This paper presents the design and implementation of a memristor-based oscillator, investigating potential analog applications for memristors. Inspired from relaxation oscillator operation, the circuit flows a constant current through a memristor in two directions alternately, increasing or decreasing the memristance and the resulting voltage. A following comparator compares the voltage against two thresholds. Switches are then used to control the current direction according to the comparators' output and close the feedback loop for oscillation. Designed and implemented with Winbond 90-nm CMOS and HfO2-ReRAM technology, measurement results show an operating frequency range of 300 - 1150 kHz, power consumptions of 700 to 830 μW, and κ of <;6.5e-5 for free-running oscillator jitter performance.

We study entropy flattening: Given a circuit C_X implicitly describing an n-bit source X (namely, X is the output of C_X on a uniform random input), construct another circuit C_Y describing a source Y such that (1) source Y is nearly flat (uniform on its support), and (2) the Shannon entropy of Y is monotonically related to that of X. The standard solution is to have C_Y evaluate C_X altogether Theta(n^2) times on independent inputs and concatenate the results (correctness follows from the asymptotic equipartition property). In this paper, we show that this is optimal among black-box constructions: Any circuit C_Y for entropy flattening that repeatedly queries C_X as an oracle requires Omega(n^2) queries. Entropy flattening is a component used in the constructions of pseudorandom generators and other cryptographic primitives from one-way functions [Johan Håstad et al., 1999; John Rompel, 1990; Thomas Holenstein, 2006; Iftach Haitner et al., 2006; Iftach Haitner et al., 2009; Iftach Haitner et al., 2013; Iftach Haitner et al., 2010; Salil P. Vadhan and Colin Jia Zheng, 2012]. It is also used in reductions between problems complete for statistical zero-knowledge [Tatsuaki Okamoto, 2000; Amit Sahai and Salil P. Vadhan, 1997; Oded Goldreich et al., 1999; Vadhan, 1999]. The Theta(n^2) query complexity is often the main efficiency bottleneck. Our lower bound can be viewed as a step towards proving that the current best construction of pseudorandom generator from arbitrary one-way functions by Vadhan and Zheng (STOC 2012) has optimal efficiency.

In this paper, we propose a synchronization scheme based on partial Zadoff-Chu sequences for cooperative MIMO systems. In the proposed training sequence design, partial Zadoff-Chu sequences are placed on disjoint sets of subcarriers in an interleaved manner. Such an arrangement not only guarantees frequency-domain training signal separation, but also produces separable time-domain training signals to lessen the mutual interference among the training signals in both the frequency domain and the time domain. Furthermore, propagation delays and carrier frequency offsets can be estimated solely in the time domain, while they are estimated in the time domain and the frequency domain, respectively, in most existing synchronization schemes for cooperative MIMO systems. The proposed training sequences also enjoy good auto-correlation and cross-correlation properties that improves synchronization performance.

MOTIVATION: Transcriptional regulatory networks, which consist of linkages between transcription factors (TF) and target genes (TGene), control the expression of a genome and play important roles in all aspects of an organism's life cycle. Accurate prediction of transcriptional regulatory networks is critical in providing useful information for biologists to determine what to do next. Currently, there is a substantial amount of fragmented gene regulation information described in the medical literature. However, current related text analysis methods designed to identify protein-protein interactions are not entirely suitable for finding transcriptional regulatory networks. RESULT: In this article, we propose an automatic regulatory network inference method that uses bootstrapping of description patterns to predict the relationship between a TF and its TGenes. The proposed method differs from other regulatory network generators in that it makes use of both positive and negative patterns for different vector combinations in a sentence. Moreover, the positive pattern learning process can be fully automatic. Furthermore, patterns for active and passive voice sentences are learned separately. The experiments use 609 HIF-1 expert-tagged articles from PubMed as the gold standard. The results show that the proposed method can automatically generate a predicted regulatory network for a transcription factor. Our system achieves an F-measure of 72.60%. AVAILABILITY: The software, training/test datasets and learned patterns are available at http://140.116.99.138/∼hcw0901/PubMedSearch.php.