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Network forensic tools enable security professionals to monitor network performance and compromises. These tools are used to monitor internal and external network attacks. Technological improvements have enabled criminals to wipe out tracks of cybercrime to elude alterations. Network forensics procedures use processes to expedite investigation by tracking each original packet and event that is generated in the network. There are many network forensic tools, both open source and commercial versions available in the market. In this work, the result of a survey participated by different experts in open source network forensic tools have been presented. The advantages, challenges, and necessities have been identified for network forensic investigation of such tools. A few open source network forensic tools have been studied and performed a comparative analysis based on six key parameters. Further, two malware datasets are analyzed using open source tools to perform investigation and present a comprehensive network forensic analysis comprising IO graphs, Flow graphs, TCP stream, UDP multicast stream, mac-based analysis, and operating system analysis.

The economics of wind and solar generation face two opposing drivers. Technological progress leads to lower costs and both wind and solar have shown dramatic price reductions in recent decades. At the same time, adding wind and solar lowers market electricity prices and thus revenue during periods when they produce energy. In this work, we analyze these two opposing effects of renewable integration: learning and diminishing marginal revenue, investigated using a model that assumes the status quo with regards to generation technology mix and demand. Our modeling results suggest that reduction in revenue from market forces may offset or even outpace technological progress. If deployed on current grids without changes to demand response, storage or other integrating technologies, the cost of mitigating CO2 with wind will increase and will be no cheaper in the future than it is today for solar. This study highlights the need to deploy grid technologies such as storage and new transmission in order to integrate wind and solar in an economically sustainable manner.

This paper presents a novel pulse position-coded PDU (PPCP) paradigm for multi-access wireless sensor nodes and IoT networks with thin energy budgets. The core idea is to encode a protocol data unit (PDU) in terms of the silence duration between two sets of delimiter pulses, whose positions are modulated based on the value of the PDU. This PPCP architecture achieves significant energy savings by using a lesser amount of bit/pulse transmissions, and by eliminating long multi-bit preambles and headers, which are normally used in traditional packets. The proposed multi-access pulse-based PDU scheme enables medium sharing among many sensor nodes without requiring per-PDU frame synchronization. Unlike previous Communication through Silence (CtS) literature, a full architecture design and prototype hardware platform of PPCP is presented here to demonstrate its advantages in terms of energy savings, reduced transmission delays, and ease of implementation. The advantages of PPCP compared to state-of-the-art Bluetooth Low Energy (BLE) and legacy binary packets are also shown. The prototype implementation shows reliable error detection based on a novel mechanism that relies on the inherent pulse-position modulation-based coding technique.

Numerous methods have been developed in the context of expert and intelligent systems for structural health monitoring (SHM) with wireless sensor networks (WSNs). However, these techniques have been proven to be efficient when dealing with continuous signals, and the applicability of such expert systems with discrete noisy signals has not yet been explored. This study presents an intelligent data mining methodology as part of an expert system developed for SHM with noisy and delayed signals, which are generated by a through-substrate self-powered sensor network. The noted sensor network has been demonstrated as an effective means for minimizing energy consumption in WSNs for SHM. Experimental vibration tests were conducted on a cantilever plate to evaluate the developed expert system for SHM. The proposed data mining method is based on the integration of pattern recognition, an innovative probabilistic approach, and machine learning. The novelty of the proposed system for SHM with data interpretation methodology lies in the integration of the noted intelligent techniques on discrete, binary, noisy, and delayed patterns of signals collected from self-powered sensing technology in the application to a practical engineering problem, i.e., data-driven energy-efficient SHM. Results confirm that the proposed data mining method employing a probabilistic approach can be effectively used to reconstruct delayed and missing signals, thereby addressing the important issue of energy availability for intelligent SHM systems being used for damage identification in civil and aerospace structures. The applicability and effectiveness of the expert system with the data mining approach in detecting damage with noisy signals was demonstrated for plate-like structures with an accuracy of 97%. The present study successfully contributes to advance data mining and signal processing techniques in the SHM domain, indicating a practical application of expert and intelligent systems applied to damage detection in SHM platforms. Findings from this research pave a way for development of the data analysis techniques that can be employed for interpreting noisy and incomplete signals collected from various expert systems such as those being used in intelligent infrastructure monitoring systems and smart cities.

