Showing papers by "Delhi Technological University published in 2017"

SymPy: Symbolic Computing in Python

Abstract: SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.

Medicinal Plant Leaf Extract and Pure Flavonoid Mediated Green Synthesis of Silver Nanoparticles and their Enhanced Antibacterial Property.

Abstract: The rewards of using plants and plant metabolites over other biological methods for nanoparticle synthesis have fascinated researchers to investigate mechanisms of metal ions uptake and bio-reduction by plants. Here, green chemistry were employed for the synthesis of silver nanoparticles (AgNPs) using leaf extracts of Ocimum Sanctum (Tulsi) and its derivative quercetin (flavonoid present in Tulsi) separately as precursors to investigate the role of biomolecules present in Tulsi in the formation of AgNPs from cationic silver under different physicochemical conditions such as pH, temperature, reaction time and reactants concentration. The size, shape, morphology, and stability of resultant AgNPs were investigated by optical spectroscopy (absorption, photoluminescence (PL), PL-lifetime and Fourier transform infrared), X-ray diffraction (XRD) analysis, and transmission electron microscopy (TEM). The enhanced antibacterial activity of AgNPs against E-Coli gram-negative bacterial strains was analyzed based on the zone of inhibition and minimal inhibitory concentration (MIC) indices. The results of different characterization techniques showed that AgNPs synthesized using both leaf extract and neat quercetin separately followed the same optical, morphological, and antibacterial characteristics, demonstrating that biomolecules (quercetin) present in Tulsi are mainly responsible for the reduction of metal ions to metal nanoparticles.

Pharos: Collating protein information to shed light on the druggable genome

Abstract: The 'druggable genome' encompasses several protein families, but only a subset of targets within them have attracted significant research attention and thus have information about them publicly available. The Illuminating the Druggable Genome (IDG) program was initiated in 2014, has the goal of developing experimental techniques and a Knowledge Management Center (KMC) that would collect and organize information about protein targets from four families, representing the most common druggable targets with an emphasis on understudied proteins. Here, we describe two resources developed by the KMC: the Target Central Resource Database (TCRD) which collates many heterogeneous gene/protein datasets and Pharos (https://pharos.nih.gov), a multimodal web interface that presents the data from TCRD. We briefly describe the types and sources of data considered by the KMC and then highlight features of the Pharos interface designed to enable intuitive access to the IDG knowledgebase. The aim of Pharos is to encourage 'serendipitous browsing', whereby related, relevant information is made easily discoverable. We conclude by describing two use cases that highlight the utility of Pharos and TCRD.

Recent advances in carbon based nanosystems for cancer theranostics.

Abstract: One of the major challenges in our contemporary society is to facilitate healthy life for all human beings. In this context, cancer has become one of the most deadly diseases around the world, and despite many advances in theranostics techniques the treatment of cancer still remains an important problem. With recent advances made in the field of nano-biotechnology, carbon-based nanostructured materials have drawn special attention because of their unique physicochemical properties, giving rise to great potential for the diagnosis and therapy of cancer. This review deals with four different types of carbon allotrope including carbon nanotubes, graphene, fullerenes and nanodiamonds and summarizes the results of recent studies that are likely to have implications in cancer theranostics. We discuss the applications of these carbon allotropes for cancer imaging and drug delivery, hyperthermia, photodynamic therapy and acoustic wave assisted theranostics. We focus on the results of different studies conducted on functionalized/conjugated carbon nanotubes, graphene, fullerenes and nanodiamond based nanostructured materials reported in the literature in the current decade. The emphasis has been placed on the synthesis strategies, structural design, properties and possible mechanisms that are perhaps responsible for their improved theranostic characteristics. Finally, we discuss the critical issues that may accelerate the development of carbon-based nanostructured materials for application in cancer theranostics.

BFOA-scaled fractional order fuzzy PID controller applied to AGC of multi-area multi-source electric power generating systems

Abstract: In the fast developing world of today, automatic generation control (AGC) plays an incredibly significant role in offering inevident demand of good quality power supply in power system. To deliver a quality power, AGC system requires an efficient and intelligent control algorithm. Hence, in this paper, a novel fractional order fuzzy proportional-integral-derivative (FOFPID) controller is proposed for AGC of electric power generating systems. The proposed controller is tested for the first time on three structures of multi-area multi-source AGC system. The gains and fractional order parameters such as order of integrator (λ) and differentiator (µ and γ) of FOFPID controller are optimized using bacterial foraging optimization algorithm (BFOA). Initially, the proposed controller is implemented on a traditional two-area multi-source hydrothermal power system and its effectiveness is established by comparing the results with FOPID, fuzzy PID (FPID) and PI/PID controller based on recently published optimization techniques like hybrid firefly algorithm-pattern search (hFA-PS) and grey wolf optimization (GWO) algorithm. The approach is further extended to restructured multi-source hydrothermal and thermal gas systems. It is observed that the dynamic performance of the proposed BFOA optimized FOFPID controller is superior to BFOA optimized FPID/FOPID/PID and differential evolution (DE)/genetic algorithm (GA) optimized PID controllers. It is also detected that the dynamic responses obtained under different power transactions with/without appropriate generation rate constraint, time delay and governor dead-zone effectively satisfy the AGC requirement in deregulated environment. Moreover, robustness of the suggested approach is verified against wide variations in the nominal initial loading, system parameters, distribution company participation matrix structure and size and position of uncontracted power demand.

