Este documento describe diferentes tipos de medios de transmisión de información, incluyendo medios confinados y no confinados. Explica que un medio es necesario para transmitir señales de un host emisor a un host receptor. Luego menciona que los medios de comunicación permitieron transmitir información a las familias de voluntarios que iban a la guerra. Finalmente lista diferentes tipos de medios como cable coaxial, cable trenzado y fibra óptica.
Qué representa cada una de las potencias, en que se utiliza, como Elementos de un sistema de comunicación electrónica
-Sistemas unidireccionales y bidireccionales. Medios de transmisión.
-Antecedentes históricos de los sistemas de comunicación.
-Limitaciones fundamentales de la comunicación electrónica. Modulación
-Velocidad de propagación y longitud de onda. Ondas transversales y longitudinales
-Espectro electromagnético. Espectro de longitud de onda. Bandas VHF Y UHF.
-Modos de transmisión: simplex, half-duplex y full – dúplex.
En esta presentación se abordan las ideas principales de la Teoría Matemática de la Información realizada por Claude Shannon y Warren Weaver en los años 40.
Qué representa cada una de las potencias, en que se utiliza, como Elementos de un sistema de comunicación electrónica
-Sistemas unidireccionales y bidireccionales. Medios de transmisión.
-Antecedentes históricos de los sistemas de comunicación.
-Limitaciones fundamentales de la comunicación electrónica. Modulación
-Velocidad de propagación y longitud de onda. Ondas transversales y longitudinales
-Espectro electromagnético. Espectro de longitud de onda. Bandas VHF Y UHF.
-Modos de transmisión: simplex, half-duplex y full – dúplex.
En esta presentación se abordan las ideas principales de la Teoría Matemática de la Información realizada por Claude Shannon y Warren Weaver en los años 40.
Design patterns are acknowledged as powerful conceptual tools to improve design quality and to reduce the time and cost of design
by effect of the reuse of “good” solutions. In many fields such as software engineering, web engineering, and interface design,
patterns are widely used by practitioners and are also investigated from a research perspective. Still, the concept of design pattern
has received marginal attention in the arena of user interfaces (UIs) for Recommender Systems (RSs). To our knowledge, a little
is known about the use of patterns in this specific class of applications, in spite of their increasing popularity, and no RS
specific interface pattern is available in existing pattern languages. We have performed a systematic analysis of 28 real-world RSs in
a variety of sectors, in order to: (i) discover occurrences of existing general (i.e., domain independent) UI patterns; (ii)
identify recurrent UI design solutions for RS specific features; (iii) elicit a set of new UI patterns for RS interfaces. The analysis
of patterns occurrences highlights the degree at which “good” UI design solutions are adopted in RSs for the different sectors. The
new patterns can be used by UI designers of RSs to improve the UX of their systems.
Active Learning in Collaborative Filtering Recommender Systems : a SurveyUniversity of Bergen
In collaborative filtering recommender systems user’s preferences are expressed as ratings for items, and each additional rating extends the knowledge of the system and affects the system’s recommendation accuracy. In general, the more ratings are elicited from the users, the more effective the recommendations are. However, the usefulness of each rating may vary significantly, i.e., different ratings may bring a different amount and type of information about the user’s tastes. Hence, specific techniques, which are defined as “active learning strategies”, can be used to selectively choose the items to be presented to the user for rating. In fact, an active learning strategy identifies and adopts criteria for obtaining data that better reflects users’ preferences and enables to generate better recommendations.
Empirical Evaluation of Active Learning in Recommender SystemsUniversity of Bergen
The accuracy of collaborative-filtering recommender systems largely depends on three factors: the quality of the rating prediction algorithm, and the quantity and quality of available ratings. While research in the field of recommender systems often concentrates on improving prediction algorithms, even the best algorithms will fail if they are fed poor quality data during training. Active learning aims to remedy this problem by focusing on obtaining better quality data that more aptly reflects a user’s preferences. In attempt to do that, an active learning strategy selects the best items to be presented to the user in order to acquire her ratings and hence improve the output of the RS.
