A novel MLSD receiver architecture for nonlinear channels

A new architecture for maximum likelihood sequence detec- tion (MLSD) in nonlinear dispersive channels (NLCs) is presented, and its robustness to inaccurate channel knowledge is analyzed. This architecture is developed by considering a novel orthogonal representation of the NLC, which is exploited to develop a front-end capable of obtaining uncorrelated symbol rate samples, representing a sufficient statistic for information decoding. This front-end is a special form of space-time whitened matched filter (ST-WMF), and the MLSD obtained by using this front-end (ST-WMF-MLSD) requires simple branch metrics due to the signal whitening. The ST-WMF also allows for space-time compression of the equivalent channel, which is exploited for further complexity reduction of the ST-WMF-MLSD. Simulation results show the good trade-off in performance and complexity obtained with the ST-WMF- MLSD, even in the presence of inaccurate channel knowledge.Sociedad Argentina de Informática e Investigación Operativa (SADIO

A novel MLSD receiver architecture for nonlinear channels

A new architecture for maximum likelihood sequence detec- tion (MLSD) in nonlinear dispersive channels (NLCs) is presented, and its robustness to inaccurate channel knowledge is analyzed. This architecture is developed by considering a novel orthogonal representation of the NLC, which is exploited to develop a front-end capable of obtaining uncorrelated symbol rate samples, representing a sufficient statistic for information decoding. This front-end is a special form of space-time whitened matched filter (ST-WMF), and the MLSD obtained by using this front-end (ST-WMF-MLSD) requires simple branch metrics due to the signal whitening. The ST-WMF also allows for space-time compression of the equivalent channel, which is exploited for further complexity reduction of the ST-WMF-MLSD. Simulation results show the good trade-off in performance and complexity obtained with the ST-WMF- MLSD, even in the presence of inaccurate channel knowledge.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Polaronic signature in the metallic phase of La0.7Ca0.3MnO3 films detected by scanning tunneling spectroscopy

In this work we map tunnel conductance curves with nanometric spatial resolution, tracking polaronic quasiparticle excitations when cooling across the insulator-to-metal transition in La0.7Ca0.3MnO3 films. In the insulating phase the spectral signature of polarons, a depletion of conductance at low bias flanked by peaks, is detected all over the scanned surface. These features are still observed at the transition and persist on cooling into the metallic phase. Polaron-binding energy maps reveal that polarons are not confined to regions embedded in a highly-conducting matrix but are present over the whole field of view both above and below the transition temperature.Comment: 10 pages, 4 figure

Strong-coupling analysis of scanning tunneling spectra in Bi2_2Sr2_2Ca2_2Cu3_3O10+δ_{10+\delta}

We study a series of spectra measured in the superconducting state of optimally-doped Bi-2223 by scanning tunneling spectroscopy. Each spectrum, as well as the average of spectra presenting the same gap, is fitted using a strong-coupling model taking into account the band structure, the BCS gap, and the interaction of electrons with the spin resonance. After describing our measurements and the main characteristics of the strong-coupling model, we report the whole set of parameters determined from the fits, and we discuss trends as a function of the gap magnitude. We also simulate angle-resolved photoemission spectra, and compare with recent experimental results.Comment: Published versio

Kawasaki disease in Sicily: clinical description and markers of disease severity

Background: Kawasaki disease (KD) is an acute systemic vasculitis of small and middle size arteries; 15-25 % of untreated patients and 5 % of patients treated with intravenous immunoglobulin (IVIG) develop coronary artery lesions (CAL). Many studies tried to find the most effective treatment in the management of resistant KD and to select the risk factors for CAL. Our data are assessed on children from west Sicily, characterized by a genetic heterogeneity. Methods: We studied the clinical data of 70 KD Sicilian children (36 males: 51 %; 34 females: 49 %), analysed retrospectively, including: demographic and laboratory parameters; echocardiographic findings at diagnosis, at 2, 6 and 8 weeks, and at 1 year after the onset of the illness. Results: Forty-seven had Typical KD, three Atypical KD and twenty Incomplete KD. Age at the disease onset ranged from 0.1 to 8.9 years. IVIG were administered 5 \ub1 2 days after the fever started. Defervescence occurred 39 \ub1 26 hours after the first IVIG infusion. Fifty-six patients (80 %) received 1 dose of IVIG (responders); 14 patients (20 %) had a resistant KD, with persistent fever after the first IVIG dose (non responders). Ten (14 %) non responders responded to the second dose, 4 (5 %) responded to three doses; one needed treatment with high doses of steroids and Infliximab. Cardiac involvement was documented in twenty-two cases (eighteen with transient dilatation/ectasia, fifteen with aneurysms). Pericardial effusion, documented in eleven, was associated with coronaritis and aneurysms, and was present earlier than coronary involvement in seven. Hypoalbuminemia, D-dimer pre-IVIG, gamma-GT pre-IVIG showed a statistically significant direct correlation with IVIG doses, highlighting the role of these parameters as predictor markers of refractory disease. The persistence of elevated CRP, AST, ALT levels, a persistent hyponatremia and hypoalbuminemia after IVIG therapy, also had a statistical significant correlation with IVIG doses. Non responders showed higher levels of D-dimer and gamma-GT pre-IVIG, persistent high levels of D-dimer, CRP, AST, ALT, hypoalbuminemia and hyponatremia after IVIG. Conclusions: This is the first study on KD in Sicily. We suggest some laboratory parameters as predictive criteria for resistant KD. Patients who show early pericarditis need careful surveillance for coronary lesions