A simple colloidal drop generally forms ring like patterns after drying. The deposition morphology of the dried drop changes significantly when such a drop dries in the vicinity of another similar drop. Here we present an observational study and statistical analysis of the patterns formed inside an isolated as well as interacting drops of gelatin containing sodium sulfate (Na2SO4). In all the cases, multiple concentric regions of solute particles combined with the polymer gel appear as the drops dry up. Needle crystals of sulfur and coacervates of salt and gelatin are visible in some regions. The outer region becomes non-uniform, so does the size distribution of the needle crystals and coacervates. The non-uniformity increases with proximity of the drops. Here we propose a novel mechanism of growing patterns inside the single drop during drying and correlate that with the results obtained for interacting drops. This study and the proposed mechanism provide insights into the future studies of drying drops under different physical conditions. Further we explore the statistical characteristics of the single and interacting drops using the field emission scanning electron microscopy (FESEM) images. Next, we report fractal and image texture analyses along with object shape statistics of the drop FESEM images, under various experimental conditions. Several statistical hypothesis tests have been carried out to identify the most significant features.

Recent advances in energy harvesting technologies have led to the development of self-powered structural health monitoring (SHM) techniques that are power-efficient. Energy-aware data transmission protocols, on the other hand, have evolved due to the emergence of self-powered sensing. The pulse switching architecture is among such protocols employing ultrasonic pulses for event reporting through the substrate material. However, the noted protocol raises the necessity for new types of signal/data interpretation methods for SHM purposes. This is because a system using such technology demands dealing with power budgets for sensing and communication of binary signals that leads to unique time delay constraints. This study presents a novel computational approach to reconstruct delayed and incomplete binary signals provided by a through-substrate ultrasonic self-powered sensor network for SHM of plate-like structures. An algorithmic framework incorporating low-rank matrix completion, a data fusion model, and a statistical approach is proposed for damage identification. Performance and effectiveness of the proposed method for the case of dynamically loaded plates was evaluated using finite element simulations and experimental vibration tests. Results demonstrate that the energy-lean damage identification methodology employing the proposed algorithmic framework enables dependable detection of damage using reconstructed time-delayed binary signals.

Industrially relevant substrates, such as amorphous oxides and metallic substrates, typically cannot withstand the elevated temperatures (>800 °C) required for metalorganic chemical vapor deposition (MOCVD), thus limiting the applicability of these substrates for the realization of electronic-grade 2D materials. This work demonstrates MOCVD of crystalline MoS2 on multicomponent glass at temperatures ranging from 400 to 600 °C. Incorporated to understand the effects of ions in the glass on 2D layer growth, nitrogen plasma treatment of the glass surface enables increased domain size and minimized the effects of low temperature on electronic characteristics. Trends of growth morphology, crystalline quality, and film stoichiometry are examined as a function of glass chemistry, growth temperature, and degree of surface treatment. It is found that glass surface chemistry and growth temperature are the dominant factors in controlling domain size, which is improved with increasing plasma treatment, introducing alkali-containing glass, and growth temperature. While crystalline quality is improved with higher temperature, the films no longer remain stoichiometric. Finally, we demonstrate that glass surface chemistry has important impacts on electronic transport properties.

Given increasing demand and importance of rare earth elements (REE), exploration is underway to find alternatives to ore-extracted product. With REE concentrations varying between 270 and 1480 ppm, coal ash has been deemed as one such potential source. A number of research groups are exploring technologies to separate REEs from coal ash and supercritical extraction has emerged as a high yield contender. Estimating the economic viability of this lab-scale process at the industrial scale is both important and challenging. In this study we estimate industrial scale cost and revenues of production of REEs from coal ash by combining prior laboratory results, scaling models, combinatorial scenarios and sensitivity analysis. The processing cost of extracting REEs from one ton of coal ash using supercritical CO2 and tributylphosphate (TBP) is found to vary between $380 and $1200 for 550 g of REE. The value of REE oxides that may be obtained per ton of ash is estimated to vary between $6 and $557, with a median of $250. Scandium is the most expensive REE and can account for up to 90% of the value of the yield. The results suggest that factors critical to the economic viability of the process include scandium content & yield, reagent use, and processing time.

This paper derives new formulations for designing dominant pole placement based proportional-integral-derivative (PID) controllers to handle second order processes with time delays (SOPTD). Previously, similar attempts have been made for pole placement in delay-free systems. The presence of the time delay term manifests itself as a higher order system with variable number of interlaced poles and zeros upon Pade approximation, which makes it difficult to achieve precise pole placement control. We here report the analytical expressions to constrain the closed loop dominant and non-dominant poles at the desired locations in the complex s-plane, using a third order Pade approximation for the delay term. However, invariance of the closed loop performance with different time delay approximation has also been verified using increasing order of Pade, representing a closed to reality higher order delay dynamics. The choice of the nature of non-dominant poles e.g. all being complex, real or a combination of them modifies the characteristic equation and influences the achievable stability regions. The effect of different types of non-dominant poles and the corresponding stability regions are obtained for nine test-bench processes indicating different levels of open-loop damping and lag to delay ratio. Next, we investigate which expression yields a wider stability region in the design parameter space by using Monte Carlo simulations while uniformly sampling a chosen design parameter space. Various time and frequency domain control performance parameters are investigated next, as well as their deviations with uncertain process parameters, using thousands of Monte Carlo simulations, around the robust stable solution for each of the nine test-bench processes., Comment: 50 pages, 42 figures, Knowledge-Based Systems, 2018

