Rajshahi University of Engineering & Technology

This chapter delves into the multifaceted world of polymer composites, comprehensively examining their processing techniques, advantages, properties, and diverse applications. The processing section explores various manufacturing methods, including hand lay-up, filament winding, pultrusion, and resin transfer molding. The discussion on advantages elucidates the unique strengths of polymer composites, such as their lightweight nature, high strength-to-weight ratio, corrosion resistance, and tailorable properties. In exploring properties, the chapter covers mechanical, thermal, electrical, and other key characteristics that define the performance of polymer composites. Finally, the applications section showcases the versatility of polymer composites across industries, from aerospace and automotive to construction and sports. This chapter provides an extensive guide for researchers, engineers, and hobbyists who are interested in gaining a more profound comprehension of polymer composites and their crucial significance in contemporary materials science and engineering.

In the realm of the Internet of Things, reconfigurable intelligent surfaces (RISs) have emerged as a pivotal technology, offering unprecedented opportunities to enhance signal quality, coverage, and energy efficiency as part of the ongoing pursuit to overcome the limitations of conventional wireless communication systems. In this context, this article focuses on the analysis of performance in an integrated air-to-underwater network under the amplified-and-forward relay with variable gain, specifically examining the impact of RISs on a mixed terahertz (THz)-underwater optical communication system. This study utilizes the $\alpha -\mu $ distribution to characterize the fading effects and pointing error on the THz signal. On the other hand, the underwater turbulence on the optical signal is modeled using the mixture of the Exponential Generalized Gamma distribution with pointing error impairments. To provide a basis for comparison, the heterodyne detection technique and the intensity modulation with the direct detection technique are also incorporated. Therefore, analytical expressions of outage probability, average bit error rate, and average channel capacity are demonstrated in terms of the Meijer- $G$ function. To provide more insights, high signal-to-noise approximations of these metrics are also presented. Furthermore, the impact of various modulation schemes, fading severity, pointing errors, atmospheric turbulence conditions, and receiver detection techniques are inspected on the system performance. Finally, the analytical findings are validated through Monte Carlo simulations, ensuring the robustness of the results.

Landslides significantly threaten human life, infrastructure, and environmental balance. In the hilly regions of Bangladesh, including Sylhet and Rangamati, landslides are frequent, causing 727 deaths and 1017 injuries between 2000 and 2018. The northeastern section of Bangladesh is projected to receive over 500–600 mm of precipitation in 2023, breaking records over the past 122 years, according to the European Centre for Medium-Range Weather Forecasts (ECMWF). With an elevation range of 0 to 195 m above sea level and 18% of its total land area covered by water bodies, Rangamati is particularly vulnerable to landslides. Despite the devastating impact of landslides, susceptibility assessment and risk management strategies are lacking. This research aims to address this gap by developing a comprehensive framework for sustainable landslide risk mitigation using geostatistical and geospatial modeling techniques. Factors such as land use and land cover (LULC), elevation, slope, topographic wetness index (TWI), precipitation, lithology, soil type, normalized difference vegetation index (NDVI), and distance from roads are used to create a frequency ratio (FR) model and identify landslide susceptibility and risk zones. The resulting high-resolution landslide susceptibility map (LSM) and risk assessment models provide valuable insights for policymakers, land-use planners, and stakeholders involved in disaster risk reduction and sustainable development. By applying geostatistical and geospatial modeling techniques to assess landslide susceptibility, manage risk, and promote sustainability, this research enhances resilience to landslides and highlights the importance of proactive planning and informed decision-making in mitigating the impact of landslides for promoting sustainable development in hilly regions.

A confidential communication over multiple-input multiple-output (MIMO) fading channels is considered, in which a transmitter communicates with a group of receivers in the presence of multiple eavesdroppers. The antenna correlation depends on both the antenna spacing and the angular spectrum of the incoming radio waves. We derive the closed-form analytical expressions of the ergodic secrecy multicast capacity for Rayleigh and Ricean fading MIMO channels considering a linear array with equally spaced antennas and employing the classical Jakes correlation. Then, we compare the performance of Rayleigh and Ricean fading channels showing the effects of fading, antenna correlation and the spacing between antenna elements. Our results show that antenna correlations significantly reduces the ergodic secrecy multicast capacity of Rayleigh as well as Ricean fading MIMO channels and this effect is more severe in the case of Rayleigh fading channel compared to Ricean fading.

