Molecular Logic Computation with Debugging Method

Seesaw gate concept, which is based on a reversible DNA strand branch process, has been found to have the potential to be used in the construction of various computing devices. In this study, we consider constructing full adder and serial binary adder, using the new concept of seesaw gate. Our simulation of the full adder preformed properly as designed; however unexpected exception is noted in the simulation of the serial binary adder. To identify and address the exception, we propose a new method for debugging the molecular circuit. The main idea for this method is to add fan-outs to monitor the circuit in a reverse stepwise manner. These fan-outs are fluorescent signals that can obtain the real-time concentration of the target molecule. By analyzing the monitoring result, the exception can be identified and located. In this paper, examples of XOR and serial binary adder circuits are described to prove the practicability and validity of the molecular circuit debugging method

A resonant feature near the Perseus arm revealed by red clump stars

We investigate the extinction together with the radial velocity dispersion and distribution of red clump stars in the anti-center direction using spectra obtained with Hectospec on the MMT. We find that extinction peaks at Galactocentric radii of about 9.5 and 12.5 kpc, right in front of the locations of the Perseus and Outer arms and in line with the relative position of dust and stars in external spiral galaxies. The radial velocity dispersion peaks around 10kpc, which coincides with the location of the Perseus arm, yields an estimated arm-interarm density contrast of 1.3-1.5 and is in agreement with previous studies. Finally, we discover that the radial velocity distribution bifurcates around 10-11 kpc into two peaks at +27 km/s and -4 km/s. This seems to be naturally explained by the presence of the outer Lindblad resonance of the Galactic bar, but further observations will be needed to understand if the corotation resonance of the spirals arms also plays a role.Comment: 8 pages, 2 figures, accepted for publication in ApJ

Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

With the booming development of Internet-of-Things (IoT) and communication technologies such as 5G, our future world is envisioned as an interconnected entity where billions of devices will provide uninterrupted service to our daily lives and the industry. Meanwhile, these devices will generate massive amounts of valuable data at the network edge, calling for not only instant data processing but also intelligent data analysis in order to fully unleash the potential of the edge big data. Both the traditional cloud computing and on-device computing cannot sufficiently address this problem due to the high latency and the limited computation capacity, respectively. Fortunately, the emerging edge computing sheds a light on the issue by pushing the data processing from the remote network core to the local network edge, remarkably reducing the latency and improving the efficiency. Besides, the recent breakthroughs in deep learning have greatly facilitated the data processing capacity, enabling a thrilling development of novel applications, such as video surveillance and autonomous driving. The convergence of edge computing and deep learning is believed to bring new possibilities to both interdisciplinary researches and industrial applications. In this article, we provide a comprehensive survey of the latest efforts on the deep-learning-enabled edge computing applications and particularly offer insights on how to leverage the deep learning advances to facilitate edge applications from four domains, i.e., smart multimedia, smart transportation, smart city, and smart industry. We also highlight the key research challenges and promising research directions therein. We believe this survey will inspire more researches and contributions in this promising field

Embedded Based Miniaturized Universal Electrochemical Sensing Platform

We created an embedded sensing platform based on STM32 embedded system, with integrated carbon-electrode ionic sensor by using a self-made plug. Given ration of concentration-unknown nitrate liquid samples, this platform is able to measure the nitrate concentration in neutral environment. Response signals which were transmitted by the sensor can be displayed via a serial port to the computer screen or via Bluetooth to the smartphone. Processed by a fitting function, signals are transformed into related concentration. Through repeating the experiment many times, the accuracy and repeatability turned out to be excellent. The results can be automatically stored on smartphone via Bluetooth. We created this embedded sensing platform for field water quality measurement. This platform also can be applied for other micro sensors’ signal acquisition and data processing