Design and demonstration of digital pre-distortion using software defined radio

Abstract. High data rates for large number of users set tight requirements for signal quality measured in terms of error vector magnitude (EVM). In radio transmitters, nonlinear distortion dominated by power amplifiers (PAs) often limits the achievable EVM. However, the linearity can be improved by linearization techniques. Digital pre-distortion (DPD) is one of these widely used linearization techniques for an effective distortion reduction over a wide bandwidth. In DPD, the nonlinearity of the transmitter is pre-compensated in the digital domain to achieve linear output. Moreover, DPD is used to enable PAs to operate in the power-efficient region with a decent linearity. As we are moving towards millimetre-wave frequencies to enable the wideband communications, the design of the DPD algorithm must be optimized in terms of performance and power consumption. Moreover, continuous development of wireless infrastructure motivates to make research on programmable and reconfigurable platforms in order to decrease the demonstration cost and time, especially for the demonstration purposes. This thesis illustrates and presents how software defined radio (SDR) platforms can be used to demonstrate DPD. Universal software defined peripheral (USRP) X300 is a commercial software defined radio (SDR) platform. The chosen model, X300, has two independent channels equipped with individual transceiver cards. SIMULINK is used to communicate with the device and the two channels of X300 are used as transmitter and receiver simultaneously in full-duplex mode. Hence, a single USRP device is acting as an operational transmitter and feedback receiver, simultaneously. The implemented USRP design consists of SIMULINK based transceiver design and lookup table based DPD in which the coefficients are calculated in MATLAB offline. An external PA, i.e. ZFL-2000+ together with a directional coupler and attenuator are connected between the TX/RX port and RX2 port to measure the nonlinearity. The nonlinearity transceiver is measured with and without the external PA. The experimental results show decent performance for linearization by using the USRP platform. However, the results differ widely due to the used USRP transceiver parameterization and PA operational point. The 16 QAM test signal with 500 kHz bandwidth is fed to the USRP transmit chain. As an example, the DPD algorithm improves the EVM from 7.6% to 2.1% and also the ACPR is reduced around 10 dB with the 16 QAM input signal where approximately + 2.2 dBm input power applied to the external PA

Teacher Education for University Teachers: Bangladesh Perspective

In this age of globalization every nation is struggling to keep pace with the upcoming demands to maintain quality. The progress of a nation largely depends on its citizens who are the products of its education system. This study is an attempt to unfold the status of university teachers of Bangladesh. Data collected through document review and in-depth interview of the university teachers that was analysed qualitatively. Researchers found that, most of the countries assiduously work for reshaping their teacher education while Bangladesh is focusing on needs for university teachers. However, most of the teachers express their opinion regarding emergence of teacher education and they agree that a good student might not be a good teacher always unless and until quality teacher education is there

A review on the mobile applications developed for COVID-19: An exploratory analysis

The objective of this research is to explore the existing mobile applications developed for the COVID-19 pandemic. To obtain this research objective, firstly the related applications were selected through the systematic search technique in the popular application stores. Secondly, data related to the app objectives, functionalities provided by the app, user ratings, and user reviews were extracted. Thirdly, the extracted data were analyzed through the affinity diagram, noticing-collecting-thinking, and descriptive analysis. As outcomes, the review provides a state-of-the-art view of mobile apps developed for COVID-19 by revealing nine functionalities or features. It revealed ten factors related to information systems design characteristics that can guide future app design. The review outcome highlights the need for new development and further refinement of the existing applications considering not only the revealed objectives and their associated functionalities, but also revealed design characteristics such as reliability, performance, usefulness, supportive, security, privacy, flexibility, responsiveness, ease of use, and cultural sensitivity.Comment: 11 pages, 3 figures, 4 table

Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review

This paper presents a comprehensive review of the developments made in rotating bearing fault diagnosis, a crucial component of a rotatory machine, during the past decade. A data-driven fault diagnosis framework consists of data acquisition, feature extraction/feature learning, and decision making based on shallow/deep learning algorithms. In this review paper, various signal processing techniques, classical machine learning approaches, and deep learning algorithms used for bearing fault diagnosis have been discussed. Moreover, highlights of the available public datasets that have been widely used in bearing fault diagnosis experiments, such as Case Western Reserve University (CWRU), Paderborn University Bearing, PRONOSTIA, and Intelligent Maintenance Systems (IMS), are discussed in this paper. A comparison of machine learning techniques, such as support vector machines, k-nearest neighbors, artificial neural networks, etc., deep learning algorithms such as a deep convolutional network (CNN), auto-encoder-based deep neural network (AE-DNN), deep belief network (DBN), deep recurrent neural network (RNN), and other deep learning methods that have been utilized for the diagnosis of rotary machines bearing fault, is presented

Why do People Share Misinformation during the COVID-19 Pandemic?

