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(Volume: 4, Issue: 5)
Towards 6G...
Mobile communication has taken a great leap in about 35 years that the wireless transfer of data from simple text or audio to live High-Definition (HD) videos is now possible in less than a millisecond. With the commercial rollout of fifth-generation mobile technology in 2019, termed as 5G, high-speed and reliable data transfer beyond 1 Gigabits per second (Gbps) is achievable across multiple devices. However, this data rate will also become insufficient in the future, owing to the growing worldly population and their all-time need of mobile devices to carry out any task. Moreover, the advancing technological era is foreseen to achieve Internet of Everything (IoE) to connect numerous people, devices, processes and computing devices in a wireless mode to support any application. 5G is actually known for achieving IoE to some extent than its precursors with its mm-Wave communication, network slicing and edge-computing-based architecture, but its ubiquitous presence is not ensured. Hence, the aim to impart massive, reliable and low-latent data transfer in a ubiquitous manner and to support various IoE applications in the near future has led to the emergence of the sixth-generation mobile technology, called as 6G. So, what is special about 6G, compared to 5G? The mere answer is: “Achieving Ubiquitous Intelligent Mobile Society (UIMS) with Ultra-Reliable Low Latency Communications (URLLC) ”. In fact, 6G is believed to ensure ubiquity and ultra-reliable low latency data communication by integrating aerial/ satellite communications, underwater communications and terrestrial/non-terrestrial device to device communications in the TeraHertz (THz) frequency ranges. But, how can Intelligent Mobile Society be achieved with 6G? Naturally, 6G is hoped to attain device connections of about 10 million per square kilometer or even more. Such massive device connections are incredibly huge for a human to monitor or control!!! Hence, Artificial Intelligence (AI) techniques are sought in 6G to handle the issues with mobile data transmission, the data traffic or the user experience, rendering the Intelligent Mobile Society. Recently, many researchers have begun to investigate and pool their ideas on 6G development. Arun Agarwal, Chandan Mohanta and Gourav Misra have also brought the true vision, the working principles, the associated applications and the challenges of 6G in a nutshell. In their article in Journal of Information Technology and Digital World, vol.3 (4), the researchers have elucidated the ways to improve network coverage using 6G, like the change of modulation techniques, the integration of AI and the modification of the cellular network architecture, together with the related issues in achieving them. Since most applications of the future employ Augmented Reality (AR), Virtual Reality (VR) and Extended Reality (XR), researching on 6G deployment is very essential to attain massive data rates in a low-latent and ubiquitous sense.
Image courtesy: www.freepik.com
Image courtesy: www.freepik.com
Improving Brain-Controlled Wheel Chair Navigation With Quantum Computing
Controlling a device and accomplishing a task with it by just a thinking was a fiction decades ago. But this fiction has come to live with Brain-Computer Interfaces (BCIs), which directly use the neural signals of the brain to take control of a device. Brain-controlled wheelchair is one of the best BCI inventions assisting people with motor disabilities. With the brain signals being acquired invasively or non-invasively, followed by the extraction and classification of the motor imagery features, the brain-controlled wheelchair actually allows a disabled individual to navigate independently. Generally, the non-invasive means of brain data acquisition, like electroencephalogram (EEG) is most-preferred to avoid any surgical operations. However, the EEG data always have noise and artifacts associated with it, adversely affecting the accurate feature extraction. Moreover, continuous control of wheel-chair without time lapse is mostly desired, which again depends on feature extraction and classification. Hence, the researchers working on the BCI paradigm are concentrating more on feature extraction methods involving time-domain, frequency-domain, spatial-domain or non-linear features. Some of those researchers have also used deep learning approaches to yield enhanced feature. However, obtaining increased accuracy in low processing time is again a problem, when noise adds further complexity to the high-dimensional EEG data processing. Seeking ways to solve this issue in the EEG feature extraction and classification phase of brain-controlled wheelchair, Prabhat Kumar Upadhyay and Kumar Avinash Chandra from the Birla Institute of Technology, Mesra, Jharkhand have sought the use of Quantum computing principles. In their article published in Neuroscience, vol. 591, Elsevier, the researchers have put forth the Hybrid Quantum Enhanced CNN-LSTM model EEG Classifier (HQeCL) to interpret the motor-imagery EEG signals, as to rest or to accurately navigate the wheelchair to the left or the right direction. In fact, the researchers have opted the combined features from spatial, frequency and non-linear domain to yield a low-noise, enhanced feature abstraction, rather than relying on a single feature. Although their quantum-inspired approach provided beneficial results, the researchers also alert that hardware implementation might be challenging. So, an aspiring researcher can contribute towards motor imagery BCI in both the software and the hardware arenas. Not only that, the gradual transformation of the health care sector towards the Internet of Things (IoT) platform also uncovers new research opportunities. For instance, the upcoming researchers can integrate the motor imagery BCIs with IoT to ensure safety of life to the house-monitored disabled persons as well.
