Effects of TNT Blast on Multistory Structure

For the last few years, rebel activities in addition to related intimidation have been a rising concern throughout the world which not only impacts the human life but also leads to loss of property, both structural impacts and its corporeal integrity. Special prominence has also been agreed to nuisance such as explosion and seismic activity. Different STAAD models generated were analyzed, the results of analysis have been discussed in this section. It contains different amount of TNT and varying standoff distance. Each case has its own maximum and minimum displacement which was found by using STAAD Pro V8i. Two different framed structures were used. In one of the cases, we used simple concrete bare frame having only beams and columns, whereas in the other case we used concrete framed structure with shear wall

Creating variability through interspecific hybridization and its utilization for genetic improvement in mungbean [Vigna radiata (L.) Wilczek]

Interspecific hybridization is important for genetic enhancement of crop plants. The present study was conducted to study genetic variation in advanced interspecific lines of mungbean for yield and its component traits, to determine the association among different traits and their contribution towards seed yield through correlation and path coefficient analysis. A set of 64 genotypes including 51 advanced interspecific lines derived from mungbean (Vigna radiata L. Wilczek) × urdbean (Vigna mungo L. Hepper) and mungbean (Vigna radiata L. Wilczek) × ricebean (Vignaumbellata Thumb.) crosses and 13 parents (mungbean, urdbean and ricebean) was the experimental material for this study. The mean sums of squares for genotypes were highly significant for all the traits. Mean sum of squares for replications were also highly significant for all traits except days to 50 % flowering, days to maturity and harvest index at 1 % and 5 % level of significance. This indicated substantial magnitude of diversity and variability in the interspecific lines and parents under study, which could be further exploited. High to moderate PCV and GCV along with high heritability and genetic advance was observed for biological yield per plant, seed yield per plant and plant height, indicating that these traits could be easy targets for phenotypic selection and consequently, may be improved genetically via simple plant selection methods. On the basis of correlation studies, it could be concluded that all the traits under investigation except number of seeds per pod and harvest index were important for selection for yield improvement. Path analysis further revealed that harvest index could also be one of the criteria of selection for higher yield in these interspecific lines

Transport Spectroscopy of Sublattice-Resolved Resonant Scattering in Hydrogen-Doped Bilayer Graphene

We report the experimental observation of sublattice-resolved resonant scattering in bilayer graphene by performing simultaneous cryogenic atomic hydrogen doping and electron transport measurements in an ultrahigh vacuum. This allows us to monitor the hydrogen adsorption on the different sublattices of bilayer graphene without atomic-scale microscopy. Specifically, we detect two distinct resonant scattering peaks in the gate-dependent resistance, which evolve as a function of the atomic hydrogen dosage. Theoretical calculations show that one of the peaks originates from resonant scattering by hydrogen adatoms on the a sublattice (dimer site) while the other originates from hydrogen adatoms on the beta sublattice (nondimer site), thereby enabling a method for characterizing the relative sublattice occupancy via transport measurements. Utilizing this new capability, we investigate the adsorption and thermal desorption of hydrogen adatoms via controlled annealing and conclude that hydrogen adsorption on the beta sublattice is energetically favored. Through site-selective desorption from the alpha sublattice, we realize hydrogen doping with adatoms primarily on a single sublattice, which is highly desired for generating ferromagnetism

Hardware Security Primitives using Passive RRAM Crossbar Array: Novel TRNG and PUF Designs

With rapid advancements in electronic gadgets, the security and privacy aspects of these devices are significant. For the design of secure systems, physical unclonable function (PUF) and true random number generator (TRNG) are critical hardware security primitives for security applications. This paper proposes novel implementations of PUF and TRNGs on the RRAM crossbar structure. Firstly, two techniques to implement the TRNG in the RRAM crossbar are presented based on write-back and 50% switching probability pulse. The randomness of the proposed TRNGs is evaluated using the NIST test suite. Next, an architecture to implement the PUF in the RRAM crossbar is presented. The initial entropy source for the PUF is used from TRNGs, and challenge-response pairs (CRPs) are collected. The proposed PUF exploits the device variations and sneak-path current to produce unique CRPs. We demonstrate, through extensive experiments, reliability of 100%, uniqueness of 47.78%, uniformity of 49.79%, and bit-aliasing of 48.57% without any post-processing techniques. Finally, the design is compared with the literature to evaluate its implementation efficiency, which is clearly found to be superior to the state-of-the-art.Comment: To appear at ASP-DAC 202