Cardiovascular diagnostics relies heavily on the ECG (ECG), which reveals significant information about heart rhythm and function. Despite their significance, traditional ECG measures employing electrodes have limitations. As a result of extended electrode attachments, patients may experience skin irritation or pain, and motion artifacts may interfere with signal accuracy. Additionally, ECG monitoring usually requires highly trained professionals and specialized equipment, which increases the treatment's complexity and cost. In critical care scenarios, such as continuous monitoring of hospitalized patients, wearable sensors for collecting ECG data may be difficult to use. Although there are issues with ECG, it remains a valuable tool for diagnosing and monitoring cardiac disorders due to its non-invasive nature and the detailed information it provides about the heart. The goal of this study is to present an innovative method for generating continuous ECG waveforms from non-contact radar data by using Deep Learning. The method can eliminate the need for invasive or wearable biosensors and expensive equipment to collect ECGs. In this paper, we propose the MultiResLinkNet, a one-dimensional convolutional neural network (1D CNN) model for generating ECG signals from radar waveforms. With the help of a publicly accessible radar benchmark dataset, an end-to-end DL architecture is trained and assessed. There are six ports of raw radar data in this dataset, along with ground truth physiological signals collected from 30 participants in five distinct scenarios: Resting, Valsalva, Apnea, Tilt-up, and Tilt-down. By using strong temporal and spectral measurements, we assessed our proposed framework's ability to convert ECG data from Radar signals in three distinct scenarios, namely Resting, Valsalva, and Apnea (RVA). ECG segmentation performed better by MultiResLinkNet than by state-of-the-art networks in both combined and individual cases. As a result of the simulations, the resting, Valsalva, and RVA scenarios showed the highest average temporal values, respectively: 66.09523 ± 19.33, 60.13625 ± 21.92, and 61.86265 ± 21.37. In addition, it exhibited the highest spectral correlation values (82.4388 ±18.42 (Resting), 77.05186 ± 23.26 (Valsalva), 74.65785 ± 23.17 (Apnea), and 79.96201 ± 20.82 (RVA)), along with minimal temporal and spectral errors in almost every case. The qualitative evaluation revealed strong similarities between generated and actual ECG waveforms. As a result of our method of forecasting ECG patterns from remote radar data, we can monitor high-risk patients, especially those undergoing surgery.

Seismic events are a matter of concern for structural engineers because of their devastating effects. These events are neither can be predicted. nor can be prevented. To mitigate its impacts on the structures several measures are taken. But none of them can be treated as 100% effective, there is a lot of scope for improvement. In this study, the dynamic behavior of structures under seismic loading was investigated, focusing on the combined influence of geometric shapes, different types of shear walls, and their position on lateral displacement and drift. Specifically, four different geometric shapes of plans of identical floor areas were taken for this study. Two types of shear walls, oupled shear walls, and core shear walls were placed in four different positions. The Static Equivalent Load method to assess the seismic response of structures is employed in this study, following the guidelines given in the Bangladesh National Building Code 2006 using ETABS software. Key findings of the study are the efficacy of shear walls in mitigating lateral displacement. The optimum results were achieved for central positions and core wall configurations. The study discloses the significance of different geometric shapes on seismic responses. This study provides a perception of effective seismic retrofitting strategies.

