A cubic B-spline quasi-interpolation algorithm to capture the pattern formation of coupled reaction-diffusion models

Diffusion plays a significant role in complex pattern formulations occurred in biological and chemical reactions. In this work, the authors study the effect of diffusion in coupled reaction-diffusion systems named the Gray-Scott model for complex pattern formation with the help of cubic B-spline quasi-interpolation (CBSQI) method and capture various formates of these patterns. The idea of Kronecker product is used first time with CBSQI method for 2D problems. Linear stability analysis of the reaction-diffusion system as well as stability of the proposed method is studied. Four test problems are considered to check the accuracy and efficiency of the method and found the stable patterns.

     

Formal modeling and verification of software‐defined networks: A survey

Unlike traditional networking devices, control and management plane are decoupled from data plane in software‐defined networks (SDN). The logically centralized control and management plane facilitate dynamic orchestration of network resources, services, and policies by writing software programs. This provides much needed flexibility and programmability where networking rules and policies can be modified dynamically depending upon the application context. As the operation of network services entirely depends on a program, a small fault may induce several issues which can adversely affect the expected behavior of the network. Formal modeling and verification help in catching inconsistencies and existence of errors prior to the deployment of the programs that control the behavior of a network. In this paper, we provide a comprehensive survey of tools and techniques available in the literature for formal modeling and verification of SDN. These tools and techniques are classified based on their types, the components of SDN where they can be applied, and the design and development phase when they are utilized. In particular, their respective benefits and limitations are discussed in terms of ease of use, interfaces, and the ability to capture and verify intended network properties.     

Automated TB classification using ensemble of deep architectures

Multimedia Tools and Applications - Tuberculosis (TB) is an infectious disease that mainly affects the lung region. Its initial screening is mostly performed using chest radiograph, which is also...     

Preparation,characterization, and in vivo evaluation of nano formulations of ferulic acid in diabetic wound healing

Diabetes mellitus is most common disorder characterize by hyperglycemia. Chronic hyperglycemia may lead to over production of free radicals thereby results in oxidative stress which impaired healing of wounds. Ferulic acid (FA) has been shown to have antidiabetic and antioxidant properties. The aim of the present study was to develop Ferulic acid nanoparticles and to study its hypoglycemic and wound healing activities. Ferulic acid-poly(lactic-co-glycolic acid) (FA-PLGA) nanoparticles were prepared by nano precipitation method. The prepared FA-PLGA nanoparticles had an average size of 240?nm. Scanning electron microscopy (SEM) and atomic force microscopy (AFM) analysis showed the prepared FA-PLGA nanoparticles were spherical in shape. Drug encapsulation assay showed that 88.49% FA was encapsulated in PLGA. Carbopol 980 was used to formulate FA-PLGA nanoparticles loaded hydrogel. FA-loaded polymeric nanoparticles dispersion (oral administration) and FA-loaded polymeric nanoparticles based hydrogel (topical administration) treated wounds were found to epithelize faster as compared with diabetic wound control group. The hydroxyproline content increased significantly when compared with diabetic wound control. Therefore, the results indicate that FA significantly promotes wound healing in diabetic rats.     

Residual U-Network for Breast Tumor Segmentation from Magnetic Resonance Images

Breast cancer positions as the most well-known threat and the main source of malignant growth-related morbidity and mortality throughout the world. It is apical of all new cancer incidences analyzed among females. Two features substantially influence the classification accuracy of malignancy and benignity in automated cancer diagnostics. These are the precision of tumor segmentation and appropriateness of extracted attributes required for the diagnosis. In this research, the authors have proposed a ResU-Net (Residual U-Network) model for breast tumor segmentation. The proposed methodology renders augmented, and precise identification of tumor regions and produces accurate breast tumor segmentation in contrast-enhanced MR images. Furthermore, the proposed framework also encompasses the residual network technique, which subsequently enhances the performance and displays the improved training process. Over and above, the performance of ResU-Net has experimentally been analyzed with conventional U-Net, FCN8, FCN32. Algorithm performance is evaluated in the form of dice coefficient and MIoU (Mean Intersection of Union), accuracy, loss, sensitivity, specificity, F1score. Experimental results show that ResU-Net achieved validation accuracy & dice coefficient value of 73.22% & 85.32% respectively on the Rider Breast MRI dataset and outperformed as compared to the other algorithms used in experimentation.     

