ALBERT

Description: Time–frequency packing (TFP) transmission provides the highest achievable spectral efficiency with a constrained symbol alphabet and detector complexity. In this paper, the application of the TFP technique to fiber-optic systems is investigated and experimentally demonstrated. The main theoretical aspects, design guidelines, and implementation issues are discussed, focusing on those aspects which are peculiar to TFP systems. In particular, adaptive compensation of propagation impairments, matched filtering, and maximum a posteriori probability detection are obtained by a combination of a two-dimensional equalizer and four eight-state parallel Bahl–Cocke–Jelinek–Raviv (BCJR) detectors. A novel algorithm that ensures adaptive equalization, channel estimation, and a proper distribution of tasks between the equalizer and BCJR detectors is proposed. A set of irregular low-density parity-check codes with different rates is designed to operate at low error rates and approach the spectral efficiency limit achievable by TFP at different signal-to-noise ratios. An experimental demonstration of the designed system is finally provided with five dual-polarization QPSK-modulated optical carriers, densely packed in a 100-GHz bandwidth, employing a recirculating loop to test the performance of the system at different transmission distances.

Description: We investigated nonlinear optical characteristics of Tungsten disulfide (WS 2 ) films and experimentally demonstrated their high potential for application as nonlinear saturable absorbers in passively mode-locked fiber lasers. Side polished fiber (SPF) was fabricated and WS 2 film was overlaid to provide an efficient evanescent field interaction. The WS 2 film was prepared using two methods: liquid phase exfoliation to form few-layer nano-sheets, and chemical vapor deposition (CVD) to grow uniform multilayer WS 2 on a SiO 2 substrate. Two SPF saturable absorbers were prepared by either spin coating WS 2 solution or lifting off the multilayer CVD WS 2 on SPF. An all-fiber ring cavity was built and the WS 2 film overlaid on SPF was employed as a mode locker along with Er-doped fiber as a gain medium. Using the spin-coated WS 2 SPF, stable soliton-like pulses were generated with a spectral width of 5.6 nm and 467 fs pulse duration. The fiber laser cavity containing CVD WS2 SPF generated a transform-limited soliton pulse train with a spectral width of 8.23 nm and a pulse duration of 332 fs. Our study confirmed a high potential of WS 2 film as a novel 2-D nonlinear optical material for laser applications.

Description: We propose and experimentally demonstrate an on-chip all-optical differential-equation solver capable of solving second-order ordinary differential equations (ODEs) characterizing continuous-time linear time-invariant systems. The photonic device is implemented by a self-coupled microresonator on a silicon-on-insulator platform with mutual coupling between the cavity modes. Owing to the mutual mode coupling within the same resonant cavity, the resonance wavelengths induced by different cavity modes are self-aligned, thus avoiding precise wavelength alignment and unequal thermal wavelength drifts as in the case of cascaded resonators. By changing the mutual mode coupling strength, the proposed device can be used to solve second-order ODEs with tunable coefficients. System demonstration using the fabricated device is carried out for 10-Gb/s optical Gaussian and super-Gaussian input pulses. The experimental results are in good agreement with theoretical predictions of the solutions, which verify the feasibility of the fabricated device as a tunable second-order photonic ODE solver.

Description: We demonstrate a 100 Gb/s short reach system using a multicarrier transmitter based on a gain switched monolithically integrated laser. An optical comb source with 12.5-GHz free spectral range is achieved by gain-switching an integrated passive feedback laser. The 100 Gb/s wavelength division multiplexed, single sideband, direct detection, orthogonal frequency division multiplexed (WDM-SSB-DD-OFDM) system operates over 25 km standard single mode fiber exhibiting a spectral efficiency of 1.8 b/s/Hz. Receiver sensitivity of –14.2 dBm is achieved after 25 km transmission. Performance optimization with phase and amplitude precompensation is employed to improve the SSB OFDM modulation thereby reducing the interchannel interference and overcoming the power fading induced by the optical filter. We also present a theoretical analysis of the SSB-OFDM modulation.

Description: In this paper, narrow-band emission lines are generated by means of two random distributed feedback fiber laser schemes. Spectral line-widths as narrow as 3.2 pm have been measured, which significantly improves previous reported results. The laser is analyzed with the aim of obtaining a spectral line-width as narrow as possible. Additionally a variation of this setup for multi-wavelength operation is also validated. Both schemes present a simple topology that use a combination of phase-shifted fiber Bragg gratings and regular fiber Bragg gratings as filtering elements.

Description: We report on the fabrication and characterization of 19-cell hypocycloid-shape Kagome fibers with core size larger than 100 μm. These inhibited coupling fibers present low propagation loss (100 dB/km) over broad transmission range with low chromatic dispersion combined with ultra-low power overlap with silica surround, making them an efficient solution for ultra-high power laser handling, ultra-fast laser delivery, and plasma photonics applications.

Description: The iterative demodulation and decoding algorithm introduced in 2005 by Colavolpe, Barbieri, and Caire to cope with channels affected by phase noise needs pilot symbols to bootstrap. However, pilot symbols reduce the spectral efficiency of the system and, consequently, system's throughput. The aim of this paper is to show that trellis-based demodulation can be used to bootstrap the iterative process without the need of pilot symbols. Also, the complexity issue of trellis-based demodulation is addressed in this paper. The result is that the performance of iterative demodulation and decoding after the iterations is virtually unaffected by complexity reduction, provided that the reduced-complexity demodulator guarantees cycle-slip-free operation. From the numerical results presented in this paper, we show that cycle-slip-free operation can be achieved with substantial complexity reduction also for phase noise associated with linewidths of practical interest.

Description: The optical pulse evolution in a highly nonlinear normal dispersion-increasing fiber has been considered, both experimentally and theoretically. It was found that large spectral broadening in tapered waveguides could occur without temporal instabilities and impose the linear frequency modulation, i.e., chirp, required for high-quality pulse compression. The pedestal-free pulses have been demonstrated after dechirping in a standard single-mode fiber.

Description: This work proposes a novel localized surface plasmon resonance (LSPR) biochemical sensor featuring high sensitivity and a high resolution. The sensor was divided into two subcomponents according to their distinct functions; namely, single-mode fiber and metal array. Single-mode fibers located on the left and right sides of the sensors function as the input and output for optical fiber signals. A metal array comprising an arrangement of cylindrical nanometal particles served as the detection area of the sensor. To effectively reduce the memory capacity and calculation time, two innovative techniques (i.e., object meshing and boundary meshing) were integrated with the finite element method. With the area of the triangular elements used as a basis, the object boundary, small object, medium object, and large objects were meshed at a ratio of 1:8:160:1600. The improved numerical simulation methods and six design procedures were adopted to develop and analyze the proposed LSPR biochemical sensor. The results show that the novel LSPR biochemical sensor outperformed two current high-performance biochemical sensors and provided additional advantages such as short length (approximately 430 μm), high resolution (approximately –120 dB), and high sensitivity (approximately 127 604 nm/RIU).

Description: We propose an ultra-broadband super light absorber by integrating different-sized tapered hyperbolic metamaterial (HMM) waveguides, each of which has a different and wide absorption band due to broadband slow-light response, into a unit cell. We numerically demonstrate that such an absorber is superior to a single-sized HMM absorber in terms of absorption bandwidth, while maintaining a comparable absorption efficiency. A three different-sized HMM absorber presents the capability of working with an ultra-wide frequency band ranging from 1 to 30 THz, which is much larger than previously proposed absorbers working in the same spectral region. Such a design shows great promise for a broad range of applications such as thermal emitters, photovoltaics, optical-chemical energy harvesting, and stealth technology, where ultra-wideband absorption is in very high demand.

Description: This paper proposes a ring-based integrated wireless optical network architecture and an associated protocol that involves Ethernet passive optical network (EPON) and long term evolution (LTE) wireless network. The architecture along with the proposed protocol is instrumental toward the reduction of handover delay. The proposed ring-based EPON architecture facilitates the implementation of the X2 interface for LTE network by enabling the optical network units of the EPON backhaul to directly communicate with each other. The work further discusses about an open access network architecture where a single EPON can be used by multiple mobile service providers without compromising information security. Extensive simulations have been carried out to evaluate the performance of the proposed network. An analytical model has been introduced to calculate the queuing delay experienced by the X2 interface. The model has been validated with the simulations results.

Description: High spectrum efficiency and fast restoration speed are highly desired for survivable elastic optical networks (EONs). In this paper, we take the advantages of failure-independent path-protecting preconfigured cycles (FIPP p -cycles) and investigate how to realize spectrum efficient resilience design with them. We first study the problem of offline service provisioning with FIPP p -cycles. We formulate an integer linear programming model and prove that the problem is $mathcal{NP}$ hard. Then, several time-efficient heuristics are designed for FIPP p -cycle formulation and related routing, modulation format, and spectrum assignment. Extensive simulations on offline provisioning verify that the heuristics can obtain near-optimal solutions. Next, we consider online service provisioning with FIPP p -cycles in dynamic EONs. In order to overcome the decrease of protection efficiency during dynamic network operation, we propose a p -cycle reconfiguration scheme to reoptimize protection structures on-the-fly. Simulation results demonstrate that the proposed algorithms can improve spectrum efficiency and reduce bandwidth blocking probability simultaneously.