Heavy urbanization along with population increase in the major cities of Bangladesh generates a massive amount of solid waste, which the government is incapable of handling effectively with the current management system, manpower, infrastructures, monetary backing, and technical aptitudes. With identifying the primary concerns for solid waste management, the research analyzed the present circumstances of waste generation, collection, dumping site transfer system, waste recycling, and treatment facilities of Khulna City Corporation (KCC). This study was completed using both an analytical and descriptive research design. A total of 12 KIIs and two focus group discussions were organized with important stakeholders such as garbage collectors, transporters, homeowners, conservancy field inspectors, and NGO staff. According to the study, a single individual generates 0.3 kg of garbage in KCC, where vegetable and food waste account for approx. 79% of the garbage stream, or nearly four-fifths of all litter created. The KCC has been instrumental in disposing of rubbish from the municipal corporation’s roadside collection points. In accordance with the results of the questionnaire study, the majority of the residences do not have a collection system, and most of them bring their garbage to the nearest STS or storage site. At worst, nearly half of the litter produced from the residence is not accumulated and transferred to the disposal area and is instead disposed of at the Rajbandh landfilling site, causing environmental pollution and posing dangers to surrounding residents. The study discovered that KCC lacks sufficient rules or a framework for scaling up programs to divert household garbage to an organic system. If individuals are specifically informed about transforming organic wastes into compost for fertilizing purposes, a resource recovery technique can precede to an enhancement in managing the MSW successfully. If a living environment is to be assured for citizens, KCC must keep a broad eye on environmental losses by developing effective plans or frameworks and putting them into action to preserve the environment safe and sound.

Nano-crystalline Zinc sulfide (ZnS) thin films were deposited on glass substrates by spin coating method using thiol-amine co-solvent through Triton X-100 (TX-100) surfactant. The structural, morphological, and optical properties of the deposited ZnS films for with and without TX-100 surfactant was investigated by X-ray diffraction, scanning electron microscopy, and optical transmission spectroscopy. The X-ray diffraction results showed that hexagonal phase with (008) plane. The highest peak intensity was found from the surfactant-mediated film which originated at 2θ value of ~29.30°. The crystalline size increased but the dislocation density and lattice parameters are decreased for with surfactant than without surfactant. The SEM results showed surfactant mediated film offered a smoother and uniform surface with fewer crack than without surfactant film. The optical transmittance was found 62–70% and 80–86% in the visible region without and with surfactant respectively. At the same time, the band gap energy was found 3.71 eV and 3.80 eV for without and with surfactant correspondingly. All the results showed that the high-quality ZnS film using TX-100 surfactant may be used as buffer layer for TFSCs. Graphical Abstract

Proper solid waste management is crucial to urban development, ensuring environmental sustainability and public health. This research study intends to investigate the sources of solid waste generation, analyze the quality of solid waste management techniques, and determine if the existing landfill site is suitable for effective trash disposal in Rajshahi City Corporation, Bangladesh. The study takes a mixed-methods approach, gathering both quantitative and qualitative data. While secondary data is taken from pertinent literature, publications, and government records, primary data is gathered through field surveys and interviews. The study looks into many facets of managing solid waste, such as garbage generation rates, collection and transportation networks, recycling and waste segregation procedures, and ways of disposal at the end of the process. The landfill site's physical characteristics, such as location, geology, hydrogeology, and proximity to residential areas, are examined. Furthermore, the research evaluates the effectiveness of the current solid waste management strategies in terms of environmental impact, resource utilization, and public satisfaction. Number of factors, including as the landfill's capacity, location relative to water bodies, geological stability, and adherence to environmental standards, are taken into account when determining the landfill's acceptability. To analyze and visualize the data and enable informed decision-making, geospatial techniques are used, such as Geographic Information System (GIS). The study's conclusions will help inform policy suggestions and initiatives aimed at enhancing Rajshahi City Corporation's solid waste management procedures. The results will help identify suitable alternatives for waste disposal and promote sustainable waste management strategies that minimize environmental pollution, optimize resource recovery, and enhance public health and well-being.

