The performance of a direct-sequence spread-spectrum acquisition system using a linear prediction filter for narrowband interference suppression

The coarse acquisition performance of a direct sequence spread-spectrum receiver is analyzed when a linear prediction filter is used for narrowband interference suppression. We show that once an appropriate matching strategy is identified, the linear prediction filter can provide favorable performance when narrowband interference is present over a considerable range of both interference power and bandwidth. In addition, the presence of the filter dramatically improves the performance over the case where there is no filter, except when the interference bandwidth and the power are both small (i.e., when the processing gain provides sufficient interference immunity without the filter). If long spreading sequences are used with moderately sized observation windows, the acquisition performance can be severely degraded when a parallel acquisition scheme is used due to the linear predication filter. We show, however, that a slower serial receiver will provide reliable performance.This work was supported in part by the Office of Naval Research under contract ONR N00014-91-J-1234, the Army Research Office under contract ARO DAAL03-91-0071, and the NSF Center for Ultra-High Speed Circuits and Systems (ICAS).     

A New Sidelobe Correction Algorithm for Microwave Radiometers: Application to the Envisat Instrument

The antenna temperature measured by a microwave radiometer is converted in brightness temperature (TB) by removing the different contributions that do not come from the main lobe of the antenna. Among them, the Earth contribution in the sidelobes may be significant as for the Environmental Satellite mission due to the antenna position on the platform. In such a case, simple corrections commonly applied on previous altimetry missions are inadequate, and a more accurate correction should be determined. We propose in this paper a new method based on global seasonal tables of contamination. This allows application of an accurate sidelobe correction in space and time in the retrieved TB computation     

Adaptive Neural Network for Nuclear Medicine Image Restoration

A novel adaptive neural network is proposed for image restoration using a nuclear medicine gamma camera based on the point spread function of measured system. The objective is to restore image degradation due to photon scattering and collimator photon penetration with the gamma camera and allow improved quantitative external measurements of radionuclides in-vivo. The specific clinical model proposed is the imaging of bremsstrahlung radiation using ³²P and ⁹⁰Y because of the enhanced image degradation effects of photon scattering, photon penetration and poor signal/noise ratio in measurements of this type with the gamma camera. This algorithm model avoids the common inverse problem associated with other image restoration filters such as the Wiener filter. The relative performance of the adaptive NN for image restoration is compared to a previously reported order statistic neural network hybrid (OSNNH) filter by these investigators, a traditional Weiner filter and a modified Hopfield neural network using simulated degraded images with different noise levels. Quantitative metrics such as the change of signal to noise ratio (SNR) are used to compare filter performance. The adaptive NN yields comparable results for image restoration with a slightly better performance for the images with higher noise level as often encountered in bremsstrahlung detection with the gamma camera. Experimental attenuation measurements were also performed in a water tank using two radionuclides, ³²P and ⁹⁰Y, typically used for antibody therapy. Similar values for an effective attenuation coefficient was observed for the restored images using the OSNNH filters and adaptive NN which demonstrate that the restoration filters preserves the total counts in the image as required for quantitative in-vivo measurements. The adaptive NN was computationally more efficient by a factor 4–6 compared to the OSNNH filter. The filter architecture, in turn, is also optimum for parallel processing or VLSI implementation as required for planar and particularly for tomographic mode of detection using the gamma camera. The proposed adaptive NN method should also prove to be useful for quantitative imaging of single photon emitters for other nuclear medicine tomographic imaging applications using positron emitters and direct X-ray photon detection.     