Description: Photo-thermo-refractive (PTR) glasses are the class of polyfunctional materials that combine the properties of several monofunctional materials such as photo-refractive, laser, luminescent, and plasmonic ones. Based on PTR glasses, various diffractive holographic elements and photonic devices were developed in both the volume and fiber versions. In this paper, the fabrication of optical planar waveguides on PTR glass by low-temperature ion exchange is reported for the first time. Planar waveguides were fabricated through substituting the sodium ions in glass by silver, potassium, rubidium, and cesium ones from the nitrate melts. The silver waveguides were shown to have the largest depth (27 μm) and reveal no birefringence. For the silver waveguides, an increase in the refractive index is caused by differences in the polarizabilities of cations exchanged. The maximum increment of the refractive index was observed in the cesium waveguides (0.0512). An increase in the refractive index and also appearing the birefringence in potassium, rubidium, and cesium waveguides are found to be due to the compressive mechanical stresses and their relaxation. The potentialities of the ion exchange technology for fabricating, in PTR glasses, planar gradient waveguides with low losses (0.5 dB/cm) are demonstrated, the potentialities extending the application field of PTR glasses in photonics.

Description: A multi-sensor addressing method for fiber Bragg grating aided fiber loop ringdown (FBG-FLRD) sensor array is proposed. It is capable of simultaneous measurement of temperature and force. Light from a wavelength-swept fiber laser (WSFL) is modulated into pulsed light to illuminate FBG-FLRD sensor array. Based on the time sequence of pulsed lights reflected by fiber Bragg grating array, each FBG-FLRD sensor can be distinguished. The time interval between the trigger signal of the WSFL and the pulsed light reflected by FBG offers temperature variation information. By measuring the ringdown time of each FBG-FLRD, the loss of fiber ring induced by temperature and force can be obtained. And then simultaneous measurement can be implemented in real time. To prove the validity of the proposed system, a six elements array is experimentally validated with interrogation frequency of 27.5 Hz.

Description: Histopathological grading of cancer not only offers an insight to the patients’ prognosis but also helps in making individual treatment plans. Mitosis counts in histopathological slides play a crucial role for invasive breast cancer grading using the Nottingham grading system. Pathologists perform this grading by manual examinations of a few thousand images for each patient. Hence, finding the mitotic figures from these images is a tedious job and also prone to observer variability due to variations in the appearances of the mitotic cells. We propose a fast and accurate approach for automatic mitosis detection from histopathological images. We employ area morphological scale space for cell segmentation. The scale space is constructed in a novel manner by restricting the scales with the maximization of relative-entropy between the cells and the background. This results in precise cell segmentation. The segmented cells are classified in mitotic and non-mitotic category using the random forest classifier. Experiments show at least 12% improvement in $F_{1}$ score on more than 450 histopathological images at $40times $ magnification.

Description: This paper proposes a fast multi-band image fusion algorithm, which combines a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image. The well admitted forward model is explored to form the likelihoods of the observations. Maximizing the likelihoods leads to solving a Sylvester equation. By exploiting the properties of the circulant and downsampling matrices associated with the fusion problem, a closed-form solution for the corresponding Sylvester equation is obtained explicitly, getting rid of any iterative update step. Coupled with the alternating direction method of multipliers and the block coordinate descent method, the proposed algorithm can be easily generalized to incorporate prior information for the fusion problem, allowing a Bayesian estimator. Simulation results show that the proposed algorithm achieves the same performance as the existing algorithms with the advantage of significantly decreasing the computational complexity of these algorithms.

Description: In recent years, baggage screening at airports has included the use of dual-energy X-ray computed tomography (DECT), an advanced technology for nondestructive evaluation. The main challenge remains to reliably find and identify threat objects in the bag from DECT data. This task is particularly hard due to the wide variety of objects, the high clutter, and the presence of metal, which causes streaks and shading in the scanner images. Image noise and artifacts are generally much more severe than in medical CT and can lead to splitting of objects and inaccurate object labeling. The conventional approach performs object segmentation and material identification in two decoupled processes. Dual-energy information is typically not used for the segmentation, and object localization is not explicitly used to stabilize the material parameter estimates. We propose a novel learning-based framework for joint segmentation and identification of objects directly from volumetric DECT images, which is robust to streaks, noise and variability due to clutter. We focus on segmenting and identifying a small set of objects of interest with characteristics that are learned from training images, and consider everything else as background. We include data weighting to mitigate metal artifacts and incorporate an object boundary field to reduce object splitting. The overall formulation is posed as a multilabel discrete optimization problem and solved using an efficient graph-cut algorithm. We test the method on real data and show its potential for producing accurate labels of the objects of interest without splits in the presence of metal and clutter.

Description: Feature point matching is a fundamental and challenging problem in many computer vision applications. In this paper, a robust feature point matching algorithm named spatial order constraints bilateral-neighbor vote (SOCBV) is proposed to remove outliers for a set of matches (including outliers) between two images. A directed ${k}$ nearest neighbor ( knn ) graph of match sets is generated, and the problem of feature point matching is formulated as a binary discrimination problem. In the discrimination process, the class labeled matrix is built via the spatial order constraints defined on the edges that connect a point to its knn . Then, the posterior inlier class probability of each match is estimated with the knn density estimation and spatial order constraints. The vote of each match is determined by averaging all posterior class probabilities that originate from its associative inliers set and is used for removing outliers. The algorithm iteratively removes outliers from the directed graph and recomputes the votes until the stopping condition is satisfied. Compared with other popular algorithms, such as RANSAC, RSOC, GTM, SOC and WGTM, experiments under various testing data sets demonstrate strong robustness for the proposed algorithm.

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Description: Provides a listing of the editors, board members, and current staff for this issue of the publication.

Description: This paper presents a novel low-complexity motion estimation and mode decision algorithm for encoding multiple quality layers following the H.264/scalable video coding standard, considering both coarse grain scalability (CGS) and medium grain scalability (MGS). The proposed algorithm conducts motion estimation and mode decision only at the base layer (BL) and enforces the higher layers to inherit the motion and mode decisions of the BL. In order for the decision made at the BL to be nearly optimal for all layers, we use the highest layer reconstructed frame as the reference frame for motion estimation and set the Lagrangian multipliers according to the quantization parameter of the current and higher layers. We also propose a simple early skip/direct decision to further boost the encoding speed. Mode decision and motion estimation is conducted at a higher layer only if the layer below it uses the skip/direct mode for a block. Significant complexity reduction can be achieved because the mode and motion estimation is performed at most once for each macroblock. Because the mode and motion information only needs to be transmitted once, we also achieve a slightly better rate-distortion (R–D) performance for typical videos. Experiments have shown more than $2times $ (up to $5times $ ) speedup for a three-layer encoder against the conventional R–D optimized reference software JSVM on both CIF and HD sequences, and for both CGS and MGS, with the tradeoff of the coding efficiency measured by the Bjontegaard delta rate.

Description: In this paper, we propose a novel unifying framework using a Markov network to learn the relationships among multiple classifiers. In face recognition, we assume that we have several complementary classifiers available, and assign observation nodes to the features of a query image and hidden nodes to those of gallery images. Under the Markov assumption, we connect each hidden node to its corresponding observation node and the hidden nodes of neighboring classifiers. For each observation-hidden node pair, we collect the set of gallery candidates most similar to the observation instance, and capture the relationship between the hidden nodes in terms of a similarity matrix among the retrieved gallery images. Posterior probabilities in the hidden nodes are computed using the belief propagation algorithm, and we use marginal probability as the new similarity value of the classifier. The novelty of our proposed framework lies in the method that considers classifier dependence using the results of each neighboring classifier. We present the extensive evaluation results for two different protocols, known and unknown image variation tests, using four publicly available databases: 1) the Face Recognition Grand Challenge ver. 2.0; 2) XM2VTS; 3) BANCA; and 4) Multi-PIE. The result shows that our framework consistently yields improved recognition rates in various situations.

Description: Ellipse fitting is widely applied in the fields of computer vision and automatic manufacture. However, the introduced edge point errors (especially outliers) from image edge detection will cause severe performance degradation of the subsequent ellipse fitting procedure. To alleviate the influence of outliers, we develop a robust ellipse fitting method in this paper. The main contributions of this paper are as follows. First, to be robust against the outliers, we introduce the maximum correntropy criterion into the constrained least-square (CLS) ellipse fitting method, and apply the half-quadratic optimization algorithm to solve the nonlinear and nonconvex problem in an alternate manner. Second, to ensure that the obtained solution is related to an ellipse, we introduce a special quadratic equality constraint into the aforementioned CLS model, which results in the nonconvex quadratically constrained quadratic programming problem. Finally, we derive the semidefinite relaxation version of the aforementioned problem in terms of the trace operator and thus determine the ellipse parameters using semidefinite programming. Some simulated and experimental examples are presented to illustrate the effectiveness of the proposed ellipse fitting approach.