The management of food waste (FWM) is a globally pervasive issue characterized by inherent risks. The issue of reducing food losses and food waste is receiving growing global, regional, and national recognition. It is widely recognized that addressing this issue can help alleviate other sustainability concerns, such as food security and climate change. Furthermore, there is an increasing consensus among political and scientific communities regarding the imperative to mitigate global food waste (FW). Food waste is a prevalent issue that arises across several stages of the food supply chain, including production, refining, transport, and consumption. Furthermore, an additional factor contributing to the issue is the absence of adequate planning. Food waste is responsible for the onset of several diseases and contributes to the degradation of the environment. The primary source of loss in low-income nations such as Bangladesh is predominantly observed during the production stage. The consequences stemming from global food waste encompass the depletion of biodiversity, a substantial volume of water amounting to 250 km3, and the squandering of approximately 30% of the Earth's agricultural area. However, the principal ramifications mostly manifest in the form of global warming and the release of greenhouse gases. According to the Food and Agriculture Organization (FAO), it has been estimated that food losses (FL) and waste contribute to a proportion above 10% of the total global energy consumption. Therefore, food waste has a substantial role in the exacerbation of global warming. Despite the fact that Bangladesh exhibits a range of 67–75% freshwater (FW) resources, there remains a dearth of comprehensive knowledge and study pertaining to the management of FW in the country. Given the aforementioned context, it is imperative to do an analysis of the current situation and consistently evaluate the challenges, opportunities, and significant policy ramifications associated with the management of food waste. The objective of this study is to perform a comprehensive review of the current body of literature, with the intention of examining the contextual obstacles and suggesting potential strategies for mitigating and controlling food waste. It is imperative for the government of Bangladesh to implement initiatives aimed at mitigating and effectively managing the issue of food waste.

The electromyography (EMG) signal provides insight into neuromuscular activity which is used in medical and technological fields. Traditional needle electrodes and surface electrodes have several drawbacks making them less suitable for portable and long-term use. In contrast, emerging capacitive electrodes offer promising features over the existing electrodes. Yet, the full potential of capacitive electrodes remains untapped due to the lack of comprehensive design optimization for consistently reliable signal quality. This study highlights the complex interplay of factors influencing correlation in capacitive EMG (CEMG) and surface EMG (SEMG) signals. The study emphasizes the importance of the surface area of capacitive electrodes, muscle force, preprocessing, and sampling frequency in understanding and improving the correlation between CEMG and SEMG signals, providing valuable insights for future research and applications in the field. The study reveals that the electrode area has no significant effect on the correlation. However, the correlation significantly depends on the muscle force. In addition, removing artifacts from the CEMG increases the correlation, especially for lower force where artifacts are significant. Again, oversampling the EMG signal above 800 Hz does not have any impact on increasing the correlation but the correlation decreases with higher inter-electrode distance (IED). In this research, the highest correlation of 92.94% between CEMG and SEMG has been achieved for high muscle force with a plate area of 4 cm2. Therefore, the capacitive electrode can be an alternative for EMG signal acquisition.

Nowadays, the use of advanced pulse width modulation (PWM) techniques is becoming a research hot topic for improving the performance of multilevel inverter (MLI) based induction motor drives (IMDs). However, existing triangular carrier-based sinusoidal PWM (SPWM), conventional space vector PWM (CSVPWM), and existing bus-clamping PWM (BCPWM) techniques suffer from power quality problems. For further improvement of the performance of existing PWM techniques, an advanced level shifted carrier-based bus-clamping PWM (ALSBCPWM) technique is proposed in this paper for a 54- pulse ac-dc converter fed 7-level cascaded H-bridge (CHB) inverter based IMD. The carrier signal of the ALSBCPWM is formed by center-aligned harmonic injected saturated level shifted sinusoidal carriers. On the contrary, 3 rd harmonic injected 60° bus clamping modulating signals are used along with the new carrier signals to develop the ALSBCPWM technique. The total harmonic distortion (THD) of inverter output voltage or motor voltage, motor current, inverter switching and conductor power losses of the CHB-MLI are all reduced by the presented ALSBCPWM technique. Excellent thermal stability of the insulated gate bipolar transistors (IGBTs) of the CHB-MLI is also achieved with the proposed PWM technique. The performances of the IMD with the proposed ALSBCPWM technique are studied based on the considerations for industry applications, such as power quality, power losses, and thermal stability. The performance of the proposed ALSBCPWM technique is validated through simulations in MATLAB/Simulink environment and testing in a reduced scale laboratory test platform.