This work reports the development of a novel bilayered Zn Ni/Ni Co SiC nanocomposite coating on mild steel (MS) by electrodeposition. The top Ni-Co-SiC nanocomposite layer, when deposited directly on MS (monolayer) shows a hardness of 734 HV and a corrosion current density of ∼28μA/cm2 in 3.5 wt% NaCl. However, the lack of adherence of the coating with MS results in poor wear and scratch resistance. Mild steel is anodic to Ni Co SiC nanocomposite and any kind of discontinuity in the coating may lead to severe galvanic corrosion. This problem has been successfully overcome with the incorporation of an anodic (with respect to mild steel)Zn 12%Ni under-layerin between the electrodeposited Ni Co SiC nanocomposite coating and MS, which is adherent to both the materials. This bilayer coating has a hardness of 760 HV and corrosion current density of ∼19μA/cm2 in 3.5 wt% NaCl which are slightly better than that of the monolayer. Further, the bilayer coating assembly exhibits exceptionally low volume loss under wear (∼350 mm3) compared to bare mild steel (∼1700 mm3). This Zn Ni/Ni Co SiC nanocomposite bilayercoating has the potential to emerge as hard, wear resistant and anti-corrosive coating which is essential in the field of heavy duty machines.

A facile electrophoretic deposition technique is developed to prepare composite films of ZnFe2O4 (ZFO) and different carbonaceous materials such as carbon nanotubes and reduced graphene oxide for stable lithium-ion battery anode. The as-deposited films exhibit superior cycling stability with no capacity fade and good rate capability. High reversible capacities of 840 and 870 mAhg−1 were obtained after 100 cycles at 0.5 Ag−1 for ZFO-CNT and ZFO-RGO electrodes, respectively. The contribution of capacitive charge storage in the electrochemical performance is analyzed.

This article demonstrates the applicability of electrophoretically fabricated CuO-carbon black (CB) film as negative electrodes for lithium and sodium-ion rechargeable cells. CuO powder is derived from a thermally decomposed copper metal-organic framework (MOF) template. Electrophoretic deposition (EPD) of CuO-CB electrode has been carried out from a stable CuO-CB suspension, which is prepared in isopropanol medium containing polyacrylic acid and nickel nitrate as dispersant and charging agent respectively. The obtained EPD films are found to be porous and strongly adherent to underlying current collector, with a uniform distribution of CB and CuO. These films are observed to retain a reversible specific capacity of 500 mA h g−1 at 0.5 A g−1 and 256 mA h g−1 at 0.1 A g−1 after 100 cycles as Li-ion battery and Na-ion battery anode respectively. The uniformly dispersed CuO within the conductive network of CB particles is found to sustain the high-rate battery operations efficiently and exhibit a specific capacity of 265 mA h g−1 at 5 A g−1 (LIB) and 140 mA h g−1 at 3 A g−1 (SIB). Owing to the simple and cost-effective route for fabricating these negative electrodes, electrophoretically deposited CuO-CB porous film proves to be a suitable candidate for high-rate Li-ion and Na-ion batteries.

We report the use of nickel ferrite (NFO) with different morphologies as a superior anode material for lithium-ion batteries. The NFO nanoparticles are synthesized using simple auto-combustion process (CNFO) and micron-sized faceted particles are synthesized through pyrolysis of a nickel and iron metal-organic framework (MOF-NFO). Facile electrophoretic deposition (EPD) technique is employed to fabricate electrodes from a stable suspension of the active material (NFO) and conducting carbon-black. The EPD grown electrodes are uniform, porous and well adhered to the copper current collector. The CNFO and MOF-NFO electrodes retain a reversible capacity of 560 and 650 mAhg−1, respectively, after 150 cycles at a specific current of 0.5 Ag−1. Such EPD grown electrodes exhibit excellent rate capability delivering a capacity of 150 and 275 mAhg−1 at a high specific current of 8 Ag−1 for CNFO and NFO-MOF electrodes, respectively. The role of pseudocapacitance in enhancing the rate performance has been explained with the help of electrochemical impedance spectroscopy (EIS) and cyclic voltammetry. It is observed that even with significantly different particle size and morphology, the method of electrode fabrication plays a key role in accommodating the large volumetric changes during cycling and thereby improves the electrochemical properties of the electrode.

