Collaborative spectrum sensing optimisation algorithms for cognitive radio networks

The main challenge for a cognitive radio is to detect the existence of primary users reliably in order to minimise the interference to licensed communications. Hence, spectrum sensing is a most important requirement of a cognitive radio. However, due to the channel uncertainties, local observations are not reliable and collaboration among users is required. Selection of fusion rule at a common receiver has a direct impact on the overall spectrum sensing performance. In this paper, optimisation of collaborative spectrum sensing in terms of optimum decision fusion is studied for hard and soft decision combining. It is concluded that for optimum fusion, the fusion centre must incorporate signal-to-noise ratio values of cognitive users and the channel conditions. A genetic algorithm-based weighted optimisation strategy is presented for the case of soft decision combining. Numerical results show that the proposed optimised collaborative spectrum sensing schemes give better spectrum sensing performance

Insights and approaches for low-complexity 5G small-cell base-station design for indoor dense networks

This paper investigates low-complexity approaches to small-cell base-station (SBS) design, suitable for future 5G millimeter-wave (mmWave) indoor deployments. Using large-scale antenna systems and high-bandwidth spectrum, such SBS can theoretically achieve the anticipated future data bandwidth demand of 10000 fold in the next 20 years. We look to exploit small cell distances to simplify SBS design, particularly considering dense indoor installations. We compare theoretical results, based on a link budget analysis, with the system simulation of a densely deployed indoor network using appropriate mmWave channel propagation conditions. The frequency diverse bands of 28 and 72 GHz of the mmWave spectrum are assumed in the analysis. We investigate the performance of low-complexity approaches using a minimal number of antennas at the base station and the user equipment. Using the appropriate power consumption models and the state-of-the-art sub-component power usage, we determine the total power consumption and the energy efficiency of such systems. With mmWave being typified nonline-of-sight communication, we further investigate and propose the use of direct sequence spread spectrum as a means to overcome this, and discuss the use of multipath detection and combining as a suitable mechanism to maximize link reliability

Self-Esteem & Academic Performance among University Students

The current study was conducted to assess the self-esteem and academic performance among university students after arising of several behavioral and educational problems. A total number of 80 students, 40 male students and 40 female students were selected through purposive sampling from G.C University Faisalabad. The participants were administered Rosenberg Self-Esteem Scale and Academic Performance Rating Scale to measure their self-esteem and academic performance. The score of male and female students was compared. Pearson’s Product Moment and the t-test were used for statistical significance of data. It was found that there was a significant relationship (r=0.879, p<.01) between self-esteem and academic performance. Moreover a significant difference was found between male and female students on self-esteem and academic performance scores, which indicate that female students have high scores on academic performance as compared to male students and male students have high scores on self-esteem as compared to female students Key word:  Self-esteem, Academic Performance, Behavioral & Educational Problems.

Mobile internet activity estimation and analysis at high granularity: SVR model approach

Understanding of mobile internet traffic patterns and capacity to estimate future traffic, particularly at high spatiotemporal granularity, is crucial for proactive decision making in emerging and future cognizant cellular networks enabled with self-organizing features. It becomes even more important in the world of `Internet of Things' with machines communicating locally. In this paper, internet activity data from a mobile network operator Call Detail Records (CDRs) is analysed at high granularity to study the spatiotemporal variance and traffic patterns. To estimate future traffic at high granularity, a Support Vector Regression (SVR) based traffic model is trained and evaluated for the prediction of maximum, minimum and average internet traffic in the next hour based on the actual traffic in the last hour. Performance of the model is compared with that of the State-of-the-Art (SOTA) deep learning models recently proposed in the literature for the same data, same granularity, and same predicates. It is concluded that this SVR model outperforms the SOTA deep and non-deep learning methods used in the literature

A privacy-preserving framework for smart context-aware healthcare applications

Smart connected devices are widely used in healthcare to achieve improved well-being, quality of life, and security of citizens. While improving quality of healthcare, such devices generate data containing sensitive patient information where unauthorized access constitutes breach of privacy leading to catastrophic outcomes for an individual as well as financial loss to the governing body via regulations such as the General Data Protection Regulation. Furthermore, while mobility afforded by smart devices enables ease of monitoring, portability, and pervasive processing, it introduces challenges with respect to scalability, reliability, and context awareness. This paper is focused on privacy preservation within smart context-aware healthcare emphasizing privacy assurance challenges within Electronic Transfer of Prescription. We present a case for a comprehensive, coherent, and dynamic privacy-preserving system for smart healthcare to protect sensitive user data. Based on a thorough analysis of existing privacy preservation models, we propose an enhancement to the widely used Salford model to achieve privacy preservation against masquerading and impersonation threats. The proposed model therefore improves privacy assurance for smart healthcare while addressing unique challenges with respect to context-aware mobility of such applications. © 2019 John Wiley & Sons, Ltd