Among different types of transformerless photovoltaic inverters, multi‐level inverters based on switched‐capacitors (SC) are the burning topic of recent decades due to their potential advantages, such as, single source requirement, voltage boosting capability, and high power density. However, for a seven level output voltage, a conventional SC based inverter architecture uses more than two units of SC, a large amount of power switches, which then lead to capacitor voltage balancing problems. This paper presents a seven‐level switched‐capacitor transformerless inverter (SCTI), which is structured with only two SC units, ten power switches, and a single DC source. The proposed SCTI ensures voltage boosting capability, self‐voltage balancing, and low power semiconductor losses. Apart from these, a modified sinusoidal pulse width modulation (MSPWM) is also proposed in this work, which guarantees better thermal performance and low inverter output voltage THD for the proposed SCTI. The proposed SCTI along with the MSPWM is simulated in MATLAB/Simulink and PLECS computer simulation environments. A reduced scale laboratory prototype is also built and tested to ensure the feasibility of the proposed SCTI.

The global need for energy is increasing at a high rate and is expected to double or increase by 50%, according to some studies, in 30 years. As a result, it is essential to look into alternative methods of producing power. Solar photovoltaic (PV) power plants utilize the sun's clean energy, but they're not always dependable since they depend on weather patterns and requires vast amount of land. Space-based solar power (SBSP) has emerged as the potential solution to this issue. SBSP can provide 24/7 baseload carbon-free electricity with power density over 10 times greater than terrestrial alternatives while requiring far less land. Solar power is collected and converted in space to be sent back to Earth via Microwave or laser wirelessly and used as electricity. However, harnessing its full potential necessitates tackling substantial technological obstacles in wireless power transmission across extensive distances in order to efficiently send power to receivers on the ground. When it comes to achieving a net-zero goal, the SBSP is becoming more viable option. This paper presents a review of wireless power transmission systems and an overview of SBSP as a comprehensive system. To introduce the state-of-the-art information, the properties of the system and modern SBSP models along with application and spillover effects with regard to different sectors was examined. The challenges and risks are discussed to address the key barriers for successful project implementation. The technological obstacles stem from the fact that although most of the technology is already available none are actually efficient enough for deployment so with, private enterprises entering space race and more efficient system, the cost of the entire system that prevented this notion from happening is also decreasing. With incremental advances in key areas and sustained investment, SBSP integrated with other renewable could contribute significantly to cross-sector decarbonization.

Person identification is one of the most vital tasks for network security. People are more concerned about their security due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprints and faces have been widely used for person identification, which has the risk of information leakage as a result of reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiable pattern, which will not be reproducible falsely by capturing psychological and behavioral information of a person using vision and sensor-based techniques. In existing studies, most of the researchers used very complex patterns in this direction, which need special training and attention to remember the patterns and failed to capture the psychological and behavioral information of a person properly. To overcome these problems, this research devised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. This study developed two hand gesture-based pattern datasets for performing the experiments, which contained more than 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the hand geometry. Random forest was used to measure feature importance using the Gini Index. Finally, the support vector machine was implemented for person identification and evaluate its performance using identification accuracy. The experimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitrary hand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicated that the proposed system can be used for person identification in the field of security.

In this study, a broadband polarization and angle‐independent metamaterial absorber (MA) is investigated in the microwave range. It is made up of a periodic array of multi‐layered metal‐dielectric stepped cones. Since the dimensions of the layers forming the unit cell are different, each layer resonates at different frequencies with overlapping bands. The overall response of the structure, with its extremely wide bandwidth, can be obtained by summing all the overlapping frequency responses corresponding to each layer. In numerical simulation, it is observed that the absorption at normal incidence is above 90% in the frequency range between 9.68 and 17.45 GHz and 95% in the frequency range between 9.91 and 14.86 GHz. The energy harvesting ratios of the structure are also evaluated in a wide spectral band. A power ratio of around 90% is obtained in the same frequency range in accordance with absorption response. A noticeable harvesting efficiency of up to 82% is observed, which represents the energy level converted into electrical energy on resistors.

