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

Drivers to Climate Change Mitigation Strategies in the Cement Industry in India: A Framework based on Interpretive Structural Modeling

Cement manufacturing is recognized as one of the most energy-intensive and high-emission industries worldwide. India ranks as the second-largest producer and consumer of cement globally. Within the country, the cement sector is currently the third-highest in energy consumption and the second-largest contributor to greenhouse gas (GHG) emissions. The ongoing release of GHGs significantly contributes to global warming and severe climate change. As a result, the cement industry faces increasing pressure to curb its emissions. This study aims to investigate the key factors influencing climate change mitigation strategies within India’s cement sector. To achieve this, Interpretive Structural Modeling (ISM) has been employed to analyze and structure these drivers. The ISM method is used to establish the interrelationships among the drivers associated with climate mitigation efforts. The findings reveal a total of thirty drivers linked to mitigation practices. The ISM analysis ranks these drivers based on their driving power, showing that those with high driving power but low dependency are foundational and occupy the lowest levels in the ISM hierarchy. These core drivers should be prioritized when designing and implementing climate mitigation strategies within the Indian cement industry

Donor-acceptor stacking arrangements in bulk and thin-film high-mobility conjugated polymers characterized using molecular modelling and MAS and surface-enhanced solid-state NMR spectroscopy

Conjugated polymers show promising properties as cheap, sustainable and solution-processable semiconductors. A key challenge in the development of these materials is to determine the polymer chain structure, conformation and packing in both the bulk polymer and in thin films typically used in devices. However, many characterisation techniques are unable to provide atomic-level structural information owing to the presence of disorder. Here, we use molecular modelling, magic-angle spinning (MAS) and dynamic nuclear polarisation surface-enhanced NMR spectroscopy (DNP SENS) to characterise the polymer backbone group conformations and packing arrangement in the high-mobility donor-acceptor copolymer diketopyrrolo-pyrrole-dithienylthieno[3,2-b] thiophene (DPP-DTT). Using conventional H-1 and C-13 solid-state MAS NMR coupled with density functional theory calculations and molecular dynamics simulations, we find that the bulk polymer adopts a highly planar backbone conformation with a laterally-shifted donor-on-acceptor stacking arrangement. DNP SENS enables acquisition of C-13 NMR data for polymer films, where sensitivity is limiting owing to small sample volumes. The DNP signal enhancement enables a two-dimensional H-1-C-13 HETCOR spectrum to be recorded for a drop-cast polymer film, and a C-13 CPMAS NMR spectrum to be recorded for a spin-coated thin-film with a thickness of only 400 nm. The results show that the same planar backbone structure and intermolecular stacking arrangement is preserved in the films following solution processing and annealing, thereby rationalizing the favourable device properties of DPP-DTT, and providing a protocol for the study of other thin film materials

Review of gait recognition systems: approaches and challenges

Gait recognition (GR) has emerged as a significant biometric identification technique, leveraging an individual's walking pattern for various applications such as surveillance, forensic analysis, and person identification. Despite its non-intrusive nature, GR systems face challenges due to their sensitivity to pose variations, limiting functionality in real-world scenarios where people exhibit diverse walking styles and body orientations. This review paper aims to comprehensively discuss GR systems, focusing on approaches and challenges in designing accurate and robust systems capable of handling bodily variations. GR's prominence spans across domains including surveillance, security, healthcare, and human-computer interaction, positioning it as a versatile biometric modality complementary to the traditional methods like fingerprint and face recognition. The review offers an in-depth analysis of GR systems, detailing silhouette-based, model-based, and deep-learning approaches. Silhouette-based methods capture gait information by analyzing the outline and locomotion of a person’s silhouette, while model-based approaches utilize skeletal models to describe gait patterns. The paper elucidates the challenges and limitations of GR systems, encompassing factors such as walking conditions, clothing, viewpoint, and environmental influences. Additionally, it explores potential future directions in GR research, highlighting the technology’s ongoing evolution and integration into diverse applications. As a valuable resource, this review serves researchers, practitioners, and policymakers by providing insights into the current state of GR systems and avenues for further research and development. It underscores the importance of addressing challenges to enhance GR’s accuracy and robustness, ensuring its continued relevance in biometric identification across various domains

