Themed issue: Optofluidics

The term optofluidics defines a growing research area that integrates optics and microfluidics in ways that enable unique strengths and advantages for a broad range of applications. The First International Conference on Optofluidics (Optofluidics- 2011) organized by Xi’an Jiaotong University and Lab on a Chip on 11–12 December 2011 featured work in this field, with an exciting two-day program of presentations and discussions. We are happy that Lab on a Chip, a major publication destination for optofluidic research, has scheduled this themed issue on Optofluidics. We are especially heartened that the optofluidics community has responded enthusiastically with a large number of excellent manuscript submissions

Self-Learning Hot Data Prediction: Where Echo State Network Meets NAND Flash Memories

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Well understanding the access behavior of hot data is significant for NAND flash memory due to its crucial impact on the efficiency of garbage collection (GC) and wear leveling (WL), which respectively dominate the performance and life span of SSD. Generally, both GC and WL rely greatly on the recognition accuracy of hot data identification (HDI). However, in this paper, the first time we propose a novel concept of hot data prediction (HDP), where the conventional HDI becomes unnecessary. First, we develop a hybrid optimized echo state network (HOESN), where sufficiently unbiased and continuously shrunk output weights are learnt by a sparse regression based on L2 and L1/2 regularization. Second, quantum-behaved particle swarm optimization (QPSO) is employed to compute reservoir parameters (i.e., global scaling factor, reservoir size, scaling coefficient and sparsity degree) for further improving prediction accuracy and reliability. Third, in the test on a chaotic benchmark (Rossler), the HOESN performs better than those of six recent state-of-the-art methods. Finally, simulation results about six typical metrics tested on five real disk workloads and on-chip experiment outcomes verified from an actual SSD prototype indicate that our HOESN-based HDP can reliably promote the access performance and endurance of NAND flash memories.Peer reviewe

Aperiodic conductivity oscillations in quasi-ballistic graphene heterojunctions

We observe conductivity oscillations with aperiodic spacing to only one side of the tunneling current in a dual-gated graphene field effect transistor with an n-p-n type potential barrier. The spacing and width of these oscillatoins were found to be inconsistent with pure Farbry-Perot-type interferences, but are in quantitative agreement with theoretical predictions that attribute them to resonant tunneling through quasi-bound impurity states. This observation may be understood as another signature of Klein tunneling in graphene heterojunctions and is of importance for future development and modeling of graphene based nanoelectronic devices.Comment: 3 pages, 3 figure