Ester-functionalized poly(3-alkylthiophene) copolymers: Synthesis,physicochemical characterization and performance in bulk heterojunction organic solar cells

The introduction of functional moieties in the donor polymer (side chains) offers a potential pathway toward selective modification of the nanomorphology of conjugated polymer:fullerene active layer blends applied in bulk heterojunction organic photovoltaics, pursuing morphology control and solar cell stability. For this purpose, two types of poly(3-alkylthiophene) random copolymers, incorporating different amounts (10/30/50%) of ester-functionalized side chains, were efficiently synthesized using the Rieke method. The solar cell performance of the functionalized copolymers was evaluated and compared to the pristine P3HT:PCBM system. It was observed that the physicochemical and opto-electronic characteristics of the polythiophene donor material can be modified to a certain extent via copolymerization without (too much) jeopardizing the OPV efficiency, as far as the functionalized side chains are introduced in a moderate ratio (<30%) and that preference is given to side chains with a small molar volume. A range of complementary techniques – UV–Vis spectroscopy, (modulated temperature) differential scanning calorimetry, transmission electron microscopy and X-ray diffraction analysis – indicated that variations in polymer crystallinity, while maintaining a high level of regioregularity, are probably the main factor responsible for the observed differences.     

High dynamic range image rendering with a Retinex-based adaptive filter.

We propose a new method to render high dynamic range images that models global and local adaptation of the human visual system. Our method is based on the center-surround Retinex model. The novelties of our method is first to use an adaptive filter, whose shape follows the image high-contrast edges, thus reducing halo artifacts common to other methods. Second, only the luminance channel is processed, which is defined by the first component of a principal component analysis. Principal component analysis provides orthogonality between channels and thus reduces the chromatic changes caused by the modification of luminance. We show that our method efficiently renders high dynamic range images and we compare our results with the current state of the art.     

Combinatorial Particle Patterning by Nanoxerography

In their article ( https://doi.org/10.1002/adfm.201703511 ), Bojnicic‐Kninski et al. expose the known methods used for creating, on a same substrate, patterns composed of different types of particles. This approach is called “combinatorial particle patterning.” They describe various techniques and group them depending on the used methods or driving forces that enable the directed assemblies of particles. A substantial part of the article (Part 2: Electrical Particle Patterning) referred to particle assemblies guided by electrostatic forces, i.e., electrophoretic or dielectrophoretic forces. However, this part, especially the paragraph on “Nanoxerography” (cf. 2.2 of the article) suffers from some inaccuracies that lead to partially wrong conclusions regarding combinatorial particle patterning. The goal of this comment is double: i) giving a complete and up to date definition of nanoxerography ii) reporting the results on combinatorial particle patterning using nanoxerography to correct the article inaccuracies.     

Error-resilient video communications over CDMA networks with a bandwidth constraint.

We present an adaptive video transmission scheme for use in a code-division multiple-access network, which incorporates efficient bandwidth allocation among source coding, channel coding, and spreading under a fixed total bandwidth constraint. We derive the statistics of the received signal, as well as a theoretical bound on the packet drop rate at the receiver. Based on these results, a bandwidth allocation algorithm is proposed at the packet level, which incorporates the effects of both the changing channel conditions and the dynamics of the source content. Detailed simulations are done to evaluate the performance of the system, and the sensitivity of the system to estimation error is presented.     

Individuality of understanding and assessment of sensory attributes of foods, in particular, tenderness of meat

This study examines the extent to which variations in consumers' sensory assessments of food arise from the inability to report accurately sensory perceptions, from confusion regarding the criteria by which to assess samples, or from differences in their oral breakdown of the products.

Twenty consumers assessed the tenderness of a range of 8 hot, freshly roasted meat samples using Time Intensity (TI). Overall a significant correlation was found between the maximum recorded intensity (Imax) of their TI curves and single sensory scores given by a trained panel. Correlation was significant for only 42% of the consumers individually. Significant correlations were found between the amount of masticatory muscle activity undertaken during chewing (measured using electromyography) and Imax for all but 2 of the consumers. Thus subjects' perceptions were accurately described by their chewing work, suggesting between subject differences in perception arose from differences in the way chewing work was applied to break down the samples. The sensory input from the masticatory muscles may represent the major determinant of perceived tenderness of meat.