Intelligent process analytics for CRM

Many organisations use decision models in their processes such as tables or trees to provide decision support to their operational divisions. For example, in fault management, customer contact centre operators usually use a decision model in the form of prescribed interviews. Based on the answers given by customers, the operator navigates through the decision model to reach an assessment of the problem. In order to achieve customer satisfaction and operational excellence, it is very important to constantly monitor the performance of a decision model not only on an overall level, but also on the level of individual decisions. In this paper we present a configurable business process analytics tool, known as the intelligent Universal Service Management System, that constantly monitors decision data and is capable of optimising the decisions based on high-level business objectives. We explain the various features of the software and show how it can be used to optimise decision processes. We also show how we can easily provide a customised version to monitor the performance of provision processes.     

Learning in multiple model adaptive control switch

Including a learning mechanism in SMMAC (switched, multiple-model adaptive control) avoids the need for a priori knowledge of the model set of the plant to control and leads to a significant performance improvement with respect to the sole inclusion of an adaptive control channel in combination with switched fixed local controllers.     

Holographic sensors for the determination of ionic strength

Holographic sensors for monitoring ionic strength have been fabricated from charged sulphonate and quaternary ammonium monomers, incorporated into thin, polymeric hydrogel films which were transformed into volume holograms. The diffraction wavelength or reflected colour of the holograms was used to characterise their swelling or de-swelling behaviour as a function of ionic strength in various media. The effects of co-monomer structure, buffer composition, ion composition, pH and temperature were evaluated, whilst the reversibility and reproducibility of the sensor was also assessed. An acrylamide-based hologram containing equal molar amounts of negatively and positively charged monomers was shown to be able to quantify ionic strength independent of the identity of the ionic species present in the test solution. The sensor was fully reversible, free of hysteresis and exhibited little response to pH between 3 and 9 and temperature within the range 20-45 °C. The system was successfully used to quantify the ionic strength of milk solutions, which contain a complex mixture of ions and biological components.     

1/f noise in large-area Hg<Subscript>1−x</Subscript>Cd<Subscript>x</Subscript>Te photodiodes

The 1/f noise in photovoltaic (PV) molecular-beam epitaxy (MBE)-grown Hg_1−xCd_xTe double-layer planar heterostructure (DLPH) large-area detectors is a critical noise component with the potential to limit sensitivity of the cross-track infrared sounder (CrIS) instrument. Therefore, an understanding of the origins and mechanisms of noise currents in these PV detectors is of great importance. Excess low-frequency noise has been measured on a number of 1000-μm-diameter active-area detectors of varying “quality” (i.e., having a wide range of I-V characteristics at 78 K). The 1/f noise was measured as a function of cut-off wavelength under illuminated conditions. For short-wave infrared (SWIR) detectors at 98 K, minimal 1/f noise was measured when the total current was dominated by diffusion with white noise spectral density in the mid-10⁻¹⁵A/Hz^1/2 range. For SWIR detectors dominated by other than diffusion current, the ratio, α, of the noise current in unit bandwidth i_n(f = 1 Hz, V_d = −60 mV, and Δf = 1 Hz) to dark current I_d(V_d = −60 mV) was α_SW-d = i_n/I_d ∼ 1 × 10⁻³. The SWIR detectors measured at 0 mV under illuminated conditions had median α_SW-P = i_n/I_ph ∼ 7 × 10⁻⁶. For mid-wave infrared (MWIR) detectors, α_MW-d = i_n/I_d ∼ 2 × 10⁻⁴, due to tunneling current contributions to the 1/f noise. Measurements on forty-nine 1000-μm-diameter MWIR detectors under illuminated conditions at 98 K and −60 mV bias resulted in α_MW-P = i_n/I_ph = 4.16 ± 1.69 × 10⁻⁶. A significant point to note is that the photo-induced noise spectra are nearly identical at 0 mV and 100 mV reverse bias, with a noise-current-to-photocurrent ratio, α_MW-P, in the mid 10⁻⁶ range. For long-wave infrared (LWIR) detectors measured at 78 K, the ratio, α_LW-d = i_n/I_d ∼ 6 × 10⁻⁶, for the best performers. The majority of the LWIR detectors exhibited α_LW-d on the order of 2 × 10⁻⁵. The photo-induced 1/f noise had α_LW-P = i_n/I_ph ∼ 5 × 10⁻⁶. The value of the noise-current-to-dark-current ratio, α appears to increase with increasing bandgap. It is not clear if this is due to different current mechanisms impacting 1/f noise performance. Measurements on detectors of different bandgaps are needed at temperatures where diffusion current is the dominant current. Excess low-frequency noise measurements made as a function of detector reverse bias indicate 1/f noise may result primarily from the dominant current mechanism at each particular bias. The 1/f noise was not a direct function of the applied bias.     