Description: State-of-the-art web image search frameworks are often based on the bag-of-visual-words (BoVWs) model and the inverted index structure. Despite the simplicity, efficiency, and scalability, they often suffer from low precision and/or recall, due to the limited stability of local features and the considerable information loss on the quantization stage. To refine the quality of retrieved images, various postprocessing methods have been adopted after the initial search process. In this paper, we investigate the online querying process from a graph-based perspective. We introduce a heterogeneous graph model containing both image and feature nodes explicitly, and propose an efficient reranking approach consisting of two successive modules, i.e., incremental query expansion and image-feature voting, to improve the recall and precision, respectively. Compared with the conventional reranking algorithms, our method does not require using geometric information of visual words, therefore enjoys low consumptions of both time and memory. Moreover, our method is independent of the initial search process, and could cooperate with many BoVW-based image search pipelines, or adopted after other postprocessing algorithms. We evaluate our approach on large-scale image search tasks and verify its competitive search performance.

Description: The study of fluid flow through solid matter by computed tomography (CT) imaging has many applications, ranging from petroleum and aquifer engineering to biomedical, manufacturing, and environmental research. To avoid motion artifacts, current experiments are often limited to slow fluid flow dynamics. This severely limits the applicability of the technique. In this paper, a new iterative CT reconstruction algorithm for improved a temporal/spatial resolution in the imaging of fluid flow through solid matter is introduced. The proposed algorithm exploits prior knowledge in two ways. First, the time-varying object is assumed to consist of stationary (the solid matter) and dynamic regions (the fluid flow). Second, the attenuation curve of a particular voxel in the dynamic region is modeled by a piecewise constant function over time, which is in accordance with the actual advancing fluid/air boundary. Quantitative and qualitative results on different simulation experiments and a real neutron tomography data set show that, in comparison with the state-of-the-art algorithms, the proposed algorithm allows reconstruction from substantially fewer projections per rotation without image quality loss. Therefore, the temporal resolution can be substantially increased, and thus fluid flow experiments with faster dynamics can be performed.

Description: Most existing approaches for RGB-D indoor scene labeling employ hand-crafted features for each modality independently and combine them in a heuristic manner. There has been some attempt on directly learning features from raw RGB-D data, but the performance is not satisfactory. In this paper, we propose an unsupervised joint feature learning and encoding (JFLE) framework for RGB-D scene labeling. The main novelty of our learning framework lies in the joint optimization of feature learning and feature encoding in a coherent way, which significantly boosts the performance. By stacking basic learning structure, higher level features are derived and combined with lower level features for better representing RGB-D data. Moreover, to explore the nonlinear intrinsic characteristic of data, we further propose a more general joint deep feature learning and encoding (JDFLE) framework that introduces the nonlinear mapping into JFLE. The experimental results on the benchmark NYU depth dataset show that our approaches achieve competitive performance, compared with the state-of-the-art methods, while our methods do not need complex feature handcrafting and feature combination and can be easily applied to other data sets.

Description: Out-of-focus blur occurs frequently in multispectral imaging systems when the camera is well focused at a specific (reference) imaging channel. As the effective focal lengths of the lens are wavelength dependent, the blurriness levels of the images at individual channels are different. This paper proposes a multispectral image deblurring framework to restore out-of-focus spectral images based on the characteristic of interchannel correlation (ICC). The ICC is investigated based on the fact that a high-dimensional color spectrum can be linearly approximated using rather a few number of intrinsic spectra. In the method, the spectral images are classified into an out-of-focus set and a well-focused set via blurriness computation. For each out-of-focus image, a guiding image is derived from the well-focused spectral images and is used as the image prior in the deblurring framework. The out-of-focus blur is modeled as a Gaussian point spread function, which is further employed as the blur kernel prior. The regularization parameters in the image deblurring framework are determined using generalized cross validation, and thus the proposed method does not need any parameter tuning. The experimental results validate that the method performs well on multispectral image deblurring and outperforms the state of the arts.

Description: We report the results of a study of an optical sensor based on a channel-drop technique with two cascaded cavities in photonic-crystal slabs. Quality factors and intensities of the resonant modes of the sensor were analyzed with three-dimensional simulations. With the introduction of a reflector in the bus-channel and by control of the coupling between the two cavities and the drop-channel, the drop efficiency can be remarkably increased. In addition to the simulation, the two cavity sensor is fabricated and tested for optical response to water and oil infiltration. Both direct visual imaging and quantitative analysis were applied in experiment. A difference of refractive index $Delta n = 0.12$ between water and oil samples results in a wavelength shift of 18.3 nm, which greatly matches the simulation result of 20 nm and indicates a sensitivity of 153 nm RIU −1 . Both resonant peaks for water and oil infiltration have good selectivity in their transmission spectrum. The contrast between the broadband output of the bus-channel and the highly wavelength-selective outputs of the drop-channel opens opportunities for the two cascaded-cavity system as a fundamental building block for a multiplex drop-channel array for all-optical sensing, which can be widely used for bio/chemical detection and environmental monitoring.

Description: Nonlinearity-induced phase noise has become a major obstacle in long-haul coherent fiber-optic communication systems. Such phase noise has been shown to be signal dependent, and correlated over time. We propose a code-aided expectation-maximization algorithm to mitigate such nonlinear phase noise, iteratively utilizing both the time correlation of the nonlinearity-induced impairments and a soft-decision error-control code. Simulation and experimental results show that on a dual-polarization wavelength-division-multiplexed 16 QAM system, launch-power tolerance can be increased by ${1.5}$ dB, and the optical signal-to-noise ratio requirement can be relaxed by ${0.3}$ dB to achieve the same ${Q^2}$ -factor.

Description: Microring resonators have been the fundamental building blocks of integrated photonic networks. They play key roles in a number of on-chip photonic elements, such as filters, modulators, switches, and sensors. However, the applications of microring resonators in these components are hindered by the evanescent coupling, which requires very precise position control and is hard to be realized in standard photolithography. Here, we demonstrate a novel, general, and robust mechanism to achieve microdisk-based end-fire injection and collection devices. Instead of coupling light via evanescent waves, here the light is injected into the cavity by connecting a waveguide and a deformed microdisk directly. The light on resonance can be reflected back and detected at the same waveguide, making the devices to be “easy come easy go.” The reflectance is as high as 95% and can be periodically obtained from the long-lived resonances with free spectral range around 18 nm. The proposed mechanism is found to be robust to cavity shape, refractive index, and waveguide width and all the parameters can be larger than 500 nm, making the designed devices to be easier realized by standard photolithography. We believe this research will boost the development of low-cost silicon photonic devices.

Description: We proposed theoretically analyzed and experimentally demonstrated novel 1 × 4 channel flatband optical multiplexer/demultiplexer (MUX/DeMUX) that is based on Si-nanowire microring-assisted multiple delaylines. The proposed optical MUX/DeMUX scheme, which includes all-pass microring-type phase controllers and a Banyan-type 4 × 4 coupler, exhibits superior spectral flatness to any Si-nanowire optical DeMUXs that have previously been experimentally demonstrated.

Description: Analysis of the experimental data on microscopic, absorptive, fluorescent, and Raman-scattering properties of Bismuth (Bi)-doped yttria-alumino-silicate glass (Y-Al-SiO 2 :Bi)-based nanoengineered optical fibers, exhibiting broadband near-infrared fluorescence, is presented. Among the other well-established characteristics, inherent to Bi-doped silica fibers codoped with Aluminum (Al), a trend of spatial distributions of Bi atoms and Bi-related fluorescence-active centers is determined, being their concentrating in ring-like areas around the core's center, at approximately a half-distance to core/cladding interface. At the same time, the formation in this region of nanosized Bi clusters, supposedly weakly or nonfluorescing, is revealed for the fibers. These phenomena are argued to underlie worsening of the pump-to-signal overlap factor, which deteriorates efficiency of lasers and amplifiers based on such or similar Bi-doped alumino-silicate fibers.

Description: The paper develops a statistical model for the signals received in phase-sensitive optical time domain reflectometry (OTDR) probed by highly coherent sources. The backscattering process is modelled by a set of discrete scatterers with properly chosen parameters. Explicit equations for calculating the amplitude and the phase of the backscattered signal are obtained. The developed model predicts spectral and autocorrelation characteristics of the amplitude signals that are validated by experimental results. Characteristics of the phase signals, practicable for studying the sensing applications of the OTDR system, are presented and studied as well, demonstrating good correspondence with experiment. A more detailed modelling of distributed vibration sensing systems and their response to disturbances along an optical fiber will be possible as an extension of the developed formalism.

Description: A five channel step index plastic optical fiber proposal for a multiplexer/demultiplexer having insertion losses ( IL ) of 2.9–4 dB, pass bandwidths at −3 dB > 30 nm, crosstalk attenuation >30 dB and size of ∼65 mm × 55 mm, is demonstrated. It is based on a reflective diffraction grating with blazed profile and an aspheric lens. The theoretical analysis presented is used to further reduce the system size to ∼37 mm × 30 mm and to increase the number of channels to 8 keeping ILs 〈 4.5 dB. Experimental results have good agreement with theoretical expectations.

Description: We present a novel spatiotemporal saliency detection method to estimate salient regions in videos based on the gradient flow field and energy optimization. The proposed gradient flow field incorporates two distinctive features: 1) intra-frame boundary information and 2) inter-frame motion information together for indicating the salient regions. Based on the effective utilization of both intra-frame and inter-frame information in the gradient flow field, our algorithm is robust enough to estimate the object and background in complex scenes with various motion patterns and appearances. Then, we introduce local as well as global contrast saliency measures using the foreground and background information estimated from the gradient flow field. These enhanced contrast saliency cues uniformly highlight an entire object. We further propose a new energy function to encourage the spatiotemporal consistency of the output saliency maps, which is seldom explored in previous video saliency methods. The experimental results show that the proposed algorithm outperforms state-of-the-art video saliency detection methods.

