Opportunistic Spectrum Utilization by Cognitive Radio Networks: Challenges and Solutions

Cognitive Radio Network (CRN) is an emerging paradigm that makes use of Dynamic Spectrum Access (DSA) to communicate opportunistically, in the un-licensed Industrial, Scientific and Medical bands or frequency bands otherwise licensed to incumbent users such as TV broadcast. Interest in the development of CRNs is because of severe under-utilization of spectrum bands by the incumbent Primary Users (PUs) that have the license to use them coupled with an ever-increasing demand for unlicensed spectrum for a variety of new mobile and wireless applications. The essence of Cognitive Radio (CR) operation is the cooperative and opportunistic utilization of licensed spectrum bands by the Secondary Users (SUs) that collectively form the CRN without causing any interference to PUs\u27 communications. CRN operation is characterized by factors such as network-wide quiet periods for cooperative spectrum sensing, opportunistic/dynamic spectrum access and non-deterministic operation of PUs. These factors can have a devastating impact on the overall throughput and can significantly increase the control overheads. Therefore, to support the same level of QoS as traditional wireless access technologies, very closer interaction is required between layers of the protocol stack. Opportunistic spectrum utilization without causing interference to the PUs is only possible if the SUs periodically sense the spectrum for the presence of PUs\u27 signal. To minimize the effects of hardware capabilities, terrain features and PUs\u27 transmission ranges, DSA is undertaken in a collaborative manner where SUs periodically carry out spectrum sensing in their respective geographical locations. Collaborative spectrum sensing has numerous security loopholes and can be favorable to malicious nodes in the network that may exploit vulnerabilities associated with DSA such as launching a spectrum sensing data falsification (SSDF) attack. Some CRN standards such as the IEEE 802.22 wireless regional area network employ a two-stage quiet period mechanism based on a mandatory Fast Sensing and an optional Fine Sensing stage for DSA. This arrangement is meant to strike a balance between the conflicting goals of proper protection of incumbent PUs\u27 signals and optimum QoS for SUs so that only as much time is spent for spectrum sensing as needed. Malicious nodes in the CRN however, can take advantage of the two-stage spectrum sensing mechanism to launch smart denial of service (DoS) jamming attacks on CRNs during the fast sensing stage. Coexistence protocols enable collocated CRNs to contend for and share the available spectrum. However, most coexistence protocols do not take into consideration the fact that channels of the available spectrum can be heterogeneous in the sense that they can vary in their characteristics and quality such as SNR or bandwidth. Without any mechanism to enforce fairness in accessing varying quality channels, ensuring coexistence with minimal contention and efficient spectrum utilization for CRNs is likely to become a very difficult task. The cooperative and opportunistic nature of communication has many challenges associated with CRNs\u27 operation. In view of the challenges described above, this dissertation presents solutions including cross-layer approaches, reputation system, optimization and game theoretic approaches to handle (1) degradation in TCP\u27s throughput resulting from packet losses and disruptions in spectrum availability due non-deterministic use of spectrum by the PUs (2) presence of malicious SUs in the CRN that may launch various attacks on CRNs\u27 including SSDF and jamming and (3) sharing of heterogeneous spectrum resources among collocated CRNs without a centralized mechanism to enforce cooperation among otherwise non-cooperative CRN

A comprehensive review on role of phytochemicals in human health

Phytochemicals are plant extracts that are widely used due to their ability to provide health benefits and their therapeutic activity against several disorders. They are also called phytonutrients as they are not really the part of our diet but have several positive effects on ourhealth. They are found to decrease the risk factor of cardiovascular disorders and coronary heart disorders. It has also been found that phytochemicals diminish the risk ratio of several types of cancers including breast, colon and skin cancers. This article revolves around taking carotenoids, chlorophyll and fibers as examples of phytonutrients to evaluate their beneficial role in several diseases. These phytochemicals have been found to be associated with antioxidant activity, retinol activity, cell signaling, improvement of glycemic control, protection against high blood pressure, facilitation in defecation and anti-cancerous activity

Factors Affecting Job Turnover: A Case Study of Private Schools of District Swat

 Several factors force employees to leave the organization. This study examines the relationship among job turnover intention, workload, low pay and job stress in private schools at district swat. For this purpose, the data has been collected from of two hundred and forty eight employees. The results of the study indicated that workload, low pay and job stress are significantly positive related to turnover intention. This study also suggested that for overcoming the turnover from the schools enough salary should be given to employees to motivate them and retained

Quantification of carbon dioxide released from effervescent granules as a predictor of formulation quality using modified Chittick apparatus

