On-Demand Cooperation MAC Protocols with Optimal Diversity-Multiplexing Tradeoff

This paper presents access protocols with optimal Diversity-Multiplexing Tradeoff (DMT) performance in the context of IEEE 802.11-based mesh networks. The protocols are characterized by two main features: on-demand cooperation and selection of the best relay terminal. The on-demand characteristic refers to the ability of a destination terminal to ask for cooperation when it fails in decoding the message transmitted by a source terminal. This approach allows maximization of the spatial multiplexing gain. The selection of the best relay terminal allows maximization of the diversity order. Hence, the optimal DMT curve is achieved with these protocols

DMT Optimal On-Demand Relaying for Mesh Networks

This paper presents a new cooperative MAC (Medium Access Control) protocol called BRIAF (Best Relay based Incremental Amplify-and-Forward). The proposed protocol presents two features: on-demand relaying and selection of the best relay terminal. “On-demand relaying” means that a cooperative transmission is implemented between a source terminal and a destination terminal only when the destination terminal fails in decoding the data transmitted by the source terminal. This feature maximizes the spatial multiplexing gain r of the transmission. “Selection of the best relay terminal” means that a selection of the best relay among a set of (m-1) relay candidates is implemented when a cooperative transmission is needed. This feature maximizes the diversity order d(r) of the transmission. Hence, an optimal DMT (Diversity Multiplexing Tradeoff) curve is achieved with a diversity order d(r) = m(1-r) for 0 ≤ r ≤ 1

Influences of the technological parameters on the surface twist in grinding

The requirement towards to the functional surfaces of the machine components became higher over the time. The final steps in the machining are supposed to create a surface which ensures wear resistance, longer lifetime and has special features on demand as well. The twist free surface can be considered as a special surface condition. The following paper presents the effect of the grinding parameters to the surface twist.Требования в отношении функциональных поверхностей деталей машин повысились с течением времени. Предполагается, что на окончательных этапах механической обработки должна создаваться поверхность, которая обеспечивает износостойкость, длительный срок службы, а также обладает специальными требуемыми характеристиками. Закручивание свободной поверхности может рассматриваться как особое состояние поверхности. В представленной статье исследуется влияние режимов шлифования на закручивание поверхности

Platform-based Plug and Play of Automotive Safety Features - Challenges and Directions

Optional software-based features are increasingly becoming an important cost driver in automotive systems. These include features pertaining to active safety, infotainment, etc. Currently, these optional features are integrated into the vehicles at the factory during assembly. This severely restricts the flexibility of the customer to select and use features on-demand and therefore, the customer will either have to be satisfied with an available set of feature options or pre-order a car with the required features from the manufacturer resulting in considerable delay. In order to increase flexibility and reduce the delay, it is necessary to provide the option to configure the vehicle on-demand at the dealership or remotely. In this paper, we present our vision and challenges involved in developing a platform infrastructure that allows on-demand deployment of automotive safety features and ensures their correct execution

RAPID WEBGIS DEVELOPMENT FOR EMERGENCY MANAGEMENT

The use of spatial data during emergency response and management helps to make faster and better decisions. Moreover spatial data should be as much updated as possible and easy to access. To face the challenge of rapid and updated data sharing the most efficient solution is largely considered the use of internet where the field of web mapping is constantly evolving. ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action) is a non profit association founded by Politecnico di Torino and SITI (Higher Institute for the Environmental Systems) as a joint project with the WFP (World Food Programme). The collaboration with the WFP drives some projects related to Early Warning Systems (i.e. flood and drought monitoring) and Early Impact Systems (e.g. rapid mapping and assessment through remote sensing systems). The Web GIS team has built and is continuously improving a complex architecture based entirely on Open Source tools. This architecture is composed by three main areas: the database environment, the server side logic and the client side logic. Each of them is implemented respecting the MCV (Model Controller View) pattern which means the separation of the different logic layers (database interaction, business logic and presentation). The MCV architecture allows to easily and fast build a Web GIS application for data viewing and exploration. In case of emergency data publication can be performed almost immediately as soon as data production is completed. The server side system is based on Python language and Django web development framework, while the client side on OpenLayers, GeoExt and Ext.js that manage data retrieval and user interface. The MCV pattern applied to javascript allows to keep the interface generation and data retrieval logic separated from the general application configuration, thus the server side environment can take care of the generation of the configuration file. The web application building process is data driven and can be considered as a view of the current architecture composed by data and data interaction tools. Once completely automated, the Web GIS application building process can be performed directly by the final user, that can customize data layers and controls to interact with the

Optimal Cooperative MAC Protocol with Efficient Selection of Relay Terminals

A new cooperative protocol is proposed in the context of wireless mesh networks. The protocol implements ondemand cooperation, i.e. cooperation between a source terminal and a destination terminal is activated only when needed. In that case, only the best relay among a set of available terminals is re-transmitting the source message to the destination terminal. This typical approach is improved using three additional features. First, a splitting algorithm is implemented to select the best relay. This ensures a fast selection process. Moreover, the duration of the selection process is now completely characterized. Second, only terminals that improve the outage probability of the direct link are allowed to participate to the relay selection. By this means, inefficient cooperation is now avoided. Finally, the destination terminal discards the source message when it fails to decode it. This saves processing time since the destination terminal does not need to combine the replicas of the source message: the one from the source terminal and the one from the best relay. We prove that the proposed protocol achieves an optimal performance in terms of Diversity-Multiplexing Tradeoff (DMT)

Collaborative Spatio-temporal Feature Learning for Video Action Recognition

Spatio-temporal feature learning is of central importance for action recognition in videos. Existing deep neural network models either learn spatial and temporal features independently (C2D) or jointly with unconstrained parameters (C3D). In this paper, we propose a novel neural operation which encodes spatio-temporal features collaboratively by imposing a weight-sharing constraint on the learnable parameters. In particular, we perform 2D convolution along three orthogonal views of volumetric video data,which learns spatial appearance and temporal motion cues respectively. By sharing the convolution kernels of different views, spatial and temporal features are collaboratively learned and thus benefit from each other. The complementary features are subsequently fused by a weighted summation whose coefficients are learned end-to-end. Our approach achieves state-of-the-art performance on large-scale benchmarks and won the 1st place in the Moments in Time Challenge 2018. Moreover, based on the learned coefficients of different views, we are able to quantify the contributions of spatial and temporal features. This analysis sheds light on interpretability of the model and may also guide the future design of algorithm for video recognition.Comment: CVPR 201