Description: Hyperspectral unmixing is one of the crucial steps for many hyperspectral applications. The problem of hyperspectral unmixing has proved to be a difficult task in unsupervised work settings where the endmembers and abundances are both unknown. In addition, this task becomes more challenging in the case that the spectral bands are degraded by noise. This paper presents a robust model for unsupervised hyperspectral unmixing. Specifically, our model is developed with the correntropy-based metric where the nonnegative constraints on both endmembers and abundances are imposed to keep physical significance. Besides, a sparsity prior is explicitly formulated to constrain the distribution of the abundances of each endmember. To solve our model, a half-quadratic optimization technique is developed to convert the original complex optimization problem into an iteratively reweighted nonnegative matrix factorization with sparsity constraints. As a result, the optimization of our model can adaptively assign small weights to noisy bands and put more emphasis on noise-free bands. In addition, with sparsity constraints, our model can naturally generate sparse abundances. Experiments on synthetic and real data demonstrate the effectiveness of our model in comparison to the related state-of-the-art unmixing models.

Description: The design and development of a plastic optical fiber macrobend temperature sensor is presented. The sensor can operate in a temperature range from −55 to 70 °C and has a linear response versus temperature with a sensitivity of 8.95·10 −4 °C −1 . The sensor system uses the ratio of transmittance at two wavelengths to implement a self-referencing technique in order to avoid undesirable power fluctuations influence. The transmittance ratio precision is 0.1%. An analysis has been developed to find the two wavelengths which ratio offers the highest linearity and sensitivity response. Experimental results are successfully compared with theoretical approaches.

Description: We review recent high capacity ultralong haul demonstrations using amplification schemes with extended C+L optical bandwidth and optimized nonlinear performance. These demonstrations achieve 49.3 Tb/s capacity over 9100 km with the use of C+L EDFAs and 54 Tb/s capacity over 9150 km using hybrid-Raman EDFAs. Using the same hybrid-Raman EDFAs, a capacity of 52.2 Tb/s over 10 230 km (534 Pb/s*km) was also demonstrated. Different combinations of three 16QAM-based coded modulation schemes with spectral efficiencies SE = 4.86, 5.4/5.45, 6.14 bit/s/Hz were used in order to maximize capacity for each amplification scheme and transmission distance. We also compare the merits of optimized transmission performance of hybrid Raman-EDFA and C+L EDFA amplification schemes based on our loop experiments. At the nonlinear limit, the use of hybrid Raman-EDFA provides a modest 9.5% increase in capacity at a cost of ∼2× higher electrical power.

Description: Flexible grid optical networks allow a better exploitation of fiber capacity, by enabling a denser frequency allocation. A tighter channel spacing, however, requires narrower filters, which increase linear intersymbol interference (ISI), and may dramatically reduce system reach. Commercial coherent receivers are based on symbol by symbol detectors, which are quite sensitive to ISI. In this context, Nyquist spacing is considered as the ultimate limit to wavelength-division multiplexing (WDM) packing. In this paper, we show that by employing a limited-complexity trellis processing at the receiver, either the reach of Nyquist WDM flexi-grid networks can be significantly extended, or a denser-than-Nyquist channel packing [i.e., a higher spectral efficiency (SE)] is possible at equal reach. By adopting well-known information-theoretic techniques, we design a limited-complexity trellis processing and quantify its SE gain in flexi-grid architectures where wavelength selective switches over a frequency grid of 12.5 GHz are employed.

Description: We analyze the shortest path lengths between node pairs of real optical transport networks. From the analysis, we find that Johnson ${rm S}_{B}$ distribution is suitable for the shortest path length modeling. The validity of the distributions is evaluated in terms of the Kolmogorov–Smirnov (KS) statistic. Johnson ${rm S}_{B}$ distribution provides an average KS statistic of 0.0423, which indicates its good accuracy. We also show that the key parameters of the shortest path lengths, such as the mean, the median, and the standard deviation, can be estimated from the convex area of the network. We develop the proposed Johnson ${rm S}_{B}$ distribution model for the shortest path lengths using the basic information of the networks. The developed model is able to estimate path-length dependent system parameters, such as the appropriate modulation formats in transparent optical networks with an average error of only $6.4{%}$ . It is noteworthy that these estimations can be made without full knowledge of the network. Only the node locations are required.

Description: We present a comprehensive numerical model for intrinsic small-signal modulation response of both reflective, and traveling-wave semiconductor optical amplifiers. We investigate the small-signal photon and carrier density spatial distribution, modulation response, and $-3text{dB}$ bandwidth for uniform and lossless traveling-wave modulation current model. The analysis shows that the current model does not significantly affect the modulation response as long as the modulation frequency is within the bandwidth. One of the most important results of our analysis is the discovery of the bandwidth maximum in case of a reflective semiconductor optical amplifier operating with low to moderate input optical powers and high current densities. The bandwidth can be further improved by choosing the optimal amplifier length.

Description: We study the effect of phase noise canceled polarization-insensitive all-optical wavelength conversion (AOWC) of orthogonal frequency-division multiplexing (OFDM) signals, which is based on four-wave-mixing in high-nonlinear optical fiber using dual-pump. An AOWC experiment of polarization division multiplexing OFDM (PDM-OFDM) 8/16/32-QAM signals is investigated. A DFB laser with linewidth of 10 MHz is employed as the coherent dual-pump seed. The measured results show that the received signal after wavelength conversion by utilizing coherent DFB dual-pump has the same BER performance as back-to-back. Furthermore, AOWC of 557-Gb/s superchannel discrete Fourier transform-spread PDM-OFDM with eight-QAM (DFT-S PDM-OFDM 8-QAM) signal is proposed and experimentally demonstrated based on the studied coherent dual-pump scheme. Negligible OSNR penalty (〈0.8 dB) is observed at 7% FEC limit ( ${rm BER} = 3.8 times 10^{-3}$ ) after wavelength conversion. Our demonstration shows that the phase noise transferred from the pumps can be effectively eliminated. It also shows that AOWC enables practical implementation on the polarization multiplexing dynamic optical networks at the nodes where wavelengths conflict.

Description: This article presents results from the integration of a noncontact physiological radar monitoring system (PRMS) with a type I polysomnography (PSG) system to perform sleep monitoring. The PRMS system consists of two continuous-wave Doppler radars operating at the industrial, scientific, and medical (ISM) band of 2.45 GHz. The system can acquire data, perform digital processing, and output appropriate conventional analog outputs with a latency of approximately 130 ms, which can be recorded and displayed by a gold standard sleep monitoring system along with other standard sensor measurements. Radar data was also used to successfully detect paradoxical motion that was simulated using linear movers and to categorize normal breathing, apnea, and hypopnea in sleeping subjects with obstructive sleep apnea (OSA).

Description: A quantitative and explicit validation of the performance and safety of microwave systems and devices that have electromagnetic interaction with the human body is critical in the technological development process. Although a numerical model of the system environment can be ideally simulated, it cannot reflect the realistic environment that is vulnerable to various electrical, mechanical, and environmental interferences. Hence, the presence of the human body is the best measurement environment for these systems. However, newly designed devices/ systems that rely on the human body electromagnetic field interactions require multiple tests/measurements under a controlled environment. This environment is needed to validate the performance in all the possible scenarios of operation and make sure of the safety of those devices and systems. For example, a breast imaging system needs to be evaluated by detecting tumors in multiple locations, and it is unthinkable to do that on a real patient; hence, breast phantoms are needed to obtain an optimum system design and algorithms before moving to human clinical trials. Moreover, some experiments, such as the specific absorption rate (SAR) and hyperthermia, cannot be done on human beings due to the need to monitor the variation of the power intensity and temperature inside tissues. Employing live human participants for testing devices exposes the entire test procedure to several inherent uncertainties, such as respiratory movement, cardiovascular vibration, and variable skin humidity in addition, of course, to the safety concern of the new devices. Also, the application of the devices and systems on human subjects or human-related materials is a serious ethical issue where the researchers must receive an ethical clearance from the proper authorities, and it can be difficult to reasonably estimate and investigate the level of risks from various scientific, physical, and psychological aspects beforehand. Thus, the utilization of ar- ificial tissue-emulating (ATE) phantoms is highly beneficial for testing a device or system.

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Description: We report record 10 Gb/s bi-directional data transmission over a single 10 m SI-POF, by employing blue microlight-emitting diodes (μLEDs) at a single wavelength, APD receivers, and a PAM-32 modulation scheme. The implementation of 10 Gb/s LED-POF links takes advantage of the bi-directional configuration, which doubles the overall channel capacity, and APDs, which provide an enhanced link power budget owing to their improved sensitivity compared with conventional p-i-n photodiodes. Moreover, the high spectral efficiency of the PAM-32 modulation scheme employed, together with equalization techniques, enable the full utilization of the link bandwidth and the transmission of data rates higher than those obtained with conventional on–off keying. Simulation and experimental results demonstrate the feasibility of such a bi-directional link, and simultaneous 5 Gb/s data transmission is realized in each direction, achieving an aggregate data rate of 10 Gb/s with a BER 〈 10 −3 . The crosstalk penalty between the two directions of the link is measured to be less than 0.5 dB.

