Abstract
In order to stay competitive, European manufacturing companies need to enter new market segments implementing innovative production methodologies to strengthen their collaborative networks. Consumer needs and expectations of specific target groups - such as elderly, obese, disabled, or diabetic persons - are arising as challenging opportunities for European consumer goods companies which are asked to supply small series of functional and fashionable products of high quality, affordable price and eco-compatible. This paper aims at proposing an innovative collaborative Reference Model that supports in Textile, Clothing and Footwear (TCF) supply networks in meeting the needs of specific consumer target groups. © 2012 IEEE.

Abstract
In spite of the growing interest verified in the field of technology-based interventions for Stroke rehabilitation, there is still no global solution that is both successful and suitable for a widespread use [1,2]. In this article, we present a novel tele-rehabilitation tool designed to be used for ambulatory patients, and developed towards the motor recovery of the patient's upper-limb. The SWORD system combines a movement quantification system that analyzes the quality of the motor task performed with a biofeedback console. The proposed structure defines the SWORD system as a complete tele-rehabilitation framework that enables a direct connection between clinical and ambulatory settings. Currently a randomized clinical trial is being designed in order to assess the effectiveness of the SWORD tele-rehabilitation system.

Abstract
Due to the dramatic change of the market competition for industrial manufacturers - especially in TCFI (Textile, Clothing and Footwear Industry) - in the recent past resulting from the globalization and consequent openness of the markets together with the financial crisis, resulted in an increased need of networked collaborative approaches and tools. This pressing need of collaboration among SMEs (small medium enterprises) is changing the overall strategic approaches of the companies in the way they address the market demand. Company managers are realizing that in order to compete in open markets with increased responsiveness, they need to develop and provide new, complex and high configurable products and services supported in fully functional collaborative networks. This paper presents a new collaborative aggregated planning approach for supplier networks, where groups of SMEs collaborate in the formation of the network, the definition of the operations plans, in replying to customer inquiries and the release of individual production orders for each unit in the network. The proposed approach promotes the definition of multi-criteria evaluated production plans through collaborative negotiation and individual capacity checking validation. © 2012 IEEE.

Abstract
The model used to represent musical instrument sounds plays a crucial role in the quality of sound transformations. Ideally, the representation should be compact and accurate, while its parameters should give flexibility to independently manipulate perceptually related features of the sounds. This work describes a source-filter model for musical instrument sounds based on the sinusoidal plus residual decomposition. The sinusoidal component is modeled as sinusoidal partial tracks (source) and a time-varying spectral envelope (filter), and the residual is represented as white noise (source) shaped by a time-varying spectral envelope (filter). This article presents estimation and representation techniques that give totally independent and intuitive control of the spectral envelope model and the frequencies of the partials to perform perceptually related sound transformations. The result of a listening test confirmed that, in general, the sounds resynthesized from the source-filter model are perceptually similar to the original recordings.

Abstract
Wearable technologies have been evolving towards daily usage and are a major player in the personalized health challenge. In this paper we present a personal view of their evolution, how one of them developed within our lab went to the international market and how this type of technology is being used in pHealth projects for first responder professionals and public transportation drivers.

Abstract
Ordinal data classification (ODC) has a wide range of applications in areas where human evaluation plays an important role, ranging from psychology and medicine to information retrieval. In ODC the output variable has a natural order, however, there is not a precise notion of the distance between classes. The recently proposed method for ordinal data, Kernel Discriminant Learning Ordinal Regression (KDLOR), is based on Linear Discriminant Analysis (LDA), a simple tool for classification. KDLOR brings LDA to the forefront in the ODC field, motivating further research. This paper compares three LDA based algorithms for ODC. The first method uses the generic framework of Frank and Hall for ODC instantiated with a kernel version of LDA. Similarly, the second method is based on the also generic Data Replication framework for ODC instantiated with the same kernel version of LDA. Both the Frank and Hall and Data Replication methods address the ODC problem by the use of a base binary classifier. Finally, the third method under comparison is KDLOR. The experiments are carried out on synthetic and real datasets. A comparison between the performances of the three systems is made based on t-statistics. The performance and running time complexity of the methods do not support any advantage of KDLOR over the other two methods. © 2012 IEEE.