Optimum diversity combiner based multiuser detection fortime-dispersive Rician fading CDMA channels

Multiuser detection for asynchronous code division multiple access (CDMA) data transmission over the time-dispersive two-path Rician fading channel is considered. The multiuser maximum likelihood sequence detector (MLSD) is derived, and an equivalence of the fading channel to an asynchronous Gaussian intersymbol interference (AGISI) CDMA channel is established. However, the MLSD is found to be implementationally infeasible and this motivates the derivation of the optimum linear detector with near/far resistance as the performance criterion. The optimally near/far resistant linear time-invariant K-user detector is shown to consist of a cascade of a 2 K input/K output linear multiuser diversity combining filter followed by a K input/K output decorrelator that is designed for the equivalent AGISI/CDMA channel. This detector solves the near/far problem and also supports significantly higher bandwidth efficiencies for CDMA communication over the fading channel than does the conventional near/far limited single-user diversity combiner. The performance penalties incurred by multiuser detectors designed for the Gaussian channel when used over the Rician fading channel are also analytically characterized. It is shown that these penalties can be significant, making the case for the use of multiuser detectors optimized for this fading channel, particularly the optimum linear detector due to its relative implementational simplicity     

Hypoglycaemic and Hypolipidaemic Effects of Withania somnifera Root and Leaf Extracts on Alloxan-Induced Diabetic Rats

Withania somnifera is an important medicinal plant, which is used in traditional medicine to cure many diseases. Flavonoids were determined in the extracts of W. somnifera root (WSREt) and leaf (WSLEt). The amounts of total flavonoids found in WSREt and WSLEt were 530 and 520 mg/100 g dry weight (DW), respectively. Hypoglycaemic and hypolipidaemic effects of WSREt and WSLEt were also investigated in alloxan-induced diabetic rats. WSREt and WSLEt and the standard drug glibenclamide were orally administered daily to diabetic rats for eight weeks. After the treatment period, urine sugar, blood glucose, haemoglobin (Hb), glycosylated haemoglobin (HbA1C), liver glycogen, serum and tissues lipids, serum and tissues proteins, liver glucose-6-phosphatase (G6P) and serum enzymes like aspartate transaminase (AST), alanine transaminase (ALT), acid phosphatase (ACP) and alkaline phosphatase (ALP) levels were determined. The levels of urine sugar, blood glucose, HbA1C, G6P, AST, ALT, ACP, ALP, serum lipids except high density lipoprotein-bound cholesterol (HDL-c) and tissues like liver, kidney and heart lipids were significantly (p < 0.05) increased, however Hb, total protein, albumin, albumin:globulin (A:G) ratio, tissues protein and glycogen were significantly (p < 0.05) decreased in alloxan-induced diabetic rats. Treatment of the diabetic rats with WSREt, WSLEt and glibenclamide restored the changes of the above parameters to their normal level after eight weeks of treatment, indicating that WSREt and WSLEt possess hypoglycaemic and hypolipidaemic activities in alloxan-induced diabetes mellitus (DM) rats.     

The Potentials and Pitfalls of ‘Art in Research’ Methodologies: Foregrounding Memory and Emotion in Planning Research

ABSTRACT

The article draws on a two-month project with forty-four high school students in Reston, Virginia to suggest that ‘art in research’ methodologies might be useful to shift away from the problematic histories of planning as solely a technical endeavor based in masculinist conceptions of legitimate research. I propose that we can radically reimagine planning research and practice as an emancipatory endeavor for its participants, suggesting that the iterative and longer art-making process may usefully complement traditional qualitative planning research, specifically helping to uncover relevant memories and emotions of participants.     

Exploration of Electrochemical Reactions at Organic–Inorganic Halide Perovskite Interfaces via Machine Learning in In Situ Time‐of‐Flight Secondary Ion Mass Spectrometry

The instability of hybrid organic–inorganic perovskite (HOIP) devices is one of the significant challenges preventing commercialization. Exploring these phenomena is severely limited by the complexity of the intrinsic electrochemistry of HOIPs, the presence of multiple volatile and mobile ionic species, and the possible role of environmentally induced reactions at surfaces and triple‐phase junctions. Here, in situ studies of the electrochemistry of methylammonium lead bromide perovskite with the Au electrode interface are reported via light‐ and voltage‐dependent time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) imaging of lateral perovskite heterostructures. While ToF‐SIMS allows for the visualization of the chemical composition along the surface and its evolution with light and electrical bias, the interpretation of the multidimensional data obtained is often limited due to strong correlations between chemical signatures and the need to track multiple peaks at once. Here, a machine learning workflow combining the Hough transform and non‐negative matrix factorization and non‐negative tensor decomposition is developed to avoid this limitation and extract salient features of associated chemical changes and to separate the light‐ and voltage‐dependent dynamics. Combining these in situ characterizations and the machine learning workflow provides comprehensive information on the chemical nature of moving species, ion accumulation, and interfacial electrochemical reactions in HOIP devices.     