Description: Integrated polarization converters (PCs) with high (>99%) conversion efficiency open up many new possibilities in an InP-based photonic integrated circuit. In this paper, we describe how such a PC can be added to a circuit containing semiconductor optical amplifiers (SOAs), in order to obtain polarization independent amplification. Polarization independence is obtained by placing the PC halfway between two identical SOA sections. This approach has the advantage that no compromises in design and fabrication are needed. The polarization conversion is found to be very high, above 99.5%, when using a tolerant two-section PC. The extra insertion loss due to the converter is below 0.5 dB. The polarization-dependent gain (PDG) of the SOA reduces from 17 dB to only 0.3 dB by the inclusion of the PC. This is comparable to the best PDG values found in the literature with other techniques. The reduction is achieved over the whole C-band and for varying pump currents.

Description: We present the design, fabrication, and measurement results of low-insertion-loss and low-crosstalk broadband $2times 2$ Mach–Zehnder switches for nanosecond-scale optical data routing applications. We propose a simulation framework to calculate the spectral characteristics of switches and use it to design two switches: one based on directional couplers, the other using two-section directional couplers for broader bandwidth. We show that driving the switch in a push–pull manner enables to reduce insertion loss and optical crosstalk at the expense of the optical bandwidth. We achieve a good correlation between simulations and devices fabricated in IBM's 90-nm photonics-enabled CMOS process. We demonstrate a push–pull drive switch with insertion loss of $sim$ 1 dB and an optical crosstalk smaller than $-$ 23 dB over a 45-nm optical bandwidth in the O-band. We further achieve a transition time of $sim$ 4 ns with an average phase shifter consumption of 1 mW and a heater efficiency of $sim$ 25 mW $/pi$ .

Description: The optical spectra of composite one-dimensional photonic crystals (1-D PCs), fabricated by microstructuring of Si, are investigated. The composite PC is based on a Si-air structure and consists of two periodic 1-D PCs with various lattice constants a with $a_{1} = 2.7$ μm and $a_{2} = 4$ μm and various filling fractions f . The PCs are fabricated using photolithography followed by anisotropic chemical etching of (1 1 0) Si. Reflection and transmission spectra of this structure are measured using a Fourier transform infrared spectrometer combined with an IR microscope. The transmission spectra obtained demonstrate extended photonic stop bands (SBs), with characteristic transmission bands between the SBs. Theoretical and experimental transmission results indicate that the position of the extended SBs does not depend on the sequence of the individual PCs within the composite structure. In general, the calculated electric field distribution in the composite structure is similar to that of the individual PC components.

Description: We investigate five-channel polarization division multiplexed Nyquist WDM superchannel using offset-16QAM and 16QAM modulation format, under the condition of transmitter-side digital spectral shaping technique, respectively. The offset-16QAM scheme can greatly relax the implementation complexity, because the tap number of 35 is sufficient for the required finite impulse response (FIR) filter, compared with 81-tap FIR filter of the 16QAM scheme. About 1.3-dB back-to-back sensitivity improvement is obtained for the offset-16QAM scheme. In particular, the offset-16QAM scheme has better tolerance of DACs’ constraints, including the untracked jitter, sampling rate, and resolution. Finally, the practical implementation of offset-16QAM-based Nyquist WDM superchannel with TS-DSS is evaluated, by taking the phase difference deviation among wavelength channels and phase noise into account. No more than 1.3-dB required-OSNR penalty can be obtained, in case the phase difference is varied from 0° to 360°. When the laser with a linewidth of 100 kHz is used, there still exists about 1-dB overall performance improvement, compared with the 16QAM scheme.

Description: We investigate the optimum linewidth of the spectrum-sliced incoherent light (SSIL) source using a gain-saturated semiconductor optical amplifier (SOA) for the maximum capacity and longest transmission distance. For this purpose, we carry out experimental and simulation studies on the transmission performance of a 10-Gb/s on–off keying signal generated by using the SSIL source over a wide range of the SSIL linewidth. We find out that there are two windows of the linewidth for the high-speed operation of the SSIL source: ultra-narrow (i.e., linewidth $ll$ receiver bandwidth) and very wide (i.e., linewidth $gg$ receiver bandwidth). However, when the linewidth of the SSIL source is very wide, the 10-Gb/s signal generated by using this SSIL suffers severely from fiber chromatic dispersion and optical filtering. The simulation results are confirmed by experimental data measured by using an ultranarrow fiber Fabry–Perot filter (bandwidth = 700 MHz) and a bandwidth-tunable optical filter (bandwidth = 20 ∼ 53 GHz). Thus, we can conclude that the optimum linewidth of SSIL for capacity and transmission distance is ultranarrow. We also present a couple of drawbacks of the ultranarrow SSIL source, compared to the conventional wide-linewidth SSIL one, such as a large spectrum-slicing loss, a large SOA input power required for the suppression of excess intensity noise inherent in the incoherent light source, and the susceptibility to in-band crosstalk.

Description: We present a hierarchical grid-based, globally optimal tracking-by-detection approach to track an unknown number of targets in complex and dense scenarios, particularly addressing the challenges of complex interaction and mutual occlusion. Frame-by-frame detection is performed by hierarchical likelihood grids, matching shape templates through a fast oriented distance transform. To allow recovery from misdetections, common heuristics such as nonmaxima suppression within observations is eschewed. Within a discretized state-space, the data association problem is formulated as a grid-based network flow model, resulting in a convex problem casted into an integer linear programming form, giving a global optimal solution. In addition, we show how a behavior cue (body orientation) can be integrated into our association affinity model, providing valuable hints for resolving ambiguities between crossing trajectories. Unlike traditional motion-based approaches, we estimate body orientation by a hybrid methodology, which combines the merits of motion-based and 3D appearance-based orientation estimation, thus being capable of dealing also with still-standing or slowly moving targets. The performance of our method is demonstrated through experiments on a large variety of benchmark video sequences, including both indoor and outdoor scenarios.

Description: Many impulse noise (IN) reduction methods suffer from two obstacles, the improper noise detectors and imperfect filters they used. To address such issue, in this paper, a weighted couple sparse representation model is presented to remove IN. In the proposed model, the complicated relationships between the reconstructed and the noisy images are exploited to make the coding coefficients more appropriate to recover the noise-free image. Moreover, the image pixels are classified into clear, slightly corrupted, and heavily corrupted ones. Different data-fidelity regularizations are then accordingly applied to different pixels to further improve the denoising performance. In our proposed method, the dictionary is directly trained on the noisy raw data by addressing a weighted rank-one minimization problem, which can capture more features of the original data. Experimental results demonstrate that the proposed method is superior to several state-of-the-art denoising methods.

Description: In this paper, a hierarchical multi-task structural learning algorithm is developed to support large-scale plant species identification, where a visual tree is constructed for organizing large numbers of plant species in a coarse-to-fine fashion and determining the inter-related learning tasks automatically. For a given parent node on the visual tree, it contains a set of sibling coarse-grained categories of plant species or sibling fine-grained plant species, and a multi-task structural learning algorithm is developed to train their inter-related classifiers jointly for enhancing their discrimination power. The inter-level relationship constraint, e.g., a plant image must first be assigned to a parent node (high-level non-leaf node) correctly if it can further be assigned to the most relevant child node (low-level non-leaf node or leaf node) on the visual tree, is formally defined and leveraged to learn more discriminative tree classifiers over the visual tree. Our experimental results have demonstrated the effectiveness of our hierarchical multi-task structural learning algorithm on training more discriminative tree classifiers for large-scale plant species identification.

Description: The paper demonstrates a standardized process of vapor phase doping to fabricate large core Yb–doped preforms with longer useful length in reproducible manner. The optimization of the process led to successful achievement of Yb-doped core thickness of 4.5 mm (in 14.8 mm of preform diameter) by depositing up to 30 number of core layers with controlled amount of generated precursor vapors. The influence of the process parameters was studied rigorously to enhance the useful preform length up to 380 mm. A combination of Yb and Al in different proportions was doped into the core with uniform dopant concentration along the length by adjusting few process parameters efficiently. The Al 2 O 3 concentration up to the level of 17.8 mol% has been achieved successfully which resulted in NA of 0.31. This is the highest ever doping of Al in passive fibers by any modified chemical vapor deposition process. The Yb 2 O 3 content in the active fibers is as high as 0.47 mol%.

Description: In view of the increasing frequency and damage severity of disasters, network operators have become more concerned with providing disaster-resiliency measures for their optical network infrastructure, whereas mitigating network service interruption due to the disaster region failures in the optical physical medium merely by increasing network redundancy is deemed spatially inefficient and very costly, with recent advancements, wireless technology is a potential candidate solution for efficient medium diversification. This paper addresses the challenge of efficiently designing disaster-resilient wireless-link-augmented optical network infrastructure. We formulate this problem as an optimization model of finding the subset of links in an optical network topology whose wireless augmentation maximizes postdisaster recovery of overall network availability for a given budget constraint. To overcome the computational complexity of finding the optimal design solution, a novel greedy heuristic algorithm is proposed. Performance comparisons with an exhaustive enumeration search and simple heuristics demonstrate the efficiency and scalability of our heuristic algorithm.