Among different cardiac arrhythmias, Ventricular Arrhythmias (VA) are fatal and life-threatening. Therefore, the detection and classification of VA is crucial task for cardiologists. However, in some cases, the ECG morphologies of two kinds of VA - Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF) are similar and difficult to distinguish by human eyes. In this study, we present a low computational complexity arrhythmia classifier with high accuracy based on Phase Space Reconstruction (PSR). It is used to classify normal electrocardiogram (ECG), atrial fibrillation (AF), VT, VF and VT followed by VF. The Creighton University Ventricular Tachyarrhythmia Database (CUDB), Physikalisch-Technische Bundesanstalt Diagnostic Database (PTBDB), MIT-BIH Atrial Fibrillation Database (MIT-BIH AFDB) from PhysioNet databank and University Hospital Southampton database (UHSDB) are used for evaluation and comparison of the proposed algorithm. Two PSR diagrams were plotted based on a window length of 5 s ECG with two different time delays and the PSR-based features were extracted from them using the box-counting technique. This process was applied on 122 records with more than 5500 windows of ECG signals. The results show an average sensitivity of 98.73%, specificity of 99.71% and accuracy of 99.56%. The average computational time of our proposed method for one 5 s window processing is 1.9 s and therefore has the potential in real-time arrhythmia classification applications.

This paper proposes and evaluates an energy-aware pulse switching architecture for a through-substrate ultrasonic sensor network. The network is run from vibration energy harvested on an airplane stabilizer structure, whose health is monitored using the through-substrate network. Pulse- switching-based protocols use single pulses instead of multi-bit packets for information delivery with ultra-high energy-efficiency. Pulse switching using ultrasound is particularly well suited for event reporting through metal / composite substrates used in structures such as bridges, aircraft wings, etc. This can eliminate the need for out-of-substrate radio or wired links. This paper presents a large-scale simulation model in which structural vibration modeling using finite element methods, energy harvesting modeling from such vibrations, and energy-aware pulse networking models are integrated for end-to-end architecture level performance evaluation. Simulation results are used for demonstrating the sensitivity of network performance to key system parameters, namely, structural vibration intensity, energy harvesting efficiency of the used piezoelectric material, and the energy storage capacity at the pulse switching sensor nodes. In addition to event reporting delay, the impacts of pulse loss have been thoroughly characterized using the integrated simulator.

This paper develops a novel formulation for quality of service (QoS) aware networked observer and PI/PID controller design in the presence of specified rate of random packet dropouts. We here derive the stability conditions for the controller and observer gains using asynchronous dynamical system (ADS) formulation of the networked control system (NCS) in discrete time which yields a set of linear matrix inequalities (LMIs). The control design has been validated on two test-bench energy systems viz. battery with DC/DC converter and synchronous generator with power system stabilizer. Numerical simulations report the joint observer and controller's performance in the presence of random packet drops within output feedback control scheme using PI/PID controllers and employing state estimation for the battery and synchronous generator systems. The major numerical challenge includes handling nonconvex constraints in LMIs and stabilizing the observer-based controller with specified rate of packet dropouts. Monte Carlo simulations are provided to compare the uncertain state trajectories, state estimation errors and control efforts under different probability of packet dropouts for the two test-bench energy systems.

This paper describes a generic methodology to develop mathematical and computational models of different components of the smart grid architecture model (SGAM). The SGAM inspired integrated mathematical modelling will help develop interoperable complex system simulations for integrating different smart grid components, associated communication models for data exchange and software modules, control, estimation, and data analytics functionalities with the business perspectives. This paper is based on the existing component models inspired by SGAM, which provides a holistic view for integrating the models under operational and security constraints. Achievable results and open research problems for the SGAM mapping have also been discussed in these models supporting the interoperability challenges. The models described in this paper can serve as a guideline to design efficient and robust control strategies for smart grids against uncertain loading, generation, and communication constraints, thus optimizing and improving the whole system’s performance. Mathematical and computational models of cyber-physical systems have also been discussed along with their potential challenges. Based on the above concepts, unsolved and open challenges in the smart grid control, optimization and data analytics are highlighted.

The placement of energy storage systems (ESS) in smart grids is challenging due to the high complexity of the underlying model and operational datasets. In this paper, non-parametric multivariate statistical analyses of the energy storage operations in base and contingency scenarios are carried out to address these issues. Monte Carlo simulations of the optimization process for the overall cost involving unit commitment and dispatch decisions are performed with different wind and load demand ensembles. The optimization is performed for different grid contingency scenarios like transmission line trips and generator outages along with the location of the ESS in different parts of the grid. The stochastic mixed-integer programming technique is used for optimization. The stochastic model load demand and wind power are obtained from real data. The uncertainty in the operational decisions is obtained, considering the different stochastic realizations of load demand and wind power. The data analytics is performed on ESS operations in the base and its corresponding contingency scenarios with different locations in the grid. Moreover, it is aided by non-parametric multivariate hypothesis tests to understand their dependence amongst various parameters and locations in the grid. The numerical analysis has been shown on a simple 3-bus system considering all the locational and contingency scenarios.

