Error exponents for decentralized detection in feedback architectures

We consider the decentralized Bayesian binary hypothesis testing problem in feedback architectures, in which the fusion center broadcasts information based on the messages of some sensors to some or all sensors in the network. We show that the asymptotically optimal detection performance (as quantified by error exponents) does not benefit from the feedback messages. In addition, we determine the corresponding optimal error exponents.National Science Foundation (U.S.) (Grant ECCS-0701623

The value of feedback for decentralized detection in large sensor networks

We consider the decentralized binary hypothesis testing problem in networks with feedback, where some or all of the sensors have access to compressed summaries of other sensors' observations. We study certain two-message feedback architectures, in which every sensor sends two messages to a fusion center, with the second message based on full or partial knowledge of the first messages of the other sensors. Under either a Neyman-Pearson or a Bayesian formulation, we show that the asymptotically optimal (in the limit of a large number of sensors) detection performance (as quantified by error exponents) does not benefit from the feedback messages

Holographic injection locking of a broad area laser diode via a photorefractive thin-film device

We demonstrate locking of a high power broad area laser diode to a single frequency using holographic feedback from a photorefractive polymer thin-film device for the first time. A four-wave mixing setup is used to generate feedback for the broad area diode at the wavelength of the single frequency source (Ti:Sapphire laser) while the spatial distribution adapts to the preferred profile of the broad area diode. The result is an injection-locked broad area diode emitting with a linewidth comparable to the Ti:Sapphire laser

Two-pump parametric amplification in the presence of fiber dispersion fluctuations: a comparative study

Fiber optical parametric amplifiers (FOPAs) operating based on four-wave mixing (FWM) are versatile devices with increasing applications in optical communication systems. In this paper, the effects of dispersion fluctu�ations on the performance of bandwidth, ripple, parametric gain, and saturation power of a two-pump FOPA based on four-wave and six-wave models are studied and compared. Coupled-amplitude equations representing the non-degenerate FWM process in optical fiber are solved numerically to compute the parametric gain over the communication wavelengths. The behaviors of the performance parameters are critically analyzed and compared with different types of fluctuation strengths (or amplitudes) specified by the combinations of correlation length (Lc) and fluctuation amplitude (σ). Based on the results, it was found that the flat gain bandwidth for the four-wave model remains unchanged and is insensitive to the strengths of fluctuations. The gain ripples, however, get higher as the fluctuation strengths increase. On the other hand, the flat gain bandwidths of the six-wave model are hardly identified due to the tremendous and continuous ripples within the pump wavelengths. In addition, the minimum parametric gain values for both four-wave and six-wave models reduce as the fluctuation strengths increase. Also, the lowest value of parametric gain leads to the highest saturation power and vice versa. The dispersion fluctuations affect the FWM process’s efficiency and deteriorate the overall amplifier performance, particularly for the six-wave model. The numerical analysis obtained via the six-wave model is especially useful since this model closely matches with practical circumstances

Gain prediction of dual-pump fiber optic parametric amplifier based on artificial neural network

Optimized parameters of dual-pump fiber optic parametric amplifier (FOPA) to give optimized FOPA gain can be obtained through optimization techniques. However, it is complicated to determine the multi-objective functions (gain, bandwidth and flatness), multi decision variables and multiple global solutions. Optimization works only considered undepleted pump configura�tion or pump depletion but without fiber loss. Recently, a machine learning approach was applied to design a Raman amplifier. Thus, this study intends to design a desired dual-pump FOPA gain utilizing an artificial neural network (ANN) to predict pump powers and pump wavelength by considering pump depletion and fiber loss. First of all, the FOPA training gain data were obtained through the 6-wave model and supplied into the ANN to learn the relation between the gains with their pump wavelengths and pump powers. Once the smallest mean square error (MSE) between input and target was obtained, the ANN model was saved. The ANN model can be used to predict the desired pump wavelengths and pump powers if the desired gain is given. The desired gains of constant values from 10 to 45 dB over 1540–1589 nm for optical communication are predicted very well with mean absolute error (MAE) of 1 dB variations

Decolorization of reactive red-120 by using macrofungus and microfungus

The objectives of the study are to investigate the growth of Aspergillus sp. and Pleurotus sp. and decolorization of Reactive Red – 120 in Minimal Salt Solution (MSS). The growths of fungi were measured every 3 days by using spectrophotometer at 540 nm. For decolorization, the fungi were cultured in 10 mg/L and 20 mg/L of dye concentration. Furthermore, pH of 5, 7 and 9 were used to determine the optimum pH for dye decolorization. The 10 mg/L concentration and pH 5 were chosen as optimum conditions with the maximum performance of reactive dye decolorization ranging of 60%-70%. The Aspergillus sp. was more efficient todecolourize synthetic dye Reactive Red – 120 when compared to Pleurotus sp. This study contributes to the knowledge of mycoremediation and product of mycoremediation kit that could be developed and applied in industry.Keywords: aspergillus sp.; dye decolorization; mycoremediation; pleurotus sp.; reactive red – 120; synthetic dye

Bayesian Detection in Bounded Height Tree Networks

We study the detection performance of large scale sensor networks, configured as trees with bounded height, in which information is progressively compressed as it moves towards the root of the tree. We show that, under a Bayesian formulation, the error probability decays exponentially fast, and we provide bounds for the error exponent. We then focus on the case where the tree has certain symmetry properties. We derive the form of the optimal exponent within a restricted class of easily implementable strategies, as well as optimal strategies within that class. We also find conditions under which (suitably defined) majority rules are optimal. Finally, we provide evidence that in designing a network it is preferable to keep the branching factor small for nodes other than the neighbors of the leaves

Biodegradation of carbamazepine using fungi and bacteria

Carbamazepine is an anti-epileptic pharmaceuticalcompound which is frequently detected in wastewater. However, this compound is hardly degraded naturally due to its persistency. Thus, carbamazepine presents in water stream and household water supply as well as wastewater treatment plant. This paper focuses on various species of fungi and bacteria used in carbamazepine biodegradation and the carbamazepine degrading-enzymes involved in the degradation pathways. Selected research papers on carbamazepine biodegradation using fungi and bacteria were reviewed. The efficiency and approaches in term of methodologies and technologies used were highlighted in this paper. Such study sheds light on gaps of study and future research direction on carbamazepine biodegradation.Keywords: biodegradation; carbamazepine; method; pharmaceuticals