Analytic investigation of frequency sensitivity in microwave oscillators: Application to the computation of phase noise in a dielectric resonator oscillator

The conversion of low frequency noise into phase noise in microwave oscillators is studied through an analytical calculation of the pushing factor. This calculation is based on a simplified equivalent circuit for two types of active devices : field effect transistors (Fet) and heterojunction bipolar transistors (hbt). The preeminence in the conversion process of the gate- source capacitance in theFet and the base- emitter junction in thehbt is pointed out. Practical methods are proposed to reduce the phase noise in these circuits.     

An improved algorithm for disaggregating microwave-derived soil moisture based on red, near-infrared and thermal-infrared data

Accurate high-resolution soil moisture data are needed for a range of agricultural and hydrologic activities. To improve the spatial resolution of ∼ 40 km resolution passive microwave-derived soil moisture, a methodology based on 1 km resolution MODIS (MODerate resolution Imaging Spectroradiometer) red, near-infrared and thermal-infrared data has been implemented at 4 km resolution. The three components of that method are (i) fractional vegetation cover, (ii) soil evaporative efficiency (defined as the ratio of actual to potential evaporation) and (iii) a downscaling relationship. In this paper, 36 different disaggregation algorithms are built from 3 fractional vegetation cover formulations, 3 soil evaporative efficiency models, and 4 downscaling relationships. All algorithms differ with regard to the representation of the nonlinear relationship between microwave-derived soil moisture and optical-derived soil evaporative efficiency. Airborne L-band data collected over an Australian agricultural area are used to both generate ∼ 40 km resolution microwave pixels and verify disaggregation results at 4 km resolution. Among the 36 disaggregation algorithms, one is identified as being more robust (insensitive to soil, vegetation and atmospheric variables) than the others with a mean slope between MODIS-disaggregated and L-band derived soil moisture of 0.94. The robustness of that algorithm is notably assessed by comparing the disaggregation results obtained using composited (averaged) Terra and Aqua MODIS data, and using data from Terra and Aqua separately. The error on disaggregated soil moisture is systematically reduced by compositing daily Terra and Aqua data with an error of 0.012 vol./vol.     

A survey on fraud and service misuse in voice over IP (VoIP) networks

The migration from circuit-switched networks to packet-switched networks necessitates the investigation of related issues such as service delivery, QoS, security, and service fraud and misuse. The latter can be seen as a combination of accounting and security aspects. In traditional telecommunication networks, fraud accounts for annual losses at an average of 3%–5% of the operators’ revenue and still increasing at a rate of more than 10% yearly. It is also expected that in VoIP networks, the situation will be worse due to the lack of strong built-in security mechanisms, and the use of open standards. This paper discusses the fraud problem in VoIP networks and evaluates the related available solutions.     

Deliberate word access: an intuition,a roadmap and some preliminary empirical results

No doubt, words play a major role in language production, hence finding them is of vital importance, be it for writing or for speaking (spontaneous discourse production, simultaneous translation). Words are stored in a dictionary, and the general belief holds, the more entries the better. Yet, to be truly useful the resource should contain not only many entries and a lot of information concerning each one of them, but also adequate navigational means to reveal the stored information. Information access depends crucially on the organization of the data (words) and the access keys (meaning/form), two factors largely overlooked. We will present here some ideas of how an existing electronic dictionary could be enhanced to support a speaker/writer to find the word s/he is looking for. To this end we suggest to add to an existing electronic dictionary an index based on the notion of association, i.e. words co-occurring in a well balanced corpus, the latter being supposed to represent the average citizen’s knowledge of the world. Before describing our approach, we will briefly take a critical look at the work being done by colleagues working on automatic, spontaneous or deliberate language production,—that is, computer-generated language, simulation of the mental lexicon, or WordNet (WN),—to see how adequate they are with regard to our goal.     

Boosted multi-task learning

In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model, explicitly addressing their commonalities through shared parameters and their differences with task-specific ones. This enables implicit data sharing and regularization. Our algorithm is derived using the relationship between ? ₁-regularization and boosting. We evaluate our learning method on web-search ranking data sets from several countries. Here, multi-task learning is particularly helpful as data sets from different countries vary largely in size because of the cost of editorial judgments. Further, the proposed method obtains state-of-the-art results on a publicly available multi-task dataset. Our experiments validate that learning various tasks jointly can lead to significant improvements in performance with surprising reliability.     

Neural mass activity, bifurcations, and epilepsy

In this letter, we propose a general framework for studying neural mass models defined by ordinary differential equations. By studying the bifurcations of the solutions to these equations and their sensitivity to noise, we establish an important relation, similar to a dictionary, between their behaviors and normal and pathological, especially epileptic, cortical patterns of activity. We then apply this framework to the analysis of two models that feature most phenomena of interest, the Jansen and Rit model, and the slightly more complex model recently proposed by Wendling and Chauvel. This model-based approach allows us to test various neurophysiological hypotheses on the origin of pathological cortical behaviors and investigate the effect of medication. We also study the effects of the stochastic nature of the inputs, which gives us clues about the origins of such important phenomena as interictal spikes, interictal bursts, and fast onset activity that are of particular relevance in epilepsy.     

Characterization of renal tumours based on Raman spectra classification

In this study, we propose to evaluate the potential of Raman spectroscopy (RS) to assess renal tumours at surgery. Different classes of Raman renal spectra acquired during a clinical protocol are discriminated using support vector machines classifiers. The influence on the classification scores of various preprocessing steps generally involved in RS are also investigated and evaluated in the particular context of renal tumour characterization. Encouraging results show the interest of RS to evaluate kidney cancer and suggest the potential of this technique as a surgical assistance during partial nephrectomy.