Description: In order to characterize the channel capacity of a wavelength channel in a wavelength-division multiplexed (WDM) system, statistical models are needed for the transmitted signals on the other wavelengths. For example, one could assume that the transmitters for all wavelengths are configured independently of each other, that they use the same signal power, or that they use the same modulation format. In this paper, it is shown that these so-called behavioral models have a profound impact on the single-wavelength achievable information rate. This is demonstrated by establishing, for the first time, upper and lower bounds on the maximum achievable rate under various behavioral models, for a rudimentary WDM channel model.

Description: This paper proposes a two-stage texture synthesis algorithm. At the first stage, a structure tensor map carrying information about the local orientation is synthesized from the exemplar’s data and used at the second stage to constrain the synthesis of the texture. Keeping in mind that the algorithm should be able to reproduce as faithfully as possible the visual aspect, statistics, and morphology of the input sample, the method is tested on various textures and compared objectively with existing methods, highlighting its strength in successfully synthesizing the output texture in many situations where traditional algorithms fail to reproduce the exemplar’s patterns. The promising results pave the way towards the synthesis of accurately large and multi-scale patterns as it is the case for carbon material samples showing laminar structures, for example.

Description: An image search reranking (ISR) technique aims at refining text-based search results by mining images’ visual content. Feature extraction and ranking function design are two key steps in ISR. Inspired by the idea of hypersphere in one-class classification, this paper proposes a feature extraction algorithm named hypersphere-based relevance preserving projection (HRPP) and a ranking function called hypersphere-based rank (H-Rank). Specifically, an HRPP is a spectral embedding algorithm to transform an original high-dimensional feature space into an intrinsically low-dimensional hypersphere space by preserving the manifold structure and a relevance relationship among the images. An H-Rank is a simple but effective ranking algorithm to sort the images by their distances to the hypersphere center. Moreover, to capture the user’s intent with minimum human interaction, a reversed $k$ -nearest neighbor (KNN) algorithm is proposed, which harvests enough pseudorelevant images by requiring that the user gives only one click on the initially searched images. The HRPP method with reversed KNN is named one-click-based HRPP (OC-HRPP). Finally, an OC-HRPP algorithm and the H-Rank algorithm form a new ISR method, H-reranking. Extensive experimental results on three large real-world data sets show that the proposed algorithms are effective. Moreover, the fact that only one relevant image is required to be labeled makes it has a strong practical significance.

Description: In this paper, we propose a novel method for image fusion with a high-resolution panchromatic image and a low-resolution multispectral (Ms) image at the same geographical location. The fusion is formulated as a convex optimization problem which minimizes a linear combination of a least-squares fitting term and a dynamic gradient sparsity regularizer. The former is to preserve accurate spectral information of the Ms image, while the latter is to keep sharp edges of the high-resolution panchromatic image. We further propose to simultaneously register the two images during the fusing process, which is naturally achieved by virtue of the dynamic gradient sparsity property. An efficient algorithm is then devised to solve the optimization problem, accomplishing a linear computational complexity in the size of the output image in each iteration. We compare our method against six state-of-the-art image fusion methods on Ms image data sets from four satellites. Extensive experimental results demonstrate that the proposed method substantially outperforms the others in terms of both spatial and spectral qualities. We also show that our method can provide high-quality products from coarsely registered real-world IKONOS data sets. Finally, a MATLAB implementation is provided to facilitate future research.

Description: Automatic fluorescent particle tracking is an essential task to study the dynamics of a large number of biological structures at a sub-cellular level. We have developed a probabilistic particle tracking approach based on multi-scale detection and two-step multi-frame association. The multi-scale detection scheme allows coping with particles in close proximity. For finding associations, we have developed a two-step multi-frame algorithm, which is based on a temporally semiglobal formulation as well as spatially local and global optimization. In the first step, reliable associations are determined for each particle individually in local neighborhoods. In the second step, the global spatial information over multiple frames is exploited jointly to determine optimal associations. The multi-scale detection scheme and the multi-frame association finding algorithm have been combined with a probabilistic tracking approach based on the Kalman filter. We have successfully applied our probabilistic tracking approach to synthetic as well as real microscopy image sequences of virus particles and quantified the performance. We found that the proposed approach outperforms previous approaches.

Description: In this paper, we propose a novel model, a discriminatively learned iterative shrinkage (DLIS) model, for color image denoising. The DLIS is a generalization of wavelet shrinkage by iteratively performing shrinkage over patch groups and whole image aggregation. We discriminatively learn the shrinkage functions and basis from the training pairs of noisy/noise-free images, which can adaptively handle different noise characteristics in luminance/chrominance channels, and the unknown structured noise in real-captured color images. Furthermore, to remove the splotchy real color noises, we design a Laplacian pyramid-based denoising framework to progressively recover the clean image from the coarsest scale to the finest scale by the DLIS model learned from the real color noises. Experiments show that our proposed approach can achieve the state-of-the-art denoising results on both synthetic denoising benchmark and real-captured color images.

Description: In cross-view action recognition, what you saw in one view is different from what you recognize in another view, since the data distribution even the feature space can change from one view to another. In this paper, we address the problem of transferring action models learned in one view (source view) to another different view (target view), where action instances from these two views are represented by heterogeneous features. A novel learning method, called heterogeneous transfer discriminant-analysis of canonical correlations (HTDCC), is proposed to discover a discriminative common feature space for linking source view and target view to transfer knowledge between them. Two projection matrices are learned to, respectively, map data from the source view and the target view into a common feature space via simultaneously minimizing the canonical correlations of interclass training data, maximizing the canonical correlations of intraclass training data, and reducing the data distribution mismatch between the source and target views in the common feature space. In our method, the source view and the target view neither share any common features nor have any corresponding action instances. Moreover, our HTDCC method is capable of handling only a few or even no labeled samples available in the target view, and can also be easily extended to the situation of multiple source views. We additionally propose a weighting learning framework for multiple source views adaptation to effectively leverage action knowledge learned from multiple source views for the recognition task in the target view. Under this framework, different source views are assigned different weights according to their different relevances to the target view. Each weight represents how contributive the corresponding source view is to the target view. Extensive experiments on the IXMAS data set demonstrate the effectiveness of HTDCC on learning the common feature space for heterogeneous cross-view action rec- gnition. In addition, the weighting learning framework can achieve promising results on automatically adapting multiple transferred source-view knowledge to the target view.

Description: A complete encoding solution for efficient intra-based depth map compression is proposed in this paper. The algorithm, denominated predictive depth coding (PDC), was specifically developed to efficiently represent the characteristics of depth maps, mostly composed by smooth areas delimited by sharp edges. At its core, PDC involves a directional intra prediction framework and a straightforward residue coding method, combined with an optimized flexible block partitioning scheme. In order to improve the algorithm in the presence of depth edges that cannot be efficiently predicted by the directional modes, a constrained depth modeling mode, based on explicit edge representation, was developed. For residue coding, a simple and low complexity approach was investigated, using constant and linear residue modeling, depending on the prediction mode. The performance of the proposed intra depth map coding approach was evaluated based on the quality of the synthesized views using the encoded depth maps and original texture views. The experimental tests based on all intra configuration demonstrated the superior rate-distortion performance of PDC, with average bitrate savings of 6%, when compared with the current state-of-the-art intra depth map coding solution present in the 3D extension of a high-efficiency video coding (3D-HEVC) standard. By using view synthesis optimization in both PDC and 3D-HEVC encoders, the average bitrate savings increase to 14.3%. This suggests that the proposed method, without using transform-based residue coding, is an efficient alternative to the current 3D-HEVC algorithm for intra depth map coding.

Description: This paper demonstrates the feasibility of layered space-time coding (STC) in an outdoor image-sensor-based (IS-based) visible light communication (VLC) system. We examined that for low-resolution IS-based VLC channel where intensity-modulated signals from two different light emitting diodes (LEDs) are detected by one pixel of an IS, STC allows us to decouple them; thus, succeeding to receive them with no errors. Consequently, STC offers extended transmission distance to pixel-resolution-limited IS-based VLC links. In the layered STC presented in this paper, additional bit streams are laid on the ${2^ntimes 2^n}$  LED array for increasing the transmission rate per symbol duration for the case where the pixel resolution is improved. A prototype of a three-layered STC is built with an ${8times 8}$  LED array, where each of the LEDs is modulated at 1 kb/s and a high-speed camera with IS operating at 1000 fps. Our experimental results validate that the two additional bit streams (layer-2 and -3), aligned in the layer-1 STC matrix pair, are extracted with no errors when the receiver comes within 155 and 55 m, respectively, from the LED array, without decreasing 210 m of the transmission distance of layer-1 bit stream.

Description: We present simulation and experimental results on a silicon photonic strictly nonblocking $4times 4$ electrooptic Mach–Zehnder-based switch fabric. We propose a simulation framework based on the transfer matrix approach that enables calculating the transmission spectra of any type of multistage interconnect switch network. The model is used to analyze the spectral characteristics of the switch fabric. We also show experimental results on a fabric designed and fabricated in IBM's 90-nm photonics-enabled CMOS process. The fabric monolithically integrates the CMOS logic, the switch drivers, and all the photonics. We fully characterized all the transmittances of the switch and demonstrate onchip insertion loss between 1.5 and 3 dB and a crosstalk less than –25 dB for all the signal paths.

