Design of a 2.4 GHz High-Performance Up-Conversion Mixer with Current Mirror Topology

In this paper, a low voltage low power up-conversion mixer, designed in a Chartered 0.18 μm RFCMOS technology, is proposed to realize the transmitter front-end in the frequency band of 2.4 GHz. The up-conversion mixer uses the current mirror topology and current-bleeding technique in both the driver and switching stages with a simple degeneration resistor. The proposed mixer converts an input of 100 MHz intermediate frequency (IF) signal to an output of 2.4 GHz radio frequency (RF) signal, with a local oscillator (LO) power of 2 dBm at 2.3 GHz. A comparison with conventional CMOS up-conversion mixer shows that this mixer has advantages of low voltage, low power consumption and high-performance. The post-layout simulation results demonstrate that at 2.4 GHz, the circuit has a conversion gain of 7.1 dB, an input-referred third-order intercept point (IIP3) of 7.3 dBm and a noise figure of 11.9 dB, while drawing only 3.8 mA for the mixer core under a supply voltage of 1.2 V. The chip area including testing pads is only 0.62×0.65 mm2

Caging phenomena in reactions: Femtosecond observation of coherent, collisional confinement

We report striking observations of coherent caging of iodine, above the B state dissociation threshold, by single collisions with rare gas atoms at room-temperature. Despite the random nature of the solute–solvent interaction, the caged population retains coherence of the initially prepared unbound wave packet. We discuss some new concepts regarding dynamical coherent caging and the one-atom cage effect

An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks

This paper aims to develop a novel model to assess the risk factors of maritime supply chains by incorporating a fuzzy belief rule approach with Bayesian networks. The new model, compared to traditional risk analysis methods, has the capability of improving result accuracy under a high uncertainty in risk data. A real case of a world leading container shipping company is investigated, and the research results reveal that among the most significant risk factors are transportation of dangerous goods, fluctuation of fuel price, fierce competition, unattractive markets, and change of exchange rates in sequence. Such findings will provide useful insights for accident prevention

Mesh reinforced membrane and its wrinkling characteristics

Membrane structures have received a wide range of attention in applications of large-scale spacecrafts, such as inflated wing, light-than-air (LTA) airship and membrane antenna reflector, and so on. These spacecrafts need to be designed as high loading-efficiency components or structures, especially with high shape precision [1]. Therefore, some special processing and design should be done on the membranes so as to satisfy with special requirements, such as free wrinkle, ultralightweight, high shape precision, and high load-carrying ability, etc.. Several applications may give us good ideas to deal with abovementioned problems in membrane structures. One example is easy to be remembered, that is, the Super-Pressure Balloons (SPB) [2-4] covered by some ropes on membrane surface to make the balloon stable and strong. The similar considerations are also applied to some gossamer spacecraft components or structures, taking the Lunar habitat [5] as an example

A Deep Relevance Matching Model for Ad-hoc Retrieval

In recent years, deep neural networks have led to exciting breakthroughs in speech recognition, computer vision, and natural language processing (NLP) tasks. However, there have been few positive results of deep models on ad-hoc retrieval tasks. This is partially due to the fact that many important characteristics of the ad-hoc retrieval task have not been well addressed in deep models yet. Typically, the ad-hoc retrieval task is formalized as a matching problem between two pieces of text in existing work using deep models, and treated equivalent to many NLP tasks such as paraphrase identification, question answering and automatic conversation. However, we argue that the ad-hoc retrieval task is mainly about relevance matching while most NLP matching tasks concern semantic matching, and there are some fundamental differences between these two matching tasks. Successful relevance matching requires proper handling of the exact matching signals, query term importance, and diverse matching requirements. In this paper, we propose a novel deep relevance matching model (DRMM) for ad-hoc retrieval. Specifically, our model employs a joint deep architecture at the query term level for relevance matching. By using matching histogram mapping, a feed forward matching network, and a term gating network, we can effectively deal with the three relevance matching factors mentioned above. Experimental results on two representative benchmark collections show that our model can significantly outperform some well-known retrieval models as well as state-of-the-art deep matching models.Comment: CIKM 2016, long pape