Frontiers in the Growth of Complex Oxide Thin Films: Past,Present, and Future of Hybrid MBE

Driven by an ever‐expanding interest in new material systems with new functionality, the growth of atomic‐scale electronic materials by molecular beam epitaxy (MBE) has evolved continuously since the 1950s. Here, a new MBE technique called hybrid‐MBE (hMBE) is reviewed that has been proven a powerful approach for tackling the challenge of growing high‐quality, multicomponent complex oxides, specifically the ABO₃ perovskites. The goal of this work is to (1) discuss the development of hMBE in a historical context, (2) review the advantageous surface kinetics and chemistry that enable the self‐regulated growth of ABO₃ perovskites, (3) layout the key components and technical challenges associated with hMBE, (4) review the status of the field and the materials that have been successfully grown by hMBE which demonstrate its general applicability, and (5) discuss the future of hMBE in regards to technical innovations and expansion into new material classes, which are aimed at expanding into industrial realm and at tackling new scientific endeavors.     

Shape Evolution of Highly Lattice-Mismatched InN/InGaN Nanowire Heterostructures

We have investigated the structure and shape of GaN-based nanowires grown on (001) Si substrates for optoelectronic device applications. The nanowire heterostructures contained InN disks and In_0.4Ga_0.6N barrier layers in the active region. The resulting nanowire array comprised two differently shaped nanowires: shorter pencil-like nanowires and longer bead-like nanowires. The two different nanowire shapes evolve due to a variation in the In incorporation rate, which was faster for the bead-like nanowires. Both types of nanowires exhibited evidence of significant migration of both Ga and In during growth. Ga tended to diffuse away and down along the sidewalls, resulting in a Ga-rich shell for all nanowires. Despite the complex structure and great variability in the In composition, the optical properties of the nanowire arrays were very good, with strong luminescence peaking at ～ 1.63 μm.     

FT-SDN: A Fault-Tolerant Distributed Architecture for Software Defined Network

Wireless Personal Communications - The recent evolution in wireless technologies has brought a new notion called Internet of Things (IoT), in which all objects can communicate to each other....     

Ab initio study of mono-layer 2-D insulators (X-(OH)2 and h-BN) and their use in MTJ memory device

The paper presents an ab initio study of the 2-D insulators and their effect on the performance of a magnetic tunnel junction memory (MTJ) device. MTJ devices has been considered as an alternate to the charge based data storage cells due to its spin-polarised operation and high scaling probability. The use of 2-D insulators like X-(OH)₂ (X: Ca and Mg) and h-BN (hexagonal-Boron Nitride) in such device would be interesting. The authors have calculated the band structures, density of states and effective mass of electrons and holes for the mono-layer of these three non-conventional 2-D insulators using the first principle calculations in density functional theory framework using Quantumwise ATK tool. The ab initio calculation yielded band gap (E_g) of 4.633, 4.685 and 4.249 eV for h-BN, Ca(OH)₂ and Mg(OH)₂, respectively. The effective mass of electrons was calculated as 0.621, 0.604 and 0.478 for single layer h-BN, Ca(OH)₂ and Mg(OH)₂, respectively. While for holes it is 0.834, 0.446 and 0.407, respectively for h-BN, Ca(OH)₂ and Mg(OH)₂. The MTJ device properties as tunneling-magneto resistance, differential TMR, parallel and anti-parallel resistance, differential resistance and spin transfer torque components (in-plane and out-of-plane) with these materials as composite dielectric has been reported in this paper using MTJ Lab tool. The performance of MTJ memory device with h-BN based composite dielectric is found better.

     

An investigation of byte n-gram features for malware classification

Malware classification using machine learning algorithms is a difficult task, in part due to the absence of strong natural features in raw executable binary files. Byte n-grams previously have been used as features, but little work has been done to explain their performance or to understand what concepts are actually being learned. In contrast to other work using n-gram features, in this work we use orders of magnitude more data, and we perform feature selection during model building using Elastic-Net regularized Logistic Regression. We compute a regularization path and analyze novel multi-byte identifiers. Through this process, we discover significant previously unreported issues with byte n-gram features that cause their benefits and practicality to be overestimated. Three primary issues emerged from our work. First, we discovered a flaw in how previous corpora were created that leads to an over-estimation of classification accuracy. Second, we discovered that most of the information contained in n-grams stem from string features that could be obtained in simpler ways. Finally, we demonstrate that n-gram features promote overfitting, even with linear models and extreme regularization.     

