Disordered, strongly scattering porous materials as miniature multipass gas cells

Spectroscopic gas sensing is both a commercial success and a rapidly advancing scientific field. Throughout the years, massive efforts have been directed towards improving detection limits by achieving long interaction pathlengths. Prominent examples include the use of conventional multipass gas cells, sophisticated high-finesse cavities, gas-filled holey fibers, integrating spheres, and diffusive reflectors. Despite this rich flora of approaches, there is a continuous struggle to reduce size, gas volume, cost and alignment complexity. Here, we show that extreme light scattering in porous materials can be used to realise miniature gas cells. Near-infrared transmission through a 7 mm zirconia (ZrO2) sample with a 49% porosity and subwavelength pore structure (on the order of 100 nm) gives rise to an effective gas interaction pathlength above 5 meters, an enhancement corresponding to 750 passes through a conventional multipass cell. This essentially different approach to pathlength enhancement opens a new route to compact, alignment-free and low-cost optical sensor systems

Latent Class Model with Application to Speaker Diarization

In this paper, we apply a latent class model (LCM) to the task of speaker diarization. LCM is similar to Patrick Kenny's variational Bayes (VB) method in that it uses soft information and avoids premature hard decisions in its iterations. In contrast to the VB method, which is based on a generative model, LCM provides a framework allowing both generative and discriminative models. The discriminative property is realized through the use of i-vector (Ivec), probabilistic linear discriminative analysis (PLDA), and a support vector machine (SVM) in this work. Systems denoted as LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid are introduced. In addition, three further improvements are applied to enhance its performance. 1) Adding neighbor windows to extract more speaker information for each short segment. 2) Using a hidden Markov model to avoid frequent speaker change points. 3) Using an agglomerative hierarchical cluster to do initialization and present hard and soft priors, in order to overcome the problem of initial sensitivity. Experiments on the National Institute of Standards and Technology Rich Transcription 2009 speaker diarization database, under the condition of a single distant microphone, show that the diarization error rate (DER) of the proposed methods has substantial relative improvements compared with mainstream systems. Compared to the VB method, the relative improvements of LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid systems are 23.5%, 27.1%, and 43.0%, respectively. Experiments on our collected database, CALLHOME97, CALLHOME00 and SRE08 short2-summed trial conditions also show that the proposed LCM-Ivec-Hybrid system has the best overall performance

Latent Class Model with Application to Speaker Diarization

In this paper, we apply a latent class model (LCM) to the task of speaker diarization. LCM is similar to Patrick Kenny’s variational Bayes (VB) method in that it uses soft information and avoids premature hard decisions in its iterations. In contrast to the VB method, which is based on a generative model, LCM provides a framework allowing both generative and discriminative models. The discriminative property is realized through the use of i-vector (Ivec), probabilistic linear discriminative analysis (PLDA), and a support vector machine (SVM) in this work. Systems denoted as LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid are introduced. In addition, three further improvements are applied to enhance its performance. (1) Adding neighbor windows to extract more speaker information for each short segment. (2) Using a hidden Markov model to avoid frequent speaker change points. (3) Using an agglomerative hierarchical cluster to do initialization and present hard and soft priors, in order to overcome the problem of initial sensitivity. Experiments on the National Institute of Standards and Technology Rich Transcription 2009 speaker diarization database, under the condition of a single distant microphone, show that the diarization error rate (DER) of the proposed methods has substantial relative improvements compared with mainstream systems. Compared to the VB method, the relative improvements of LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid systems are 23.5%, 27.1%, and 43.0%, respectively. Experiments on our collected database, CALLHOME97, CALLHOME00, and SRE08 short2-summed trial conditions also show that the proposed LCM-Ivec-Hybrid system has the best overall performance

Quantum Yield Characterization and Excitation Scheme Optimization of Upconverting Nanoparticles

Upconverting nanoparticles suffer from low quantum yield in diffuse optical imaging, especially at low excitation intensities. Here, the power density dependent quantum yield is characterized, and the excitation scheme is optimized based on such characterizatio