Exploring Diverse Facets of Small Molecules by NMR Spectroscopy

The thesis entitled “Exploring Diverse Facets of Small Molecules by NMR Spectroscopy” consists of six chapters. The main theme of the thesis is to exploit one and two dimensional NMR methodologies for understanding the diverse facets of small organic molecules, such as, weak intra- and inter- molecular interactions, chiral discrimination, quantification of enantiomeric excess and assignment of absolute configuration. Several new pulse sequences have also been designed to solve specific chemical problems, in addition to extensive utility of existing one and two dimensional NMR experiments. The results obtained on different problems, are discussed under six chapters in the thesis. The brief summary of each of these chapters is given below. Chapter 1 begins with the discussion on the importance of small molecules and their various facets, the analytical techniques available in the literature to study them. The role of NMR spectroscopy as powerful analytical technique to understand the diverse facets of organic molecules and their importance is set out in brief. A short introduction to the basic principles of NMR, the interaction parameters, the commonly employed one and two dimensional homo- and herero- nuclear NMR experiments are also given. The basic introduction to product operators essential for understanding the spin dynamics in the developed pulse sequences is given. The application of diffusion ordered spectroscopy (DOSY), the general problems encountered in the analysis of combinatorial mixtures and the matrix assisted method in circumventing such problems are discussed. Chapter 2 focuses on the chiral discrimination and the measurement of enatiomeric excess. The NMR approach to discriminate enantiomers using chiral auxiliaries such as, solvating agents, derivatizing agents, lanthanide shift reagents, the choice of such auxiliaries and the limitations are discussed in detail. The in-depth discussion on the new protocols developed using both the solvating and derivatizing agents for enantiomeric discrimination of chiral amines, hydroxy acids and diacids are discussed. The new three-component protocols that serve as chiral derivatizing agents for the discrimination of primary amines, diacids and hydroxy acids are discussed. Also the role of organic base such as DMAP in the chiral discrimination is explored for discrimination of acids using BINOL as a chiral solvating agent. Accordingly the discussion is classified into two sections. In the first section the protocol developed utilizing an enantiopure mandelic acid, a primary amine substrate and 2-formylphenylboronic acid that is ideally suited for testing the enantiopurity of chiral primary amines is discussed. The broad applicability of the protocols for testing enantiopurity has been demonstrated on number of chiral molecules using 1H and 19F NMR. The second section contains the results on the new concept developed for discrimination of hydroxy acids. The strategy involves the formation of three component protocol using chiral hydroxy acid, R-alphamethylbenzylamine and 2-formylphenylboronic acid for 1H-NMR discrimination of diacids. The section also includes the utility of ternary ion-pair complex for the discrimination of acids. The ternary ion-pair not only permitted the testing of enantiopurity of chiral acids, but is also found useful for the measurement of enantiomeric excess. Chapter 3 discusses the utilization of the developed three-component protocols for the assignment of absolute configurations of molecules of different functionality. The protocols for the assignments of absolute configuration of primary amines using 2-formylphenylboronic acid and mandelic acid yielded the substantial chemical shift differences between diastereomers. The consistent trend in the direction of change of chemical shifts of the discriminated proton(s) gave significant evidence for employing them as parameters for the assignment of spatial configuration of primary amines. Another protocol using 2-formylphenylboronic acid, hydroxy acids and enantiopure alphamethylbenzylamine permitted their configurational assignment. In the second section a novel solvating agent, obtained by the formation of an ion-pair complex among enantiopure BINOL, DMAP and chiral hydroxy acid for the assignment of the spatial configuration of hydroxy acids is discussed. Chapter 4 focuses on the development of novel NMR methodologies, and also the utility of existing two-dimensional experiments for addressing certain challenging problems. This chapter has been divided into three sections. In Section-I the utilization of well-known homonuclear 2D-J-resolved methodology for unravelling the overlapped NMR spectra of enantiomers, an application for chiral discrimination and the measurement of enantiomeric excess is discussed. The utilization of the chiral auxiliaries, such as, chiral derivatizing agents, chiral solvating agents and lanthanide shift reagents permits enantiodiscrimination and the measurement of excess of one form over the other. Nevertheless many a times one encounters severe problems due to small chemical shift difference, overlap of resonances, complex multiplicity pattern because of the presence of number of interacting spins, and enormous line broadening due to paramagnetic nature of the metal complex. This section is focused on combating such problems utilizing 2D-J-1JNH resolved spectroscopy where a 450 tilting of the spectrum in the F2 dimension, yielded the pure shift NMR spectrum. The method circumvents several problems involved in chiral discrimination and allows the accurate measurement of enantiomeric excess. In Section-II, the development of novel NMR experimental methodology cited in the literature as C-HetSERF and its application for the study of symmetric molecules, such as, double bonded cis- and trans- isomers, and extraction of magnitudes and signs of long range homo- and hetero- nuclear scalar couplings among chemically equivalent protons in polycylic aromatic hydrocarbons is discussed. The extensive utility of the new pulse sequence has been demonstrated on number of symmetric molecules, where the conventional one dimensional experiment fails to yield spectral parameters. In section III, yet another novel pulse sequence called RES-TOCSY developed for unravelling of the overlapped NMR spectrum of enantiomers and the measurement of enantiomeric contents, has been utilized for the accurate measurement of magnitudes and signs of 1H-19F couplings in fluorine containing molecules. The method has distinct advantages as the strengths of the couplings and their relative signs could be extracted on diverse situations, such as, couplings smaller than line widths, the spectrum where the coupling fine structures are absent. Chapter 5 covers the study of nature of intra- and inter- molecular hydrogen bond in amide and its derivative. The chapter is accordingly divided into two sections. In the first section the study of acid and amide hydrogen bonding is discussed and the hydrogen bonded interactions are probed by extensive utility of 1H, 13C and 15N-NMR. The temperature perturbation experiments, measurements of the variation in the couplings, monitoring of diffusion coefficients and the association constants, detection of through space correlation have given unambiguous evidence for the hydrogen bond formation. The results were also supported by DFT calculations. Similar interaction in the solid state has also been derived by obtaining the crystal structure of complex phenylacetic acid with benzamide. In the second section of the chapter the hydrogen bond interaction of organic fluorine in trifluoromethyl derivatives of benzanilides has been explored and the involvement of CF3 group in the hydrogen bonding has been detected. The evidence for the participation of CF3 group in hydrogen bond has been confirmed by number of experiments, such as, the detection of through space couplings, viz., 1hJFH, 1hJFN, and 2hJFF , where the spin polarization between the interacting spins is transmitted through hydrogen bond, the temperature and solvent dependent studies, variation in the 1JNH and two dimensional heteronuclear correlation experiments. In an interesting example of a molecule containing two CF3 groups situated on two phenyl rings of benzanilide, the simultaneous participation of fluorines of two CF3 groups in hydrogen bond has been detected. The confirmatory evidence for such an interaction, where hydrogen bond mediated couplings are not reflected in the NMR spectrum, has been derived by 19F−19F NOESY. Significant deviations in the strengths of 1JNH, in addition to variable temperature, and the solvent induced perturbation studies yielded additional evidence. The NMR results are corroborated by both DFT calculations and MD simulations, where the quantitative information on different ways of involvement of fluorine in two and three centered hydrogen bonds, their percentage of occurrences, and geometries have been obtained. The hydrogen bond interaction energies have also been calculated. The study revealed the rare observation and the first example of the C-F…H-N hydrogen bond in solution state in the molecules containing CF3 groups. Chapter 6 focuses on the mixture analysis using the diffusion ordered spectroscopy (DOSY). High Resolution-DOSY works when the NMR spectrum is well resolved and the diffusion coefficients of the combinatorial mixtures are substantially different from each other. DOSY technique fails when the mixture contains the molecules of nearly identical weights and similar hydrodynamic radii. Thus, the positional isomers, enantiomers consequent to their nearly identical rates of diffusion, are not differentiated. Some of these problems can be overcome by Matrix-Assisted Diffusion Order Spectroscopy (MAD-spectroscopy), where an external reagent acts as a matrix and aids in their diffusion edited separation, provided the molecules embedded in it possess differential binding abilities with the matrix. Such different binding properties of the matrix are the basis for resolution of many isomeric species. In the present study three different novel auxiliaries, micelles-reverse micelles, crown ether and cyclodextrin are introduced for the resolution of positional isomers, double bonded isomers, viz., fumaric acid and maleic acid and also enantiomers. Accordingly, the results of each of these studies are discussed in three different sections