Training and optimization of an artificial neural network controlling a hybrid power filter

A hybrid power compensator (HPC) consisting of a static VAr compensator and a dynamic compensator needs to be optimally controlled during the compensation of nonlinear loads. The HPC must be controlled to meet minimum requirements in terms of power factor and harmonic distortion, while at the same time minimizing its total cost. An artificial neural network (ANN) is used to control the HPC amidst a very dynamic power system environment. The performance of a reference ANN is evaluated while controlling an HPC connected to a typical nonlinear industrial load. The training and performance of the ANN is then optimized in terms of training set size, training set packing and ANN topology and the performance compared to the reference ANN. This paper highlights the importance of optimising the mentioned ANN parameters to achieve optimum ANN training and modeling accuracy. The results obtained reveals that the application of an ANN in controlling an HPC is feasible given that the ANN parameters are chosen appropriately.     

Synthesis of ZSM‐5 Films and Monoliths with Bimodal Micro/Mesoscopic Structures

A route to synthesize ZSM‐5 crystals with a bimodal micro/mesoscopic pore system has been developed in this study; the successful incorporation of the mesopores within the ZSM‐5 structure was performed using tetrapropylammonium hydroxide (TPAOH)‐impregnated mesoporous materials containing carbon nanotubes in the pores, which were encapsulated in the ZSM‐5 crystals during a solid rearrangement process within the framework. Such mesoporous ZSM‐5 zeolites can be readily obtained as powders, thin films, or monoliths.     

The First Template‐Free Growth of Crystalline Silicon Microtubes

A simple template‐free high‐temperature evaporation method was developed for the growth of crystalline Si microtubes for the first time. As‐grown Si microtubes were characterized using X‐ray diffraction, scanning electron microscopy, transmission electron microscopy, and room‐temperature photoluminescence. The lengths of the Si tubes can reach several hundreds of micrometers; some of them have lengths on the order of millimeters. Each tube has a uniform outer diameter along its entire length, and the typical outer diameter is ≈ 2–3 μm. Most of the tubes have a wall thickness of ≈ 400–500 nm, though a considerable number of them exhibit a very thin wall thickness of ≈ 50 nm. Room‐temperature photoluminescence measurement shows the as‐synthesized Si microtubes have two strong emission peaks centered at ≈ 589 nm and ≈ 617 nm and a weak emission peak centered at ≈ 455 nm. A possible mechanism for the formation of these Si tubes is proposed. We believe that the present discovery of the crystalline Si microtubes will promote further experimental studies on their physical properties and smart applications.     

Securing Mobile Ad hoc Networks — A Motivational Approach

Mobile ad hoc networks rely on the co-operation of devices that route for each other. This immediately presents security problems. Each device's data passes through the not so friendly hands of other devices. Forwarding devices must use their processing power and battery power to route packets for others. When bandwidth is limited they must also use bandwidth that they might want to use for themselves. These are not great issues with the first incarnations of ad hoc networks — military systems or others where all the devices are owned by a single organisation. However, if ad hoc networks are to be generally deployed and become an alternative or adjunct to future cellular systems, then ways to encourage co-operation are required. This revised version was published online in July 2006 with corrections to the Cover Date.     

Scaling planar silicon devices

The generation-over-generation scaling of critical CMOS technology parameters is ultimately bound by nonscalable limitations, such as the thermal voltage and the elementary electronic charge. Sustained improvement in performance and density has required the introduction of new device structures and materials. Partially depleted SOI, a most recent MOSFET innovation, has extended VLSI performance while introducing unique idiosyncrasies. Fully depleted SOI is one logical extension of this device design direction. Gate dielectric tunneling, device self-heating, and single-event upsets present developers of these next-generation devices with new challenges. Strained silicon and high-permittivity gate dielectric are examples of new materials that will enable CMOS developers to continue to deliver device performance enhancements in the sub-100 nm regime.     

Effects of SiN passivation and high-electric field on AlGaN-GaN HFET degradation

The authors report on the effects of silicon nitride (SiN) surface passivation and high-electric field stress (hot electron stress) on the degradation of undoped AlGaN-GaN power HFETs. Stressed devices demonstrated a decrease in the drain current and maximum transconductance and an increase in the parasitic drain series resistance, gate leakage, and subthreshold current. The unpassivated devices showed more significant degradation than SiN passivated devices. Gate lag phenomenon was observed from unpassivated devices and removed by SiN passivation. However, SiN passivated devices also showed gate lag phenomena after high-electric field stress, which suggests possible changes in surface trap profiles occurred during high-electric field stress test.