Quick and accurate diagnosis of COVID‐19 is crucial in preventing its transmission. Chest X‐ray (CXR) imaging is often used for diagnosis, however, even experienced radiologists may misinterpret the results, necessitating computer‐aided diagnosis. Deep learning has yielded favourable results previously, but overfitting, excessive variance, and generalization errors may occur due to noise and limited datasets. Ensemble learning can improve predictions by using robust techniques. Therefore, this study, proposes two‐fold strategy that combines advanced and robust algorithms, including DenseNet201, EfficientNetB7, and Xception, to achieve faster and more accurate COVID‐19 detection. Segmented lung images were generated from CXR images using the residual U‐Net model, and two attention‐based ensemble neural networks were used for classification. The COVID‐19 radiography dataset was used to evaluate the proposed approach, which achieved an accuracy of 98.21%, 93.4%, and 89.06% for two, three, and four classes respectively which outperformed previous studies by a significant margin considering COVID, viral pneumonia, and lung opacity simultaneously. Despite the similarity in CXR images of COVID, pneumonia, and lung opacity, the proposed approach achieved 89.06% accuracy, demonstrating its ability to recognize distinguishable features. The developed algorithm is expected to have applications in clinics for diagnosing different diseases using X‐ray images.

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia with substantial health consequences. Persistent atrial fibrillation (PersAF), an earlier stage of AF, persists over a week and may spontaneously return to a normal rhythm but untreated cases can escalate to chronic AF. Due to growing intricacy, there is a rising demand for automated detection of PersAF. This study introduces a novel approach for PersAF classification using bidirectional long-short-term memory (BiLSTM) that harnesses both time series and image features extracted from electrocardiogram signals. The minimum redundancy maximum relevance algorithm was executed to optimize the feature set, enhancing the model's effectiveness. The model was evaluated via both 5-fold cross-validation and blindfold validation, utilizing three publicly available datasets. Training_set_I achieved 98.87±0.78 % accuracy, 99.14±0.70% sensitivity, and an F1 score of 0.992±0.005, while Training_set_II demonstrated a detection accuracy of 96.35±3.09% with 91.00±8.39% sensitivity and 92.00±6.90% F1 score. The model retained its performance when subjected to independent, unseen data with 94.91% accuracy, 98.57% sensitivity, and 96.10% F1 score. The tremendous performance of the model with a small number of features makes it a promising tool for wearable healthcare devices in the clinical applications of PersAF classification.

Post occupancy evaluation of housing reconstruction after a disaster is imperative for every nation that pushes ahead the idea of disaster resilient housing, which is obligatory for the social, cultural, and economic growth of a society or a country. Cyclone SIDR is the most dreadful of all catastrophes that have struck Bangladesh and aftermath of this devastation, a number of donor organizations stepped forward to help disaster victims by providing housing solutions. The aim of this study is to effectiveness analysis of these housing reconstruction programs from planning and construction standpoints with a post occupancy framework. Based on three months of detailed field survey at the most devastated location of cyclone SIDR, Southkhali Union (small rural area) of Sarankhola Upazila (sub-district) of Bagerhat District, this research infers that the donor organizations didn’t pay much attention in terms of choice, need and indigenous practices to provide reconstructed houses for the target beneficiaries. The study found that about 78% of the reconstructed houses hold dissatisfaction of the beneficiaries. As a result, people prompt to transform or modify 54% of the ‘Donor Driven’ houses soon after completion of the project. Another 16% donor driven house remained abandoned and rest 8% demolished. On the other hand, ‘Owner Driven’ houses were functionally perfect. They had freedom to build their houses with locally available natural materials with resilient planning and construction features which reduces disaster vulnerability and sustains for long time in comparison with donor driven houses.

Penguins are proficient swimmers, and their survival depends on their ability to catch prey. The diving behaviour of these fascinating birds should then minimize the associated energy cost. For the first time, the energy cost of penguin dives is computed from the free-ranging dive data, on the basis of an existing biomechanical model. Time-resolved acceleration and depth data collected for 300 dives of little penguins (Eudyptula minor) are specifically employed to compute the bird dive angles and swimming speeds, which are needed for the energy estimate. We find that the numerically obtained energy cost by using the free-ranging dive data is not far from the minimum cost predicted by the model. The outcome, therefore, supports the physical soundness of the chosen model; however, it also suggests that, for closer agreement, one should consider previously neglected effects, such as those due to water currents and those associated with motion unsteadiness. Additionally, from the free-ranging dive data, we calculate hydrodynamic forces and non-dimensional indicators of propulsion performance – Strouhal and Reynolds numbers. The obtained values further confirm that little penguins employ efficient propulsion mechanisms, in agreement with previous investigations.