Practical application of antimony alloy based electrodes in alkali-ion secondary batteries are viable due to their low-cost and widespread abundance. Employing unique nanostructuring strategies is vital for significantly improving their electrochemical performance while simultaneously addressing their inherent drawbacks. In this study, we report a simple microwave-hydrothermal approach for preparing antimony and tin-antimony alloy nanorods for the first time. Detailed X-ray diffraction and transmission electron microscopy measurements reveal that the preferential adsorption of cetrimonium bromide molecules on the (1 1 0) planes of the precursor alloy nanoparticles results in the formation of self-assembled nanorods with an average aspect ratio of 6. When mixed with nitrogen-doped graphene flakes, these nanorods deliver reversible discharge capacities of 500 mAh g−1 and 350 mAh g−1 at the end of 200 cycles in Li+ and Na+ half-cell configurations, respectively. When coupled with a LiNi0.5Mn1.5O4 cathode, the resulting Li+ full-cell delivers a nominal working voltage of 4.3 V, a high energy density of 344 Wh kg−1full-cell, and an 87.5% capacity retention after 200 cycles. Similarly, when coupled with a Prussian blue analog cathode, the resulting Na+ full-cell also delivers a nominal working voltage of 2.5 V, an energy density of 250 Wh kg−1full-cell, and an 82.5% capacity retention after 100 cycles.

In this work, we demonstrate the advantage of introducing extrinsic pseudocapacitance behavior in the active material for designing low-cost, high energy, and power density sodium-ion full cells. Here, tin and antimony based alloy nanopowder is synthesized via microwave assisted hydrothermal process (MAHP) for use as a negative electrode. Ex-situ x-ray diffraction studies reveal that the alloy nanopowder stores Na+ via a diffusion-controlled alloying mechanism. Extrinsic pseudocapacitance behavior is subsequently introduced by sandwiching the alloy nanopowder between nitrogen doped reduced graphene oxide nanosheets via MAHP. When coupled with a diffusion-controlled positive electrode comprising of an iron-based prussian blue analog, the resulting full cell demonstrates a high energy density of 262.5 Wh kg−1 at 1C rate with 85.7% capacity retention at the end of 100 cycles. Detailed cyclic voltammetry and electrochemical impedance studies provide insight into the effect of extrinsic pseudocapacitance in improving the electrochemical performance of the full cell.

Electroencephalogram (EEG) signals are often corrupted with unintended artifacts which need to be removed for extracting meaningful clinical information from them. Typically a priori knowledge of the nature of the artifacts is needed for such purpose. Artifact contamination of EEG is even more prominent for pervasive EEG systems where the subjects are free to move and thereby introducing a wide variety of motion-related artifacts. This makes hard to get a priori knowledge about their characteristics rendering conventional artifact removal techniques often ineffective. In this paper, we explore the performance of two hybrid artifact removal algorithms: Wavelet packet transform followed by Independent Component Analysis (WPTICA) and Wavelet Packet Transform followed by Empirical Mode Decomposition (WPTEMD) in pervasive EEG recording scenario, assuming existence of no a priori knowledge about the artifacts and compare their performance with two existing artifact removal algorithms. Artifact cleaning performance has been measured using Root Mean Square Error (RMSE) and Artifact to Signal Ratio (ASR) - an index similar to traditional Signal to Noise Ratio (SNR), and also by observing normalized power distribution topography over the scalp. Comparison has been made first using semi-simulated signals and then with real experimentally acquired EEG data with commercially available 19-channel pervasive EEG system Enobio corrupted by eight types of artifact. Our explorations show that WPTEMD consistently gives best artifact cleaning performance not only in semi-simulated scenario but also in the case of real EEG data containing artifacts., 45 pages, 47 figures

Analyses of monetary and emissions savings from residential efficiency upgrades usually neglect behavioral differences between consumers. Variations in behavior are often large: 13% of the U.S. population sets thermostats for cooling at 20 °C (68 °F) or lower and 15% at 25.5 °C (78 °F) or higher. Efficiency analyses should account for behavioral heterogeneity as well as variations in climate and housing characteristics. We model energy and economic savings of efficiency upgrades for U.S. single-family detached houses, accounting for differences in thermostat settings, climate zone, fuel prices, and home characteristics. We consider five efficiency interventions: wall insulation, attic insulation, air sealing, high-efficiency furnaces, and high-efficiency air conditioners. Energy and economic savings vary widely by consumer; the average net present value for wall insulation is $3,020 United States Dollar (USD), but considering heterogeneity, it has a 10th percentile value of -$231 and 90th percentile value of $5,000. Neglecting heterogeneity, technology upgrades can be prioritized from lower to higher carbon abatement costs: air conditioners, wall insulation, furnace, air sealing, and attic insulation. Accounting for heterogeneity reorders this prioritization, and consumer-specific conditions affect mitigation costs such that technologies are mixed in with one another and are no longer arranged one after another. These results indicate that interventions to improve efficiency should consider differences between consumers.