Miniaturized Microstrip Patch Antenna Array for ISM Band Using Complementary Split Ring Resonator

Wireless Personal Communications - These days patch antenna arrays are widely used in various communication systems. In this research design of miniaturized microstrip patch antenna array is...     

Trust-aware energy-efficient stable clustering approach using fuzzy type-2 Cuckoo search optimization algorithm for wireless sensor networks

Wireless Networks - With the advancement of communication and sensor technologies, it has become possible to develop low-cost circuitry to sense and transmit the state of surroundings. Wireless...     

Low Cost Low Power Smart Helmet for Real-Time Remote Underground Mine Environment Monitoring

Underground mining production process is vulnerable and highly dynamic in nature. Among the various causes of accidents in underground mine, major one is presence of flammable and noxious gases. Though many existing safety gadgets are there but they could not work reliably because of the typical nature of mines structure and production variability. Wireless data and communication network is also not successful because wireless communication in underground mine is significantly more challenging than through air. This work introduces the application of mobile wireless sensor network in order to monitor a variety of parameters in underground mines which have life threatening effects towards them. Each node of the network placed over the safety gear (helmet wore statutorily by every miner) comprises of various sensors depending on the requirement with microcontroller unit and other low power accessories. The proposed work has a unique feature that it will make the personnel aware about the situation of the gases present and surrounding by automatically generating different alarms and different light indicators. Other function of this device will be to transmit the data sensed by the sensors in the device to the control room wirelessly so that the responsible person would be aware of the situation. This work is focused on the design of such a prototype model for the underground mines with the aforementioned specification.     

A BER Based Selection Combining Protocol for Adaptive Cooperative Cognitive Radios

This paper illustrates the performance of bit error rate based selection combining (BER-SC) protocol for adaptive cooperative cognitive radios. In the proposed framework, the unlicensed (i.e. secondary) system utilizes an adaptive mode of transmission to help the licensed (i.e. primary) system to achieve the desired quality of service in exchange for opportunistic spectrum access. The total transmission is divided in two phases. In Phase I, the primary transmitter (PT) broadcasts the data to the primary receiver (PR), which is overheard by the secondary transmitter (ST) and secondary receiver (SR). In Phase II, ST decodes the primary data and linearly combines its own data with the primary data. Using M-QAM the combined data is adaptively modulated, where M = 4, 16 or 64 depending on the received channel feedback, and relayed to PR and SR. At PR, BER-SC is employed to retrieve the primary data, and at SR interference cancellation is used to retrieve the secondary data. The analytical expressions are derived for the BER and the outage probability. The obtained results demonstrate the higher performance gains for both primary and secondary system by using adaptive mode of transmission at ST and BER-SC at PR.

     

A stable energy efficient clustering protocol for wireless sensor networks

Sensor networks comprise of sensor nodes with limited battery power that are deployed at different geographical locations to monitor physical events. Information gathering is a typical but an important operation in many applications of wireless sensor networks (WSNs). It is necessary to operate the sensor network for longer period of time in an energy efficient manner for gathering information. One of the popular WSN protocol, named low energy adaptive clustering hierarchy (LEACH) and its variants, aim to prolong the network lifetime using energy efficient clustering approach. These protocols increase the network lifetime at the expense of reduced stability period (the time span before the first node dies). The reduction in stability period is because of the high energy variance of nodes. Stability period is an essential aspect to preserve coverage properties of the network. Higher is the stability period, more reliable is the network. Higher energy variance of nodes leads to load unbalancing among nodes and therefore lowers the stability period. Hence, it is perpetually attractive to design clustering algorithms that provides higher stability, lower energy variance and are energy efficient. In this paper to overcome the shortcomings of existing clustering protocols, a protocol named stable energy efficient clustering protocol is proposed. It balances the load among nodes using energy-aware heuristics and hence ensures higher stability period. The results demonstrate that the proposed protocol significantly outperforms LEACH and its variants in terms of energy variance and stability period.