Underwater Fish Species Classification using Convolutional Neural Network and Deep Learning

Abstract: The target of this paper is to recommend a way for Automated classification of Fish species. A high accuracy fish classification is required for greater understanding of fish behavior in Ichthyology and by marine biologists. Maintaining a ledger of the number of fishes per species and marking the endangered species in large and small water bodies is required by concerned institutions. Majority of available methods focus on classification of fishes outside of water because underwater classification poses challenges such as background noises, distortion of images, the presence of other water bodies in images, image quality and occlusion. This method uses a novel technique based on Convolutional Neural Networks, Deep Learning and Image Processing to achieve an accuracy of 96.29%. This method ensures considerably discrimination accuracy improvements than the previously proposed methods.

Hydroprocessed vegetable oil as a fuel for transportation sector: A review

Abstract: Renewable fuels produced from vegetable oils are an attractive alternative to fossil-based fuel. Different type of fuels can be derived from these triglycerides. One of them is biodiesel which is a mono alkyl ester of the vegetable oil. The biodiesel is produced by transesterification of the oil with an alcohol in the presence of a catalyst. Another kind of fuel (which is similar to petroleum-derived diesel) can be produced from the vegetable oil using hydroprocessing technique. This method uses elevated temperature and pressure along with a catalyst to produce a fuel termed as ‘renewable diesel’. The fuel produced has properties that are beneficial for the engine as well as the environment. It has high cetane number, low density, excellent cold flow properties and same materials can be used as are used for engine running on petrodiesel. It can effectively reduce NOx, PM, HC, CO emissions and unregulated emissions as well as greenhouse gases as compared to diesel. The fuel is also beneficial for the after-treatment systems. Trials in the field have shown that the volumetric fuel consumption of renewable diesel is higher than petrodiesel and nearly proportional to the volumetric heating value. The present review focuses on the hydroprocessing technique used for the renewable diesel production and the effect of different parameters such as catalyst, reaction temperature, hydrogen pressure, liquid hourly space velocity (LHSV) and H2/oil ratio on oil conversion, diesel selectivity, and isomerization. The review also summarizes the effect; renewable diesel has on combustion, performance, and emission characteristics of a compression ignition engine.

Fuzzy-Contextual Contrast Enhancement

Abstract: This paper presents contrast enhancement algorithms based on fuzzy contextual information of the images. We introduce fuzzy similarity index and fuzzy contrast factor to capture the neighborhood characteristics of a pixel. A new histogram, using fuzzy contrast factor of each pixel is developed, and termed the fuzzy dissimilarity histogram (FDH). A cumulative distribution function is formed with normalized values of an FDH and used as a transfer function to obtain the contrast enhanced image. The algorithm gives good contrast enhancement and preserves the natural characteristic of the image. In order to develop a contextual intensity transfer function, we introduce a fuzzy membership function based on fuzzy similarity index and coefficient of variation of the image. The contextual intensity transfer function is designed using the fuzzy membership function to achieve final contrast enhanced image. The overall algorithm is referred as the fuzzy contextual contrast-enhancement algorithm. The proposed algorithms are compared with the conventional and the state-of-the-art contrast enhancement algorithms. The quantitative and visual assessment of the results is performed. The results of quantitative measures are statistically analyzed using t-test. The exhaustive experimentation and analysis show the proposed algorithm efficiently enhances contrast and yields in natural visual quality images.

