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 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.

     

Engineering the Interlayer Spacing by Pre-Intercalation for High Performance Supercapacitor MXene Electrodes in Room Temperature Ionic Liquid

MXenes exhibit excellent capacitance at high scan rates in sulfuric acid aqueous electrolytes, but the narrow potential window of aqueous electrolytes limits the energy density. Organic electrolytes and room-temperature ionic liquids (RTILs) can provide higher potential windows, leading to higher energy density. The large cation size of RTIL hinders its intercalation in-between the layers of MXene limiting the specific capacitance in comparison to aqueous electrolytes. In this work, different chain lengths alkylammonium (AA) cations are intercalated into Ti₃C₂T_x, producing variation of MXene interlayer spacings (d-spacing). AA-cation-intercalated Ti₃C₂T_x (AA-Ti₃C₂), exhibits higher specific capacitances, and cycling stabilities than pristine Ti₃C₂T_x in 1 m 1-ethly-3-methylimidazolium bis-(trifluoromethylsulfonyl)-imide (EMIMTFSI) in acetonitrile and neat EMIMTFSI RTIL electrolytes. Pre-intercalated MXene with an interlayer spacing of ≈2.2 nm, can deliver a large specific capacitance of 257 F g⁻¹ (1428 mF cm⁻² and 492 F cm⁻³) in neat EMIMTFSI electrolyte leading to high energy density. Quasi elastic neutron scattering and electrochemical impedance spectroscopy are used to study the dynamics of confined RTIL in pre-intercalated MXene. Molecular dynamics simulations suggest significant differences in the structures of RTIL ions and AA cations inside the Ti₃C₂T_x interlayer, providing insights into the differences in the observed electrochemical behavior.     

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.

     

A Hybrid Genetic Algorithm and Back-Propagation Classifier for Gearbox Fault Diagnosis

An Artificial Neural Network (ANN) classifier trained by a hybrid GA-BP method for diagnosis of gear faults is presented here that can be incorporated in an online fault diagnostic system of vital gearboxes. The distinctive features obtained from vibration signals of a running gearbox; that was operated in normal and with faults induced conditions were used to feed the GA-BP hybrid classifier. Time domain vibration signals were divided in 40segments. From each segment features such as magnitude of peaks in time domain and spectrum along with statistical features such as central moments and standard deviations were extracted to feed the classifier. Based on the experimental results it was shown that the GA-BP hybrid classifier can successfully identify gear condition. It was also shown that the network trained by GA-BP hybrid method performs much better than ANN that is trained by standard BP or GA individually. Further, it was also shown that if prior to extraction of features; the vibration signals are pre-processed by Discrete Wavelet Transform (DWT) then efficacy of the GA-BP hybrid is significantly enhanced.     

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.     

Efficacy of Ozone and Lactic Acid as Nonthermal Hurdles for Preservation of Sugarcane Juice

Thermal processing is the most widely adopted technology for preservation of juices; however, it is associated with significant changes in nutritional quality and flavor. Combination of nonthermal hurdles (ozone 1.2 g/h, 10 min, and lactic acid 0.5%) reduced total bacterial count of sugarcane juice by 4.3 log and controlled enzymatic activity to a moderate level (reduction of 60% and 72% activity of polyphenol oxidase and peroxidase, respectively). Combined treatment was comparable to thermal treatment in maintaining microbial and sensory quality of sugarcane juice during 1-month storage under refrigerated conditions; however, additional hurdles may be required for extended storage.     

Solution‐Processed High‐Performance Tetrathienothiophene‐Based Small Molecular Blends for Ambipolar Charge Transport

Four soluble dialkylated tetrathienoacene ( TTAR) ‐based small molecular semiconductors featuring the combination of a TTAR central core, π‐conjugated spacers comprising bithiophene ( bT ) or thiophene ( T ), and with/without cyanoacrylate ( CA ) end‐capping moieties are synthesized and characterized. The molecule DbT‐TTAR exhibits a promising hole mobility up to 0.36 cm² V^?1 s^?1 due to the enhanced crystallinity of the microribbon‐like films. Binary blends of the p‐type DbT‐TTAR and the n‐type dicyanomethylene substituted dithienothiophene‐quinoid ( DTTQ‐11 ) are investigated in terms of film morphology, microstructure, and organic field‐effect transistor (OFET) performance. The data indicate that as the DbT‐TTAR content in the blend film increases, the charge transport characteristics vary from unipolar (electron‐only) to ambipolar and then back to unipolar (hole‐only). With a 1:1 weight ratio of DbT‐TTAR DTTQ‐11 in the blend, well‐defined pathways for both charge carriers are achieved and resulted in ambipolar transport with high hole and electron mobilities of 0.83 and 0.37 cm² V^?1 s^?1, respectively. This study provides a viable way for tuning microstructure and charge carrier transport in small molecules and their blends to achieve high‐performance solution‐processable OFETs.