Description: The FEC limit paradigm is the prevalent practice for designing optical communication systems to attain a certain bit error rate (BER) without forward error correction (FEC). This practice assumes that there is an FEC code that will reduce the BER after decoding to the desired level. In this paper, we challenge this practice and show that the concept of a channel-independent FEC limit is invalid for soft-decision bit-wise decoding. It is shown that for low code rates and high-order modulation formats, the use of the soft-decision FEC limit paradigm can underestimate the spectral efficiencies by up to 20%. A better predictor for the BER after decoding is the generalized mutual information, which is shown to give consistent post-FEC BER predictions across different channel conditions and modulation formats. Extensive optical full-field simulations and experiments are carried out in both the linear and nonlinear transmission regimes to confirm the theoretical analysis.

Description: We propose an analytical time-domain model for microring and microdisk modulators, which considers both their electrical and optical properties. Theory of the dynamics of microring/microdisk is discussed, and general solutions to the transfer matrix representation are presented. Both static and dynamic predictions from the model are compared to measurement results to demonstrate the accuracy of our model. Static predictions and measurements are presented for power and phase responses, whereas dynamic predictions and measurements are presented for small-signal and large-signal operations. The model verifies that the chirping and modulation bandwidth of the modulators depend on the detuning state. Finally, the accuracy and scalability of several techniques employed in the model are discussed.

Description: We experimentally investigate an improved fast orthogonal frequency division multiplexing (OFDM) scheme using intensity modulation and full-field detection. This new fast OFDM algorithm exhibits better back-to-back and transmission performance than the conventional one, and is shown to support a 40-Gb/s signal over a 480-km re-circulating loop-based single-mode fiber (SMF) with 1-dB penalty. We compare this scheme with the direct-detection (DD) system using the same algorithm, and show that the DD system cannot support 60-km SMF at 40 Gb/s. Finally, we demonstrate the proposed scheme over 124 km of BT Ireland's field-installed fiber without inline optical amplification. The results show that this scheme can be a promising solution to address the gap between direct detection and coherent detection for applications in short metro networks and long-reach Ethernet and access networks.

Description: We describe branched-fiber sensing with a novel Brillouin time-domain analysis configuration for temperature or strain sensing within a 2-D expanded area. By using a branch configuration with an appropriate number of branches and the newly proposed multiprobe pulse arrangement, the system dynamic range may surpass that of the conventional Brillouin time-domain analyzer, which employs a unicursal fiber configuration. The proposed Brillouin sensing would be useful particularly when the sensed area is 2-D, rather than when the area expands 1-D.

Description: The boundaryless beam propagation method has been reported to suffer from reflections due to frequency aliasing. We propose an alternate explanation of reflection in the method, based on eikonal analysis of the wave equation in the mapped space, and show that reflection starts much before aliasing happens. We theoretically predict the reflection coefficient profile in a simulation and introduce an internal absorbing boundary condition (ABC), where reflection becomes appreciable. The a priori knowledge of the window size of the ABC does away with the arbitrariness in the design of such boundaries. Finally, we use the boundaryless scheme with the ABC to successfully model a dielectric bend.

Description: This study presents a photonic integrated transceiver for application in a data readout unit of a sensor network. The device was realized in a generic InP-based technology. The circuit uses an asymmetric coupler and a PIN photodiode for input signal detection and a Mach–Zehnder amplitude modulator for encoding of output sensor data. Small-signal modulation bandwidth of 11.1 GHz was measured, eye-diagrams with a dynamic extinction ratio of 11 dB were recorded, and transmission of a 10-Gb/s signal over 25 km of SMF fiber with bit error rate below 10 −10 was achieved.

Description: We report a new low-noise compact fiber acoustic sensor that implements a microfabricated silicon diaphragm with a π/2 phase step combined to a single-mode fiber to form a simple interferometric sensor head. Compared to high-sensitivity diaphragm-based fiber Fabry–Perot sensors, this sensor has a similar strain resolution, it operates over a much broad range of wavelengths (∼±150 nm), its response depends very weakly on temperature and on the distance between the fiber and the diaphragm, and both microfabrication and assembly are simpler. A prototype sensor is reported with an average minimum detectable pressure as low as 5.4 μPa/√Hz between 1 and 30 kHz, in agreement with a theoretical model.

Description: Polymer waveguides with different lengths have been fabricated between two single mode fibers by using a new approach of self-writing using single photon polymerization via a xenon lamp instead of a monochromatic laser source. A photopolymerizable liquid drop deposited between two aligned fibers forms a polymer bridge between the cores of the two fibers when irradiated by light from one of the fibers. Polymer waveguides bridges 40 to 600 μm long have been fabricated between two optical fibers with single mode transmission loss 0.5 to 1.26 dB over a broad wavelength range.

Description: Person re-identification aims to match people across non-overlapping camera views, which is an important but challenging task in video surveillance. In order to obtain a robust metric for matching, metric learning has been introduced recently. Most existing works focus on seeking a Mahalanobis distance by employing sparse pairwise constraints, which utilize image pairs with the same person identity as positive samples, and select a small portion of those with different identities as negative samples. However, this training strategy has abandoned a large amount of discriminative information, and ignored the relative similarities. In this paper, we propose a novel relevance metric learning method with listwise constraints (RMLLCs) by adopting listwise similarities, which consist of the similarity list of each image with respect to all remaining images. By virtue of listwise similarities, RMLLC could capture all pairwise similarities, and consequently learn a more discriminative metric by enforcing the metric to conserve predefined similarity lists in a low-dimensional projection subspace. Despite the performance enhancement, RMLLC using predefined similarity lists fails to capture the relative relevance information, which is often unavailable in practice. To address this problem, we further introduce a rectification term to automatically exploit the relative similarities, and develop an efficient alternating iterative algorithm to jointly learn the optimal metric and the rectification term. Extensive experiments on four publicly available benchmarking data sets are carried out and demonstrate that the proposed method is significantly superior to the state-of-the-art approaches. The results also show that the introduction of the rectification term could further boost the performance of RMLLC.

Description: Tomographic iterative reconstruction methods need a very thorough modeling of data. This point becomes critical when the number of available projections is limited. At the core of this issue is the projector design, i.e., the numerical model relating the representation of the object of interest to the projections on the detector. Voxel driven and ray driven projection models are widely used for their short execution time in spite of their coarse approximations. Distance driven model has an improved accuracy but makes strong approximations to project voxel basis functions. Cubic voxel basis functions are anisotropic, accurately modeling their projection is, therefore, computationally expensive. Both smoother and more isotropic basis functions better represent the continuous functions and provide simpler projectors. These considerations have led to the development of spherically symmetric volume elements, called blobs. Set apart their isotropy, blobs are often considered too computationally expensive in practice. In this paper, we consider using separable B-splines as basis functions to represent the object, and we propose to approximate the projection of these basis functions by a 2D separable model. When the degree of the B-splines increases, their isotropy improves and projections can be computed regardless of their orientation. The degree and the sampling of the B-splines can be chosen according to a tradeoff between approximation quality and computational complexity. We quantitatively measure the good accuracy of our model and compare it with other projectors, such as the distance-driven and the model proposed by Long et al. From the numerical experiments, we demonstrate that our projector with an improved accuracy better preserves the quality of the reconstruction as the number of projections decreases. Our projector with cubic B-splines requires about twice as many operations as a model based on voxel basis functions. Higher accuracy projectors can be used to - mprove the resolution of the existing systems, or to reduce the number of projections required to reach a given resolution, potentially reducing the dose absorbed by the patient.

Description: Despite important recent advances, the vulnerability of biometric systems to spoofing attacks is still an open problem. Spoof attacks occur when impostor users present synthetic biometric samples of a valid user to the biometric system seeking to deceive it. Considering the case of face biometrics, a spoofing attack consists in presenting a fake sample (e.g., photograph, digital video, or even a 3D mask) to the acquisition sensor with the facial information of a valid user. In this paper, we introduce a low cost and software-based method for detecting spoofing attempts in face recognition systems. Our hypothesis is that during acquisition, there will be inevitable artifacts left behind in the recaptured biometric samples allowing us to create a discriminative signature of the video generated by the biometric sensor. To characterize these artifacts, we extract time-spectral feature descriptors from the video, which can be understood as a low-level feature descriptor that gathers temporal and spectral information across the biometric sample and use the visual codebook concept to find mid-level feature descriptors computed from the low-level ones. Such descriptors are more robust for detecting several kinds of attacks than the low-level ones. The experimental results show the effectiveness of the proposed method for detecting different types of attacks in a variety of scenarios and data sets, including photos, videos, and 3D masks.

Description: Target representation is a necessary component for a robust tracker. However, during tracking, many complicated factors may make the accumulated errors in the representation significantly large, leading to tracking drift. This paper aims to improve the robustness of target representation to avoid the influence of the accumulated errors, such that the tracker only acquires the information that facilitates tracking and ignores the distractions. We observe that the locally mutual relations between the feature observations of temporally obtained targets are beneficial to the subspace representation in visual tracking. Thus, we propose a novel subspace learning algorithm for visual tracking, which imposes joint row-wise sparsity structure on the target subspace to adaptively exclude distractive information. The sparsity is induced by exploiting the locally mutual relations between the feature observations during learning. To this end, we formulate tracking as a subspace sparsity inducing problem. A large number of experiments on various challenging video sequences demonstrate that our tracker outperforms many other state-of-the-art trackers.