Design and analysis of BFOA-optimized fuzzy PI/PID controller for AGC of multi-area traditional/restructured electrical power systems

Abstract: In this paper, design and performance analysis of bacterial foraging optimization algorithm (BFOA)-optimized fuzzy PI/PID (FPI/FPID) controller for automatic generation control of multi-area interconnected traditional/restructured electrical power systems is presented. Firstly a traditional two-area non-reheat thermal system is considered, and gains of the fuzzy controller are tuned employing BFOA using integral of squared error objective function. The supremacy of this controller is demonstrated by juxtaposing the results with particle swarm optimization (PSO), firefly algorithm (FA), BFOA, hybrid BFOA–PSO-based PI and fuzzy PI controllers based upon pattern search (PS) and PSO algorithms for the same power system structure. The approach is then extended to a two-area reheat system, and improved results are found with the purported FPI/FPID controller in comparison with PSO and artificial bee colony optimized PI controller. Further, the approach is implemented on a traditional multi-source multi-area (MSMA) hydrothermal system and its superb performance is observed over genetic algorithm and hybrid FA–PS tuned PI controller. Additionally, to demonstrate the scalability of the designed controller to cope with restructured power system, the study is also protracted to a restructured MSMA hydrothermal power system. Finally, sensitivity analysis is performed to ascertain the robustness of the controller designed for the systems under study. It is observed that the suggested FPI/FPID controller optimized for nominal conditions is able to handle generation rate constraints and wide variations in nominal loading condition as well as system parameters.

Thermodynamic analysis and optimization of a supercritical CO2 regenerative recompression Brayton cycle coupled with a marine gas turbine for shipboard waste heat recovery

Abstract: This communication presents the modelling and analysis of a waste-heat-recovery-system (WHRS) using supercritical carbon dioxide (SC-CO2) based regenerative recompression Brayton cycle (RRCBC) for shipboard applications following energy and exergy analyses. The influence of key operating parameters such as, the gas compositions, turbine and compressor inlet temperatures, pressure drop irreversibility, pressure ratio etc., on the overall performance of the system including the exergy destruction rate has been investigated. The results show that the proposed integration improves the overall efficiency of the system by 10% while the net power is found to be increasing up to 25% of the rated power. It is also found that the topping gas turbine exhaust gas compositions and temperatures have a significant influence on the WHRS performance. The results are also found in good agreement with those already available in the published literature.

Structural and emission properties of Eu3+-doped alkaline earth zinc-phosphate glasses for white LED applications

Abstract: Quaternary alkaline earth zinc-phosphate glasses in molar composition (40 − x)ZnO – 35P2O5 – 20RO – 5TiO2 – xEu2O3 (where x=1 and R=Mg, Ca, Sr, and Ba) were prepared by melt quenching technique. These glasses were studied with respect to their thermal, structural, and photoluminescent properties. The maximum value of the glass transition temperature (Tg) was observed for BaO network modifier mixed glass and minimum was observed for MgO network modifier glass. All the glasses were found to be amorphous in nature. The FT-IR suggested the glasses to be in pyrophosphate structure, which matches with the theoretical estimation of O/P atomic ratio and the maximum depolymerization was observed for glass mixed with BaO network modifier. The intense emission peak was observed at 613 nm (5D0→7F2) under excitation of 392 nm, which matches well with excitation of commercial n-UV LED chips. The highest emission intensity and quantum efficiency was observed for the glass mixed with BaO network modifier. Based on these results, another set of glass samples was prepared with molar composition (40 − x)ZnO – 35P2O5 – 20BaO – 5TiO2 – xEu2O3 (x=3, 5, 7, and 9) to investigate the optimized emission intensity in these glasses. The glasses exhibited crystalline features along with amorphous nature and a drastic variation in asymmetric ratio at higher concentration (7 and 9 mol%) of Eu2O3. The color of emission also shifted from red to reddish orange with increase in the concentration of Eu2O3. These glasses are potential candidates to use as a red photoluminsecent component in the field of solid-state lighting devices.

Highly sensitive electrochemical immunosensor based on graphene-wrapped copper oxide-cysteine hierarchical structure for detection of pathogenic bacteria

Abstract: We report results of the studies relating to fabrication of the graphene wrapped copper (II) assisted cysteine hierarchical structure (rGO-CysCu, 10 μm) synthesised using facile, aqueous and environmental-friendly conditions. The results of electrochemical impedance spectroscopic investigations indicate that self-assembly of rGO-CysCu molecules onto gold electrode provides a high surface area and high electron transfer rate constant (1.82 × 10 −6 cm/s). Further, an ultrasensitive label-free electrochemical immunosensor for quantitative determination of Escherichia coli O157: H7 ( E. coli ) has been developed using rGO-CysCu as the sensing layer. Under optimal conditions, the calibration plot pertaining to sensing characteristics of the fabricated immunoelectrode for E. coli O157: H7 was approximately linear in the wide detection range of 10 CFU mL −1 to 10 8 CFU mL −1 with a detection limit of 3.8 CFU mL −1 . Moreover, the proposed method was successfully used to differentiate the E. coli O157: H7 cells from the non-pathogenic E. coli (DH5α) and other bacterial cells in the synthetic samples.

