Selection of window sizes for optimizing occupational comfort and hygiene based on computational fluid dynamics and neural networks

The present paper presents a novel computational method to optimize window sizes for thermal comfort and indoor air quality in naturally ventilated buildings. The methodology is demonstrated by means of a prototype case, which corresponds to a single-sided naturally ventilated apartment. Initially, the airflow in and around the building is simulated using a Computational Fluid Dynamics model. Local prevailing weather conditions are imposed in the CFD model as inlet boundary conditions. The produced airflow patterns are utilized to predict thermal comfort indices, i.e. the PMV and its modifications for non-air-conditioned buildings, as well as indoor air quality indices, such as ventilation effectiveness based on carbon dioxide and volatile organic compounds removal. Mean values of these indices (output/objective variables) within the occupied zone are calculated for different window sizes (input/design variables), to generate a database of input–output data pairs. The database is then used to train and validate Radial Basis Function Artificial Neural Network input–output “meta-models”. The produced meta-models are used to formulate an optimization problem, which takes into account special constraints recommended by design guidelines. It is concluded that the proposed methodology determines appropriate windows architectural designs for pleasant and healthy indoor environments.     

Behavioral-Level Performance and Power Exploration of Data-Intensive Applications Mapped on Programmable Processors

The continuous increase of the computational power of programmable processors has established them as an attractive design alternative, for implementation of the most computationally intensive applications, like video compression. To enforce this trend, designers implementing applications on programmable platforms have to be provided with reliable and in-depth data and instruction analysis that will allow for the early selection of the most appropriate application for a given set of specifications. To address this need, we introduce a new methodology for early and accurate estimation of the number of instructions required for the execution of an application, together with the number of data memory transfers on a programmable processor. The high-level estimation is achieved by a series of mathematical formulas; these describe not only the arithmetic operations of an application, but also its control and addressing operations, if it is executed on a programmable core. The comparative study, which is done using three popular processors (ARM, MIPS, and Pentium), shows the high efficiency and accuracy of the methodology proposed, in terms of the number of executed (micro-)instructions (i.e. performance) and the number of data memory transfers (i.e. memory power consumption). Using the proposed methodology we estimated an average deviation of 23% in our estimated figures compared with the measurements taken from the real execution on the CPUs. This work was supported by the project PENED ’99 ED501 funded by GSRT of the Greek Ministry of Development, and the project PRENED ’99 KE 874 funded by the Research Committee of the Democritus University of Thrace. This work was partially sponsored by a scholarship from the Public Benefit Foundation of Alexander S. Onassis (Minas Dasygenis). Nikolaos Kroupis was born in Trikala in 1976. He receiver the engineering degree and Ms.C. degree in Department of Electrical and Computer Engineering from Democritous University of Thrace, Greece, in 2000 and 2002, respectively. Since 2002 he has been a Ph.D. student at the Laboratory of Electrical and Electronic Materials Technology. His research interests are in software/hardware co-design of embedded system for signal processing applications. Nikos D. Zervas received a Diploma in Electrical & Computer Engineering from University of Patras, Greece in 1997. He received the Ph.D. degree in the Department of Electrical and Computer Engineering of the same University in 2004. His research interests are in the area of high-level, power optimization techniques and methodologies for multimedia and telecommunication applications. He has received an award from IEEE Computer Society in the context of Low-Power Design Contest of 2000 IEEE Computer Elements Mesa Workshop. Mr. Zervas is a member of the IEEE, ACM and of the Technical Chamber of Greece. Minas Dasygenis was born in Thessaloniki in 1976. He received his Diploma in Electrical and Computer Engineering in 1999, from the Democritus University of Thrace, Greece, and for his diploma Thesis he was honored by The Technical Chamber of Greece and Ericsson Hellas. In 2005, he received his PhD Degree from the Democritus University of Thrace. His research interests include low-power VLSI design of arithmetic circuits, residue number system, embedded architectures, DSPs, hardware/ software codesign and IT security. He has published more than 20 papers in international journals and conferences and he has been a principal researcher in three European research projects. Konstantinos Tatas received his degree in Electrical and Computer Engineering from the Democritus University of Thrace, Greece in 1999. He received his Ph.D. in the VLSI Design and Testing Center in the same University by June 2005. He has been employed as an RTL designer in INTRACOM SA, Greece between 2000 and 2003. His research interests include low-power VLSI design of DSP and multimedia systems, computer arithmetic, IP core design and design for reuse. Antonios Argyriou received the degree in Electrical and Computer engineering from the Democritous University of Thrace, Greece, in 2001, and the M.S. and Ph.D. degrees in Electrical and Computer engineering from the Georgia Institute of Technology, Atlanta, in 2003 and 2005, respectively. His primary research interests include wireless networks, mobile computing and multimedia communications. He is a member of the IEEE and ACM. Dimitrios Soudris received his Diploma in Electrical Engineering from the University of Patras, Greece, in 1987. He received the Ph.D. Degree in Electrical Engineering, from the University of Patras in 1992. He is currently working as Ass. Professor in Dept. of Electrical and Computer Engineering, Democritus University of Thrace, Greece. His research interests include low power design, parallel architectures, embedded systems design, and VLSI signal processing. He has published more than 140 papers in international journals and conferences. He was leader and principal investigator in numerous research projects funded from the Greek Government and Industry as well as the European Commission (ESPRIT II-III-IV and 5th and 6th IST). He has served as General Chair and Program Chair for the International Workshop on Power and Timing Modelling, Optimisation, and Simulation (PATMOS). He received an award from INTEL and IBM for the project results of LPGD #25256 (ESPRIT IV). He is a member of the IEEE, the VLSI Systems and Applications Technical Committee of IEEE CAS and the ACM. Antonios Thanailakis was born in Greece on August 5, 1940. He received B.Sc. degrees in physics and electrical engineering from the University of Thessaloniki, Greece, 1964 and 1968, respectively, and the Msc. and Ph.D. Degrees in electrical engineering and electronics from UMIST, Manchester, U.K. in 1968 and 1971, respectively. He has been a Professor of Microelectronics in Dept. of Electrical and Computer Eng., Democritus Univ. of Thrace, Xanthi, Greece, since 1977. He has been active in electronic device and VLSI system design research since 1968. His current research activities include microelectronic devices and VLSI systems design. He has published a great number of scientific and technical papers, as well as five textbooks. He was leader for carrying out research and development projects funded by Greece, EU, or other organizations on various topics of Microlectronics and VLSI Systems Design (e.g. NATO, ESPRIT, ACTS, STRIDE).     