The World Health Organization have emphasised that misinformation - spreading rapidly through social media - poses a serious threat to the COVID-19 response. Drawing from theories of health perception and cognitive load, we develop and test a research model hypothesizing why people share unverified COVID-19 information through social media. Our findings suggest a person's trust in online information and perceived information overload are strong predictors of unverified information sharing. Furthermore, these factors, along with a person's perceived COVID-19 severity and vulnerability influence cyberchondria. Females were significantly more likely to suffer from cyberchondria, however, males were more likely to share news without fact checking their source. Our findings suggest that to mitigate the spread of COVID-19 misinformation and cyberchondria, measures should be taken to enhance a healthy skepticism of health news while simultaneously guarding against information overload

Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks

Information fusion is an essential part of numerous engineering systems and biological functions, e.g., human cognition. Fusion occurs at many levels, ranging from the low-level combination of signals to the high-level aggregation of heterogeneous decision-making processes. While the last decade has witnessed an explosion of research in deep learning, fusion in neural networks has not observed the same revolution. Specifically, most neural fusion approaches are ad hoc, are not understood, are distributed versus localized, and/or explainability is low (if present at all). Herein, we prove that the fuzzy Choquet integral (ChI), a powerful nonlinear aggregation function, can be represented as a multi-layer network, referred to hereafter as ChIMP. We also put forth an improved ChIMP (iChIMP) that leads to a stochastic gradient descent-based optimization in light of the exponential number of ChI inequality constraints. An additional benefit of ChIMP/iChIMP is that it enables eXplainable AI (XAI). Synthetic validation experiments are provided and iChIMP is applied to the fusion of a set of heterogeneous architecture deep models in remote sensing. We show an improvement in model accuracy and our previously established XAI indices shed light on the quality of our data, model, and its decisions.Comment: IEEE Transactions on Fuzzy System

Frequency of nodal metastasis in differentiated thyroid carcinoma

Background: Thyroid cancer is the most common endocrine malignancy. Moreover, within thyroid cancer, differentiated thyroid carcinoma (DTC) is the most common variety, with the incidence rising over the past decade. Often, most of the cases present with multicentric disease and presenting with lymph node metastases at the time of diagnosis. Nodal metastasis has prognostic importance, and it may guide surgeons regarding further management. Considering this scientific fact, the study was performed to see the frequency of lymph node metastasis in DTC among the patients admitted to a tertiary care hospital. Methods: This cross-sectional study was conducted in the department of otolaryngology and head-neck surgery, Sher-E-Bangla Medical College Hospital, for 9 months after the acceptance of the protocol. A total of 50 patients (in all age groups) who attended the relevant department due to thyroid malignancies were approached and interviewed. Thorough history taking, physical examination, and relevant investigation were done and recorded into separate case record forms. Informed written consent was taken from each subject. Following collection, data were coded and inputted into statistical software. Data analysis was done with SPSS 21 according to the objective of the study. Data were presented in the form of tables, and charts. Results: Among the 50 patients, the mean age of the patients was 47.86±15.69 SD (years) with minimum and maximum ages of 14 and 78 years respectively. The male-female ratio was 1:4 (20% male vs 80% female). Papillary carcinoma was the most frequent (88%) followed by follicular (10%). Nodal metastasis was most common in the papillary variety, about 54.55% of cases, whereas 20% were present in the follicular variety. The majority of the patients underwent total thyroidectomy with neck dissection (90%) and the remaining cases were managed by lobectomy (10%). Conclusions: This study concluded that nodal metastasis was present in 54.55% of cases of papillary carcinoma.

Effect of Corpora on Classification of Fake News using Naive Bayes Classifier

At the present world, one of the main sources of the news is an online platform like different websites and social media i.e. Facebook, Twitter, Linkedin, Youtube, Instagram and so on. However, due to the lack of proper knowledge or deliberate activity of some cunning people, fake news is spreading more than ever. People in general, struggling to filter which news to trust and which one to discard. Even the sly people take advantage of the situation by spreading false news and misleading the people. Natural Language Processing, one of the major branch of Machine Learning, the wealth of research is remarkable. However, new challenges underpinning this development. Here in this work, Naive Bayes Classifier, a Bayesian approach of Machine Learning algorithm has applied to identify the fake news. We showed, besides the algorithms, how the wealth of corpora can assist to improve the performance. The dataset collected from an open-source, has been used to classify whether the news is authenticated or not. Initially, we achieved classification accuracy about 87% which is higher than previously reported accuracy and then 92% by the same Naive Bayes Algorithm with enriched corpora