Description: Color-to-gray (C2G) image conversion is the process of transforming a color image into a grayscale one. Despite its wide usage in real-world applications, little work has been dedicated to compare the performance of C2G conversion algorithms. Subjective evaluation is reliable but is also inconvenient and time consuming. Here, we make one of the first attempts to develop an objective quality model that automatically predicts the perceived quality of C2G converted images. Inspired by the philosophy of the structural similarity index, we propose a C2G structural similarity (C2G-SSIM) index, which evaluates the luminance, contrast, and structure similarities between the reference color image and the C2G converted image. The three components are then combined depending on image type to yield an overall quality measure. Experimental results show that the proposed C2G-SSIM index has close agreement with subjective rankings and significantly outperforms existing objective quality metrics for C2G conversion. To explore the potentials of C2G-SSIM, we further demonstrate its use in two applications: 1) automatic parameter tuning for C2G conversion algorithms and 2) adaptive fusion of C2G converted images.

Description: In this paper, we propose a skin classification method exploiting faces and bodies automatically detected in the image, to adaptively initialize individual ad hoc skin classifiers. Each classifier is initialized by a face and body couple or by a single face, if no reliable body is detected. Thus, the proposed method builds an ad hoc skin classifier for each person in the image, resulting in a classifier less dependent from changes in skin color due to tan levels, races, genders, and illumination conditions. The experimental results on a heterogeneous data set of labeled images show that our proposal outperforms the state-of-the-art methods, and that this improvement is statistically significant.

Description: Local binary descriptors are attracting increasingly attention due to their great advantages in computational speed, which are able to achieve real-time performance in numerous image/vision applications. Various methods have been proposed to learn data-dependent binary descriptors. However, most existing binary descriptors aim overly at computational simplicity at the expense of significant information loss which causes ambiguity in similarity measure using Hamming distance. In this paper, by considering multiple features might share complementary information, we present a novel local binary descriptor, referred as ring-based multi-grouped descriptor (RMGD), to successfully bridge the performance gap between current binary and floated-point descriptors. Our contributions are twofold. First, we introduce a new pooling configuration based on spatial ring-region sampling, allowing for involving binary tests on the full set of pairwise regions with different shapes, scales, and distances. This leads to a more meaningful description than the existing methods which normally apply a limited set of pooling configurations. Then, an extended Adaboost is proposed for an efficient bit selection by emphasizing high variance and low correlation, achieving a highly compact representation. Second, the RMGD is computed from multiple image properties where binary strings are extracted. We cast multi-grouped features integration as rankSVM or sparse support vector machine learning problem, so that different features can compensate strongly for each other, which is the key to discriminativeness and robustness. The performance of the RMGD was evaluated on a number of publicly available benchmarks, where the RMGD outperforms the state-of-the-art binary descriptors significantly.

Description: In perturbation-based digital fiber nonlinearity mitigation methods, such as pre-distortion and post-equalization, quantization of perturbation coefficients is employed to reduce the computational and implementation complexity. However, quantization introduces errors, which results in performance penalties. By analyzing the occurrence probability of the perturbation coefficients, an MMSE-based optimization of perturbation coefficients quantization is proposed and demonstrated in ultra-large-area-fiber transmission systems with a 36Gbd DP-16QAM modulation format. Compared to conventional uniform quantization schemes, a significant reduction in quantization errors has been realized for both the real and imaginary parts of the perturbation coefficients. Compared with the nonquantization case, computational term reduction by a factor of 50 is achieved in 2400 km ULAF transmission with 〈 0.2 dB Q degradation. For the same Q improvement, optimized quantization scheme further reduce the computational term by 50% compared to that of the uniform quantization method. We also analyze the tolerance to link dispersion.

Description: Palmprint recognition (PR) is an effective technology for personal recognition. A main problem, which deteriorates the performance of PR, is the deformations of palmprint images. This problem becomes more severe on contactless occasions, in which images are acquired without any guiding mechanisms, and hence critically limits the applications of PR. To solve the deformation problems, in this paper, a model for non-linearly deformed palmprint matching is derived by approximating non-linear deformed palmprint images with piecewise-linear deformed stable regions. Based on this model, a novel approach for deformed palmprint matching, named key point-based block growing (KPBG), is proposed. In KPBG, an iterative M-estimator sample consensus algorithm based on scale invariant feature transform features is devised to compute piecewise-linear transformations to approximate the non-linear deformations of palmprints, and then, the stable regions complying with the linear transformations are decided using a block growing algorithm. Palmprint feature extraction and matching are performed over these stable regions to compute matching scores for decision. Experiments on several public palmprint databases show that the proposed models and the KPBG approach can effectively solve the deformation problem in palmprint verification and outperform the state-of-the-art methods.

Description: Nonnegative Tucker decomposition (NTD) is a powerful tool for the extraction of nonnegative parts-based and physically meaningful latent components from high-dimensional tensor data while preserving the natural multilinear structure of data. However, as the data tensor often has multiple modes and is large scale, the existing NTD algorithms suffer from a very high computational complexity in terms of both storage and computation time, which has been one major obstacle for practical applications of NTD. To overcome these disadvantages, we show how low (multilinear) rank approximation (LRA) of tensors is able to significantly simplify the computation of the gradients of the cost function, upon which a family of efficient first-order NTD algorithms are developed. Besides dramatically reducing the storage complexity and running time, the new algorithms are quite flexible and robust to noise, because any well-established LRA approaches can be applied. We also show how nonnegativity incorporating sparsity substantially improves the uniqueness property and partially alleviates the curse of dimensionality of the Tucker decompositions. Simulation results on synthetic and real-world data justify the validity and high efficiency of the proposed NTD algorithms.

Description: The problem of estimating the parameters of a Rayleigh-Rice mixture density is often encountered in image analysis (e.g., remote sensing and medical image processing). In this paper, we address this general problem in the framework of change detection (CD) in multitemporal and multispectral images. One widely used approach to CD in multispectral images is based on the change vector analysis. Here, the distribution of the magnitude of the difference image can be theoretically modeled by a Rayleigh-Rice mixture density. However, given the complexity of this model, in applications, a Gaussian-mixture approximation is often considered, which may affect the CD results. In this paper, we present a novel technique for parameter estimation of the Rayleigh-Rice density that is based on a specific definition of the expectation-maximization algorithm. The proposed technique, which is characterized by good theoretical properties, iteratively updates the parameters and does not depend on specific optimization routines. Several numerical experiments on synthetic data demonstrate the effectiveness of the method, which is general and can be applied to any image processing problem involving the Rayleigh-Rice mixture density. In the CD context, the Rayleigh-Rice model (which is theoretically derived) outperforms other empirical models. Experiments on real multitemporal and multispectral remote sensing images confirm the validity of the model by returning significantly higher CD accuracies than those obtained by using the state-of-the-art approaches.

Description: In this paper, we propose a very simple deep learning network for image classification that is based on very basic data processing components: 1) cascaded principal component analysis (PCA); 2) binary hashing; and 3) blockwise histograms. In the proposed architecture, the PCA is employed to learn multistage filter banks. This is followed by simple binary hashing and block histograms for indexing and pooling. This architecture is thus called the PCA network (PCANet) and can be extremely easily and efficiently designed and learned. For comparison and to provide a better understanding, we also introduce and study two simple variations of PCANet: 1) RandNet and 2) LDANet. They share the same topology as PCANet, but their cascaded filters are either randomly selected or learned from linear discriminant analysis. We have extensively tested these basic networks on many benchmark visual data sets for different tasks, including Labeled Faces in the Wild (LFW) for face verification; the MultiPIE, Extended Yale B, AR, Facial Recognition Technology (FERET) data sets for face recognition; and MNIST for hand-written digit recognition. Surprisingly, for all tasks, such a seemingly naive PCANet model is on par with the state-of-the-art features either prefixed, highly hand-crafted, or carefully learned [by deep neural networks (DNNs)]. Even more surprisingly, the model sets new records for many classification tasks on the Extended Yale B, AR, and FERET data sets and on MNIST variations. Additional experiments on other public data sets also demonstrate the potential of PCANet to serve as a simple but highly competitive baseline for texture classification and object recognition.

Description: Presents the 2015 author/subject index for this publication.

Description: The segmentation of brain MR images into different tissue classes is an important task for automatic image analysis technique, particularly due to the presence of intensity inhomogeneity artifact in MR images. In this regard, this paper presents a novel approach for simultaneous segmentation and bias field correction in brain MR images. It integrates judiciously the concept of rough sets and the merit of a novel probability distribution, called stomped normal (SN) distribution. The intensity distribution of a tissue class is represented by SN distribution, where each tissue class consists of a crisp lower approximation and a probabilistic boundary region. The intensity distribution of brain MR image is modeled as a mixture of finite number of SN distributions and one uniform distribution. The proposed method incorporates both the expectation-maximization and hidden Markov random field frameworks to provide an accurate and robust segmentation. The performance of the proposed approach, along with a comparison with related methods, is demonstrated on a set of synthetic and real brain MR images for different bias fields and noise levels.