Novel Materials and Devices for Terahertz Detection and Emission for Sensing, Imaging and Communication

Technical advancement is required to attain a high data transmission rate, which entails expanding beyond the currently available bandwidth and establishing a new standard for the highest data rates, which mandates a higher frequency range and larger bandwidth. The THz spectrum (0.1-10 THz) has been considered as an emerging next frontier for the future 5G and beyond technology. THz frequencies also offer unique characteristics, such as penetrating most dielectric materials like fabric, plastic, and leather, making them appealing for imaging and sensing applications. Therefore, employing a high-power room temperature, tunable THz emitters, and a high responsivity THz detector is essential. Dyakonov-theory Shur\u27s was applied in this dissertation to achieve tunable THz detection and emission by plasma waves in high carrier density channels of field-effect devices. The first major contribution of this dissertation is developing graphene-based THz plasmonics detector with high responsivity. An upside-down free-standing graphene in a field effect transistor based resonant room temperature THz detector device with significantly improved mobility and gate control has been presented. The highest achieved responsivity is ~3.1kV/W, which is more than 10 times higher than any THz detector reported till now. The active region is predominantly single-layer graphene with multi-grains, even though the fabricated graphene THz detector has the highest responsivity. The challenges encountered during the fabrication and measurement of the graphene-based detector have been described, along with a strategy to overcome them while preserving high graphene mobility. In our new design, a monolayer of hBN underneath the graphene layer has been deposited to increase the mobility and electron concentration rate further. We also investigated the diamond-based FETs for their potential characteristics as a THz emitters and detectors. Diamond\u27s wide bandgap, high breakdown field, and high thermal conductivity attributes make it a potential semiconductor material for high voltage, high power, and high-temperature operation. Diamond is a good choice for THz and sub-THz applications because of its high optical phonon scattering and high momentum relaxation time. Numerical and analytical studies of diamond materials, including p-diamond and n-diamond materials, are presented, indicating their effectiveness as a prospective contender for high temperature and high power-based terahertz applications These detectors are expected to be a strong competitor for future THz on-chip applications due to their high sensitivity, low noise, tunability, compact size, mobility, faster response time, room temperature operation, and lower cost. Furthermore, when plasma wave instabilities are induced with the proper biasing, the same devices can be employed as THz emitters, which are expected to have a higher emission power. Another key contribution is developing a method for detecting counterfeit, damaged, forged, or defective ICs has been devised utilizing a new non-destructive and unobtrusive terahertz testing approach to address the crucial point of hardware cybersecurity and system reliability. The response of MMICs, VLSI, and ULSIC to incident terahertz and sub-terahertz radiation at the circuit pins are measured and analyzed using deep learning. More sophisticated terahertz response profiles and signatures of specific ICs can be created by measuring a more significant number of pins under different frequencies, polarizations, and depth of focus. The proposed method has no effect on ICs operation and could provide precise ICs signatures. The classification process between the secure and unsecure ICs images has been explained using data augmentation and transfer learning-based convolution neural network with ~98% accuracy. A planar nanomatryoshka type core-shell resonator with hybrid toroidal moments is shown both experimentally and analytically, allowing unique characteristics to be explored. This resonator may be utilized for accurate sensing, immunobiosensing, quick switching, narrow-band filters, and other applications

A review of thz technologies for rapid sensing and detection of viruses including SARS-CoV-2

Virus epidemics such as Ebola virus, Zika virus, MERS-coronavirus, and others have wreaked havoc on humanity in the last decade. In addition, a coronavirus (SARS-CoV-2) pandemic and its continuously evolving mutants have become so deadly that they have forced the entire technical advancement of healthcare into peril. Traditional ways of detecting these viruses have been successful to some extent, but they are costly, time-consuming, and require specialized human resources. Terahertz-based biosensors have the potential to lead the way for low-cost, non-invasive, and rapid virus detection. This review explores the latest progresses in terahertz technology-based biosensors for the virus, viral particle, and antigen detection, as well as upcoming research directions in the field

Level of Awareness about HIV/AIDS among Ever Married Women in Bangladesh

Abstract Ever married women are more vulnerable group to sexually transmitted diseases (STDs), HIV/AIDS infection, and unplanned pregnancies. The aims of this study are to assess the level of awareness among ever married women on HIV/AIDS and to determine the affecting factors influenced knowledge and awareness about HIV/AIDS regarding its prevention and control. The data on 10,996 ever married women in their reproductive span (15-49 years) was taken from the Bangladesh Demographic and Health Survey (BDHS), 2007. The statistical tools, Chi square (χ 2 ) test and binary logistic regression analysis have been performed to analyse the data. Both bivariate and multivariate analyses identified that respondent's education, husband's education, husband's occupation, age at marriage, watching TV, electricity in the household, marital status, and residence are found to have statistically significant effects with HIV/AIDS awareness (p<0.01). Marriage in the older age (>18 years), education, and mass media campaigns are strongly suggested for increasing knowledge and awareness to be controlled the spread of HIV/AIDS as well as STDs among ever married women in Bangladesh

'ICT-Periodism' in the Classroom: Lessons Learnt from English in Action

This paper illustrates the factors affecting the use of ICT in the classroom in a large scale language development project in Bangladesh. The project, called English in Action (EIA) provides ICT materials along with print based materials for the teachers as well as face-to-face training to bring changes in primary and secondary classroom practices. On-going monitoring data showed that the use of audio materials increases as the intervention progresses through the year, however, the pattern changes when a decrease is observed during the third quarter. The study focused on the factors that have an influence on the use of audio in EIA classrooms during different quarters of the year and explored the underlying causes. The study adopted a mixed method approach and used classroom observations and semi-structured interviews with fifteen primary teachers to gather data. Findings showed that different nature and the pressure of the contents during different quarters of the year and the examination focused education system are the key factors that affect the use of ICT in English classes at the primary level. This raises the concern regarding ‘negative back-wash effect’ (Pan, 2009) of the exam system, which can stop effective pedagogical changes in the classroom from happening

