Security and privacy in sensor networks

Sensor networks offer economically viable solutions for a variety of applications. For example, current implementations monitor factory instrumentation, pollution levels, freeway traffic, and the structural integrity of buildings. Other applications include climate sensing and control in office buildings and home environmental sensing systems for temperature, light, moisture, and motion. Sensor networks are key to the creation of smart spaces, which embed information technology in everyday home and work environments. The miniature wireless sensor nodes, or motes, developed from low-cost off-the-shelf components at the University of California, Berkeley, as part of its smart dust projects, establish a self-organizing sensor network when dispersed into an environment. The privacy and security issues posed by sensor networks represent a rich field of research problems. Improving network hardware and software may address many of the issues, but others will require new supporting technologies.     

Symbolic connectionism in natural language disambiguation

Natural language understanding involves the simultaneous consideration of a large number of different sources of information. Traditional methods employed in language analysis have focused on developing powerful formalisms to represent syntactic or semantic structures along with rules for transforming language into these formalisms. However, they make use of only small subsets of knowledge. This article describes how to use the whole range of information through a neurosymbolic architecture which is a hybridization of a symbolic network and subsymbol vectors generated from a connectionist network. Besides initializing the symbolic network with prior knowledge, the subsymbol vectors are used to enhance the system's capability in disambiguation and provide flexibility in sentence understanding. The model captures a diversity of information including word associations, syntactic restrictions, case-role expectations, semantic rules and context. It attains highly interactive processing by representing knowledge in an associative network on which actual semantic inferences are performed. An integrated use of previously analyzed sentences in understanding is another important feature of our model. The model dynamically selects one hypothesis among multiple hypotheses. This notion is supported by three simulations which show the degree of disambiguation relies both on the amount of linguistic rules and the semantic-associative information available to support the inference processes in natural language understanding. Unlike many similar systems, our hybrid system is more sophisticated in tackling language disambiguation problems by using linguistic clues from disparate sources as well as modeling context effects into the sentence analysis. It is potentially more powerful than any systems relying on one processing paradigm     

Extraction of salient textual patterns: synergy between lexical cohesion and contextual coherence

Most current information retrieval systems rely solely on lexical item repetition, which is notorious for its vulnerability. In this research, we propose a novel method for the extraction of salient textual patterns. One of our major objectives is to move away from keywords and their associated limitations in textual information retrieval. How individual sentences in text fit together to be perceived as a salient pattern is identified. A text network that exhibits textual continuity, arising from a connectionist model, is described. The network facilitates a dynamic extraction of salient textual segments by capturing semantics from two different categories of natural language, namely lexical cohesion and contextual coherence. We also present the results of an empirical study designed to compare our model with the performance of human judges in the identification of salient textual patterns. The preliminary results show that our model has the potential for automatic salient patterns discovery in text.     

Cryogenic Exfoliation of 2D Stanene Nanosheets for Cancer Theranostics

Stanene(Sn)-based materials have been extensively applied in industrial production and daily life,but their potential biomedi-cal application remains largely un...     

A Low-Power Fullband 802.11a/b/g WLAN Transceiver With On-Chip PA

A low-power fullband 802.11a/b/g WLAN transceiver in 0.15-mum CMOS technology is described. The zero-IF transceiver achieves a receiver noise figure of 4.4/4 dB for the 2.4-GHz/5-GHz bands, respectively. The corresponding sensitivity at 54-Mb/s operation is -72 dBm for 802.11g and -74 dBm for 802.11a using actual PER measurement. An on-chip PA delivers 20 dBm output P1-dB. A new I/Q compensation scheme is implemented in local oscillator (LO) and an image rejection of better than 52 dB is observed. The transmitter delivers 10/1.5 dBm (2.4-/5-GHz) EVM-compliant output power for a 64-QAM OFDM signal at 54-Mb/s. The power consumption is 117/135 mW (1.8-V) in the receive mode and 570/233.1 mW in the transmit mode for 2.4/5 GHz, respectively. The low power consumption, high integration and robustness (-40 to 140degC) make this transceiver suitable for portable applications     

Hypoxia Regulates the Self-Renewal of Endometrial Mesenchymal Stromal/Stem-like Cells via Notch Signaling

Human endometrium is an incredibly dynamic tissue undergoing cyclic regeneration and shedding during a woman’s reproductive life. Endometrial mesenchymal stromal/stem-like cells (eMSC) contribute to this process. A hypoxic niche with low oxygen levels has been reported in multiple somatic stem cell types. However, the knowledge of hypoxia on eMSC remains limited. In mice, stromal stem/progenitor cells can be identified by the label-retaining technique. We examined the relationship between the label-retaining stromal cells (LRSC) and hypoxia during tissue breakdown in a mouse model of simulated menses. Our results demonstrated that LRSC resided in a hypoxic microenvironment during endometrial breakdown and early repair. Immunofluorescence staining revealed that the hypoxic-located LRSC underwent proliferation and was highly colocalized with Notch1. In vitro studies illustrated that hypoxia activated Notch signaling in eMSC, leading to enhanced self-renewal, clonogenicity and proliferation of cells. More importantly, HIF-1α played an essential role in the hypoxia-mediated maintenance of eMSC through the activation of Notch signaling. In conclusion, our findings show that some endometrial stem/progenitor cells reside in a hypoxic niche during menstruation, and hypoxia can regulate the self-renewal activity of eMSC via Notch signaling.     

Measurement of periodic micro flows using micro-particle image velocimetry with phase sampling

A phase-sampling method has been developed to measure periodic flows at a high temporal resolution using conventional micro-particle image velocimetry (PIV). In this technique, the sampling is set such that each velocimetry dataset represents a unique point in phase of a periodic flow. The flow characteristics over a single cycle are reconstructed from measurements over a number of cycles, thus allowing measurement at a higher temporal resolution than the PIV system. The flow measurements were performed for AC electroosmotic flows and verified with results from the phase-locking technique. The temporal resolution is limited by the shortest camera exposure time and the time separation between laser pulses. The theoretical sampling resolution can be as low as 20 μs for 100 Hz periodic flows. A resolution of 200 μs was obtained in the experiment using 40 velocimetry datasets.