Observation of the Inverse Cotton-Mouton Effect

We report the observation of the Inverse Cotton-Mouton Effect (ICME) i.e. a magnetization induced in a medium by non resonant linearly polarized light propagating in the presence of a transverse magnetic field. We present a detailed study of the ICME in a TGG crystal showing the dependence of the measured effect on the light intensity, the optical polarization, and on the external magnetic field. We derive a relation between the Cotton-Mouton and Inverse Cotton-Mouton effects that is roughly in agreement with existing experimental data. Our results open the way to applications of the ICME in optical devices

A simulation system for behaviour evaluation of off-rooad mobile robots

Abstract: This study deals with optimization of planetary rover traversability over challenging outdoor terrains. In this aim, it is necessary to simulate rover behaviour on soft soil with a high degree of realism. The paper describes a simulation system for mobile robots evolving on natural and unstructured surfaces. This simulator integrates the whole dynamics of the multibody systems and complex interactions with soft ground as well as control schemes.

Deep Task-Based Analog-To-Digital Conversion

Analog-To-digital converters (ADCs) allow physical signals to be processed using digital hardware. Their conversion consists of two stages: Sampling, which maps a continuous-Time signal into discrete-Time, and quantization, i.e., representing the continuous-Amplitude quantities using a finite number of bits. ADCs typically implement generic uniform conversion mappings that are ignorant of the task for which the signal is acquired, and can be costly when operating in high rates and fine resolutions. In this work we design task-oriented ADCs which learn from data how to map an analog signal into a digital representation such that the system task can be efficiently carried out. We propose a model for sampling and quantization that facilitates the learning of non-uniform mappings from data. Based on this learnable ADC mapping, we present a mechanism for optimizing a hybrid acquisition system comprised of analog combining, tunable ADCs with fixed rates, and digital processing, by jointly learning its components end-To-end. Then, we show how one can exploit the representation of hybrid acquisition systems as deep networks to optimize the sampling and quantization rates given the task by utilizing Bayesian meta-learning techniques. We evaluate the proposed deep task-based ADC in two case studies: The first considers synthetic multi-variate symbol detection, where multiple analog signals are simultaneously acquired in order to recover a set of discrete symbols. The second application is beamforming of analog channel data acquired in ultrasound imaging. Our numerical results demonstrate that the proposed approach achieves performance which is comparable to operating with high sampling rates and fine resolution quantization, while operating with reduced overall bit rate. For instance, we demonstrate that deep task-based ADCs enable accurate reconstruction of ultrasound images while using 12.5% of the overall number of bits used by conventional ADCs.</p

Experimental Investigation on Microstructure and Mechanical Properties of Direct Squeeze Cast Al–13%Si Alloy

Для литья под давлением характерно приложение давления к заготовке при ее затвердевании, вследствие чего активизируются различные физические процессы, которые влияютна металлургические свойства литых сплавов. Выполненное экспериментальное исследование свидетельствует о влиянии уровня давления на микроструктуру и механические характеристики сплава Al–13%Si. Показано, что давление при литье в интервале 0,1...100 МПа приводит к уменьшению зерен, улучшению прочностных свойств и повышению твердости по Виккерсу в центральной части образцов. При давлении 100...150 МПа наблюдаются высокие предварительные деформации при высокой температуре литья и укрупнение зерен, в результате чего ухудшаются прочностные свойства и уменьшается твердость. Анализ микрофотографий на электронном микроскопе показывает, что при давлении до 100 МПа механизм разрушения является более вязким, чем при повышенном давлении. Это позволяет оптимизировать уровень давления с целью предотвращения ухудшения свойств и ликвации материала в процессе литья под давлением.Для лиття під тиском характерно прикладення тиску до заготовки при її твердінні, в результаті чого активізуються різні физичні процеси, що впливають на металургійні властивості литих сплавів. Експериментальне дослідження свідчить про вплив рівня тиску намікроструктуру і механічні характеристики сплаву Al–13%Si. Показано, що тиск при литті в інтервалі 0,1...100 МПа призводить до зменшення зерен, покращанню міцнісних властивостей і підвищенню твердості по Віккерсу в центральній частині зразка. При дії тиску 100...150 МПа мають місце високі попередні деформації за високої температури лиття і збільшення зерен. У результаті цього погіршуються міцнісні властивості і зменшується твердість. Аналіз мікрофотографій на електронному мікроскопі показує, що при дії тиску до 100 МПа механізм руйнування є більш в’язким, ніж при підвищеному тиску. Це дозволяє оптимізувати рівень тиску з метою запобігання погіршення властивостей і ліквації матеріалу в процесі лиття під тиском