Systemic lupus erythematosus and bullous pemphigoid with dramatic response to dapsone

Objective: Unusual clinical course Background: Bullous pemphigoid is an autoimmune blistering disease, with relapses, isolated or associated with other autoimmune diseases such as systemic lupus erythematosus (SLE). Joint manifestations rapidly respond to small or moderate doses of corticosteroids, whereas skin manifestations usually respond to antimalarial drugs. Case Report: We describe the clinical case of an 11-year-old girl with SLE. She showed bullous skin lesions with arthralgia, mild proteinuria, resolved after steroid treatment. At the tapering of her prednisone dose, the patient had new skin lesions requiring an increased dose of prednisone. She started dapsone at the dosage of 1 mg/kg/day, maintaining low dose prednisone; this treatment was successfully followed by the dramatic disappearance of skin lesions and limb pain. Conclusions: Bullous skin lesions can represent the first clinical presentation of pediatric SLE and could influence the treatment and the outcome of these patients. This case showed an atypical course as both skin manifestations and arthritis promptly and persistently resolved with dapsone without the use of high-dose glucocorticoids. Only a few cases of patients with SLE associated with bullous pemphigoid have been reported in the literature, and very few in the pediatric population

Deep transfer learning for machine diagnosis: From sound and music recognition to bearing fault detection

Today’s deep learning strategies require ever‐increasing computational efforts and demand for very large amounts of labelled data. Providing such expensive resources for machine diagnosis is highly challenging. Transfer learning recently emerged as a valuable approach to address these issues. Thus, the knowledge learned by deep architectures in different scenarios can be reused for the purpose of machine diagnosis, minimizing data collecting efforts. Existing research provides evidence that networks pre‐trained for image recognition can classify machine vibrations in the time‐frequency domain by means of transfer learning. So far, however, there has been little discussion about the potentials included in networks pre‐trained for sound recognition, which are inherently suited for time‐frequency tasks. This work argues that deep architectures trained for music recognition and sound detection can perform machine diagnosis. The YAMNet convolutional network was designed to serve extremely efficient mobile applications for sound detection, and it was originally trained on millions of data extracted from YouTube clips. That framework is employed to detect bearing faults for the CWRU dataset. It is shown that transferring knowledge from sound and music recognition to bearing fault detection is successful. The maximum accuracy is achieved using a few hundred data for fine‐tuning the fault diagnosis model

Fingerprint of dynamical charge/spin correlations in the tunneling spectra of colossal magnetoresistive manganites

We present temperature-dependent scanning tunneling spectroscopy measurements on $La_{1-x}Ca_{x}MO_{3}$ ($x\sim0.33$) films with different degrees of biaxial strain. A depletion in normalized conductance around the Fermi level is observed both above and below the insulator-to-metal transition temperature $T_{MI}$, for weakly as well as highly-strained films. This pseudogap-like depletion globally narrows on cooling. The zero-bias conductance decreases on cooling in the insulating phase, reaches a minimum close to $T_{MI}$ and increases on cooling in the metallic phase, following the trend of macroscopic conductivity. These results support a recently proposed scenario in which dynamical short-range antiferromagnetic/charge order correlations play a preeminent role in the transport properties of colossal magnetoresistive manganites [R. Yu \textit{et al}., Phys. Rev. B \textbf{77}, 214434 (2008)].Comment: 9 pages, 4 figure