Evidence for a dynamical ground state in the frustrated pyrohafnate Tb2Hf2O7

We report the physical properties of Tb2Hf2O7 based on ac magnetic susceptibility \chi_ac(T), dc magnetic susceptibility \chi(T), isothermal magnetization M(H), and heat capacity C_p(T) measurements combined with muon spin relaxation (\muSR) and neutron powder diffraction measurements. No evidence for long-range magnetic order is found down to 0.1 K. However, \chi_ac(T) data present a frequency-dependent broad peak (near 0.9 K at 16 Hz) indicating slow spin dynamics. The slow spin dynamics is further evidenced from the \muSR data (characterized by a stretched exponential behavior) which show persistent spin fluctuations down to 0.3 K. The neutron powder diffraction data collected at 0.1 K show a broad peak of magnetic origin (diffuse scattering) but no magnetic Bragg peaks. The analysis of the diffuse scattering data reveals a dominant antiferromagnetic interaction in agreement with the negative Weiss temperature. The absence of long-range magnetic order and the presence of slow spin dynamics and persistent spin fluctuations together reflect a dynamical ground state in Tb2Hf2O7.Comment: 11 pages and 8 figure

Deep tissue optical imaging of upconverting nanoparticles enabled by exploiting higher intrinsic quantum yield through use of millisecond single pulse excitation with high peak power

We have accomplished deep tissue optical imaging of upconverting nanoparticles at 800 nm, using millisecond single pulse excitation with high peak power. This is achieved by carefully choosing the pulse parameters, derived from time-resolved rate-equation analysis, which result in higher intrinsic quantum yield that is utilized by upconverting nanoparticles for generating this near infrared upconversion emission. The pulsed excitation approach thus promises previously unreachable imaging depths and shorter data acquisition times compared with continuous wave excitation, while simultaneously keeping the possible thermal side-effects of the excitation light moderate. These key results facilitate means to break through the general shallow depth limit of upconverting-nanoparticle-based fluorescence techniques, necessary for a range of biomedical applications, including diffuse optical imaging, photodynamic therapy and remote activation of biomolecules in deep tissues

Fluorescence diffuse optical tomography using upconverting nanoparticles

Fluorescence diffuse optical tomography (FDOT) can provide important information in biomedical studies. In this ill-posed problem, suppression of background tissue autofluorescence is of utmost importance. We report a method for autofluorescence-insensitive FDOT using nonlinear upconverting nanoparticles (NaYF4:Yb3+/Tm3+) in a tissue phantom under excitation intensities well below tissue-damage thresholds. Even with the intrinsic autofluorescence from the phantom only, the reconstruction of the nanoparticles is of much better quality than the reconstruction of a Stokes-shifting dye. In addition, the nonlinear power dependence leads to more confined reconstructions and may increase the resolution in FDOT

MHW-PD: a robust rice panicles counting algorithm based on deep learning and multiscale hybrid window

In-field assessment of rice panicle yields accurately and automatically has been one of the key ways to realize high-throughput rice breeding in the modern smart farming. However, practical rice fields normally consist of many different, often very small sizes of panicles, particularly when large numbers of panicles are captured in the imagery. In these cases, the integrity of panicle feature is difficult to extract due to the limited panicle original information and substantial clutters caused by heavily compacted leaves and stems, which results in poor counting efficacy. In this paper, we propose a simple, yet effective method termed as Multi-Scale Hybrid Window Panicle Detect (MHW-PD), which focuses on enhance the panicle features to detect and count the large number of small-sized rice panicles in the in-field scene. On the basis of quantifying and analyzing the relationship among the receptive field, the size of input image and the average dimensions of panicles, the MHW-PD gives dynamic strategies for choosing the appropriate feature learning network and constructing adaptive multi-scale hybrid window (MHW), which maximizes the richness of panicle feature. Besides, a fusion algorithm is involved to remove the repeated counting of the broken panicles to get the final panicle number. With extensive experimental results, the MHW-PD has achieved ~87% of panicle counting accuracy; and the counting accuracy just decreases by ~8% when the number of panicles per image increases from 0 to 80, which shows better in stability than all the competing methods adopted in this work. The MHW-PD is demonstrated qualitatively and quantitatively that is able to deal with high density of panicles

Autofluorescence insensitive imaging using upconverting nanocrystals in scattering media

Autofluorescence is a nuisance in the field of fluorescence imaging and tomography of exogenous molecular markers in tissue, degrading the quality of the collected data. In this letter, we report autofluorescence insensitive imaging using highly efficient upconverting nanocrystals (NaYF4: Yb3+ /Tm3+) in a tissue phantom illuminated with near- infrared radiation of 85 mW/cm(2). It was found that imaging with such nanocrystals leads to an exceptionally high contrast compared to traditional downconverting fluorophores due to the absence of autofluorescence. Upconverting nanocrystals may be envisaged as important biological markers for tissue imaging purposes. c 2008 American Institute of Physics. [DOI: 10.1063/1.3005588