Deep Task-Based Analog-to-Digital Conversion

Analog-to-digital converters (ADCs) allow physical signals to be processed using digital hardware. Their conversion consists of two stages: Sampling, which maps a continuous-time signal into discrete-time, and quantization, i.e., representing the continuous-amplitude quantities using a finite number of bits. ADCs typically implement generic uniform conversion mappings that are ignorant of the task for which the signal is acquired, and can be costly when operating in high rates and fine resolutions. In this work we design task-oriented ADCs which learn from data how to map an analog signal into a digital representation such that the system task can be efficiently carried out. We propose a model for sampling and quantization that facilitates the learning of non-uniform mappings from data. Based on this learnable ADC mapping, we present a mechanism for optimizing a hybrid acquisition system comprised of analog combining, tunable ADCs with fixed rates, and digital processing, by jointly learning its components end-to-end. Then, we show how one can exploit the representation of hybrid acquisition systems as deep network to optimize the sampling rate and quantization rate given the task by utilizing Bayesian meta-learning techniques. We evaluate the proposed deep task-based ADC in two case studies: the first considers symbol detection in multi-antenna digital receivers, where multiple analog signals are simultaneously acquired in order to recover a set of discrete information symbols. The second application is the beamforming of analog channel data acquired in ultrasound imaging. Our numerical results demonstrate that the proposed approach achieves performance which is comparable to operating with high sampling rates and fine resolution quantization, while operating with reduced overall bit rate

A moving boundary model motivated by electric breakdown: II. Initial value problem

An interfacial approximation of the streamer stage in the evolution of sparks and lightning can be formulated as a Laplacian growth model regularized by a 'kinetic undercooling' boundary condition. Using this model we study both the linearized and the full nonlinear evolution of small perturbations of a uniformly translating circle. Within the linear approximation analytical and numerical results show that perturbations are advected to the back of the circle, where they decay. An initially analytic interface stays analytic for all finite times, but singularities from outside the physical region approach the interface for $t\to\infty$, which results in some anomalous relaxation at the back of the circle. For the nonlinear evolution numerical results indicate that the circle is the asymptotic attractor for small perturbations, but larger perturbations may lead to branching. We also present results for more general initial shapes, which demonstrate that regularization by kinetic undercooling cannot guarantee smooth interfaces globally in time.Comment: 44 pages, 18 figures, paper submitted to Physica

Kinetics of Heterogeneous Single-Species Annihilation

We investigate the kinetics of diffusion-controlled heterogeneous single-species annihilation, where the diffusivity of each particle may be different. The concentration of the species with the smallest diffusion coefficient has the same time dependence as in homogeneous single-species annihilation, A+A-->0. However, the concentrations of more mobile species decay as power laws in time, but with non-universal exponents that depend on the ratios of the corresponding diffusivities to that of the least mobile species. We determine these exponents both in a mean-field approximation, which should be valid for spatial dimension d>2, and in a phenomenological Smoluchowski theory which is applicable in d<2. Our theoretical predictions compare well with both Monte Carlo simulations and with time series expansions.Comment: TeX, 18 page

Exponents appearing in heterogeneous reaction-diffusion models in one dimension

We study the following 1D two-species reaction diffusion model : there is a small concentration of B-particles with diffusion constant $D_B$ in an homogenous background of W-particles with diffusion constant $D_W$; two W-particles of the majority species either coagulate ($W+W \longrightarrow W$) or annihilate ($W+W \longrightarrow \emptyset$) with the respective probabilities $p_c=(q-2)/(q-1)$ and $p_a=1/(q-1)$; a B-particle and a W-particle annihilate ($W+B \longrightarrow \emptyset$) with probability 1. The exponent $\theta(q,\lambda=D_B/D_W)$ describing the asymptotic time decay of the minority B-species concentration can be viewed as a generalization of the exponent of persistent spins in the zero-temperature Glauber dynamics of the 1D $q$-state Potts model starting from a random initial condition : the W-particles represent domain walls, and the exponent $\theta(q,\lambda)$ characterizes the time decay of the probability that a diffusive "spectator" does not meet a domain wall up to time $t$. We extend the methods introduced by Derrida, Hakim and Pasquier ({\em Phys. Rev. Lett.} {\bf 75} 751 (1995); Saclay preprint T96/013, to appear in {\em J. Stat. Phys.} (1996)) for the problem of persistent spins, to compute the exponent $\theta(q,\lambda)$ in perturbation at first order in $(q-1)$ for arbitrary $\lambda$ and at first order in $\lambda$ for arbitrary $q$.Comment: 29 pages. The three figures are not included, but are available upon reques

Fission of a multiphase membrane tube

A common mechanism for intracellular transport is the use of controlled deformations of the membrane to create spherical or tubular buds. While the basic physical properties of homogeneous membranes are relatively well-known, the effects of inhomogeneities within membranes are very much an active field of study. Membrane domains enriched in certain lipids in particular are attracting much attention, and in this Letter we investigate the effect of such domains on the shape and fate of membrane tubes. Recent experiments have demonstrated that forced lipid phase separation can trigger tube fission, and we demonstrate how this can be understood purely from the difference in elastic constants between the domains. Moreover, the proposed model predicts timescales for fission that agree well with experimental findings