Effects of photoperiod and pair-feeding on lactation of cows fed corn or barley grain in total mixed rations

Effects of photoperiod and type of grain were assessed using 47 cows (wk 1; 59.5 d of lactation) exposed daily to 16 h of light (long day) and fed randomly one of two diets containing an equal amount of cracked corn or rolled barley. During wk 5, cows were assigned within each diet to long day and ad libitum feeding, short day (8 h of light: 2 h of dark: 2 h of light: 12 h of dark) and ad libitum feeding, or long day and ad libitum feeding (wk 5 to 8) followed by pair feeding with short day cows between wk 9 and 16. Milk composition was not affected by treatments. Between wk 9 and 16, long day cows fed for ad libitum consumption or pair fed produced 5 to 11% more milk than cows exposed to short day; feed intake of long day cows fed for ad libitum consumption was greater than short short day and long day cows pair fed cows. Milk yield and total feed intake were not affected by type of grain in the diet. In conclusion, long day photoperiod increased milk yield and feed intake.     

Accelerating Machine-Learning Algorithms on FPGAs using Pattern-Based Decomposition

Machine-learning algorithms are employed in a wide variety of applications to extract useful information from data sets, and many are known to suffer from super-linear increases in computational time with increasing data size and number of signals being processed (data dimension). Certain principal machine-learning algorithms are commonly found embedded in larger detection, estimation, or classification operations. Three such principal algorithms are the Parzen window-based, non-parametric estimation of Probability Density Functions (PDFs), K-means clustering and correlation. Because they form an integral part of numerous machine-learning applications, fast and efficient execution of these algorithms is extremely desirable. FPGA-based reconfigurable computing (RC) has been successfully used to accelerate computationally intensive problems in a wide variety of scientific domains to achieve speedup over traditional software implementations. However, this potential benefit is quite often not fully realized because creating efficient FPGA designs is generally carried out in a laborious, case-specific manner requiring a great amount of redundant time and effort. In this paper, an approach using pattern-based decomposition for algorithm acceleration on FPGAs is proposed that offers significant increases in productivity via design reusability. Using this approach, we design, analyze, and implement a multi-dimensional PDF estimation algorithm using Gaussian kernels on FPGAs. First, the algorithm’s amenability to a hardware paradigm and expected speedups are predicted. After implementation, actual speedup and performance metrics are compared to the predictions, showing speedup on the order of 20× over a 3.2 GHz processor. Multi-core architectures are developed to further improve performance by scaling the design. Portability of the hardware design across multiple FPGA platforms is also analyzed. After implementing the PDF algorithm, the value of pattern-based decomposition to support reuse is demonstrated by rapid development of the K-means and correlation algorithms.     

Analysis of the Electromagnetic Signals of the Human Brain: Milestones, Obstacles, and Goals

Measuring the functioning of the human brain is one of the most formidable scientific/engineering endeavors ever undertaken. It is difficult to extract information about any particular processing function from brain electromagnetic signals (BEMS) since, at any instant, only a small fraction of the brain's hundreds of simultaneously active major systems might be performing processing related to the function being studied. With recent developments, a new era of research is dawning based on an interdisciplinary approach in which advanced signal processing methods are focused on increasingly more specific neuroanatomical, neurophysiological, and neuropsychological research questions and clinical applications. This brief review highlights the major accomplishments of the last several decades in human BEMS analysis and discusses obstacles to progress. Five main topics are addressed: 1) the historical problem of developing a computerized expert clinical electroencephalogram (EEG) system; 2) advances in signal processing methods, including primary analysis, feature extraction, and statistical hypothesis testing and pattern classification; 3) integrated computing systems for BEMS analysis; 4) biophysical, basic science, practical and conceptual obstacles to progress; and 5) the long-term goal of developing a device for measuring the functional integrity of major neural systems, and the related topic of neurocybernetics. Cutting-edge issues discussed include measurement and modeling of nonstationary event-related signals, characterization of spatial processes, single-trial signal detection, location of the sources of scalp-recorded field distributions, and studies of the functional significance of BEMS.     