To ensure that new residential air conditioners and heat pumps operate optimally, care must be taken during the initial system design and installation to avoid missteps that immediately degrade a system’s performance. Laboratory and simulation studies have quantified the impact that faults can have on capacity and efficiency. Field studies have demonstrated that installation-related faults are commonplace. However, the national impact of installation-related faults cannot be accurately estimated by only simulating a limited number of homes at several fault levels because of the variety of home characteristics and climates involved. In our analysis, we use an improved residential building stock simulation tool to predict the annual energy increase and additional utility costs resulting from two common installation faults: indoor airflow rate and refrigerant charge level. Our method considers the wide range of building characteristics and climate zones of the U.S. housing stock. We use existing field data to develop fault intensity probability distributions to inform our analysis. The analysis shows that these two faults result in approximately 20.7 TWh/y of additional energy use for central air conditioners and air-source heat pumps in U.S. single-family detached homes, which is a 9% increase over baseline (no-fault) usage, costing homeowners approximately $2.5 billion annually on utility bills. Air-source heat pumps are responsible for a disproportionate fraction of this energy use increase because of the larger number of operating hours compared to central air conditioners and the sensitivity of heating mode performance to the faults analyzed compared to cooling mode.

Degree of phase synchronization between different Electroencephalogram (EEG) channels is known to be the manifestation of the underlying mechanism of information coupling between different brain regions. In this paper, we apply a continuous wavelet transform (CWT) based analysis technique on EEG data, captured during face perception tasks, to explore the temporal evolution of phase synchronization, from the onset of a stimulus. Our explorations show that there exists a small set (typically 3–5) of unique synchronized patterns or synchrostates, each of which are stable of the order of milliseconds. Particularly, in the beta ( β ) band, which has been reported to be associated with visual processing task, the number of such stable states has been found to be three consistently. During processing of the stimulus, the switching between these states occurs abruptly but the switching characteristic follows a well-behaved and repeatable sequence. This is observed in a single subject analysis as well as a multiple-subject group-analysis in adults during face perception. We also show that although these patterns remain topographically similar for the general category of face perception task, the sequence of their occurrence and their temporal stability varies markedly between different face perception scenarios (stimuli) indicating toward different dynamical characteristics for information processing, which is stimulus-specific in nature. Subsequently, we translated these stable states into brain complex networks and derived informative network measures for characterizing the degree of segregated processing and information integration in those synchrostates, leading to a new methodology for characterizing information processing in human brain. The proposed methodology of modeling the functional brain connectivity through the synchrostates may be viewed as a new way of quantitative characterization of the cognitive ability of the subject, stimuli and information integration/segregation capability.

This paper uses the Continued Fraction Expansion (CFE) method for analog realization of fractional order differ-integrator and few special classes of fractional order (FO) controllers viz. Fractional Order Proportional-Integral-Derivative (FOPID) controller, FO[PD] controller and FO lead-lag compensator. Contemporary researchers have given several formulations for rational approximation of fractional order elements. However, approximation of the controllers studied in this paper, due to having fractional power of a rational transfer function, is not available in analog domain; although its digital realization already exists. This motivates us for applying CFE based analog realization technique for complicated FO controller structures to get equivalent rational transfer functions in terms of the controller tuning parameters. The symbolic expressions for rationalized transfer function in terms of the controller tuning parameters are especially important as ready references, without the need of running CFE algorithm every time and also helps in the synthesis of analog circuits for such FO controllers., 21 pages, 11 figures

peer-reviewed This article corresponds to chapter 6 of Ph.D: Experimental and mathematical modelling of biowaste gasification in a bubbling fluidised bed reactor Pandey, Daya Shankar URI: http://hdl.handle.net/10344/7116 A multi-gene genetic programming technique is proposed as a new method to predict syngas yield production and the lower heating value for municipal solid waste gasification in a fluidized bed gasifier. The study shows that the predicted outputs of the municipal solid waste gasification process are in good agreement with the experimental dataset and also generalise well to validation (untrained) data. Published experimental datasets are used for model training and validation purposes. The results show the effectiveness of the genetic programming technique for solving complex nonlinear regression problems. The multi-gene genetic programming are also compared with a single-gene genetic programming model to show the relative merits and demerits of the technique. This study demonstrates that the genetic programming based data-driven modelling strategy can be a good candidate for developing models for other types of fuels as well.