Parallel inverter control using different conventional control methods and an improved virtual oscillator control method in a standalone microgrid

This paper develops a multi-timescale coordinated operation method for microgrids based on modern deep reinforcement learning. Considering the complementary characteristics of different storage devices, the proposed approach achieves multi-timescale coordination of battery and supercapacitor by introducing a hierarchical two-stage dispatch model. The first stage makes an initial decision irrespective of the uncertainties using the hourly predicted data to minimize the operational cost. For the second stage, it aims to generate corrective actions for the first-stage decisions to compensate for real-time renewable generation fluctuations. The first stage is formulated as a non-convex deterministic optimization problem, while the second stage is modeled as a Markov decision process solved by an entropy-regularized deep reinforcement learning method, i.e., the Soft Actor-Critic. The Soft Actor-Critic method can efficiently address the exploration–exploitation dilemma and suppress variations. This improves the robustness of decisions. Simulation results demonstrate that different types of energy storage devices can be used at two stages to achieve the multi-timescale coordinated operation. This proves the effectiveness of the proposed method.     

A robust and high precision optimal explicit guidance scheme for solid motor propelled launch vehicles with thrust and drag uncertainty

A new nonlinear optimal and explicit guidance law is presented in this paper for launch vehicles propelled by solid motors. It can ensure very high terminal precision despite not having the exact knowledge of the thrust–time curve apriori. This was motivated from using it for a carrier launch vehicle in a hypersonic mission, which demands an extremely narrow terminal accuracy window for the launch vehicle for successful initiation of operation of the hypersonic vehicle. The proposed explicit guidance scheme, which computes the optimal guidance command online, ensures the required stringent final conditions with high precision at the injection point. A key feature of the proposed guidance law is an innovative extension of the recently developed model predictive static programming guidance with flexible final time. A penalty function approach is also followed to meet the input and output inequality constraints throughout the vehicle trajectory. In this paper, the guidance law has been successfully validated from nonlinear six degree-of-freedom simulation studies by designing an inner-loop autopilot as well, which enhances confidence of its usefulness significantly. In addition to excellent nominal results, the proposed guidance has been found to have good robustness for perturbed cases as well.     

A novel residual energy‐based distributed clustering and routing approach for performance study of wireless sensor network

Non‐uniform energy consumption during operation of a cluster‐based routing protocol for large‐scale wireless sensor networks (WSN) is major area of concern. Unbalanced energy consumption in the wireless network results in early node death and reduces the network lifetime. This is because nodes near the sink are overloaded in terms of data traffic compared with the far away nodes resulting in node deaths. In this work, a novel residual energy–based distributed clustering and routing (REDCR) protocol has been proposed, which allows multi‐hop communication based on cuckoo‐search (CS) algorithm and low‐energy adaptive‐clustering–hierarchy (LEACH) protocol. LEACH protocol allows choice of possible cluster heads by rotation at every round of data transmission by a newly developed objective function based on residual energy of the nodes. The information about the location and energy of the nodes is forwarded to the sink node where CS algorithm is implemented to choose optimal number of cluster heads and their positions in the network. This approach helps in uniform distribution of the cluster heads throughout the network and enhances the network stability. Several case studies have been performed by varying the position of the base stations and by changing the number of nodes in the area of application. The proposed REDCR protocol shows significant improvement by an average of 15% for network throughput, 25% for network scalability, 30% for network stability, 33% for residual energy conservation, and 60% for network lifetime proving this approach to be more acceptable one in near future.     

Shorter Quasi-Adaptive NIZK Proofs for Linear Subspaces

We define a novel notion of quasi-adaptive non-interactive zero-knowledge (NIZK) proofs for probability distributions on parameterized languages. It is quasi-adaptive in the sense that the common reference string (CRS) generator can generate the CRS depending on the language parameters. However, the simulation is required to be uniform, i.e., a single efficient simulator should work for the whole class of parameterized languages. For distributions on languages that are linear subspaces of vector spaces over bilinear groups, we give computationally sound quasi-adaptive NIZKs that are shorter and more efficient than Groth–Sahai NIZKs. For many cryptographic applications quasi-adaptive NIZKs suffice and our constructions can lead to significant efficiency improvements in the standard model. Our construction can be based on any k-linear assumption, and in particular under the eXternal Diffie Hellman (XDH) assumption our proofs are even competitive with Random Oracle-based \(\Sigma \)-protocol NIZK proofs. We also show that our system can be extended to include integer tags in the defining linear equations, where the tags are provided adaptively by the adversary. This leads to applicability of our system to many applications that use tags, e.g., applications using Cramer–Shoup projective hash proofs. Our techniques also lead to the shortest known (ciphertext) fully secure identity-based encryption scheme under standard static assumptions. Further, we also get a short publicly verifiable CCA2-secure IBE scheme.