Distance metric-based time-efficient fuzzy algorithm for abnormal magnetic resonance brain image segmentation

Image segmentation is one of the significant computational applications of the biomedical field. Automated computational methodologies are highly preferred for medical image segmentation since these techniques are immune to human perception error. Artificial intelligence (AI)-based techniques are often used for this process since they are superior to other automated techniques in terms of accuracy and convergence time period. Fuzzy systems hold a significant position among the AI techniques because of their high accuracy. Even though these systems are exceptionally accurate, the time period required for convergence is exceedingly high. In this work, a novel distance metric-based fuzzy C-means (FCM) algorithm is proposed to tackle the low-convergence-rate problem of the conventional fuzzy systems. This modified approach involves the concept of distance-based dimensionality reduction of the input vector space that substantially reduces the iterative time period of the conventional FCM algorithm. The effectiveness of the modified FCM algorithm is explored in the context of magnetic resonance brain tumor image segmentation. Experimental results show promising results for the proposed approach in terms of convergence time period and segmentation efficiency. Thus, this algorithm proves to be highly feasible for time-oriented real-time applications.     

Investigation of the Device Degradation Mechanism in Pentacene-Based Thin-Film Transistors Using Low-Frequency-Noise Spectroscopy

The degradation process in pentacene-based organic thin-film transistors (OTFTs) is investigated. Pentacene-based OTFTs were fabricated with and without octadecyl trichlorosilane (OTS) treatment, and their device characteristics during lifetime test are evaluated using low-frequency-noise (LFN) spectroscopy. It is found that the devices exhibited the $hbox{1}/f$ type of noise behavior with generation and recombination noise superimposed. The drain-current noise was found to vary proportionally with drain current according to Hooge's empirical relation of flicker noise. Devices without any treatment show obvious interface traps and deep-level traps, while devices with OTS treatment show nonexistence of interface traps and suppression of deep-level traps. The LFN intensity is found to decrease during the device lifetime test initially, while upon the device failure, the noise level is observed to increase again. The viability of using LFN as a diagnostic tool in the organic transistor is demonstrated.      

Antlion Algorithm Optimized Fuzzy PID Supervised On-line Recurrent Fuzzy Neural Network Based Controller for Brushless DC Motor

In this paper, Antlion algorithm optimized Fuzzy PID supervised on-line Recurrent Fuzzy Neural Network based controller is proposed for the speed control of Brushless DC motor. Learning parameters of the supervised on-line recurrent fuzzy neural network controller, i.e., learning rate (η), dynamic factor (α), and number nodes (N_i) are optimized using Genetic algorithm, Particle Swarm optimization, Ant colony optimization, Bat algorithm, and Antlion algorithm. The proposed controller is tested with different operating conditions of the Brushless DC motor, such as varying load conditions and varying set speed conditions. The time domain specifications such as rise time, overshoot, undershoot, settling time, recovery time, and steady state error and also integral performance indices such as root mean square error, integral of absolute error, integral of squared error, and integral of time multiplied absolute error are measured and compared for above optimized controller. Simulation results show Antlion algorithm optimized Fuzzy PID supervised on-line recurrent fuzzy neural network based controller has proved to be superior than other considered controllers in all aspects. In addition, the experimental verification of proposed control system is presented to test the effectiveness of the proposed controller with different operating conditions of the Brushless DC motor.     

Multi-Fidelity High-Throughput Optimization of Electrical Conductivity in P3HT-CNT Composites

Combining high-throughput experiments with machine learning accelerates materials and process optimization toward user-specified target properties. In this study, a rapid machine learning-driven automated flow mixing setup with a high-throughput drop-casting system is introduced for thin film preparation, followed by fast characterization of proxy optical and target electrical properties that completes one cycle of learning with 160 unique samples in a single day, a > 10 ×  improvement relative to quantified, manual-controlled baseline. Regio-regular poly-3-hexylthiophene is combined with various types of carbon nanotubes, to identify the optimum composition and synthesis conditions to realize electrical conductivities as high as state-of-the-art 1000 S cm⁻¹. The results are subsequently verified and explained using offline high-fidelity experiments. Graph-based model selection strategies with classical regression that optimize among multi-fidelity noisy input-output measurements are introduced. These strategies present a robust machine-learning driven high-throughput experimental scheme that can be effectively applied to understand, optimize, and design new materials and composites.     

