Spectrum sensing for cognitive radios: Algorithms, performance, and limitations

Inefficient use of radio spectrum is becoming a serious problem as more and more wireless systems are being developed to operate in crowded spectrum bands. Cognitive radio offers a novel solution to overcome the underutilization problem by allowing secondary usage of the spectrum resources along with high reliable communication. Spectrum sensing is a key enabler for cognitive radios. It identifies idle spectrum and provides awareness regarding the radio environment which are essential for the efficient secondary use of the spectrum and coexistence of different wireless systems. The focus of this thesis is on the local and cooperative spectrum sensing algorithms. Local sensing algorithms are proposed for detecting orthogonal frequency division multiplexing (OFDM) based primary user (PU) transmissions using their autocorrelation property. The proposed autocorrelation detectors are simple and computationally efficient. Later, the algorithms are extended to the case of cooperative sensing where multiple secondary users (SUs) collaborate to detect a PU transmission. For cooperation, each SU sends a local decision statistic such as log-likelihood ratio (LLR) to the fusion center (FC) which makes a final decision. Cooperative sensing algorithms are also proposed using sequential and censoring methods. Sequential detection minimizes the average detection time while censoring scheme improves the energy efficiency. The performances of the proposed algorithms are studied through rigorous theoretical analyses and extensive simulations. The distributions of the decision statistics at the SU and the test statistic at the FC are established conditioned on either hypothesis. Later, the effects of quantization and reporting channel errors are considered. Main aim in studying the effects of quantization and channel errors on the cooperative sensing is to provide a framework for the designers to choose the operating values of the number of quantization bits and the target bit error probability (BEP) for the reporting channel such that the performance loss caused by these non-idealities is negligible. Later a performance limitation in the form of BEP wall is established for the cooperative sensing schemes in the presence of reporting channel errors. The BEP wall phenomenon is important as it provides the feasible values for the reporting channel BEP used for designing communication schemes between the SUs and the FC

Maximum Eigenvalue Detection based Spectrum Sensing in RIS-aided System with Correlated Fading

Robust spectrum sensing is crucial for facilitating opportunistic spectrum utilization for secondary users (SU) in the absense of primary users (PU). However, propagation environment factors such as multi-path fading, shadowing, and lack of line of sight (LoS) often adversely affect detection performance. To deal with these issues, this paper focuses on utilizing reconfigurable intelligent surfaces (RIS) to improve spectrum sensing in the scenario wherein both the multi-path fading and noise are correlated. In particular, to leverage the spatially correlated fading, we propose to use maximum eigenvalue detection (MED) for spectrum sensing. We first derive exact distributions of test statistics, i.e., the largest eigenvalue of the sample covariance matrix, observed under the null and signal present hypothesis. Next, utilizing these results, we present the exact closed-form expressions for the false alarm and detection probabilities. In addition, we also optimally configure the phase shift matrix of RIS such that the mean of the test statistics is maximized, thus improving the detection performance. Our numerical analysis demonstrates that the MED's receiving operating characteristic (ROC) curve improves with increased RIS elements, SNR, and the utilization of statistically optimal configured RIS

Stock Value Prediction System

The use of artificial neural network is gaining popularity in the research field. Neural network consist of interconnected neurons which deciphers value by using input data by feeding network values. The main aim of our project is to use backpropagation process to predict the future value.Stock market prediction models are the most challenging fields in computer science. The aim of this project is implementation of neural networks with back propagation algorithm for stock value prediction .A neural network is a powerful data-modeling tool that is able to capture and represent complex input/output relationships. We apply Data mining technology to the stock in order to research the trend of the market. Our proposed system provides methods to develop machine learning stock market predictor based on Neural Networks using Back propagationalgorithm, with intent of improving the accuracy. In this paper we have used data mining process along with artificial neural network networking to predict the future value of the stock. This paper overcomes the all traditional statistical methods of the stock market value prediction. DOI: 10.17762/ijritcc2321-8169.16049

ANTIHYPERTENSIVE EFFECT OF THE PUNICA GRANATUM JUICE IN DEOXYCORTICOSTERONE ACETATE–SALT MODEL OF HYPERTENSION IN RATS

 Objective: Hypertension an important global health challenge is the prevalent cause of cardiovascular disease. Natural products are emerging as new therapeutic tools in the management of hypertension due to side effects and the patient's adherence of the existing treatments. In the present study, we investigated the antihypertensive effect of the Punica granatum juice in deoxycorticosterone acetate (DOCA)–salt model of hypertension in rats.Methods: Antihypertensive activity was evaluated in P. granatum juice extract (PGJ) (PJ-100 mg/kg and 300 mg/kg; p.o.) for 4 weeks in DOCA treated rats. Blood pressure by non-invasive (indirect) method and invasive method was measured. Further, vascular reactivity to noradrenaline (1 μg/kg), adrenaline (1 μg/kg), phenylephrine (1 μg/kg), serotonin (1 μg/kg), and angiotensin II (25 ng/kg) was recorded. Antioxidant studies such as thiobarbituric acid reactive substances (TBARS); while enzyme activity of superoxide dismutase (SOD), catalase (CAT), and glutathione reductase (GSH) in kidney tissue was also carried out.Results: Administration of PGJ (PJ-100 mg/kg and 300 mg/kg; p.o.) for 4 weeks in DOCA treated rats significantly (p<0.05) reduced the mean arterial blood pressure and vascular reactivity changes to various catecholamines. PJ treatment significantly (p<0.05) decreased the levels of TBARS; while enzyme activity of SOD, CAT, and GSH in kidney tissue was significantly increased.Conclusion: Results of the present work suggest that PGJ has an antihypertensive action in unilateral nephrectomized DOCA-salt hypertensive rats and could be possible starting point for treatment of hypertension with increased patient adherence

