アニリノナフトキノン配位子を有するニッケル錯体によるノルボルネンと共役系炭化水素モノマーとの共重合

広島大学(Hiroshima University)博士(工学)Doctor of Engineeringdoctora

Intelligent Devices for IoT Applications

Internet of Things (IoT) devices refer to a vast network of physical devices that are connected to the internet and can communicate with each other through sensors and software. These devices range from simple household appliances, like smart thermostats and security cameras, to more complex industrial equipment, such as sensors used in manufacturing and logistics. Specially, IoT enabled wireless gas sensing systems which can withstand harsh environments without compromising the performance are getting popular day by day, which necessitates adequate developments in this field. By being the essential components of a wireless gas sensing system, both the sensor and the elements for communication should be agile and resilient when it comes to tackle unfavorable scenario. Moreover, gas sensors are prone to drift, which can lead to inaccurate readings and decreased reliability over time. Again, recent advancements in antenna design, such as fractal antennas and metamaterial structures, have shown promises in improving the bandwidth and gain parameters of the antennas built on top of high temperature tackling substrates. This piece of research targets three fundamental sections: demonstration of recent advances in data driven techniques for gas sensing system optimization, designing of antennas for different applications, and device design as well as fabrication. The Dimatix DMP-2831 inkjet printer has been optimized to operate with six different inks and two different substrates including PET and 3 mol yttria-stabilized zirconia (3YSZ) based ceramic substrate. Later, the feature oriented gas sensor data analysis to investigate correlations among stability, selectivity and long term drift is illustrated, which should significant relations among those parameters that can be considered while designing different intelligent data driven models to compensate drift. Moreover, a subspace transfer based approach is proposed to classify drifted gas sensor response to detect particular gas with higher accuracy. The model achieved an average accuracy greater than 87% while using only 40% of the total dataset to be trained. In the field of antenna technology, a co-planar waveguide (CPW) fed super wideband antenna is proposed which can cover C, X, Ku, K, Ka, Q, V, and W bands according to the simulated performance with high gain and radiation efficiency. Again, a high temperature tolerant antenna based on 3YSZ substrate is proposed which achieved good alignment between the simulated and fabricated device performance

Emerging Next Generation Solar Cells Route to High Efficiency and Low Cost

Generation of clean energy is one of the main challenges of the 21st century. Solar energy is the most abundantly available renewable energy source which would be supplying more than 50 of the global electricity demand in 2100. Solar cells are used to convert light energy into electrical energy directly with an appeal that it does not generate any harmful bi products, like greenhouse gasses. The manufacturing of solar cells is actually based on the types of semiconducting or non semiconducting materials used and commercial maturity. From the very beginning of the terrestrial use of Solar Cells, efficiency and costs are the main focusing areas of research. The definition of so called emerging technologies sometimes described as including any technology capable of overcoming the Shockley-Queisser limit of power conversion efficiency 33.7 percent for a single junction device. In this paper, few promising materials for solar cells are discussed including their structural morphology, electrical and optical properties. The excellent state of the art technology, advantages and potential research issues yet to be explored are also pointed out. Md. Samiul Islam Sadek | Dr. M Junaebur Rashid | Dr. Zahid Hasan Mahmood "Emerging Next Generation Solar Cells: Route to High Efficiency and Low Cost" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 201

Morphometric relationships between length-weight and length-length and condition factor of four small indigenous fishes from the Payra River, southern Bangladesh

The present study describes the length–weight (LWR) relationship, length– length (LLR) relationship, and condition factor (K) of four small indigenous fish species from the Payra River, southern Bangladesh, namely Mastacembelus pancalus, Lepidocephalus guntea, Salmostoma bacaila and Mystus vittatus. A total of 867 specimens, representing 4 species of 4 families used for this study were caught by traditional fishing gear from July to October 2018. Standard length (SL) and total length (TL) for each specimen were measured by digital slide calipers and each body weight (BW) was taken by a digital balance. The allometric coefficient b of the LWR was close to the isometric value (b=3.078 and 3.028) in M. pancalus and L. guntea respectively, although it suggested negative allometric growth in M. vittatus (b < 3.00), whilst positive allometric growth in S. bacaila (b > 3.00). All the LWRs were highly significant (P < 0.05) and most of the coefficients of determination values were ≥ 0.857. The results further indicated that the LLRs were highly correlated (r2 ≥ 0.939; P < 0.05). Fulton’s condition factor (K) by month basis ranged from 0.52 (in M. pancalus) through 1.89 (in M. vittatus). The results of this study can be very effective for stock assessment of this four species in Payra River as well as in the surrounding ecosystems