Optimization of thermoelectric cooling technology for an active cooling of photovoltaic panel

Abstract: The electrical efficiency of PV panel is undesirably influenced by the rise in the panel temperature and accelerates cell degradation which leads to reduction of life expectancy of PV module. A fall in PV module output power with rise in temperature is observed on non-removal of this excessive heat. For cooling such system, thermoelectric technology is appropriate for its integration adjacent to the PV module. Thermoelectric module (TEM) is attached at the back side of PV module for absorption of the heat generated in PV module by infrared spectrum. This paper deals with the active heat sinking from PV system using TEM tiles. Mathematical model for TEM is developed by consideration of temperature dependence of material properties. A temperature based maximum power point tracking (MPPT) scheme is presented for operating TEM at optimal temperature of PV system. Analysis and design of MPPT scheme, current controller and converter are also discussed. The performance improvement of PV system with thermoelectric cooling is presented through simulated results under MATLAB environment to compute the adequate heat sinking from the PV system exposed to wide spectrum other than visible light.

Numerical Simulation of N + Source Pocket PIN-GAA-Tunnel FET: Impact of Interface Trap Charges and Temperature

Abstract: This paper investigates the reliability of PIN-gate-all-around (GAA)-tunnel field-effect transistor (TFET) with N+ source pocket. The reliability of the PNIN-GAA-TFET is examined by analyzing: 1) the impact of interface trap charge (ITC) density and polarity and 2) the temperature affectability on analog/RF performance of the device. It is realized that the interface traps existing at the Si/SiO2 interface modifies the flatband voltage and, thereby, alters the analog and RF characteristics of the device. The analysis is done at various trap charge densities and polarities. The results, thus, obtained reveal that, at higher trap charge density, the device performance alters significantly. It is obtained that, for a donor trap charge density of $3 \times 10^{{12}}$ cm $^{-2}$ , the off-state current of the device degrades tremendously (increases from an order of $10^{-17}$ – $10^{-9}\text{A}$ ). The temperature affectability over the device reveals that, at lower gate bias, the Shockley–Read–Hall phenomenon dominates and degrades the subthreshold current of the device at elevated temperatures. However, for the superthreshold regime, the band-to-band tunneling (BTBT) mechanism dominates. Furthermore, the results show enormous degradation in the off-state current at elevated temperatures, such that, with an increase in the ambient temperature from 200 K to 400 K, the $I_{ \mathrm{\scriptscriptstyle OFF}}$ degrades by an order of $10^{5}$ , i.e., increases from $10^{-18}$ A to $10^{-13}$ A. The results specify that the PNIN-GAA-TFET is insusceptible to the acceptor traps existing at the Si/SiO2 interface in comparison with the donor traps.

An Optimal Fuzzy System for Edge Detection in Color Images Using Bacterial Foraging Algorithm

Abstract: This paper presents a fuzzy system for edge detection, using smallest univalue segment assimilating nucleus (USAN) principle and bacterial foraging algorithm (BFA). The proposed algorithm fuzzifies the USAN area obtained from the original image, using a USAN area histogram-based Gaussian membership function. A parametric fuzzy intensification operator (FINT) is proposed to enhance the weak edge information, which results in another fuzzy set. The fuzzy measures, i.e., fuzzy edge quality factor and sharpness factor, are defined on fuzzy sets. The BFA is used to optimize the parameters involved in the fuzzy membership function and the FINT. The fuzzy edge map is obtained using optimized parameters. The adaptive thresholding is used to defuzzify the fuzzy edge map to obtain a binary edge map. The experimental results are analyzed qualitatively and quantitatively. The quantitative measures, i.e., Pratt's figure of merit, Cohen’ Kappa, Shannon's entropy, and edge strength similarity-based edge quality metric, are used. The quantitative results are statistically analyzed using t-test. The proposed algorithm outperforms many of the traditional and state-of-the-art edge detectors.

An empirical study for software change prediction using imbalanced data

Abstract: Software change prediction is crucial in order to efficiently plan resource allocation during testing and maintenance phases of a software. Moreover, correct identification of change-prone classes in the early phases of software development life cycle helps in developing cost-effective, good quality and maintainable software. An effective software change prediction model should equally recognize change-prone and not change-prone classes with high accuracy. However, this is not the case as software practitioners often have to deal with imbalanced data sets where instances of one type of class is much higher than the other type. In such a scenario, the minority classes are not predicted with much accuracy leading to strategic losses. This study evaluates a number of techniques for handling imbalanced data sets using various data sampling methods and MetaCost learners on six open-source data sets. The results of the study advocate the use of resample with replacement sampling method for effective imbalanced learning.