Energy-efficient,fast-settling,modified nested-current-mirror,single-stage-amplifier for high-resolution LCDs in 90-nm CMOS

The Internet of Things (IoT) presents opportunities to address a variety of systemic, metabolic healthcare issues. Cardiovascular disease and diabetes are among the greatest contributors to premature death worldwide. Wireless wearable continuous monitoring systems such as ECG sensors connected to the IoT can greatly decrease the risk of death related to cardiac issues by providing valuable long-term information to physicians, as well as immediate contact with emergency services in the event of a heart attack or stroke. In this report we discuss the fabrication, characterization and validation of composite fabric ECG sensors made from Nylon^® coated with reduced graphene oxide (rGO_x) as part of a self-powered wearable IoT sensor. We utilize an electronic probing station to measure electrical properties, take live ECG data to measure signal reliability, and provide detailed surface characterization through scanning electron microscopy. Finally, bonding between the layers of the composite and between composite and the Nylon^® is analyzed by Fourier transform Infrared spectroscopy. Furthermore, a low power analog front end circuit designed in 65 nm CMOS process is presented to interface the sensor with a system on chip used in a wearable IoT healthcare device.     

A Model-Based Reinforcement Learning Protocol for Routing in Vehicular Ad hoc Network

Todays by equipping vehicles with wireless technologies, Vehicular Ad Hoc Network (VANET) has been emerged. This type of network can be utilized in many fields such as emergency, safety or entertainment. It is also considered as a main component of intelligent transportation system. However, due to the nodes velocity (vehicles velocity), varying density, obstacles and lack of fixed infrastructure, finding and maintaining a route between nodes are always challenging in VANET. Any routing protocol can be effective only if the nodes can learn and adapt themselves with such a dynamic environment. One way to achieve this adaptation is using machine learning techniques. In this paper we try to reach this goal by applying Multi-Agent Reinforcement Learning (MARL) that enables agents to solve routing optimization problems in a distributed way. Although model-free Reinforcement Learning (RL) schemes are introduced for this purpose, such techniques learn using a trial and error scheme in a real environment so they cannot reach an optimal policy in a short time. To deal with such a problem, we have proposed a mode-based RL based routing scheme. We have also developed a Fuzzy Logic (FL) system to evaluate the quality of links between neighbor nodes based on parameters such as velocity and connection quality. Outputs of this fuzzy system have been used to form the state transition model, needed in MARL. Results of evaluations have shown that our approach can improve some routing metrics like delivery ratio, end-to-end delay and traffic overhead.

     

Multi-objective algorithms for the application mapping problem in heterogeneous multiprocessor embedded system design

The Journal of Supercomputing - Design at the Electronic System-Level tackles the increasing complexity of embedded systems by raising the level of abstraction in system specification and modeling....     

Flow separation control in open-channel bends

The investigation of fluid flow in sharp open-channel bends is key to controlling undesired sedimentation in natural river reaches. The difficulties are associated with controlling the flow separation in meanderings. Flow separation decreases the width of the flow, and consequently, the conveyance capacity while increasing erosion and mixing. This study proposes a novel approach to reduce the flow separation at the inner banks of sharp open-channel bends. Three-dimensional numerical experiments were conducted. To find the most reliable procedure, five turbulence models were examined. The employed numerical approach is formulated within the framework of the finite volume method and the volume of fluid (VOF) technique to solve the Navier–Stokes equations. Water levels and velocity profiles are obtained in different sections of the channel and are compared to experimental studies of a 90° sharp open-channel bend. A close agreement is observed using the RSM (Reynolds stress model) turbulence model. Moreover, the evaluation of acquired velocity profiles demonstrates that in a regular bend, the lowest velocity occurs near the inner bank, where it has a flow separation tendency. The same numerical procedure is employed to simulate water flow through a sharp converging open-channel bend. The measurements of velocity profiles and velocity vectors in the curved sections support the idea that decreasing the channel width considerably reduces the overall velocity variations in cross-sectional areas of the test case and is effective to control flow separation.     

Gradient‐based back‐propagation dynamical iterative learning scheme for the neuro‐fuzzy inference system

In this paper, a gradient‐based back propagation dynamical iterative learning algorithm is proposed for structure optimization and parameter tuning of the neuro‐fuzzy system. Premise and consequent parameters of the neuro‐fuzzy model are initialized randomly and then tuned by the proposed iterative algorithm. The learning algorithm is based on the first order partial derivative of the output with respect to the structure parameters. The first order derivative of the model output with respect to the structure parameters determines the sensitivity of the model to structure parameters. The sensitivity values are then used to set the tuning factors and parameters updating step sizes. Therefore, an adaptive dynamical iterative scheme is achieved which adapts the learning procedure to the current state of the performance during the optimization process. Larger tuning step sizes make the convergence speed higher and vice versa. In this regard, this parameter is treated according to the calculated sensitivity of the model to the parameter. The proposed learning algorithm is compared with the least square back propagation method, genetic algorithm and chaotic genetic algorithm in the neuro‐fuzzy model structure optimization. Smaller mean square error and shorter learning time are sought in this paper, and the performance of the proposed learning algorithm is versified regarding these criteria.     

Design and fabrication of a new high gain multilayer negative refractive index metamaterial antenna for X‐band applications

This article presents the design of a planar high gain and wideband antenna using a negative refractive index multilayer superstrate in the X‐band. This meta‐antenna is composed of a four‐layer superstrate placed on a conventional patch antenna. The structure resonates at a frequency of 9.4 GHz. Each layer of the metamaterial superstrate consists of a 7 × 7 array of electric‐field‐coupled resonators, with a negative refractive index of 8.66 to 11.83 GHz. The number of layers and the separation of superstrate layers are simulated and optimized. This metamaterial lens has significantly increased the gain of the patch antenna to 17.1 dBi. Measurements and simulation results proved about 10 dB improvement of the gain.     

Cellulose nanocrystal (CNC)-based nanocomposites for UV curable high-solid coating systems

Demand for durable clear wood coatings is on the rise. Cellulose nanocrystals (CNC) constitute an organic nanomaterial widely studied in polymer composites for its reinforcing effect. In this study, CNC was used to enhance the performance of a UV curable high-solid content coating system intended for indoor environments. The CNC surface was modified by a cationic surfactant since the coating system was hydrophobic resin-based requiring hydrophobic nanomaterial reinforcement. Modified CNC was mixed with the coating system using a high-speed mixer and the ultrasonication technique. Mechanical, thermal and morphological properties and curing behavior of the newly developed UV-curing coatings were assessed. Inclusion of CNC in the coating increased the mechanical properties (hardness and reduced modulus) of the coating system to a large extent. Thermal stability of the coating system was also improved by CNC addition. The CNC did not affect the curing behavior of the coating, in contrast to most inorganic nanomaterials. The CNC dispersed well in the matrix at 1% loading. Results of this study show that CNC can be used successfully with high-solid content coating systems.     

Application of Swarm Intelligence and Evolutionary Computation Algorithms for Optimal Reservoir Operation

Water Resources Management - Real-world problems often contain complex structures and various variables, and classical optimization techniques may face difficulties finding optimal solutions....