Using GANs to synthesise minimum training data for deepfake generation

There are many applications of Generative Adversarial Networks (GANs) in fields like computer vision, natural language processing, speech synthesis, and more. Undoubtedly the most notable results have been in the area of image synthesis and in particular in the generation of deepfake videos. While deepfakes have received much negative media coverage, they can be a useful technology in applications like entertainment, customer relations, or even assistive care. One problem with generating deepfakes is the requirement for a lot of image training data of the subject which is not an issue if the subject is a celebrity for whom many images already exist. If there are only a small number of training images then the quality of the deepfake will be poor. Some media reports have indicated that a good deepfake can be produced with as few as 500 images but in practice, quality deepfakes require many thousands of images, one of the reasons why deepfakes of celebrities and politicians have become so popular. In this study, we exploit the property of a GAN to produce images of an individual with variable facial expressions which we then use to generate a deepfake. We observe that with such variability in facial expressions of synthetic GAN-generated training images and a reduced quantity of them, we can produce a near-realistic deepfake videos

Integrated Architecture for Neural Networks and Security Primitives using RRAM Crossbar

This paper proposes an architecture that integrates neural networks (NNs) and hardware security modules using a single resistive random access memory (RRAM) crossbar. The proposed architecture enables using a single crossbar to implement NN, true random number generator (TRNG), and physical unclonable function (PUF) applications while exploiting the multi-state storage characteristic of the RRAM crossbar for the vector-matrix multiplication operation required for the implementation of NN. The TRNG is implemented by utilizing the crossbar's variation in device switching thresholds to generate random bits. The PUF is implemented using the same crossbar initialized as an entropy source for the TRNG. Additionally, the weights locking concept is introduced to enhance the security of NNs by preventing unauthorized access to the NN weights. The proposed architecture provides flexibility to configure the RRAM device in multiple modes to suit different applications. It shows promise in achieving a more efficient and compact design for the hardware implementation of NNs and security primitives

MemSPICE: Automated Simulation and Energy Estimation Framework for MAGIC-Based Logic-in-Memory

Existing logic-in-memory (LiM) research is limited to generating mappings and micro-operations. In this paper, we present~\emph{MemSPICE}, a novel framework that addresses this gap by automatically generating both the netlist and testbench needed to evaluate the LiM on a memristive crossbar. MemSPICE goes beyond conventional approaches by providing energy estimation scripts to calculate the precise energy consumption of the testbench at the SPICE level. We propose an automated framework that utilizes the mapping obtained from the SIMPLER tool to perform accurate energy estimation through SPICE simulations. To the best of our knowledge, no existing framework is capable of generating a SPICE netlist from a hardware description language. By offering a comprehensive solution for SPICE-based netlist generation, testbench creation, and accurate energy estimation, MemSPICE empowers researchers and engineers working on memristor-based LiM to enhance their understanding and optimization of energy usage in these systems. Finally, we tested the circuits from the ISCAS'85 benchmark on MemSPICE and conducted a detailed energy analysis.Comment: Accepted in ASP-DAC 202

Probing Tunneling Spin Injection into Graphene via Bias Dependence

The bias dependence of spin injection in graphene lateral spin valves is systematically studied to determine the factors affecting the tunneling spin injection efficiency. Three types of junctions are investigated, including MgO and hexagonal boron nitride (hBN) tunnel barriers and direct contacts. A DC bias current applied to the injector electrode induces a strong nonlinear bias dependence of the nonlocal spin signal for both MgO and hBN tunnel barriers. Furthermore, this signal reverses its sign at a negative DC bias for both kinds of tunnel barriers. The analysis of the bias dependence for injector electrodes with a wide range of contact resistances suggests that the sign reversal correlates with bias voltage rather than current. We consider different mechanisms for nonlinear bias dependence and conclude that the energy-dependent spin-polarized electronic structure of the ferromagnetic electrodes, rather than the electrical field-induced spin drift effect or spin filtering effect of the tunnel barrier, is the most likely explanation of the experimental observations.Comment: 10 pages, 6 figure