Raman Signal Amplification in Photonic Crystal Microring Resonators

We report on microring resonator with integrated photonic crystal that is capable of supporting discrete Raman signals with 7 orders of magnitude enhancement in the spectral range of 2-5 pm. The proposed platform can be used for advanced spectroscopic sensing applications

Rice (Oryza sativa L.) establishment techniques and their implications for soil properties, global warming potential mitigation and crop yields

Rice-based intensive cropping systems require high input levels making them less profitable and vulnerable to the reduced availability of labor and water in Asia. With continuous conventional puddled rice transplanting, the situation is exacerbated by damaged soil structure, declining underground water and decreasing land and water productivity. To minimize these negative effects a range of new crop establishment practices have been developed (zero tillage, dry direct seeding, wet direct seeding, water seeding, strip planting, bed planting, non-puddled transplanting of rice, mechanical transplanting of rice crop and combinations thereof) with varying effects on soil health, crop productivity, resource saving and global warming mitigation potential. Some of these allow Conservation Agriculture (CA) to be practiced in the rice-based mono-, double- and triple cropping systems. Innovations in machinery especially for smallholder farms have supported the adoption of the new establishment techniques. Non-puddling establishment of rice together with increased crop residue retention increased soil organic carbon by 79% and total N (TN) in soil by 62% relative to conventional puddling practice. Rice establishment methods (direct seeding of rice, system of rice intensification and non-puddled transplanting of rice) improve soil health by improving the physical (reduced bulk density, increased porosity, available water content), chemical (increased phosphorus, potassium and sulphur in their available forms) and biological properties (microbiome structure, microbial biomass C and N) of the soil. Even in the first year of its practice, the non-puddled transplanting method of rice establishment and CA practices for other crops increase the productivity of the rice-based cropping systems. Estimates suggest global warming potential (GWP) (the overall net effect) can be reduced by a quarter by replacing conventional puddling of rice by direct-seeded rice in the Indo-Gangetic Plains for the rice-based cropping system. Moreover, non-puddled transplanting of rice saves 35% of the net life cycle greenhouse gases (GHGs) compared with the conventional practice by a combination of decreasing greenhouse gases emissions from soil and increasing soil organic carbon (SOC). Though the system of rice intensification decreases net GHG emission, the practice releases 1.5 times greater N2O due to the increased soil aeration. There is no single rice establishment technology that is superior to others in all circumstances, rather a range of effective technologies that can be applied to different agro-climates, demography and farm typologies

Bridging technique failure through low-tech improvisation: A case study of food microbiology

Modern technology for food safety studies includes standardized protocols and equipment. However, appropriate technology needs to step in to bridge technology dys- or malfunctioning. We examined different low-tech methods for extraction of bacteria from fresh vegetables. Standard equipment including stomacher and filter bags were compared to extraction using bread stick and alternative filter material (nylon stocking, mosquito net). Comparison of microspheres’ (ø: 53-63 µm; ø: 63-75 µm) passage through filter bags, nylon stockings with different densities (15 DEN, 20 DEN, 25 DEN, 40 DEN) and mosquito net showed no significant difference between filter bag and nylon stocking. A significantly higher number of both size microspheres (ø: 53-63 and ø: 63-75 µm) passed through the mosquito net than filter bag and nylon stocking. Manual extraction of romaine lettuce leaf was performed by three technicians. Viable counts of leaf associated bacteria were influenced by the technician and choice of filter material. Viable bacterial counts obtained from breadstick with filter bag manual extraction did not show any significant difference from standard method. We conclude that standard procedures can be replaced by low-tech approaches in the event of malfunctioning equipment. However, method validation is imperative

Deep Learning Models for Stock Market Forecasting: A Comprehensive Comparative Analysis

This study presents a comprehensive comparative analysis of deep learning models for stock market forecasting using data from two prominent stock exchanges, the National Stock Exchange (NSE) and the New York Stock Exchange (NYSE). Four deep neural network architectures—Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN)—were trained and tested on NSE data, focusing on Tata Motors in the automobile sector. The analysis included data from sectors such as Automobile, Banking, and IT for NSE and Financial and Petroleum sectors for NYSE. Results revealed that the deep neural network architectures consistently outperformed the traditional linear model, ARIMA, across both exchanges. The Mean Absolute Percentage Error (MAPE) values obtained for forecasting NSE values using ARIMA were notably higher compared to those derived from the neural networks, indicating the superior predictive capabilities of deep learning models. Notably, the CNN architecture demonstrated exceptional performance in capturing nonlinear trends, particularly in recognizing seasonal patterns within the data. Visualizations of predicted stock prices further supported the findings, showcasing the ability of deep learning models to adapt to dynamic market conditions and discern intricate patterns within financial time series data. Challenges encountered by different neural network architectures, such as difficulties in recognizing certain patterns within specific timeframes, were also analyzed, providing insights into the strengths and limitations of each model

Revolutionizing Banking Decision-Making: A Deep Learning Approach to Predicting Customer Behavior

This article explores a machine learning approach focused on predicting bank customer behavior, emphasizing deep learning methods. Various architectures, including CNNs like VGG16, ResNet50, and InceptionV3, are compared with traditional algorithms such as Random Forest and SVM. Results show deep learning models, particularly ResNet50, outperform traditional ones, with an accuracy of 86.66%. A structured methodology ensures ethical data use. Investing in infrastructure and expertise is crucial for successful deep learning integration, offering a competitive edge in banking decision-making