Fuzzy logic–based jamming detection algorithm for cluster‐based wireless sensor network

In this paper, a fuzzy logic–based jamming detection algorithm (FLJDA) is proposed to detect the presence of jamming in downstream data communication for cluster‐based wireless sensor networks. The proposed FLJDA keeps an eye on the existing nodes and new node to determine their behavior by applying fuzzy logic on measured jamming detection metrics. To monitor the behavior of the nodes, the FLJDA computes the jamming detection metrics, namely, packet delivery ratio and received signal strength indicator. The major features of this paper are the following: (1) The jamming detection algorithm is specifically implemented for downstream data communication, (2) cluster head estimates jamming detection metrics for detecting the jamming unlike the existing algorithms where individual nodes explicitly collect the jamming detection metrics, and (3) the proposed algorithm optimizes the jamming detection metrics on the basis of fuzzy logic unlike the existing approaches, which uses merely jamming detection threshold alone for jamming detection. The simulation results of the proposed system provide the true detection ratio as high as 99.89%.     

Low Complexity Linear Detection for Uplink Multiuser MIMO SC-FDMA Systems

Wireless Personal Communications - In this article, the computational complexity reduction of zero forcing (ZF) and minimum mean square error (MMSE) detection is presented for the uplink multiple...     

Jamming detection approach based on fuzzy assisted multicriteria decision‐making system for wireless sensor networks

The jamming detection approach based on fuzzy assisted multicriteria decision‐making system (JDA) is proposed to detect the presence of jamming in downstream communication for Cluster based Wireless Sensor Network (CWSN). The proposed approach is deployed in cluster head (CH). The JDA functions in two aspects: First, the CH periodically measures the jamming detection metrics namely Packet Delivery Ratio (PDR) and Received Signal Strength Indicator (RSSI) of every node in the cluster to determine the behavior of the sensor nodes. In order to determine the behavior of members in the cluster, the CH compares the measured PDR with the PDR threshold. If the measured PDR is lesser than the PDR threshold, then CH applies the TOPSIS method on the PDR and RSSI metrics to determine the presence of jamming. These metrics are considered as the criteria and the nodes or the members are considered to be the alternatives. Next, the fuzzy logic is applied on the results obtained from the TOPSIS method to optimize the jamming detection metrics and identify the presence of jamming accurately. The proposed jamming detection approach detects well and arrives at 99.6% jamming detection rate as shown in simulation.     

An efficient automated methodology for detecting and segmenting the ischemic stroke in brain MRI images

Brain tumor and brain stroke are two important causes of death in and around the world. The abnormalities in brain cell leads to brain stroke and obstruction in blood flow to brain cells leads to brain stroke. In this article, a computer aided automatic methodology is proposed to detect and segment ischemic stroke in brain MRI images using Adaptive Neuro Fuzzy Inference (ANFIS) classifier. The proposed method consists of preprocessing, feature extraction and classification. The brain image is enhanced using Heuristic histogram equalization technique. Then, texture and morphological features are extracted from the preprocessed image. These features are optimized using Genetic Algorithm and trained and classified using ANFIS classifier. The performance of the proposed ischemic stroke detection system is analyzed in terms of sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and Mathew's correlation coefficient.     

Sun light mediated synthesis of gold nanoparticles as carrier for 6-mercaptopurine: Preparation,characterization and toxicity studies in zebrafish embryo model

The objective of this study is to synthesize green chemistry based gold nanoparticles by sun light irradiation method. The prepared gold nanoparticles (AuNPs) were modified using folic acid and then coupled with 6-mercaptopurine. These modified nanoparticles were used as a tool for targeted drug delivery to treat laryngeal cancer. In the present study, novel bionanocomposites containing nutrient agar coated gold nano particles (N-AuNPs) coupled with 6-mercaptopurine (drug) (N-AuNPs-Mp), folic acid (ligand) (N-AuNPs-Mp-Fa) and rhodamine (dye) (N-AuNPs-Rd), a fluorescent agent, were prepared and characterized by IR, UV, TEM, Particle size analysis and in vitro stability. The toxicity and fluorescence of N-Au was studied using zebrafish embryo model. The in vitro cytotoxicity of free Mp, N-Au-Mp and N-Au-Mp-Fa against HEp-2 cells was compared and found that the amount of Mp required to achieve 50% of growth of inhibition (IC₅₀) was much lower in N-Au-Mp-Fa than in free Mp and N-Au-Mp.     

