Nanodot-based organic memory devices

In this study, resistor-type, diode-type, and transistor-type organic memory devices were investigated, aiming at the low-cost plastic integrated circuit applications. A series of solution-processing techniques including spin-coating, inkjet printing, and self-assembly were employed to fabricate these devices. The organic resistive memory device is based on a novel molecular complex film composed of tetracyanoquinodimethane (TCNQ) and a soluble methanofullerene derivative [6,6]-phenyl C61-butyric acid methyl ester (PCBM). It has an Al/molecules/Al sandwich structure. The molecular layer was formed by spin-coating technique instead of expensive vacuum deposition method. The current-voltage characteristics show that the device switches from the initial \u27low\u27 conduction state to \u27high\u27 conduction state upon application of external electric field at room temperature and return to \u27low\u27 conduction state when a high current pulse is applied. The on/off ratio is over 106. Each state has been found to remain stable for more than five months, even after the external electric field is removed. The PCBM nanodots wrapped by TCNQ molecules can form potential wells for charge trapping, and are believed to be responsible for the memory effects. A rewritable diode memory device was achieved in an improved configuration, i.e., ITO-PEDOT:PSS-PCBM/TCNQ-Al, where a semiconductor polymer PEDOT:PSS is used to form p+-N heterojunction with PCBM/TCNQ. It exhibits a diode characteristic (low conductive) before switching to a high-conductive Poole-Frenkel regime upon applying a positive external bias to ITO. The on/off ratio at +1.0 V is up to 105. Simulation results from Taurus-Medici are in qualitative agreement with the experimental results and the proposed charge storage model. The transistor-type memory device is fabricated on a heavily doped n-type silicon (n+-Si) substrate with a 100 nm thick thermally-grown oxide layer. The n+-Si serves as the gate electrode, while the oxide layer functions as the control gate dielectric. Gold nanoparticles as the charge storage units are deposited on the substrate by electrostatic self-assembly method. A self-assembled multilayer of polyelectrolytes, together with a thin spin-coated poly(4-vinyl phenol) layer, covers the gold nanoparticles and separates them from the poly(3-hexyl thiophene) channel. Conducting polymer PEDOT:PSS is inkjet printed to form the source/drain electrodes. The device exhibits significant hysteresis behavior in its Ids-Vgs characteristics. The charge storage in gold nanodots (diameter = 16 nm) was confirmed by comparing with devices having no gold nanoparticles, although the effects of interfacial traps may be also significant. The data retention time of the transistor memory is about 60 seconds, which needs to be further improved. It appears that this is the first demonstration of memory effects in an organic transistor caused by charge storage in metal nanodots in the gate dielectric. Therefore, the approach reported in this work offers a new direction to make low-cost organic transistor memories

High performance computing based simulation for healthcare decision support

Due to the complexity and crucial role of an Emergency Department (ED) in the healthcare system. The ability to more accurately represent, simulate and predict performance of ED will be invaluable for decision makers to solve management problems. One way to realize this requirement is by modeling and simulation. The objective of this research is to grasp the non-linear association between macro-level features and micro-level behavior with the goal of better understanding the bottleneck of ED performance and provide ability to quantify such performance on defined condition. Agent-based modeling approach was used to model the healthcare staff, patient and physical resources in ED. Instead of describe all the potential causes of this complex issue. Rather, in this thesis, a layerbased application framework will be presented to discover knowledge of a complex system through simulating micro-level behaviors of its components to facilitate a systematic understanding of the aggregate behavior

Scientific Image Restoration Anywhere

The use of deep learning models within scientific experimental facilities frequently requires low-latency inference, so that, for example, quality control operations can be performed while data are being collected. Edge computing devices can be useful in this context, as their low cost and compact form factor permit them to be co-located with the experimental apparatus. Can such devices, with their limited resources, can perform neural network feed-forward computations efficiently and effectively? We explore this question by evaluating the performance and accuracy of a scientific image restoration model, for which both model input and output are images, on edge computing devices. Specifically, we evaluate deployments of TomoGAN, an image-denoising model based on generative adversarial networks developed for low-dose x-ray imaging, on the Google Edge TPU and NVIDIA Jetson. We adapt TomoGAN for edge execution, evaluate model inference performance, and propose methods to address the accuracy drop caused by model quantization. We show that these edge computing devices can deliver accuracy comparable to that of a full-fledged CPU or GPU model, at speeds that are more than adequate for use in the intended deployments, denoising a 1024 x 1024 image in less than a second. Our experiments also show that the Edge TPU models can provide 3x faster inference response than a CPU-based model and 1.5x faster than an edge GPU-based model. This combination of high speed and low cost permits image restoration anywhere.Comment: 6 pages, 8 figures, 1 tabl

Support managing population aging stress of emergency departments in a computational way

Agraïments "Partially supported by a grant from the China Scholarship Council (CSC) under reference number: 2013062900.Old people usually have more complex health problems and use healthcare services more frequently than young people. It is obvious that the increasing old people both in number and proportion will challenge the emergency departments (ED). This paper firstly presents a way to quantitatively predict and explain this challenge by using simulation techniques. Then, we outline the capability of simulation for decision support to overcome this challenge. Specifically, we use simulation to predict and explain the impact of population aging over an ED. In which, a precise ED simulator which has been validated for a public hospital ED will be used to predict the behavior of an ED under population aging in the next 15 years. Our prediction shows that the stress of population aging to EDs can no longer be ignored and ED upgrade must be carefully planned. Based on this prediction, the cost and benefits of several upgrade proposals are evaluated

Sharp sufficient conditions for stable recovery of block sparse signals by block orthogonal matching pursuit

In this paper, we use the block orthogonal matching pursuit (BOMP) algorithm to recover block sparse signals x from measurements y = Ax + v, where v is an ℓ2-bounded noise vector (i.e., kvk2 ≤ ǫ for some constant ǫ). We investigate some sufficient conditions based on the block restricted isometry property (block-RIP) for exact (when v = 0) and stable (when v , 0) recovery of block sparse signals x. First, on the one hand, we show that if A satisfies the block-RIP with δK+1 1 and √2/2 ≤ δ < 1, the recovery of x may fail in K iterations for a sensingmatrix A which satisfies the block-RIP with δK+1 = δ. Finally, we study some sufficient conditions for partial recovery of block sparse signals. Specifically, if A satisfies the block-RIP with δK+1 < √2/2, then BOMP is guaranteed to recover some blocks of x if these blocks satisfy a sufficient condition. We further show that this condition is also sharp

Low-PMEPR Preamble Sequence Design for Dynamic Spectrum Allocation in OFDMA Systems

Orthogonal Frequency Division Multiple Access (OFDMA) with Dynamic spectrum allocation (DSA) is able to provide a wide range of data rate requirements. This paper is focused on the design of preamble sequences in OFDMA systems with low peak-to-mean envelope power ratio (PMEPR) property in the context of DSA. We propose a systematic preamble sequence design which gives rise to low PMEPR for possibly non-contiguous spectrum allocations. With the aid of Golay-Davis-Jedwab (GDJ) sequences, two classes of preamble sequences are presented. We prove that their PMEPRs are upper bounded by 4 for any DSA over a chunk of four contiguous resource blocks