The growth of single-layer graphene on copper foil by chemical vapor deposition (CVD) method has been investigated extensively by several groups, however, achieving the same for the bilayer graphene, using a fast and reproducible process, is proven to be difficult and most of the efforts in this direction so far have been on controlling the nucleation phase during active growth regime. In this article we show that by regulating the gases introduced during the cooling phase, uniform and continuous growth of both the single and bilayer graphene can be obtained on copper foils with growth phase duration reduced to 3 min (i.e., 5–60 times faster than previous methods). We demonstrated growth of bilayer graphene on 30 × 30 cm copper foils. We show that the use of vacuum cooling enhanced the growth of single-layer graphene while the introduction of hydrogen gas during the cooling phase promoted the growth of bilayer graphene. We explain observed results elucidating a crucial role of hydrogen leading to a growth of bilayer graphene. The characterization of single and bilayer graphene have been supported by extensive statistical analysis of Raman spectroscopy, selected area electron diffraction measurements as well as fabrication of graphene field effect transistors.

Fuzzy logic based PID controllers have been studied in this paper, considering several combinations of hybrid controllers by grouping the proportional, integral and derivative actions with fuzzy inferencing in different forms. Fractional order (FO) rate of error signal and FO integral of control signal have been used in the design of a family of decomposed hybrid FO fuzzy PID controllers. The input and output scaling factors (SF) along with the integro-differential operators are tuned with real coded genetic algorithm (GA) to produce optimum closed loop performance by simultaneous consideration of the control loop error index and the control signal. Three different classes of fractional order oscillatory processes with various levels of relative dominance between time constant and time delay have been used to test the comparative merits of the proposed family of hybrid fractional order fuzzy PID controllers. Performance comparison of the different FO fuzzy PID controller structures has been done in terms of optimal set-point tracking, load disturbance rejection and minimal variation of manipulated variable or smaller actuator requirement etc. In addition, multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-II) has been used to study the Pareto optimal trade-offs between the set point tracking and control signal, and the set point tracking and load disturbance performance for each of the controller structure to handle the three different types of processes., Comment: 31 pages, 20 figures

Networked Control Systems (NCSs) often suffer from random packet dropouts which deteriorate overall system's stability and performance. To handle the ill effects of random packet losses in feedback control systems, closed over communication network, a state feedback controller with predictive gains has been designed. To achieve improved performance, an optimization based controller design framework has been proposed in this paper with Linear Matrix Inequality (LMI) constraints, to ensure guaranteed stability. Different conflicting objective functions have been optimized with Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The methodology proposed in this paper not only gives guaranteed closed loop stability in the sense of Lyapunov, even in the presence of random packet losses, but also gives an optimization trade-off between two conflicting time domain control objectives., 38 pages, 30 figures

We report an interesting scaling trend in the switching time and the switching voltage of the organic ferroelectric copolymer PVDF-TrFE as a function of the device area. We have found that shrinking the lateral dimensions of the ferroelectric film results in a dramatic decrease in the switching time and the switching voltage. The phenomenological theory, that explains this abnormal scaling trend, involves in-plane interaction of the polymeric chains of the two-dimensional Langmuir-Blodgett (LB) films of the copolymer PVDF-TrFE interchain and intrachain coupling results in a weak power-law dependence of the switching field on the device area (E-SW alpha A(CH)(0.1)) which is ultimately responsible for the decrease in the switching time and switching voltage. For this scaling study we have used the organic ferroelectric copolymer as the top gate dielectric of a field-effect transistor structure with poly silicon nanowires as channel material. The gated channel area was varied by more than two orders of magnitude (0.04-5 mu m(2)) while the thickness of the ferroelectric copolymer film was kept constant at 100 nm. Our findings are believed to be of importance to both, the fundamental understanding of non-equilibrium processes in correlated condensed matter systems and the technological use of ferroelectric copolymers for non volatile memory applications. (C) 2012 Elsevier B.V. All rights reserved.

In this paper, a fractional order (FO) PI{\lambda}D\mu controller is designed to take care of various contradictory objective functions for an Automatic Voltage Regulator (AVR) system. An improved evolutionary Non-dominated Sorting Genetic Algorithm II (NSGA II), which is augmented with a chaotic map for greater effectiveness, is used for the multi-objective optimization problem. The Pareto fronts showing the trade-off between different design criteria are obtained for the PI{\lambda}D\mu and PID controller. A comparative analysis is done with respect to the standard PID controller to demonstrate the merits and demerits of the fractional order PI{\lambda}D\mu controller., Comment: 30 pages, 14 figures