Purpose: To develop a method for the measurement of carbon dioxide (CO2) released from effervescent formulations. Methods: Effervescent granules were prepared using sodium bicarbonate and citric acid by fusion and solvent-assisted granulation methods. The amount of CO2 released was determined from the maximum pressure of gas release, time profile of pressure gradient using modified Chittick apparatus and gravimetric changes following effervescence. Results: The amount of CO2 released from effervescent granules prepared by fusion method was 8.125, 8.763 and 7.98 mM/g measured by ideal gas equation, pressure gradient and gravimetric method, respectively. The formulation prepared by solvent-assisted granulation showed 5.525, 5.475 5.36 mM/g of carbon dioxide measured by the above three methods, respectively. The effervescent granules prepared by fusion method showed approximately 2 % loss in effervescence. However, approximately 39 % loss in effervescence was observed for the formulation prepared by solventassisted granulation. The commercial products showed a loss in effervescence in the range of 5 - 15%. Conclusion: Modified Chittick’s apparatus is a useful analytical tool for monitoring of the CO2 from effervescent granules as a function of method of preparation

Factors Affecting Job Turnover: A Case Study of Private Schools of District Swat

 Several factors force employees to leave the organization. This study examines the relationship among job turnover intention, workload, low pay and job stress in private schools at district swat. For this purpose, the data has been collected from of two hundred and forty eight employees. The results of the study indicated that workload, low pay and job stress are significantly positive related to turnover intention. This study also suggested that for overcoming the turnover from the schools enough salary should be given to employees to motivate them and retained

On the Efficiency of Software Implementations of Lightweight Block Ciphers from the Perspective of Programming Languages

Lightweight block ciphers are primarily designed for resource constrained devices. However, due to service requirements of large-scale IoT networks and systems, the need for efficient software implementations can not be ruled out. A number of studies have compared software implementations of different lightweight block ciphers on a specific platform but to the best of our knowledge, this is the first attempt to benchmark various software implementations of a single lightweight block cipher across different programming languages and platforms in the cloud architecture. In this paper, we defined six lookup-table based software implementations for lightweight block ciphers with their characteristics ranging from memory to throughput optimized variants. We carried out a thorough analysis of the two costs associated with each implementation (memory and operations) and discussed possible trade-offs in detail. We coded all six types of implementations for three key settings (64, 80, 128 bits) of LED (a lightweight block cipher) in four programming languages (Java, C#, C++, Python). We highlighted the impact of choice relating to implementation type, programming language, and platform by benchmarking the seventy-two implementations for throughput and software efficiency on 32 & 64-bit platforms for two major operating systems (Windows & Linux) on Amazon Web Services Cloud. The results showed that these choices can affect the efficiency of a cryptographic primitive by a factor as high as 400

Accelerated Dynamic MRI Using Kernel-Based Low Rank Constraint

We present a novel reconstruction method for dynamic MR images from highly under-sampled k-space measurements. The reconstruction problem is posed as spectrally regularized matrix recovery problem, where kernel-based low rank constraint is employed to effectively utilize the non-linear correlations between the images in the dynamic sequence. Unlike other kernel-based methods, we use a single-step regularized reconstruction approach to simultaneously learn the kernel basis functions and the weights. The objective function is optimized using variable splitting and alternating direction method of multipliers. The framework can seamlessly handle additional sparsity constraints such as spatio-temporal total variation. The algorithm performance is evaluated on a numerical phantom and in vivo data sets and it shows significant improvement over the comparison methods

Auto-MeDiSine: An Auto-Turnable Medical Decision Support Engine Using an Automated Class Outlier Detection MEthod and Auto AMLP

With advanced data analysis techniques, efforts for more accurate decision support systems for disease prediction are on the rise. According to the World Health Organization, diabetes-related illnesses and mortalities are on the rise. Hence, early diagnosis is particularly important. In this paper, we present a framework, Auto-MeDiSine, that comprises an automated version of enhanced class outlier detection using a distance-based algorithm (AutoECODB), combined with an ensemble of automatic multilayer perceptron (AutoMLP). AutoECODB is built upon ECODB by automating the tuning of parameters to optimize outlier detection process. AutoECODB cleanses the dataset by removing outliers. Preprocessed dataset is then used to train a prediction model using an ensemble of AutoMLPs. A set of experiments is performed on publicly available Pima Indian Diabetes Dataset as follows: (1) Auto-MeDiSine is compared with other state-of-the-art methods reported in the literature where Auto-MeDiSine realized an accuracy of 88.7%; (2) AutoMLP is compared with other learners including individual (focusing on neural network-based learners) and ensemble learners; and (3) AutoECODB is compared with other preprocessing methods. Furthermore, in order to validate the generality of the framework, Auto-MeDiSine is tested on another publicly available BioStat Diabetes Dataset where it outperforms the existing reported results, reaching an accuracy of 97.1%