An IoT Based Water-Logging Detection System: A Case Study of Dhaka

With a large number of populations, many problems are rising rapidly in Dhaka, the capital city of Bangladesh. Water-logging is one of the major issues among them. Heavy rainfall, lack of awareness and poor maintenance causes bad sewerage system in the city. As a result, water is overflowed on the roads and sometimes it gets mixed with the drinking water. To overcome this problem, this paper realizes the potential of using Internet of Things to combat water-logging in drainage pipes which are used to move wastes as well as rainwater away from the city. The proposed system will continuously monitor real time water level, water flow and gas level inside the drainage pipe. Moreover, all the monitoring data will be stored in the central database for graphical representation and further analysis. In addition to that if any emergency arises in the drainage system, an alert will be sent directly to the nearest maintenance office.Comment: Global Conference on Technology and Information Managemen

A Novel Framework for Mixed Reality–Based Control of Collaborative Robot: Development Study

Background: Applications of robotics in daily life are becoming essential by creating new possibilities in different fields, especially in the collaborative environment. The potentials of collaborative robots are tremendous as they can work in the same workspace as humans. A framework employing a top-notch technology for collaborative robots will surely be worthwhile for further research. Objective: This study aims to present the development of a novel framework for the collaborative robot using mixed reality. Methods: The framework uses Unity and Unity Hub as a cross-platform gaming engine and project management tool to design the mixed reality interface and digital twin. It also uses the Windows Mixed Reality platform to show digital materials on holographic display and the Azure mixed reality services to capture and expose digital information. Eventually, it uses a holographic device (HoloLens 2) to execute the mixed reality–based collaborative system. Results: A thorough experiment was conducted to validate the novel framework for mixed reality–based control of a collaborative robot. This framework was successfully applied to implement a collaborative system using a 5–degree of freedom robot (xArm-5) in a mixed reality environment. The framework was stable and worked smoothly throughout the collaborative session. Due to the distributed nature of cloud applications, there is a negligible latency between giving a command and the execution of the physical collaborative robot. Conclusions: Opportunities for collaborative robots in telerehabilitation and teleoperation are vital as in any other field. The proposed framework was successfully applied in a collaborative session, and it can also be applied in other similar potential applications for robust and more promising performance

A Comparative Analysis on IoT Communication Protocols for Future Internet Applications

With the emergence of 5G, the Internet of Things (IoT) will bring about the next industrial revolution in the name of Industry 4.0. The communication aspect of IoT devices is one of the most important factors in choosing the right device for the right usage. So far, the IoT physical layer communication challenges have been met with various communications protocols that provide varying strengths and weaknesses. And most of them are wireless protocols due to the sheer number of device requirements for IoT. In this paper, we summarize the network architectures of some of the most popular IoT wireless communications protocols. We also present them side by side and provide a comparative analysis revolving around some key features, including power consumption, coverage, data rate, security, cost, and Quality of Service (QoS). This comparative study shows that LTE-based protocols like NB-IoT and LTE-M can offer better QoS and robustness, while the Industrial, Scientific, and Medical (ISM) Band based protocols like LoRa, Sigfox, and Z-wave claim their place in usage where lower power consumption and lesser device complexity are desired. Based on their respective strengths and weaknesses, the study also presents an application perspective of the suitability of each protocol in a certain type of scenario and addresses some open issues that need to be researched in the future. Thus, this study can assist in the decision making regarding choosing the most suitable protocol for a certain field

An adaptive medical cyber-physical system for post diagnosis patient care using cloud computing and machine learning approach

Medical care is one of the most basic human needs. Due to the global shortage of doctors, nurses, and other healthcare personnel, medical cyber-physical systems are quickly becoming a viable option. Post-diagnosis surveillance is an essential application of these systems, which can be performed more successfully using various monitoring devices rather than active observation by nurses in their physical presence. However, most existing solutions for this application are rigid and do not consider current difficulties. Intelligent and adaptive systems can overcome the challenges because of the advances in relevant technology, especially healthcare 4.0. Therefore, this work presents an adaptive system based on cloud and edge computing architecture and machine learning approaches to perform post-diagnosis medical tasks on patients, thus reducing the need for nurses, especially in the post-diagnosis phase

Medical Named Entity Recognition (MedNER): Deep learning model for recognizing medical entities (drug, disease) from scientific texts

Medical Named Entity Recognition (MedNER) is an indispensable task in biomedical text mining. NER aims to recognize and categorize named entities in scientific literature, such as genes, proteins, diseases, and medications. This work is difficult due to the complexity of scientific language and the abundance of available material in the biomedical sector. Using domain-specific embedding and Bi-LSTM, we propose a novel NER model that employs deep learning approaches to improve the performance of NER on scientific publications. Our model gets 98% F1-score on a curated data-set of Covid-related scientific publications published in multiple web of science and pubmed indexed journals, significantly outperforming previous approaches deployed on the same data-set. Our findings illustrate the efficacy of our approach in reliably recognizing and classifying named entities (drug and disease) in scientific literature, opening the way for future developments in biomedical text mining