A novel conformal mapping based Fractional Order (FO) methodology is developed in this paper for tuning existing classical (Integer Order) Proportional Integral Derivative (PID) controllers especially for sluggish and oscillatory second order systems. The conventional pole placement tuning via Linear Quadratic Regulator (LQR) method is extended for open loop oscillatory systems as well. The locations of the open loop zeros of a fractional order PID (FOPID or PI{\lambda}D{\mu}) controller have been approximated in this paper vis-\`a-vis a LQR tuned conventional integer order PID controller, to achieve equivalent integer order PID control system. This approach eases the implementation of analog/digital realization of a FOPID controller with its integer order counterpart along with the advantages of fractional order controller preserved. It is shown here in the paper that decrease in the integro-differential operators of the FOPID/PI{\lambda}D{\mu} controller pushes the open loop zeros of the equivalent PID controller towards greater damping regions which gives a trajectory of the controller zeros and dominant closed loop poles. This trajectory is termed as "M-curve". This phenomena is used to design a two-stage tuning algorithm which reduces the existing PID controller's effort in a significant manner compared to that with a single stage LQR based pole placement method at a desired closed loop damping and frequency., Comment: 23 pages, 7 figures, in press; Communications in Nonlinear Science and Numerical Simulations, 2012

Genetic algorithm (GA) has been used in this study for a new approach of suboptimal model reduction in the Nyquist plane and optimal time domain tuning of proportional–integral–derivative (PID) and fractional-order (FO) P I λ D μ controllers. Simulation studies show that the new Nyquist-based model reduction technique outperforms the conventional H2-norm-based reduced parameter modeling technique. With the tuned controller parameters and reduced-order model parameter dataset, optimum tuning rules have been developed with a test-bench of higher-order processes via genetic programming (GP). The GP performs a symbolic regression on the reduced process parameters to evolve a tuning rule which provides the best analytical expression to map the data. The tuning rules are developed for a minimum time domain integral performance index described by a weighted sum of error index and controller effort. From the reported Pareto optimal front of the GP-based optimal rule extraction technique, a trade-off can be made between the complexity of the tuning formulae and the control performance. The efficacy of the single-gene and multi-gene GP-based tuning rules has been compared with the original GA-based control performance for the PID and P I λ D μ controllers, handling four different classes of representative higher-order processes. These rules are very useful for process control engineers, as they inherit the power of the GA-based tuning methodology, but can be easily calculated without the requirement for running the computationally intensive GA every time. Three-dimensional plots of the required variation in PID/fractional-order PID (FOPID) controller parameters with reduced process parameters have been shown as a guideline for the operator. Parametric robustness of the reported GP-based tuning rules has also been shown with credible simulation examples.

The issues of stochastically varying network delays and packet dropouts in Networked Control System (NCS) applications have been simultaneously addressed by time domain optimal tuning of fractional order (FO) PID controllers. Different variants of evolutionary algorithms are used for the tuning process and their performances are compared. Also the effectiveness of the fractional order PI{\lambda}D{\mu} controllers over their integer order counterparts is looked into. Two standard test bench plants with time delay and unstable poles which are encountered in process control applications are tuned with the proposed method to establish the validity of the tuning methodology. The proposed tuning methodology is independent of the specific choice of plant and is also applicable for less complicated systems. Thus it is useful in a wide variety of scenarios. The paper also shows the superiority of FOPID controllers over their conventional PID counterparts for NCS applications., Comment: 30 pages, 18 figures

An optimal PID and an optimal fuzzy PID have been tuned by minimizing the Integral of Time multiplied Absolute Error (ITAE) and squared controller output for a networked control system (NCS). The tuning is attempted for a higher order and a time delay system using two stochastic algorithms viz. the Genetic Algorithm (GA) and two variants of Particle Swarm Optimization (PSO) and the closed loop performances are compared. The paper shows that random variation in network delay can be handled efficiently with fuzzy logic based PID controllers over conventional PID controllers.

Ventricular arrhythmias comprise a group of disorders which manifest clinically in a variety of ways from ventricular premature beats (VPB) and no sustained ventricular tachycardia (in healthy subjects) to sudden cardiac death due to ventricular tachyarrhythmia in patients with and/or without structural heart disease. Ventricular fibrillation (VF) and ventricular tachycardia (VT) are the most common electrical mechanisms for cardiac arrest. Accurate and automatic recognition of these arrhythmias from electrocardiography (ECG) is a crucial task for medical professionals. The purpose of this research is to develop a new index for the differential diagnosis of normal sinus rhythm (SR) and ventricular arrhythmias, based on phase space reconstruction (PSR)., 7 pages, 2 figures

The phase curve of an open loop system is flat in nature if the derivative of its phase with respect to frequency is zero. With a flat-phase curve, the corresponding closed loop system exhibits an iso-damped property i.e. maintains constant overshoot with the change of gain. This implies enhanced parametric robustness e.g. to variation in system gain. In the recent past, fractional order (FO) phase shapers have been proposed by contemporary researchers to achieve enhanced parametric robustness. In this paper, a simple methodology is proposed to design an appropriate FO phase shaper to achieve phase flattening in a control loop, comprising a plant controlled by a classical Proportional Integral Derivative (PID) controller. The methodology is demonstrated with MATLAB simulation of representative plants and accompanying PID controllers.