Biological,Chemical, and Electronic Applications of Nanofibers

With their high‐surface‐to‐volume ratio, nanofibers have been postulated to increase interactions between nanofibrous materials and targeted substrates, which are helpful to overcome many obstacles and enhance the efficiency in a diverse number of applications. Over the past decade, many studies have been published on the fabrication of nanofibers and their applications in various fields. In this review, novel biological, chemical, and electrical characteristics of nanofibers as well as their recent status and achievements in medicine, chemistry, and electronics are analyzed. It is found that nanofibers can induce fast regeneration of many tissues/organs in medical applications and improve the efficiency of many chemical and electronics applications.

     

<Emphasis Type="Bold">Ppssm:push/pull smooth video streaming multicast protocol design and implementation for an overlay network</Emphasis>

IP multicast is one of the best techniques for video streaming on the Internet. It faces issues with respect to address allocation, routing, authorization, group management, security, and scalability. By default, local Internet Service Providers did not enable IP multicast services, because of the cost incurred in using multicast-enabled routers. To solve these issues some of the IP layer functionalities have been shifted to the Application Layer, thus leading to Application Layer Multicast (ALM) protocols. However, ALM protocols face issues related to synchronous data delivery, scalability, link stress, link stretch and node failures. Some of the existing protocols are CoolStreaming, and mTreebone. A novel ALM protocol based Push/Pull Smooth video Streaming Multicast (PPSSM) protocol is proposed in this paper, to increase the throughput and reduce the packet loss rate. The PPSSM protocol involves three stages, such as tree-mesh construction, dynamic buffer management and network coding techniques. In the tree-mesh construction, a tree consists of stable nodes and a mesh consists of unstable nodes. The proposed PPSSM optimizes the stable nodes in the tree, which minimizes or eliminates the pull operations from the unstable mesh overlay nodes, by exploring the potential of the stable nodes. Dynamic buffer management is achieved by setting the optimal buffer threshold value, using the optimization of the sensitivity parameters, such as packet loss and packet workload/delay by the Infinitesimal Perturbation Analysis and Stochastic Approximation algorithms. In addition to the tree-mesh construction and buffer management, the introduction of the network coding technique will enhance the throughput and minimize the packet loss and delay. Finally, the performance of the proposed PPSSM protocol is compared with those of CoolStreaming, and mTreebone, and it shows improvement in respect of throughput, packet loss, and average decoding time.     

Aqueous Metal Oxide Inks for Modifying Electrode Work Function in Polymer Solar Cells

A method to prepare aqueous metal oxide inks for tuning the work function (WF) of electrodes is demonstrated. Thin films prepared from the metal oxide ink based on vanadium oxide (V₂O₅) nanoparticles are found to increase the WF of an indium‐tin‐oxide (ITO) electrode. ITO substrates modified with V₂O₅ films are applied as a hole selective layer (HSL) in polymer solar cells (PSCs) using a poly(3‐hexylthiophene) and [6,6]‐phenyl‐C61 butyric acid methyl ester blend as a photoactive layer. The PSCs prepared with V₂O₅‐modified ITO show better device performance, achieving a power conversion efficiency of 3.6%, demonstrating 15% enhancement compared to conventional ITO/poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT‐PSS) based devices. Furthermore, ITO/V₂O₅‐modified devices exhibit better ambient stability with 60% improvement in device lifetime than those using PEDOT:PSS as an HSL. This solution‐processable and highly stable WF‐modifying metal oxide film can be a potential alternative material for engineering interfaces in optoelectronic devices.     

Deploying Wireless Sensor Networks under Limited Mobility Constraints

In this paper, we study the issue of sensor network deployment using limited mobility sensors. By limited mobility, we mean that the maximum distance that sensors are capable of moving to is limited. Given an initial deployment of limited mobility sensors in a field clustered into multiple regions, our deployment problem is to determine a movement plan for the sensors to minimize the variance in number of sensors among the regions and simultaneously minimize the sensor movements. Our methodology to solve this problem is to transfer the nonlinear variance/movement minimization problem into a linear optimization problem through appropriate weight assignments to regions. In this methodology, the regions are assigned weights corresponding to the number of sensors needed. During sensor movements across regions, larger weight regions are given higher priority compared to smaller weight regions, while simultaneously ensuring a minimum number of sensor movements. Following the above methodology, we propose a set of algorithms to our deployment problem. Our first algorithm is the optimal maximum flow-based (OMF) centralized algorithm. Here, the optimal movement plan for sensors is obtained based on determining the minimum cost maximum weighted flow to the regions in the network. We then propose the simple peak-pit-based distributed (SPP) algorithm that uses local requests and responses for sensor movements. Using extensive simulations, we demonstrate the effectiveness of our algorithms from the perspective of variance minimization, number of sensor movements, and messaging overhead under different initial deployment scenarios.