Communication Subsystems for Emerging Wireless Technologies

The paper describes a multi-disciplinary design of modern communication systems. The design starts with the analysis of a system in order to define requirements on its individual components. The design exploits proper models of communication channels to adapt the systems to expected transmission conditions. Input filtering of signals both in the frequency domain and in the spatial domain is ensured by a properly designed antenna. Further signal processing (amplification and further filtering) is done by electronics circuits. Finally, signal processing techniques are applied to yield information about current properties of frequency spectrum and to distribute the transmission over free subcarrier channels

Timing models for high-level synthesis

In this paper, we describe a timing model for clock estimation during high-level synthesis. In order to obtain realistic timing estimates, the proposed model considers all delay elements, including datapath, control and wire delays, and several technology factors, such as layout architecture, technology mapping, buffers insertion and loading effects. The experimental results show that this model can provide much better estimates than previous models. This model is well suited for automatic and interactive synthesis as well as feedback-driven synthesis where performance matrices must be rapidly and incrementally calculated

Modeling and Analysis of Power Processing Systems (MAPPS). Volume 1: Technical report

Computer aided design and analysis techniques were applied to power processing equipment. Topics covered include: (1) discrete time domain analysis of switching regulators for performance analysis; (2) design optimization of power converters using augmented Lagrangian penalty function technique; (3) investigation of current-injected multiloop controlled switching regulators; and (4) application of optimization for Navy VSTOL energy power system. The generation of the mathematical models and the development and application of computer aided design techniques to solve the different mathematical models are discussed. Recommendations are made for future work that would enhance the application of the computer aided design techniques for power processing systems

System-level optimization of baseband filters for communication applications

In this paper, a design approach for the high-level synthesis of programmable continuous-time baseband filters able to achieve optimum trade-off among dynamic range, distortion behavior, mismatch tolerance and power area consumptions is presented. The proposed approach relies on building programming circuit elements as arrays of switchable unit cells and defines the synthesis as a constrained optimization problem with both continuous and discrete variables, this last representing the number of enabled cells of the arrays at each configuration. The cost function under optimization is, then, defined as a weighted combination of performance indices which are estimated from macromodels of the circuit elements. The methodology has been implemented in MATLAB™ and C++, and covers all the classical approximation techniques for filters, most common circuit topologies (namely, ladder simulation and cascaded biquad realizations) and both transconductance-C (Gm-C) and active-RC implementation approaches. The proposed synthesis strategy is illustrated with a programmable equal-ripple ladder Gm-C filter for a multi-band power-line communication modem.P.R.O.F.I.T. FIT-070000-2001-84

Program in electro-physical studies - Microcircuit models and diagnostic techniques for environmental failure mode prediction

Microcircuit models and computer program for predicting failure modes under adverse environmental condition

Energy efficiency improvement through MPC-based peripherals management for an industrial process test-bench

High energy costs evince the growing need for energy efficiency in industrial companies. This paper presents a solution at the industrial machine level to obtain efficient energy consumption. Therefore, a controller inspired by the well-known model predictive control (MPC) strategy was developed for the management of peripheral devices. The validation of the control requires a test-bench to emulate the energy consumption of a manufacturing machine. The test-bench has four devices, two used to emulate the periodic and fixed energy consumption of the manufacturing process and two as peripherals, subject to rules associated with the process. Consequently, a subspace identification (SI) was employed to identify energy models to simulate the behavior of the device. As a final step, a performance comparison between a rule-based control (RBC) and the proposed predictive-like controller revealed the remarkable energy savings. The MPC results show an energy saving of around 3% with respect to RBC as well as an instant maximum energy consumption reduction of 8%, approximately.Peer ReviewedPostprint (published version

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science