Combined surface acoustic wave and surface plasmon resonance measurement of collagen and fibrinogen layers

We use an instrument combining optical (surface plasmon resonance) and acoustic (Love mode acoustic wave device) real-time measurements on a same surface for the identification of water content in collagen and fibrinogen protein layers. After calibration of the surface acoustic wave device sensitivity by copper electrodeposition, the bound mass and its physical properties -- density and optical index -- are extracted from the complementary measurement techniques and lead to thickness and water ratio values compatible with the observed signal shifts. Such results are especially usefully for protein layers with a high water content as shown here for collagen on an hydrophobic surface. We obtain the following results: collagen layers include 70+/-20 % water and are 16+/-3 to 19+/-3 nm thick for bulk concentrations ranging from 30 to 300 ug/ml. Fibrinogen layers include 50+/-10 % water for layer thicknesses in the 6+/-1.5 to 13+/-2 nm range when the bulk concentration is in the 46 to 460 ug/ml range.Comment: 50 pages, 8 figures, 1 tabl

Parameter Variation Sensing and Estimation in Nanoscale Fabrics

Parameter variations introduced by manufacturing imprecision are becoming more influential on circuit performance. This is especially the case in emerging nanoscale fabrics due to unconventional manufacturing steps (e.g., nano-imprint) and aggressive scaling. These parameter variations can lead to performance deterioration and consequently yield loss. Parameter variations are typically addressed pre-fabrication with circuit design targeting worst-case timing scenarios. However, this approach is pessimistic and much of performance benefits can be lost. By contrast, if parameter variations can be estimated post-manufacturing, adaptive techniques or reconfiguration could be used to provide more optimal level of tolerance. To estimate parameter variations during run-time, on-chip variation sensors are gaining in importance because of their easy implementation. In this thesis, we propose novel on-chip variation sensors to estimate variations in physical parameters for emerging nanoscale fabrics. Based on the characteristics of systematic and random variations, two separate sensors are designed to estimate the extent of systematic variations and the statistical distribution of random variations from measured fall and rise times in the sensors respectively. The proposed sensor designs are evaluated through HSPICE Monte Carlo simulations with known variation cases injected. Simulation results show that the estimation error of the systematic-variation sensor is less than 1.2% for all simulated cases; and for the random-variation sensor, the worst-case estimation error is 12.7% and the average estimation error is 8% for all simulations. In addition, to address the placement of on-chip sensors, we calculate sensor area and the effective range of systematic-variation sensor. Then using a processor designed in nanoscale fabrics as a target, an example for sensor placement is introduced. Based on the sensor placement, external noises that may affect the measured fall and rise times of outputs are identified. Through careful analysis, we find that these noises do not deteriorate the accuracy of the systematic-variation sensor, but affect the accuracy of the random-variation sensor. We believe that the proposed on-chip variation sensors in conjunction with post-fabrication compensation techniques would be able to improve system-level performance in nanoscale fabrics, which may be an efficient alternative to making worst-case assumptions on parameter variations in nanoscale designs

Data-driven design of intelligent wireless networks: an overview and tutorial

Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves

Design and experimental characterization of a tunable vibration-based electromagnetic micro-generator

Vibration-based micro-generators, as an alternative source of energy, have become increasingly significant in the last decade. This paper presents a new tunable electromagnetic vibration-based micro-generator. Frequency tuning is realized by applying an axial tensile force to the micro-generator. The dimensions of the generator, especially the dimensions of the coil and the air gap between magnets, have been optimized to maximize the output voltage and power of the micro-generator. The resonant frequency has been successfully tuned from 67.6 to 98 Hz when various axial tensile forces were applied to the structure. The generator produced a power of 61.6–156.6 µW over the tuning range when excited at vibrations of 0.59 ms-2. The tuning mechanism has little effect on the total damping. When the tuning force applied on the generator becomes larger than the generator’s inertial force, the total damping increases resulting in reduced output power. The resonant frequency increases less than indicated from simulation and approaches that of a straight tensioned cable when the force associated with the tension in the beam becomes much greater than the beam stiffness. The test results agree with the theoretical analysis presented

Design Optimization of Transistors Used for Neural Recording

Neurons cultured directly over open-gate field-effect transistors result in a hybrid device, the neuron-FET. Neuron-FET amplifier circuits reported in the literature employ the neuron-FET transducer as a current-mode device in conjunction with a transimpedance amplifier. In this configuration, the transducer does not provide any signal gain, and characterization of the transducer out of the amplification circuit is required. Furthermore, the circuit requires a complex biasing scheme that must be retuned to compensate for drift. Here we present an alternative strategy based on the design approach to optimize a single-stage common-source amplifier design. The design approach facilitates in circuit characterization of the neuron-FET and provides insight into approaches to improving the transistor process design for application as a neuron-FET transducer. Simulation data for a test case demonstrates optimization of the transistor design and significant increase in gain over a current mode implementation