Catalytic nanosponges of acidic aluminosilicates for plastic degradation and CO2 to fuel conversion

The synthesis of solid acids with strong zeolite-like acidity and textural properties like amorphous aluminosilicates (ASAs) is still a challenge. In this work, we report the synthesis of amorphous “acidic aluminosilicates (AAS)”, which possesses Brønsted acidic sites like in zeolites and textural properties like ASAs. AAS catalyzes different reactions (styrene oxide ring-opening, vesidryl synthesis, Friedel−Crafts alkylation, jasminaldehyde synthesis, m-xylene isomerization, and cumene cracking) with better performance than state-of-the-art zeolites and amorphous aluminosilicates. Notably, AAS efficiently converts a range of waste plastics to hydrocarbons at significantly lower temperatures. A Cu-Zn-Al/AAS hybrid shows excellent performance for CO2 to fuel conversion with 79% selectivity for dimethyl ether. Conventional and DNP-enhanced solid-state NMR provides a molecular-level understanding of the distinctive Brønsted acidic sites of these materials. Due to their unique combination of strong acidity and accessibility, AAS will be a potential alternative to zeolites

Performance Analysis of LEO Satellite-Based IoT Networks in the Presence of Interference

This paper explores a star-of-star topology for an internet-of-things (IoT) network using mega low Earth orbit constellations where the IoT users broadcast their sensed information to multiple satellites simultaneously over a shared channel. The satellites use amplify-and-forward relaying to forward the received signal to the ground station (GS), which then combines them coherently using maximal ratio combining. A comprehensive outage probability (OP) analysis is performed for the presented topology. Stochastic geometry is used to model the random locations of satellites, thus making the analysis general and independent of any constellation. The satellites are assumed to be visible if their elevation angle is greater than a threshold, called a mask angle. Statistical characteristics of the range and the number of visible satellites are derived for a given mask angle. Successive interference cancellation (SIC) and capture model (CM)-based decoding schemes are analyzed at the GS to mitigate interference effects. The average OP for the CM-based scheme, and the OP of the best user for the SIC scheme are derived analytically. Simulation results are presented that corroborate the derived analytical expressions. Moreover, insights on the effect of various system parameters like mask angle, altitude, number of satellites and decoding order are also presented. The results demonstrate that the explored topology can achieve the desired OP by leveraging the benefits of multiple satellites. Thus, this topology is an attractive choice for satellite-based IoT networks as it can facilitate burst transmissions without coordination among the IoT users.Comment: Submitted to IEEE IoT Journa

Management of comminuted fractures of the shaft of femur by interlocking nailing at a tertiary level hospital in India

Background: Fracture shaft of femur is one of the most common fractures encountered in orthopaedic practice. Fracture shaft of femur is major cause of morbidity and mortality in patients who sustain high energy trauma. This study looks at the epidemiology of patients presenting with femur fracture at a tertiary level hospital in Navi Mumbai.Methods: This prospective study was performed at a tertiary level hospital in Navi Mumbai from January 1, 2014 till July 31, 2015. All patients aged 18 years or above, who presented with comminuted femur fracture and were treated with interlocking nailing was included in the study. Various clinical and radiological parameters were collected during the course of treatment.Results: 50 patients were included in the study; 84% males. 88% aged 50 years or less. Road traffic accident was the most common mode of injury and 54% of patients had fracture in the middle one-third femur. 76% of the patients presented within 24 hours of injury. 52% of the patient’s demonstrated clinical union of the fracture in 12 to 14 weeks and majority showed radiological union in 16 to 18 weeks. Partial weight bearing was started in 36% patients in 10 weeks and full weight bearing in 42% patients in 16 weeks. Majority of the patients stayed in hospital for 10 to 14 days and the functional outcome as measured by Klemm and Borner criteria was excellent in 66% patients. Complications were seen only in 6 patients.Conclusions: In our experience, interlocking nailing had very low complication rate and excellent functional outcome in two thirds patients of comminuted femur fracture

Unusual presentation of fibrolamellar carcinoma: A rare case report

Fibrolamellar hepatocellular carcinoma (fHCC) is a distinct type of first time used hence- hepatocellular carcinoma affecting particularly young patient with no gender predilection. However, there is increasing evidence of occurrence of this tumor in elderly patients also. Abdominal imaging with pre-operative biopsy provides accurate diagnosis. However, in difficult situations, CD68, cytokeratin 7, HepPar1, etc., immunohistochemical stains provide accurate diagnosis to differentiate this condition from other malignancies. Hereby, we present a case of fHCC in a 55-year-old female with equivocal imaging features and diagnosis was made by histopathology aided by immunohistochemistry