Using recurrent neural networks for adaptive communication channelequalization

Nonlinear adaptive filters based on a variety of neural network models have been used successfully for system identification and noise-cancellation in a wide class of applications. An important problem in data communications is that of channel equalization, i.e., the removal of interferences introduced by linear or nonlinear message corrupting mechanisms, so that the originally transmitted symbols can be recovered correctly at the receiver. In this paper we introduce an adaptive recurrent neural network (RNN) based equalizer whose small size and high performance makes it suitable for high-speed channel equalization. We propose RNN based structures for both trained adaptation and blind equalization, and we evaluate their performance via extensive simulations for a variety of signal modulations and communication channel models. It is shown that the RNN equalizers have comparable performance with traditional linear filter based equalizers when the channel interferences are relatively mild, and that they outperform them by several orders of magnitude when either the channel's transfer function has spectral nulls or severe nonlinear distortion is present. In addition, the small-size RNN equalizers, being essentially generalized IIR filters, are shown to outperform multilayer perceptron equalizers of larger computational complexity in linear and nonlinear channel equalization cases.     

SedLog: A shareware program for drawing graphic logs and log data manipulation

SedLog is a free multi-platform software package for creating graphic sediment logs providing an intuitive graphical user interface. The graphic sediment logs generated by SedLog can be exported as PDF, Scalable Vector Graphics (SVG), or JPEG for use by other drawing applications or for publications. Log data can be imported and exported in Comma Separated Values (CSV) format. The logs can also be printed to any paper size the user wants. Zoom In, Zoom Out, Fit page, Fit Height and Fit Width facilities are also provided to enable the user to customise the workspace size.     

Induction Thermocoagulation?A Seed Power Study

Thermal-power output of destructive therapeutic implants composed of nickel-palladium Curie alloys or 430 stainless steel (a few watts in typical use) is presented. Eddy-current models of implant heating are discussed. A field strength of about 5000 A/m-1 is shown to be appropriate for seeds of about 2-mm OD.