Effect of screen presentation on text reading and revising. International Journal of Human-Computer Studies

Two studies using the methods of experimental psychology assessed the effects of two types of text presentation (page-by-page vs. scrolling) on participants' performance while reading and revising texts. Greater facilitative effects of the page-by-page presentation were observed in both tasks. The participants' reading task performance indicated that they built a better mental representation of the text as a whole and were better at locating relevant information and remembering the main ideas. Their revising task performance indicated a larger number of global corrections (which are the most difficult to make)

Performance Study and Enhancement of Access Barring for Massive Machine-Type Communications

[EN] Machine-type communications (MTC) is an emerging technology that boosts the development of the Internet of Things by providing ubiquitous connectivity and services. Cellular networks are an excellent choice for providing such hyper-connectivity thanks to their widely deployed infrastructure, among other features. However, dealing with a large number of connection requests is a primary challenge in the cellular-based MTC. Severe congestion episodes can occur when a large number of devices try to access the network almost simultaneously. Extended access barring (EAB) is a congestion control mechanism for the MTC that has been proposed by the 3GPP. In this paper, we carry out a thorough performance analysis of the EAB and show the limitations of its current specification. To overcome these limitations, we propose the two enhanced EAB schemes: the combined use of the EAB and access class barring, and the introduction of a congestion avoidance backoff after the barring status of a UE is switched to unbarred. It is shown through extensive simulations that our proposed solutions improve the key performance indicators. A high successful access probability can be achieved even in heavily congested scenarios, the access delay is shortened, and, most importantly, the number of required preamble retransmissions is reduced, which results in significant energy savings. Furthermore, we present an accurate congestion estimation method that solely relies on the information available at the base station. We show that this method permits a realistic and effective implementation of the EAB.This work was supported in part by the Ministerio de Ciencia, Innovacion y Universidades (MCIU), Agencia Estatal de Investigacion (AEI) y Fondo Europeo de Desarrollo Regional (FEDER), UE, under Grant PGC2018-094151-B-I00, and in part by the ITACA Institute under Grant Ayudas ITACA 2019Vidal Catalá, JR.; Tello-Oquendo, L.; Pla, V.; Guijarro, L. (2019). Performance Study and Enhancement of Access Barring for Massive Machine-Type Communications. IEEE Access. 7:63745-63759. https://doi.org/10.1109/ACCESS.2019.2917618S6374563759

Learning-based tracking area list management in 4G and 5G networks

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksMobility management in 5G networks is a very challenging issue. It requires novel ideas and improved management so that signaling is kept minimized and far from congesting the network. Mobile networks have become massive generators of data and in the forthcoming years this data is expected to increase drastically. The use of intelligence and analytics based on big data is a good ally for operators to enhance operational efficiency and provide individualized services. This work proposes to exploit User Equipment (UE) patterns and hidden relationships from geo-spatial time series to minimize signaling due to idle mode mobility. We propose a holistic methodology to generate optimized Tracking Area Lists (TALs) in a per UE manner, considering its learned individual behavior. The k -means algorithm is proposed to find the allocation of cells into tracking areas. This is used as a basis for the TALs optimization itself, which follows a combined multi-objective and single-objective approach depending on the UE behavior. The last stage identifies UE profiles and performs the allocation of the TAL by using a neural network. The goodness of each technique has been evaluated individually and jointly under very realistic conditions and different situations. Results demonstrate important signaling reductions and good sensitivity to changing conditions.This work was supported by the Spanish National Science Council and ERFD funds under projects TEC2014-60258-C2-2-R and RTI2018-099880-B-C32.Peer ReviewedPostprint (author's final draft

Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications

Cellular-based machine-to-machine (M2M) communication is expected to facilitate services for the Internet of Things (IoT). However, because cellular networks are designed for human users, they have some limitations. Random access channel (RACH) congestion caused by massive access from M2M devices is one of the biggest factors hindering cellular-based M2M services because the RACH congestion causes random access (RA) throughput degradation and connection failures to the devices. In this paper, we show the possibility exploiting the capture effects, which have been known to have a positive impact on the wireless network system, on RA procedure for improving the RA performance of M2M devices. For this purpose, we analyze an RA procedure using a capture model. Through this analysis, we examine the effects of capture on RA performance and propose an Msg3 power-ramping (Msg3 PR) scheme to increase the capture probability (thereby increasing the RA success probability) even when severe RACH congestion problem occurs. The proposed analysis models are validated using simulations. The results show that the proposed scheme, with proper parameters, further improves the RA throughput and reduces the connection failure probability, by slightly increasing the energy consumption. Finally, we demonstrate the effects of coexistence with other RA-related schemes through simulation results

Study to determine potential flight applications and human factors design guidelines for voice recognition and synthesis systems

A study was conducted to determine potential commercial aircraft flight deck applications and implementation guidelines for voice recognition and synthesis. At first, a survey of voice recognition and synthesis technology was undertaken to develop a working knowledge base. Then, numerous potential aircraft and simulator flight deck voice applications were identified and each proposed application was rated on a number of criteria in order to achieve an overall payoff rating. The potential voice recognition applications fell into five general categories: programming, interrogation, data entry, switch and mode selection, and continuous/time-critical action control. The ratings of the first three categories showed the most promise of being beneficial to flight deck operations. Possible applications of voice synthesis systems were categorized as automatic or pilot selectable and many were rated as being potentially beneficial. In addition, voice system implementation guidelines and pertinent performance criteria are proposed. Finally, the findings of this study are compared with those made in a recent NASA study of a 1995 transport concept