Directed Nanoscale Self‐Assembly of Low Molecular Weight Hydrogelators Using Catalytic Nanoparticles

The work presented here shows that the growth of supramolecular hydrogel fibers can be spatially directed at the nanoscale by catalytic negatively charged nanoparticles (NCNPs). The NCNPs with surfaces grafted with negatively charged polymer chains create a local proton gradient that facilitates an acid‐catalyzed formation of hydrogelators in the vicinity of NCNPs, ultimately leading to the selective formation of gel fibers around NCNPs. The presence of NCNPs has a dominant effect on the properties of the resulting gels, including gelation time, mechanical properties, and network morphology. Interestingly, local fiber formation can selectively entrap and precipitate out NCNPs from a mixture of different nanoparticles. These findings show a new possibility to use directed molecular self‐assembly to selectively trap target nano‐objects, which may find applications in therapy, such as virus infection prevention, or engineering applications, like water treatment and nanoparticle separation.     

Creep behavior of nuclear grade 316LN austenitic stainless steel at 873 K and 923 K

Mechanics of Time-Dependent Materials - Creep deformation and rupture behavior of nitrogen-alloyed (0.14 wt.%) nuclear grade 316LN austenitic stainless steel were investigated for the varying...     

Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

Data is always a crucial issue of concern especially during its prediction and computation in digital revolution. This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication. It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data. The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means. The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from data. The proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown variables. This scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic approach. It also demonstrates the generative function for Kalman-filer based prediction model and its observations. This paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration (CPE) for Python.     

Thermoresponsive Conductivity of Graphene-Based Fibers

Smart materials are versatile material systems which exhibit a measurable response to external stimuli. Recently, smart material systems have been developed which incorporate graphene in order to share on its various advantageous properties, such as mechanical strength, electrical conductivity, and thermal conductivity as well as to achieve unique stimuli-dependent responses. Here, a graphene fiber-based smart material that exhibits reversible electrical conductivity switching at a relatively low temperature (60 °C), is reported. Using molecular dynamics (MD) simulation and density functional theory-based non-equilibrium Green's function (DFT-NEGF) approach, it is revealed that this thermo-response behavior is due to the change in configuration of amphiphilic triblock dispersant molecules occurring in the graphene fiber during heating or cooling. These conformational changes alter the total number of graphene-graphene contacts within the composite material system, and thus the electrical conductivity as well. Additionally, this graphene fiber fabrication approach uses a scalable, facile, water-based method, that makes it easy to modify material composition ratios. In all, this work represents an important step forward to enable complete functional tuning of graphene-based smart materials at the nanoscale while increasing commercialization viability.     

Predicting the position of adjacent nodes with QoS in mobile ad hoc networks

The Mobile Ad Hoc Networks are a self-regulatory set of autonomous nodes which perform communication to all the other nodes within their communication ranges. The nodes which are not in straightforward range make use of in between nodes to perform communication with one another. In mobile ad hoc network, each and every autonomous node holds displacements and shifts based on the precise positions within the network. Hence the verification of node position is crucial in mobile ad hoc networks and it is mainly a great dispute during the existence of opponents focusing on damaging the system. The intention is to design a standard termed as Adjacent Node Location Confirmation (ANLC) for confirming the location of its transmitting adjacent nodes for interchanging the messages and confirms the location of the nodes in transmission within the network. Initially, the method focuses on finding the nodes based on which the transmission connection is set up or it is within the fixed distance. The distance is estimated based on message interchanges among the confirmer and its adjacent nodes in transmission. Soon after the estimation of distances the confirmer authenticates the location of nodes in transmission within the network based on straight balanced, traverse balanced and multi-lateration analysis. The analysis is performed based on QoS of the transmitting node choice for minimizing the delays and acquiring improved throughput. The performance of the designed scheme is estimated based on network throughput and delays.

     

Security challenges in internet of things: Distributed denial of service attack detection using support vector machine-based expert systems

The rapid development of internet of things (IoT) is to be the next generation of the IoT devices are a simple target for attackers due to the lack of security. Attackers can easily hack the IoT devices that can be used to form botnets, which can be used to launch distributed denial of service (DDoS) attack against networks. Botnets are the most dangerous threat to the security systems. Software-defined networking (SDN) is one of the developing filed, which introduce the capacity of dynamic program to the network. Use the flexibility and multidimensional characteristics of SDN used to prevent DDoS attacks. The DDoS attack is the major attack to the network, which makes the entire network down, so that normal users might not avail the services from the server. In this article, we proposed the DDoS attack detection model based on SDN environment by combining support vector machine classification algorithm is used to collect flow table values in sampling time periods. From the flow table values, the five-tuple characteristic values extracted and based on it the DDoS attack can be detected. Based on the experimental results, we found the average accuracy rate is 96.23% with a normal amount of traffic flow. Proposed research offers a better DDoS detection rate on SDN.