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

The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset

Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression. Methods: A dataset partition consisting of 3D knee MRI from 88 subjects at two timepoints with ground-truth articular (femoral, tibial, patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a hold-out test set. Similarities in network segmentations were evaluated using pairwise Dice correlations. Articular cartilage thickness was computed per-scan and longitudinally. Correlation between thickness error and segmentation metrics was measured using Pearson's coefficient. Two empirical upper bounds for ensemble performance were computed using combinations of model outputs that consolidated true positives and true negatives. Results: Six teams (T1-T6) submitted entries for the challenge. No significant differences were observed across all segmentation metrics for all tissues (p=1.0) among the four top-performing networks (T2, T3, T4, T6). Dice correlations between network pairs were high (>0.85). Per-scan thickness errors were negligible among T1-T4 (p=0.99) and longitudinal changes showed minimal bias (<0.03mm). Low correlations (<0.41) were observed between segmentation metrics and thickness error. The majority-vote ensemble was comparable to top performing networks (p=1.0). Empirical upper bound performances were similar for both combinations (p=1.0). Conclusion: Diverse networks learned to segment the knee similarly where high segmentation accuracy did not correlate to cartilage thickness accuracy. Voting ensembles did not outperform individual networks but may help regularize individual models.Comment: Submitted to Radiology: Artificial Intelligence; Fixed typo

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&lt;0.05) reduced the mean arterial blood pressure and vascular reactivity changes to various catecholamines. PJ treatment significantly (p&lt;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