Realization of Low-Voltage Modified CBTA and Design of Cascadable Current-Mode All-Pass Filter

In this paper, a low voltage modified current backward transconductance amplifier (MCBTA) and a novel first-order current-mode (CM) all-pass filter are presented. The MCBTA can operate with ±0.9 V supply voltage and the total power consumption of MCBTA is 1.27 mW. The presented all-pass filter employs single MCBTA, a grounded resistor and a grounded capacitor. The circuit possesses low input and high output impedances which make it ideal for current-mode systems. The presented all-pass filter circuit can be made electronically tunable due to the bias current of the MCBTA. Non-ideal study along with simulation results are given for validation purpose. Further, an nth-order cascadable all-pass filter is also presented. It uses n MCBTAs, n grounded resistors and n grounded capacitors. The performance of the proposed circuits is demonstrated by using PSPICE simulations based on the 0.18 µm TSMC level-7 CMOS technology parameters

Current and Voltage Mode Multiphase Sinusoidal Oscillators Using CBTAs

Current-mode (CM) and voltage-mode (VM) multiphase sinusoidal oscillator (MSO) structures using current backward transconductance amplifier (CBTA) are proposed. The proposed oscillators can generate n current or voltage signals (n being even or odd) equally spaced in phase. n+1 CBTAs, n grounded capacitors and a grounded resistor are used for nth-state oscillator. The oscillation frequency can be independently controlled through transconductance (gm) of the CBTAs which are adjustable via their bias currents. The effects caused by the non-ideality of the CBTA on the oscillation frequency and condition have been analyzed. The performance of the proposed circuits is demonstrated on third-stage and fifth-stage MSOs by using PSPICE simulations based on the 0.25 µm TSMC level-7 CMOS technology parameters

A FRAMEWORK FOR PERFORMANCE-BASED FACADE DESIGN: APPROACH FOR AUTOMATED AND MULTI-OBJECTIVE SIMULATION AND OPTIMIZATION

Buildings have a considerable impact on the environment, and it is crucial to consider environmental and energy performance in building design. Buildings account for about 40% of the global energy consumption and contribute over 30% of the CO2 emissions. A large proportion of this energy is used for meeting occupants’ thermal comfort in buildings, followed by lighting. The building facade forms a barrier between the exterior and interior environments; therefore, it has a crucial role in improving energy efficiency and building performance. In this regard, decision-makers are required to establish an optimal solution, considering multi-objective problems that are usually competitive and nonlinear, such as energy consumption, financial costs, environmental performance, occupant comfort, etc. Sustainable building design requires considerations of a large number of design variables and multiple, often conflicting objectives, such as the initial construction cost, energy cost, energy consumption and occupant satisfaction. One approach to address these issues is the use of building performance simulations and optimization methods. This research first investigates and highlights the key research methods, issues and tools associated with building performance simulations and the optimization methods. Then a novel method for improving building facade performance is presented, taking into consideration occupant comfort, energy consumption and energy costs. The dissertation discusses development of a framework, which is based on multi-objective optimization and uses a genetic algorithm in combination with building performance simulations. The framework utilizes EnergyPlus simulation engine and Python programming to implement optimization algorithm analysis and decision support. The framework enhances the process of performance-based facade design, couples simulation and optimization packages, and provides flexible and fast supplement in facade design process by rapid generation of design alternatives. The dissertation describes the components and functionality of this framework in detail, as well as two-step optimization technique which is a new technique that combines GA and machine learning. The dissertation also presents results and validation techniques and provides conclusions of the study