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Using the first principle calculation the sensing properties of the pristine MoS 2 and MoS 2 adsorbed with different transition metals (Co/Cu/Fe/Mn/V) were studied. The sensing materials used for our calculation are urea (CH 4 N 2 O) and methanol (CH 3 OH). We analyzed whether the pristine or TM adsorbed MoS 2 were considered a better sensor. The bandstructure, density of states (DOS), and effective mass parameters were calculated using Density Functional Theory (DFT). The calculation was performed using Quantum ATK. We found out, among all the materials, MoS2-Cu-CH 4 N 2 O has the highest DOS 500(eV)-1 at energy -2.2eV, implying it is a better sensing material for CH 4 N 2 O. They have the highest number of states available for occupancy. The energy bandgap was calculated for different transition metals. Among all the sensing material, we observed the maximum variation in bandgap for Fe adsorbed MoS 2 sensed with CH 3 OH and CH 4 N 2 O and hence depicting it to be a superior sensing material. The effective mass of sample MoS 2 and Co adsorbed MoS 2 increases more when sensed with sensing material CH 3 OH and CH 4 N 2 O.

Computational study of MoS2 sensing material with adsorbed transition metal (Cu/Co/Fe/Mn/V) was studied. The first principle study was performed based on density function theory (DFT) to study properties such as bandstructure, the density of states (DOS), and effective mass using the Quantum ATK tool. We used Ammonia (NH 3 ) and Methane (CH 4 ) as sensing gas and found, among all the material MoS 2 -Fe has the highest DOS 707 (eV)-1 at 1.4 eV when NH3 sensing gas is added, which indicated it is better sensing material for NH3 and has the highest number of states available for occupancy. From the calculation of bandgap, it is found that MoS 2 varies the most when NH 3 sensing gas is added from 1.6 eV to 0.36 eV making it a good material for sensors. We also calculated the effective mass of MoS 2 with adsorbed TM and found that the effective mass of the material increases more when NH 3 gas is added.

The studies were performed based on various composites to improve the features of Magnetic Tunnel Junction (MTJ) memory devices. We have studied the electrical and electronic properties of Ni 3 GeFe 2 and Fe 3 GeTe 2 . MgO-hBN-MgO is used as a composite dielectric material. The ferromagnetic material Ni 3 GeFe 2 and Fe 3 GeTe 2 have shown improvement in various electronic and magnetic properties that can be useful for further implementation in MTJ device. The Ni 3 GeFe 2 and Fe 3 GeTe 2 predicted good magneto-transport property that characterizes their ferromagnetic state. In oxide layer the direct bandgap of 1.0437eV at Gamma point is obtained. The validation of Ni 3 GeFe 2 and Fe 3 GeTe 2 as ferromagnetic layer is predicted as it showed 0eV bandgap. Local Density of State (LDOS) of FM and oxide layer is calculated along with it the electron transport is observed in z direction. It is found that these two ferromagnetic (FM) materials showed a high Curie temperature. The DOS and Band Structure of Fe is compared with Ni 3 GeFe 2 , Fe 3 GeTe 2 . In comparison with the DOS of Fe showed peak at the valance band i.e. 0.45eV and 0.69eV which shows instability of memory states, as in case of Ni 3 GeFe 2 and Fe 3 GeTe 2 (stable FM materials) the peaks were observed is conduction band. The study depicts that Ni 3 GeFe 2 and Fe 3 GeTe 2 are promising candidate for its implementation in MTJ memory device. As they show better edge boundaries and good stability of states.

The study of the electronic and electrical properties of three different samples of CNT-BNNT-CNT was calculated using Density Functional Theory (DFT). The bandgap increases as the length of Boron Nitride Nanotube (BNNT) increases for both polarized and unpolarized samples which depicts the bandgap tunability of CNT-BNNT-CNT based heterostructure nanotube. The bandgap tunability finds application in nanophotonics, nanoelectronics, and Field Effect Transistor (FETs). From the Density of States (DOS) plot, the highest peak was observed in the conduction band for both the polarized and unpolarized CNT-BNNT-CNT samples. In the unpolarized sample, the highest peak was obtained for sample2 at energy 5eV with peak of 90000(eV)-1. For spin-polarized, the highest peak was obtained for sample3 at energy 2/-2eV with peak of 7000/-7000(eV)-1. Higher the availability of peak in DOS plots better is its application of spintronic memory devices. BNNT has its application in sensor membranes, memory devices and separation membrane for separating cations and anions. The research on BNNT is still in its early stages. Hence, it is highly promising for work study and future applications.

