49,756 research outputs found
Internet of Things Cloud: Architecture and Implementation
The Internet of Things (IoT), which enables common objects to be intelligent
and interactive, is considered the next evolution of the Internet. Its
pervasiveness and abilities to collect and analyze data which can be converted
into information have motivated a plethora of IoT applications. For the
successful deployment and management of these applications, cloud computing
techniques are indispensable since they provide high computational capabilities
as well as large storage capacity. This paper aims at providing insights about
the architecture, implementation and performance of the IoT cloud. Several
potential application scenarios of IoT cloud are studied, and an architecture
is discussed regarding the functionality of each component. Moreover, the
implementation details of the IoT cloud are presented along with the services
that it offers. The main contributions of this paper lie in the combination of
the Hypertext Transfer Protocol (HTTP) and Message Queuing Telemetry Transport
(MQTT) servers to offer IoT services in the architecture of the IoT cloud with
various techniques to guarantee high performance. Finally, experimental results
are given in order to demonstrate the service capabilities of the IoT cloud
under certain conditions.Comment: 19pages, 4figures, IEEE Communications Magazin
Emotion Recognition from Acted and Spontaneous Speech
Dizertační práce se zabývá rozpoznáním emočního stavu mluvčích z řečového signálu. Práce je rozdělena do dvou hlavních častí, první část popisuju navržené metody pro rozpoznání emočního stavu z hraných databází. V rámci této části jsou představeny výsledky rozpoznání použitím dvou různých databází s různými jazyky. Hlavními přínosy této části je detailní analýza rozsáhlé škály různých příznaků získaných z řečového signálu, návrh nových klasifikačních architektur jako je například „emoční párování“ a návrh nové metody pro mapování diskrétních emočních stavů do dvou dimenzionálního prostoru. Druhá část se zabývá rozpoznáním emočních stavů z databáze spontánní řeči, která byla získána ze záznamů hovorů z reálných call center. Poznatky z analýzy a návrhu metod rozpoznání z hrané řeči byly využity pro návrh nového systému pro rozpoznání sedmi spontánních emočních stavů. Jádrem navrženého přístupu je komplexní klasifikační architektura založena na fúzi různých systémů. Práce se dále zabývá vlivem emočního stavu mluvčího na úspěšnosti rozpoznání pohlaví a návrhem systému pro automatickou detekci úspěšných hovorů v call centrech na základě analýzy parametrů dialogu mezi účastníky telefonních hovorů.Doctoral thesis deals with emotion recognition from speech signals. The thesis is divided into two main parts; the first part describes proposed approaches for emotion recognition using two different multilingual databases of acted emotional speech. The main contributions of this part are detailed analysis of a big set of acoustic features, new classification schemes for vocal emotion recognition such as “emotion coupling” and new method for mapping discrete emotions into two-dimensional space. The second part of this thesis is devoted to emotion recognition using multilingual databases of spontaneous emotional speech, which is based on telephone records obtained from real call centers. The knowledge gained from experiments with emotion recognition from acted speech was exploited to design a new approach for classifying seven emotional states. The core of the proposed approach is a complex classification architecture based on the fusion of different systems. The thesis also examines the influence of speaker’s emotional state on gender recognition performance and proposes system for automatic identification of successful phone calls in call center by means of dialogue features.
Performance prediction tools for low impact building design
IT systems are emerging that may be used to support decisions relating to the design of a built enviroment that has low impact in terms of energy use and environmental emissions. This paper summarises this prospect in relation to four complementary application areas: digital cities, rational planning, virtual design and Internet energy services
Process Framework for Subscriber Management and Retention in Nigerian Telecommunication Industry
in the global telecommunication industry. Hence, a dominant approach for subscriber
management and retention is churn control, since it is cheaper to retain an existing
subscriber than acquiring a new one. Predictive modeling employs the use of data mining
techniques to identify patterns and provide a result that a group of subscribers are likely to
churn in the near future. However, the effectiveness of subscriber retention strategy in an
organization can be further boosted if the reason for churn and the timing of churn can also
be predicted.
In this paper, we propose a data mining process framework that can be used to predict
churn, determine when a subscriber is likely to churn, provides the reason why a subscriber
may churn, and recommend appropriate intervention strategy for customer retention using
a combination of statistical and machine learning techniques. This experiment is carried
out using data from a major telecom operator in Nigeria
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