Methotrexate loaded solid lipid nanoparticles (SLN) for effective treatment of carcinoma

Solid Lipid Nanoparticles (SLN) containing Methotrexate (MTX), an anticancer drug for intravenous administration was formulated and characterized. The SLN dispersions with MTX, stearic acid, and soya lecithin in the ratio of 1:4:1, 1:4:1.5, and 1:4:2, sodium taurodeoxycholate and distilled water were prepared by micro emulsification solidification method. The results show that the prepared MTX-SLN particles (with MTX-Stearic acid-Soya lecithin--1:4:2) have an average size of 270 nm with 51.3% drug entrapment. The in-vitro release was attained up to 15th h. The pharmacokinetic study reveals that the half-life and MRT of SLNs were higher than MTX solution. The life span of EAC (Ehrlich Ascite Carcinoma) bearing mice was increased when treated with MTX-SLNs (Methotrexate nanoparticles). These results clearly indicate that SLNs are a promising sustained release drug targeting system for lipophilic antitumour drugs.     

Memristive Devices with Highly Repeatable Analog States Boosted by Graphene Quantum Dots

Memristive devices, having a huge potential as artificial synapses for low‐power neural networks, have received tremendous attention recently. Despite great achievements in demonstration of plasticity and learning functions, little progress has been made in the repeatable analog resistance states of memristive devices, which is, however, crucial for achieving controllable synaptic behavior. The controllable behavior of synapse is highly desired in building neural networks as it helps reduce training epochs and diminish error probability. Fundamentally, the poor repeatability of analog resistance states is closely associated with the random formation of conductive filaments, which consists of oxygen vacancies. In this work, graphene quantum dots (GQDs) are introduced into memristive devices. By virtue of the abundant oxygen anions released from GQDs, the GQDs can serve as nano oxygen‐reservoirs and enhance the localization of filament formation. As a result, analog resistance states with highly tight distribution are achieved with nearly 85% reduction in variations. In addition the insertion of GQDs can alter the energy band alignment and boost the tunneling current, which leads to significant reduction in both switching voltages and their distribution variations. This work may pave the way for achieving artificial neural networks with accurate and efficient learning capability.     

A Low Complexity Near Optimal Signal Detection for Large Scale MIMO SC-FDMA Uplink System

This article proposes an improved Newton algorithm as a low complexity signal detection scheme for linear receiver in large scale multiple- input multiple- output (LS-MIMO) single carrier frequency division multiple access (SC-FDMA) uplink system, where a large number of antennas are set up at the base station and active users are with a single antenna system. Data detection for uplink SC-FDMA system is one of the specific challenges due to the significant rise in the dimension of antennas and number of subcarriers. Especially for symbol detection process, LS-MIMO SC-FDMA system with linear detector requires to perform a large matrix inverse computation. Even though linear detectors such as zero forcing (ZF) and minimum mean square error (MMSE) can achieve near-optimal detection performance, they still introduce high computational complexity and obliviously involve in the computation of matrix inversion. Therefore, a design of complexity reduction algorithm based near-optimal detector for LS-MIMO SC-FDMA system attains research interest. The improved Newton algorithm is employed to obtain linear detection solution which iteratively performs matrix free-inversion operation. The new algorithm performs matrix–matrix multiplication into matrix–vector multiplication, which substantially reduces receiver detection complexity. The efficacy of the proposed method is investigated at 16-QAM. Both ZF and MMSE criteria are proposed and compared through simulations. Simulation results illustrate that the proposed scheme outperforms the conventional detection schemes and exhibits near-optimal performance with a small number of iterations. Further, bit-error-rate performance is closer to classical linear detector with affordable computational complexity.