Solar Heating and Air-Conditioning by GSHP Coupled to PV System for a Cost Effective High Energy Performance Building

Abstract Energy requirements for new buildings show the strong direction given by UE Directives to improve energy performance in buildings according to economic feasibility. Nowadays it is possibile to const ruct new buildings reaching a substancial reduction in energy consumption containing prices and time for the construction. In architectural competitions are always included architectural, energy and economic parameters of quality which are decisive in the success of the project design. A Housing Contest to collect projects with high performance and low cost for residential buildings for the Municipality of Comune di Milano, Italy, and the future construtions in the local area was launched by the to involve architects and professionals on the future development of the urban landscape giving specific requirements to achieve high performance. These requirements were focused on energy quality, acoustic quality, quality of the building site, guaranteed time schedule, prefabrication, economic affordability in comparison with the market trend of costs. The project presented in the paper is one of the chosen building by the Municipality to represent a pilot project for possible future constructions. In the Contest all the design group were in team with a builder to verify and guarantee the costs of the construction. The high energy performance required coupled to the low cost assured by the projects gave the Municipality a good example of how is possible to fulfill quality levels recommended by EU Directives and national regulations. In the Contest a high energy performance for heating was compulsory. The project described in the following paragraphs not only fulfill this energy requirement but also is almost self-sufficiency since it provides the energy for heating, cooling and common electrical demand

HP2IFS: Head Pose estimation exploiting Partitioned Iterated Function Systems

Estimating the actual head orientation from 2D images, with regard to its three degrees of freedom, is a well known problem that is highly significant for a large number of applications involving head pose knowledge. Consequently, this topic has been tackled by a plethora of methods and algorithms the most part of which exploits neural networks. Machine learning methods, indeed, achieve accurate head rotation values yet require an adequate training stage and, to that aim, a relevant number of positive and negative examples. In this paper we take a different approach to this topic by using fractal coding theory and particularly Partitioned Iterated Function Systems to extract the fractal code from the input head image and to compare this representation to the fractal code of a reference model through Hamming distance. According to experiments conducted on both the BIWI and the AFLW2000 databases, the proposed PIFS based head pose estimation method provides accurate yaw/pitch/roll angular values, with a performance approaching that of state of the art of machine-learning based algorithms and exceeding most of non-training based approaches

A Novel Cancelable FaceHashing Technique Based on Non-invertible Transformation with Encryption and Decryption Template

A novel cancelable FaceHashing technique based on non-invertible transformation with encryption and decryption template has been proposed in this paper. The proposed system has four components: face preprocessing, feature extraction, cancelable feature extraction followed by the classification, and encryption/decryption of cancelable face feature templates. During face preprocessing, the facial region of interest has been extracted out to speed the process for evaluating discriminant features. In feature extraction, some optimization techniques such as Sparse Representation Coding, Coordinate descent, and Block coordinates descent have been employed on facial descriptors to obtain the best representative of those descriptors. The representative descriptors are further arranged in a spatial pyramid matching structure to extract more discriminant and distinctive feature vectors. To preserve them, the existing BioHashing technique has been modified and extended to some higher levels of security attacks and the modified BioHashing technique computes a cancelable feature vector by the combined effect of the facial feature vector and the assigned token correspond to each user. The elements of computed cancelable feature vector are in a numeric form that has been employed to perform both verifications as well as identification task in online while the original facial feature vectors are kept offline either in hard drive or disc. Then, to enhance more security levels and also to preserve the cancelable face features, an RSA based encryption-decryption algorithm has been introduced. The proposed system has been tested using four benchmark face databases: CASIA-FACE-v5, IITK, CVL, and FERET, and performance are obtained as correct recognition rate and equal error rate. The performance are compared to the state-of-the-art methods for the superiority of the proposed feature extraction technique and individual performance analysis has been performed at all the security levels of the proposed Cancelable FaceHashing Technique. These comparisons show the superiority of the proposed system