A quaternary partial-response class-IV transceiver for 125 Mbit/sdata transmission over unshielded twisted-pair cables: principles ofoperation and VLSI realization

The paper describes an experimental transceiver for full-duplex transmission at a rate of 125 Mbit/s over unshielded twisted-pair cables of ordinary voice-grade quality, intended for use in a fiber distributed data interface (FDDI) network. Quaternary partial-response class-IV (QPRIV) overall-channel signaling with near-end crosstalk (NEXT) cancellation and maximum-likelihood sequence detection is employed. The spectral shape of the QPRIV signals facilitates equalization and achieving compliance with EMC regulations. Since in an FDDI system each transmitter can be clocked independently, the receiver must cope with phase drift between NEXT signals to be cancelled and signals received from the remote transmitter. With the chosen transceiver architecture, digital-to-analog conversion of transmit signals, analog-to-digital conversion of receive signals, and adaptive NEXT cancellation are performed synchronously with the transmitter clock. The rate change from transmit timing to controlled receive timing is accomplished by an adaptive equalizer in conjunction with an elastic buffer and occasional coefficient shifts. The equalizer is adjusted rapidly enough to allow for a maximal phase drift of ±100 ppm. The implementation of all digital signal-processing functions in a single 0.5 μm CMOS VLSI prototype chip is discussed. The employed standard-cell design resulted in a power consumption of 6 W. Significantly lower power consumption can be achieved by custom design of highly repetitive processing elements     

Compatibilization of an immiscible blend of EPDM and POM with an Ionomer

Immiscible blends of ethylene-propylene-diene-monomer (EPDM) and polyoxymethylene (POM), when EPDM is the major phase were compatibilized on the addition of an ionomer, poly(ethylene-co-methacrylic acid). The inclusion of the ionomer reduced the interfacial tension between the two phases, such that the diameter of the POM domains were significantly reduced to between 0.5 and 2 μm, typical of that required to toughen ductile polymers. The mechanical properties of the resultant compatibilized blends were significantly enhanced with increases in Young's modulus (↑54%), tensile strength (σ, ↑139%), elongation at break (ε, ↑97%), and tensile toughness (↑500%) with increasing ionomer content, relative to EPDM rubber alone. The ShoreA hardness of the compatibilized blend was 70.1 compared with 56.8 for the immiscible binary blend and, 50.2 for neat EPDM rubber.     

Radiosensitivity of V79 cells after alpha particle radiation at low doses

Low dose hyper-radiosensitivity (HRS) of V79 cells was demonstrated after irradiation with gamma rays and 4He2+ ions of various linear energy transfer (LET) values (58.9, 79.3 and 101.7 keV.micron-1). In parallel, the cytogenetic analysis showed an LET dependence of aberrations at a dose of 1 Gy, while the observed chromatid fragments appeared to vary with the number of 4He2+ ions traversing the cell nucleus. The results of both studies are correlated so as to achieve a better understanding of the so-called induced radioresistance. The cell mechanism of radioresistance appears to be induced after a certain amount of energy is deposited in the cell nucleus. This amount depends both on the radiation quality as well as the number of particles traversing the cell, inducing chromosome alterations and chromatid damage.     

Policy gradient learning for quadruped soccer robots

In real-world robotic applications, many factors, both at low level (e.g., vision, motion control and behaviors) and at high level (e.g., plans and strategies) determine the quality of the robot performance. Consequently, fine tuning of the parameters, in the implementation of the basic functionalities, as well as in the strategic decisions, is a key issue in robot software development. In recent years, machine learning techniques have been successfully used to find optimal parameters for typical robotic functionalities. However, one major drawback of learning techniques is time consumption: in practical applications, methods designed for physical robots must be effective with small amounts of data. In this paper, we present a method for concurrent learning of best strategy and optimal parameters using policy gradient reinforcement learning algorithm. The results of our experimental work in a simulated environment and on a real robot show a very high convergence rate.     

GHG balances of bioenergy systems – Overview of key steps in the production chain and methodological concerns

This paper deals with a methodology for calculating the greenhouse gas (GHG) balances of bioenergy systems producing electricity, heat and transportation biofuels from biomass residues or crops. Proceeding from the standard Life-Cycle Assessment (LCA) as defined by ISO 14040 norms, this work provides an overview of the application of the LCA methodology to bioenergy systems in order to estimate GHG balances. In this paper, key steps in the bioenergy chain are identified and the bioenergy systems are compared with fossil reference systems producing the same amount of final products/services. The GHG emission balances of the two systems can thus be compared. Afterwards, the most important methodological assumptions (e.g. functional unit, allocation, reference system, system boundaries) and key aspects affecting the final outcomes are discussed. These key aspects are: changes in organic carbon pools, land-use change effects (both direct and indirect), N₂O and CH₄ emissions from agricultural soils and effects of crop residue removal for bioenergy use. This paper finally provides some guidelines concerning the compilation of GHG balances of bioenergy systems, with recommendations and indications on how to show final results, address the key methodological issues and give homogenous findings (in order to enhance the comparison across case studies).