Intramolecular Locked Dithioalkylbithiophene‐Based Semiconductors for High‐Performance Organic Field‐Effect Transistors

New 3,3′‐dithioalkyl‐2,2′‐bithiophene ( SBT )‐based small molecular and polymeric semiconductors are synthesized by end‐capping or copolymerization with dithienothiophen‐2‐yl units. Single‐crystal, molecular orbital computations, and optical/electrochemical data indicate that the SBT core is completely planar, likely via S(alkyl)?S(thiophene) intramolecular locks. Therefore, compared to semiconductors based on the conventional 3,3′‐dialkyl‐2,2′‐bithiophene, the resulting SBT systems are planar (torsional angle <1°) and highly π‐conjugated. Charge transport is investigated for solution‐sheared films in field‐effect transistors demonstrating that SBT can enable good semiconducting materials with hole mobilities ranging from ≈0.03 to 1.7 cm² V^?1 s^?1. Transport difference within this family is rationalized by film morphology, as accessed by grazing incidence X‐ray diffraction experiments.     

Pair Interaction between Two Catalytically Active Colloids

Due to the intrinsically complex non-equilibrium behavior of the constituents of active matter systems, a comprehensive understanding of their collective properties is a challenge that requires systematic bottom–up characterization of the individual components and their interactions. For self-propelled particles, intrinsic complexity stems from the fact that the polar nature of the colloids necessitates that the interactions depend on positions and orientations of the particles, leading to a 2d − 1 dimensional configuration space for each particle, in d dimensions. Moreover, the interactions between such non-equilibrium colloids are generically non-reciprocal, which makes the characterization even more complex. Therefore, derivation of generic rules that enable us to predict the outcomes of individual encounters as well as the ensuing collective behavior will be an important step forward. While significant advances have been made on the theoretical front, such systematic experimental characterizations using simple artificial systems with measurable parameters are scarce. Here, two different contrasting types of colloidal microswimmers are studied, which move in opposite directions and show distinctly different interactions. To facilitate the extraction of parameters, an experimental platform is introduced in which these parameters are confined on a 1D track. Furthermore, a theoretical model for interparticle interactions near a substrate is developed, including both phoretic and hydrodynamic effects, which reproduces their behavior. For subsequent validation, the degrees of freedom are increased to 2D motion and resulting trajectories are predicted, finding remarkable agreement. These results may prove useful in characterizing the overall alignment behavior of interacting self-propelling active swimmer and may find direct applications in guiding the design of active-matter systems involving phoretic and hydrodynamic interactions.     

An improved block based copy-move forgery detection technique

Multimedia Tools and Applications - With the increase in demand for identification of authenticity of the digital images, researchers are widely studying the image forgery detection techniques....     

High Diversity Gain MIMO-Antenna for UWB Application with WLAN Notch Band Characteristic Including Human Interface Devices

Wireless Personal Communications - In this paper, a UWB–MIMO antenna with the WLAN band-notch (5.1–5.85&nbsp;GHz) characteristic is offered. This antenna consists of two radiated...     

An Experimental Evaluation of Link Outage Due to Beam Wander in a Turbulent FSO Link

Wireless Personal Communications - Wireless communication using free space optics (FSO) is becoming attractive for data transmission purposes. However, the system performance gets affected by...     

A policy based framework for quality of service management in software defined networks

Growth in multimedia traffic over the Internet increases congestion in the network architecture. Software-Defined Networking (SDN) is a novel paradigm that solves the congestion problem and allows the network to be dynamic, intelligent, and it centrally controls the network devices. SDN has many advantages in comparison to traditional networks, such as separation of forwarding and control plane from devices, global centralized control, management of network traffic. We design a policy-based framework to enhance the Quality of Service (QoS) of multimedia traffic flows in a potential SDN environment. We phrase a max-flow-min-cost routing problem to determine the routing paths and presented a heuristic method to route the traffic flows in the network in polynomial time. The framework monitors the QoS parameters of traffic flows and identifies policy violations due to link congestion in the network. The introduced approach dynamically implements policy rules to SDN switches upon detection of policy violations and reroutes the traffic flows. The results illustrate that the framework achieves a reduction in end-to-end delay, average jitter, and QoS violated flows by 24%, 37%, and 25%, respectively, as compared to the Delay Minimization method. Furthermore, the proposed approach has achieved better results when compared to SDN without policy-based framework and reduced end-to-end delay, average jitter, and QoS violated flows by 51%, 62%, and 28%, respectively.

     

Performance improvement of Deep Learning Models using image augmentation techniques

The major barrier while using deep learning models is lack of large number of images in the training dataset. In fact, there is a need of thousands of images in each image categories based on the complexity of problem. Prior studies have shown that picture augmentation techniques can be used to enhance the number of images in a training dataset artificially. These techniques can aid in improving the overall learning process and performance of a deep learning model. Hence, to address this problem we have proposed three algorithms. Firstly, two image acquisition algorithms have been proposed to systematically obtain real field images for testing and images from public datasets for training a model. Secondly, an algorithm is proposed to describe the procedure how the augmentations can be applied to enhance the datasets. During this study, we have investigated 52 augmentations that can allow enhancing the size of input dataset by improving the quantity of images. To perform the classification process of four maize crop diseases, a new convolutional neural network model is developed and several experiments have been performed to prove its effectiveness. Firstly, two tests were carried out using the original dataset from Kaggle public repository and the augmented dataset. When compared with the original dataset, the model improved by 5.14% with the augmented dataset. Secondly, three experiments carried out to evaluate the performance of proposed augmentation method. Experimental results demonstrated that the proposed approach outperforms the existing three approaches by 27.38%, 3.14%, and 1.34% during the classification process. The proposed IPA augmentation method has been compared with six existing methods: Full Stage Data Augmentation Framework, LeafGAN, Novel Augmentation method based on GAN, Wasserstein Generative Adversarial Network (WGAN), Activation Reconstruction-GAN, and Step-by-Step Data Augmentation Method and experimental results show that performance is better than existing methods by 28.31%, 19.76%, 20.18%, 13.75%, 2.42%, and 12.68% respectively.

     

Two dimensional analytical modeling of a high-K gate stack triple-material double gate strained silicon-on-nothing MOSFET with a vertical Gaussian doping

This paper presents the two-dimensional analytical modeling of high-k gate stack Triple material double gatestrained SON MOSFET with a vertical Gaussian-like doping profile. The expression for surface potential has been calculated by solving the 2-D Poisson’s equation and by considering the parabolic potential approximation. The threshold voltages as well as the electric field are also calculated for the proposed model. In addition, detailed studies of the device response towards the various short-channel effects are also examined. The analytical results are verified using the results obtained from a 2-D device simulator, namely ATLAS, Silvaco.     

Tent-Shaped Surface Morphologies of Silicon: Texturization by Metal Induced Etching

Silicon - Nano-metal/semiconductor junction dependent porosification of silicon has been studied here. The silicon nanostructures (SiNSs) have been textured on n- and p- type silicon wafers using...