Ripple clock schemes for quantum-dot cellular automata circuits

Quantum-dot cellular automata (QCA) is an emerging technology for building digital circuits at nano-scale. It is considered as an alternative to widely used complementary metal oxide semiconductor (CMOS) technology because of its key features, which include low power operation, high density and high operating frequency. Unlike conventional logic circuits in which information is transferred by electrical current, QCA operates with the help of coulomb interaction between two adjacent QCA cells. A QCA cell is a set of four quantum-dots that are placed near the corners of a square. Due to the fact that clocking provides power and control of data flow in QCA, it is considered to be the backbone of QCA operation. This thesis presents the design and simulation of a ripple clock scheme and an enhanced ripple clock scheme for QCA circuits. In the past, different clock schemes were proposed and studied which were focused on data flow in particular direction or reducing delay. This proposed thesis will study the design and simulation of new clock schemes which are more realistic for implementation, give a freedom to propagate logic in all directions, suitable for both combinational and sequential circuits and has potential to support testing and reconfiguration up to some extent. A variety of digital circuits including a 2–to–1 multiplexer, a 1–bit memory, an RS latch, a full adder, a 4–bit adder and a 2–to–4 decoder are implemented and simulated using these clock schemes. A 2–to–4 decoder is used to demonstrate the testing capabilities of these clock schemes. All QCA layouts are drawn and simulated in QCADesigner

Field-programmable encoding for address-event representation

In conventional frame-based image sensors, every pixel records brightness information and sends this information to a receiver serially in a scanning fashion. This full-frame readout approach suffers from high bandwidth requirements and increased power consumption with the increasing size of the pixel array. Event-based image sensors are gaining popularity for reducing the bandwidth and power requirements by sending only meaningful data in an event-driven approach with the help of address-event representation (AER) communication protocol. However, the event-based readout suffers from increased latency and timing error when the number of pixels with an event increase. In this paper, we introduce a new field-programmable AER (FP-AER) encoding scheme which offers benefits of both frame-based and event-based approaches. The readout design can be configured “in the field” using configuration bits. We also compare the performance of the proposed design against existing AER-based approaches for imaging applications and show that FP-AER performs best in both scanning and event-based readout

Neuropsychiatric Symptoms and Expenditure on Complementary and Alternative Medicine

Objective—Neuropsychiatric symptoms affect 37% of US adults. These symptoms are often refractory to standard therapies, and patients may consequently opt for complementary and alternative medicine therapies (CAM). We sought to determine the demand for CAM by those with neuropsychiatric symptoms compared to those without neuropsychiatric symptoms as measured by out-of-pocket expenditure. Method—We compared CAM expenditure between US adults with and without neuropsychiatric symptoms (n = 23,393) using the 2007 National Health Interview Survey. Symptoms included depression, anxiety, insomnia, attention deficits, headaches, excessive sleepiness, and memory loss. CAM was defined per guidelines from the National Institutes of Health as mind body therapies, biological therapies, manipulation therapies, or alternative medical systems. Expenditure on CAM by those without neuropsychiatric symptoms was compared to those with neuropsychiatric symptoms. Results—Of the adults surveyed, 37% had ≥ 1 neuropsychiatric symptom and spent $ 14.8 billion out-of-pocket on CAM. Those with ≥ 1 neuropsychiatric symptom were more likely than those without neuropsychiatric symptoms to spend on CAM (27.4% vs 20.3%, P < .001). Likelihood to spend on CAM increased with number of symptoms (27.2% with ≥ 3 symptoms, P < .001). After adjustment was made for confounders using logistic regression, those with ≥