽  
Optical Gain ◽  
Packet Delay ◽  
Mobility Model ◽  
Network Simulator ◽  
Simulation Framework ◽  
Light Spectrum ◽  
Seamless Integration

Light Fidelity (LiFi) is a new candidate for wireless networking that utilizes the visible light spectrum and exploits the existing lighting infrastructure in the form of light-emitting diodes (LEDs). It provides point-to-point and point-to-multipoint communication on a bidirectional channel at very high data rates. However, the LiFi has small coverage, and its optical gain is closely related to the receiver’s directionality vis-à-vis the transmitter, therefore it can experience frequent service outages. To provide reliable coverage, the LiFi is integrated with other networking technologies such as wireless fidelity (WiFi) thus forming a hybrid system. The hybrid LiFi/WiFi system faces many challenges including but not limited to seamless integration with the WiFi, support for mobility, handover management, resource sharing, and load balancing. The existing literature has addressed one or the other aspect of the issues facing LiFi systems. There are limited free source tools available to holistically address these challenges in a scalable manner. To this end, we have developed an open-source simulation framework based on the network simulator 3 (ns-3), which realizes critical aspects of the LiFi wireless network. Our developed ns-3 LiFi framework provides a fully functional AP equipped with the physical layer and medium access control (MAC), a mobility model for the user device, and integration between LiFi and WiFi with a handover facility. Simulation results are produced to demonstrate the mobility and handover capabilities, and the performance gains from the LiFi-WiFi hybrid system in terms of packet delay, throughput, packet drop ratio (PDR), and fairness between users. The source code of the framework is made available for the use of the research community.

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