The development of Resistive Ram (RRAM) has been greatly promising and has emerged as highly reliable nonvolatile memory. The biasing of the device creates the filament formation that is due to the moving charge and shallow localized charge. However, the conduction mechanism of resistive switching changes considerably depending on the type of the material used in the dielectric layer and the electrode. Amongst the important mechanism initiating the filament formation in the device, we have mentioned about Space Charge Limited Conduction (SCLC) and F-N tunneling in our work particularly considering the development of conductive filament (CF) with the interest in the I-V characteristics of the device. The RRAM device has huge potential and many advantages over other non-volatile technology i.e. high switching speed, high device density and low power consumption. In this paper we have considered LD (low-dimensional) forms of HfO 2 and TiO 2 dielectrics for applications in RRAM (Resistive Random Access Memory) devices. The retention of memory device is determined by its saturation curve, HfO 2 showed slower saturation at −18mA providing higher retention factor than. The faster decrease in curve for HfO 2 based RRAM at −19mA, promoting low power consumption of memory device. Therefore we have obtained that the retention and power consumption of HfO 2 is better than the TiO 2 .

Automated meter reading (AMR) systems are used to effectively help utilize resources which are dependent on electricity in the home. They find numerous areas of application such as smart home, radio frequency networks, and touch-based AMR. Electrical energy consumption is proportional to the level of usage of electricity, thus, the higher the usage of electricity, the higher the billing cost. The uncontrolled daily electricity consumption causes an increase in the monthly electricity bill to normally exceed the household budget. To address this challenge, we therefore propose a low cost system with an improved architecture to that of existing electric meters as a tool to effectively monitoring electricity consumption of household equipment. The system lets the user monitor his/her appliances from a distant location via the internet. Some advantages of the system are mobility and convenience since users requires internet in order to monitor the equipment.

Incineration is the key form of treatment for waste-to-energy. The existing incinerator developed is simple in design; it did not consist of a dustbin to store the waste. The waste disposed directly went through the heating chamber with no control technology i.e., the switch of chamber is controlled manually. This disadvantage is covered by our system as the switch is automatically controlled with the use of a controller. Arduino is used to control the sweeping of the valve. Programming is done with various condition to meet the requirement of the proposed work. The system is environmental friendly as it has a chimney with a filter installed in it. With the use of this system it is easier to dispose waste. It is portable as it is a wheel based incinerator. This portable wherein furnace and valve is automatically operated based on Arduino program. System is reliable and efficient as it is wheel based. The most attractive is it has inbuilt smart dustbin in it. About 65-80% of the energy content in the organic substances can be utilize to produce electricity.

basically the concept of designing “Smart Cabin” is been developed and modified from the concept of “Smart Cabs” which are implemented in cities in large numbers in order to develop smart and secure service to the people. People found implementing all these concepts for real time purposes are much more effective rather than just keeping the concept and prototype of the system in papers, because so much of development took place after the implementation of smart cabs in the cities where people living there started feeling much more secure than that of the days when the system was not implemented, especially for women. As the current scenario is very much advanced in case of gender discrimination but then still in some places, the areas aren’t that safe for women at night times. Especially for those areas the system of smart cabs was been designed, meaning fully eliminating of abusing female gender. These system has highly advanced devices used like sound sensors, fire flame sensors, and even some systems have live monitoring devices too that sends the information(signal) of exact situation of the cab or in an around the cab and which is directly connected with several security control rooms even to police stations. If any kind of information signal is received the security control backup is 24*7 available for the action at any point of time. Similarly the concept of this paper is also based on the above discussed applications with modifications and improvisations of few devices, as it is designed for a particular room(cabin).