Simulation of the High-Frequency Response of a Heterogeneous Ground through the Finite Difference Technique

To simulate the propagation of an electromagnetic plane wave in an inhomogeneous ground, the finite difference approach can be used. One of the main problems in using this method is imposing the boundary conditions near the ground surface, especially at high frequency. Indeed, for the E polarization, the upper top of the numerical grid must be sufficiently far away from the air-ground interface in order to neglect the field due to the heterogeneities and the discretization of the atmosphere is necessary. For magneto-telluric modeling, improved boundary conditions have already been proposed. This paper deals with a new condition, valid everywhere in air and which can be applied for E and H polarization. Thus even at high frequency, as for radar applications, only one line is added to the grid discretizing the ground.     

Analyse théorique et caractérisation expérimentale ďune jonction Y intégrée utilisée comme capteur de déplacement

The coherent coupling of radiation modes in symmetric single-mode Y-junction excited by a single mode fiber is studied in this work. Using the bpm (beam propagation method) we find that the power splitting between the two outputs of the junction depends strongly on the fiber displacement with respect to the junction axis of symmetry. A GaAs/GaAlAs single mode junction is tested and the experimental measurements show a splitting ratio as high as 12 dB that could not be explained if the radiation field is neglected. The experimental results confirm the theoretical predictions that take into account the propagation of radiation modes in the structure. These results propose the structure to be used as an integrated optical displacement sensor with a sensitivity in the order of 5 dB/μm and a dynamic range within 4 to 5 μm.     

Array-OL with delays,a domain specific specification language for multidimensional intensive signal processing

Intensive signal processing applications appear in many application domains such as video processing or detection systems. These applications handle multidimensional data structures (mainly arrays) to deal with the various dimensions of the data (space, time, frequency). A specification language allowing the direct manipulation of these different dimensions with a high level of abstraction is a key to handling the complexity of these applications and to benefit from their massive potential parallelism. The Array-OL specification language is designed to do just that. We introduce here an extension of Array-OL to deal with states or delays by the way of uniform inter-repetition dependences. We show that this specification language is able to express the main patterns of computation of the intensive signal processing domain.     

RERBEE: robust efficient registration via bifurcations and elongated elements applied to retinal fluorescein angiogram sequences

We present RERBEE (robust efficient registration via bifurcations and elongated elements), a novel feature-based registration algorithm able to correct local deformations in high-resolution ultra-wide field-of-view (UWFV) fluorescein angiogram (FA) sequences of the retina. The algorithm is able to cope with peripheral blurring, severe occlusions, presence of retinal pathologies and the change of image content due to the perfusion of the fluorescein dye in time. We have used the computational power of a graphics processor to increase the performance of the most computationally expensive parts of the algorithm by a factor of over × 1300, enabling the algorithm to register a pair of 3900 × 3072 UWFV FA images in 5-10 min instead of the 5-7 h required using only the CPU. We demonstrate accurate results on real data with 267 image pairs from a total of 277 (96.4%) graded as correctly registered by a clinician and 10 (3.6%) graded as correctly registered with minor errors but usable for clinical purposes. Quantitative comparison with state-of-the-art intensity-based and feature-based registration methods using synthetic data is also reported. We also show some potential usage of a correctly aligned sequence for vein/artery discrimination and automatic lesion detection.     

Impact of polarisation mode dispersion on 10 Gbit/s terrestrialsystems over non-dispersion-shifted fibre

Depending on the launched state of polarisation 10 Gbit/s system impairments are investigated experimentally in the presence of chromatic dispersion and variable differential group delay caused by polarisation mode dispersion. In addition an actual fibre cable, not an optical simulator was used to bring polarisation mode dispersion     

A Resource Oriented Framework for Service Choreography over Wireless Sensor and Actor Networks

Current Internet of Things (IoT) development requires service distribution over Wireless Sensor and Actor Networks (WSAN) to deal with the drastic increasing of network management complexity. Because of the specific constraints of WSAN, some limitations can be observed in centralized approaches. Multi-hop communication used by WSAN introduces transmission latency, packet errors, router congestion and security issues. As it uses local services, a model of decentralized services avoids long path communications between nodes and applications. But the two main issues are then to design (1) the composition of such services and to map (2) them over the WSAN. This contribution proposes a model for decentralized services based on Resource Oriented Architecture in which their communications are designed thanks to an adaptation of Petri Network (1). In addition, the problem of decentralized service mapping and its deployment over a WSAN is successfully resumed by a Pseudo-Boolean Optimization in order to minimize network communication load (2). These contributions are presented using a proposed EMMA middleware as unifying thread.