Association of Neutrophil to Lymphocyte Ratio and Platelet to Lymphocyte Ratio with Fatty Liver in Type II Diabetes Mellitus Patients

Objective: To find the association between neutrophil to lymphocyte ratio, platelet to lymphocyte ratio and fatty liver in type 2 diabetes.Methodology: This comparative analytical study was conducted at Shifa International Hospital on diabetic patients visiting falahee OPD clinics from June 2018 to June 2019.  Nonprobability convenient sampling was used. Patients were segregated into two groups according to fatty liver status as assessed by ultrasonography. Complete blood count, lipid profile and liver profile were done. Data was analyzed by using statistical package for the social sciences (spss) version 21. Descriptive statistics were calculated for categorical variables. Kolmogorov smirnov test was used to ascertain the normality of the quantitative variables. For normal and dispersed variables, independent student t and Mann Whitney U test were applied, respectively. P Value below 0.05 was considered significant.Results: Out of total 93 patients, 33 (35.4 %) were males and 60(64.5%) were females. Female patients had increased incidence of fatty liver as compared to males. The mean duration of disease was 7.61 ± 5.8 years with 68.8% prevalence of fatty liver. BMI was elevated significantly in patients having fatty liver. There was no significant association between NLR, PLR and fatty liver. ALT, LDL and Triglycerides were increased significantly in patients having fatty liver.Conclusion: Patients having fatty liver have more deranged levels of lipid profile and hematological parameters increasing the risk of cardiovascular and inflammatory diseases

Frequency of Thyroid Dysfunction and Congenital Heart Defects in Subjects with Down Syndrome

ABSTRACT: Background: Down syndrome (DS) is the most common chromosomal abnormality with prevalence of 1 in 700-1500 live births. Its manifestations may include congenital heart defects(CHD), thyroid dysfunctions, hematopoietic disorders, early-onset Alzheimer disease, gastrointestinal disorders, neuromuscular weakness, hearing and visual problems, characteristic facial and physical features. The prevalence of thyroid disorders and congenital heart diseases are higher in DS patients than in general population. Objective:To explore the frequency, and types of congenital heart defects (CHD) and Thyroid disorders in children with Down syndrome (DS) in the children hospital and the institute of child health (CHICH) Multan. Study design:  Descriptive cross sectional Setting: Outpatient department (OPD) of CHICH Multan Method:A total of 158 down syndrome (DS) patients of 0 to 15 years of age, of both genders were included in this study from October 2019 to October 2020. DS was diagnosed by specific clinical features and karyotyping. Age, sex and mother’s age was noted. Blood samples of all the patients were sent for karyotyping and serum T4 and Thyroid stimulation hormone (TSH). For patients more than 36 months, blood samples were also sent for Antithyroglobulin and antithyroid peroxidase antibodies. Echocardiography of all the patients was done. Data was collected and analyzed by using SPSS version 16.0. Results:Out of 158 DS children most presented below 6months of age, with male to female ratio of 1:1.4. Mostly mothers were between 20 to 40 years of age. Karyotyping revealed non disjunction in 97% of cases. Cardiac abnormalities were found in 48% of DS children. Most common Type was VSD (10.9%), Thyroid abnormalities were detected in 24% of DS patients, subclinical hypothyroidism (13.9%) was most common. Conclusion: CHD and Thyroid disorders must be ruled out in all DS patients,to start early management.  Keywords: Down syndrome, Congenital heart disease, hypothyroidism

A privacy‐preserving framework for smart context‐aware healthcare applications

Internet of things (IoT) is a disruptive paradigm with wide ranging applications including healthcare, manufacturing, transportation and retail. Within healthcare, smart connected wearable devices are widely used to achieve improved wellbeing, quality of life and security of citizens. Such connected devices generate significant amount of data containing sensitive information about patient requiring adequate protection and privacy assurance. Unauthorized access to an individual’s private data constitutes a breach of privacy leading to catastrophic outcomes for an individuals personal and professional life. Furthermore, breach of privacy may also lead to financial loss to the governing body such as those proposed as part of the General Data Protection Regulation (GDPR) in Europe. Furthermore, while mobility afforded by smart devices enables ease of monitoring, portability and pervasive processing, it also introduces challenges with respect to scalability, reliability and context-awareness for its applications. This paper is focused on privacy preservation within smart context-aware healthcare with a special emphasis on privacy assurance challenges within the Electronic Transfer of Prescription (ETP). To this extent, we present a case for a comprehensive, coherent, and dynamic privacypreserving system for smart healthcare to protect sensitive user data. Based on a thorough analysis of existing privacy preservation models we propose an enhancement for the widely used Salford model to achieve privacy preservation against masquerading and impersonation threats. The proposed model therefore improves privacy assurance for cutting edge IoT applications such as smart healthcare whilst addressing unique challenges with respect to context-aware mobility of such applications