Playing recorded speech samples of an enrolled speaker – “replay attack” – is a simple approach to bypass an automatic speaker verification (ASV) system. The vulnerability of ASV systems to such attacks has been acknowledged and studied, but there has been no research into what spoofing detection systems are actually learning to discriminate. In this paper, we analyse the local behaviour of a replay spoofing detection system based on convolutional neural networks (CNNs) adapted from a state-of-the-art CNN (LCNN FFT ) submitted at the ASVspoof 2017 challenge. We generate temporal and spectral explanations for predictions of the model using the SLIME algorithm. Our findings suggest that in most instances of spoofing the model is using information in the first 400 milliseconds of each audio instance to make the class prediction. Knowledge of the characteristics that spoofing detection systems are exploiting can help build less vulnerable ASV systems, other spoofing detection systems, as well as better evaluation databases1.

Biochar, created by burning organic waste in an oxygen-deprived environment, has a number of potential uses including improving soil fertility in agricultural applications, substituting for firewood as a household energy supply and acting as water filtering medium as well as its potential for carbon sequestration. Students from Arizona State University (ASU), University of New South Wales (UNSW) and Nepal's Tribbhuvan University (TU) have participated in three service learning courses in the small community of Amaltari, Nepal, to investigate the sustainable supply of biomass as well as analyzing the costs and benefits of biochar production in the community. Located in the south-central Nepal, Amaltari is one of the buffer zone communities across the Chitwan National Park and the research is focused on assessing the feasibility of using invasive plant species - Mikania micrantha and Lantana camara as the biochar feedstock. The distinguishing feature of our research is its focus on science-based and community-led innovation to co-generate solutions through integrating students and faculty from engineering and sustainability with the local biochar company and the buffer zone community of Amaltari. The case study will show that on-the-ground experiential and multiple stakeholder engagement processes involving disciplines ranging from sustainability to engineering to cultural anthropology have the potential to address ‘wicked’ environmental problems. We summarize the preliminary outcomes for the community and discuss the effective design and implementation of international service learning projects.

In this paper, we aim to understand what makes replay spoofing detection difficult in the context of the ASVspoof 2017 corpus. We use FFT spectra, mel frequency cepstral coefficients (MFCC) and inverted MFCC (IMFCC) frontends and investigate different backends based on Convolutional Neural Networks (CNNs), Gaussian Mixture Models (GMMs) and Support Vector Machines (SVMs). On this database, we find that IMFCC frontend based systems show smaller equal error rate (EER) for high quality replay attacks but higher EER for low quality replay attacks in comparison to the baseline. However, we find that it is not straightforward to understand the influence of an acoustic environment (AE), a playback device (PD) and a recording device (RD) of a replay spoofing attack. One reason is the unavailability of metadata for genuine recordings. Second, it is difficult to account for the effects of the factors: AE, PD and RD, and their interactions. Finally, our frame-level analysis shows that the presence of cues (recording artefacts) in the first few frames of genuine signals (missing from replayed ones) influence class prediction.

A “replay attack” involves replaying pre-recorded speech of an enrolled speaker to bypass an automatic speaker verification system. The 2017 ASVspoof Challenge focused on this kind of attack. In this paper, we describe our evaluation work after this challenge. First, we study the effectiveness of Gaussian Mixture Model (GMM) systems using six different hand-crafted features for detecting a replay attack. Second, we take a deeper look at these GMM systems and perform a frame-level analysis of log likelihoods. Our analysis shows how system performance can depend on a simple class-dependent cue in the dataset: initial silence frames of zeros appear in the genuine signals but missing in the spoofed version. Third, we show how we can fool these systems using this cue. For example, we find the equal error rate (EER) of one GMM system dramatically rises from 14.82 to 44.44 when we add the cue to the evaluation data. Finally, we explore whether this problem can be mitigated by pre-processing the 2017 ASV spoof Challenge dataset.

This Robotic things are envisaged to have more human interaction when integrated with Internet of Things (IoT). An experimental set up is framed to validate the concept of Internet of Things based Robotic Car Control (loRCar), as proof of concept. Heterogeneous hardware platforms, communication protocols, technologies, sensors, actuators, and even operating systems are assembled together to validate the underlying approach of integrating robotic things with the IoT in reality. Publish and subscribe time to and from the IoRCar are measured 46ms and 34ms. Time to delay in data push to the Xively cloud is measured as 254ms.