In this seminar, I present a set of active learning strategies with different characteristics and the evaluation results with respect to several evaluation measures (i.e., MAE, NDCG, Precision, Coverage, Recommendation Quality, and, Quantity of the acquired ratings and contextual conditions).
The traditional evaluation of active learning strategies has two major flaws: (1) Performance has been evaluated for each user independently (ignoring system-wide improvements) (2) Active learning strategies have been evaluated in isolation from unsolicited user ratings (natural acquisition). Addressing these flaws, I present that an elicited rating has effects across the system, so a typical user-centric evaluation which ignores any changes of rating prediction of other users also ignores these cumulative effects, which may be more influential on the performance of the system as a whole (system-centric). Hence, I present a novel offline evaluation methodology and use it to evaluate some novel and state of the art rating elicitation strategies.
While the first set of experiments was done offline, the true value of active learning must be evaluated in an online setting. Hence, in the second part of the seminar, I present a novel active learning approach that exploits some additional information of the user (i.e. the user’s personality) to deal with the cold start problem in an up-and-running mobile context-aware RS called STS, that provides users with recommendations for places of interest (POIs). The results of live user studies, have shown that the proposed AL approach significantly increases the quantity of the ratings and contextual conditions acquired from the user as well as the recommendation accuracy.
User Personality and the New User Problem in a Context-Aware Point of Interes...University of Bergen
The new user problem is an important and challenging issue that Context-Aware Recommender Systems (CARSs) must deal with, especially in the early stage of their deployment. It occurs when a new user is added to the system and there is not enough information about the user’s preferences in order to compute appropriate recommendations. It is common to address this problem in the recommendation algorithm, by using demographic attributes such as age, gender, and occupation, which are easy to collect and are reasonably good predictors of the user preferences. However, as we show here, user’s personality provides even better information for generating context-aware recommendations for places of interest (POI), and it is still easy to assess with a simple questionnaire. In our study, using a rating data set collected by a mobile app called STS (South Tyrol Suggests), we have found that by considering the user personality the system can better rank the recommendations for the new users.
Toward Building a Content based Video Recommendation System Based on Low-leve...University of Bergen
In this presentation, I briefly discuss the use of automatically extracted visual features of videos in the context of recommender systems that brings some novel contributions in the domain of video recommendations. The proposed content-based recommender system encompasses a technique to automatically analyze video contents and to extract a set of representative stylistic features (lighting, color, and motion) grounded on existing approaches of Applied Media Theory.
Proposed recommender can be used in combination with more traditional content-based recommendation techniques that exploit explicit content features associated to video files, in order to improve the accuracy of recommendations. Proposed recommender can also be used alone, to address the problem originated from video files that have no meta-data, a typical situation of popular movie-sharing websites (e.g., YouTube) where every day hundred millions of hours of videos are uploaded by users and may contain no associated information. As they lack explicit content, these items cannot be considered for recommendation purposes by conventional content-based techniques even when they could be relevant for the user.
Estas diapositivas resumen brevenemente, la historia y evolucion que han las tenido las comunicaciones, y la trascendencia que ha marcado en el ser humano.
Un libro sin recetas, para la maestra y el maestro Fase 3.pdfsandradianelly
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1. Medios de Transmisión
Hecho por:
Alejandra Flórez
Heidy Yuliana Castaño Franco
Grado:
10.3
1. Como futuros Ingenieros de Sistemas y
Telecomunicaciones, es importante comenzar a
apropiarnos de los términos con los cuales
estaremos relacionados, los medios confinados y
no confinados son los que estudiaremos en
nuestro trabajo, para entender esto necesitamos
saber que un medio es por el cual transmitimos
información en forma de señales, por este ellas
se desplazaran desde un host emisor a un host
2. de destino, siendo el medio el puente necesario
para esto.
2. Los medios de comunicación permitieron
transmitir información especialmente para las
familias de los voluntarios que iban a luchar en
estos conflictos
Por otra parte hubo mucha maquinación de
información especial
3.
-coaxial
- tipos de cable coaxial
- modelos de cable coaxial
- par trenzado
- tipos de cable trenzado
- fibra óptica
- características generales de la fibra óptica