Organic Transistors For Large-Area Electronics
Organic Thin-Film Transistors (OTFTs) are gradually-replacing the conventionally-used silicon transistors in modern electronic devices and circuits. The urge for a non-rigid, printable, low-cost and foldable kind of transistor to support large-area electronics is the key reason behind this transformation. Besides being flexible, OTFTs also allow effective and long-lasting device fabrication over low-cost substrates that melt even at low temperatures like, plastic. It is because of all these characteristics, OTFT have attracted several applications like, TV displays, solar panels, electronic newspapers, wearable sensors, roll-to-roll printed circuits and many more. However, it is not that easy to fabricate and use OTFTs for device realization. The reason is that the OTFTs are generally made of disordered carbon-based molecules or polymers, which disrupt smooth charge transport. Hence, the device physics of OTFTs needs a thorough analysis, followed by optimal modelling and fabrication to improve its performance in any device realization. Shubham Dadhich, Garima Mathur and A.D.D. Dwivedi, three Indian researchers in their article in International Journal of Nanoelectronics and Materials, Vol.17(2), have performed numerical simulation and compact modelling of an OTFT, called C8-BTBT. With an intention to understand and improve the device physics/behavior of C8-BTBT for ring oscillator realization, these researchers have only dealt with the Technology Computer-Aided Design-based modelling of it for the first time, providing investigations on Density of States, bandgap modelling, electrostatic modelling and capacitance modelling. Since the organic semiconductors like, Pentacene, C8-BTBT, Rubrene and many other novel variants are going to shape the future of foldable and flexible large-area electronics, pairing them with neuromorphic computing, cognitive computing and other intelligent computing techniques might work wonders in the electronics industry.
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ML Approaches For Aflatoxin Detection And Liver Cancer Prevention
Liver cancer is taking form as a global fatal disease, being estimated to take about 30,090 lives in US alone in 2025. Though there are several risk factors associated with this disease development, like excess body weight, alcohol intake, smoking or direct hepatitis virus infection, a likely dormant factor that people often ignore is “the consumption of aflatoxin-contaminated food”. Aflatoxin is a mycotoxin that fungi, like Aspergillus flavus and Aspergillus parasiticus liberate, as they proliferate on foods stored in warm and humid temperature conditions. This highly carcinogenic substance not only causes fatal liver cancers, but also suppresses immune systems and physiological growth in humans by entering the human body in two ways: (i) Intake of aflatoxin-contaminated crop foods or edible cattle products and (ii) Inhalation of the airborne aflatoxin dust from crop processing. Hence, means to identify and reduce aflatoxin levels during crop processing and storage is highly mandated. The conventional aflatoxin detection methods involved chromatography, spectrometry and immunoassay-based methods. However, they required stringent and expensive laboratory settings, while the results of which are constrained to limited regions or experimental weather conditions only. So, can Machine Learning (ML) approaches help in this regard? Obviously, yes. ML approaches can aid in detecting aflatoxin, measuring its contamination levels in food or they can even create an environment that prevents the carcinogenic fungal growth!!! Knowing its significance in aflatoxin detection and risk prediction, Mayuri Tushar Deshmukh, P.R. Wankhede, Nitin Chakole, Pawan D. Kale, Mahendra R. Jadhav, Madhusudan B. Kulkarni and Manish Bhaiyya have analyzed and expressed the pros and cons associated with various ML approaches, like supervised, unsupervised and reinforcement learning. In their article in Trends in Food Science & Technology, vol.161, Elsevier, the researchers have aimed for a real-time ML approach to aflatoxin detection-cum-control and thus, reinforcement learning has been prioritized over the common supervised and exploratory unsupervised ML approaches. However, reinforcement learning also comes with challenges, such as: (i) Unavailability of large real-time training dataset, (ii) Inefficacy of aflatoxin detection across varied crops in rural/urban regions of differing climate and (iii) Lack of amenities, which render prevention of aflatoxin-liberating fungal growth or real-time monitoring of food storage and supply chain. All these challenges can serve as the research notions for the blooming researchers, who target at achieving carcinogen-free, intelligent food safety and productively-influencing the nation’s agricultural economy.
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Pyrolysis For Mixed Plastic Waste Disposal And Recycling
Plastic is produced in massive amounts every year, chiefly to manufacture materials or products related to packaging, electronic goods, construction, healthcare and domestic applications. Yet, the disposal or recycling of this non-biodegradable, durable and lightweight polymer has detrimental environmental concerns always. This is because the traditional ways of waste disposal, like landfill creation or incineration will only heap up plastic wastes or liberate toxic pollutants, respectively. Moreover, plastic segregation from the waste mixture is compulsorily required prior to waste treatment to prevent any toxic spread. But it might require expensive and time-consuming procedures with huge equipment as well!!! So, innovative ways to plastic waste disposal and recycling are sought in recent years and one among them is “Pyrolysis”. Though its process sounds similar to incineration, pyrolysis differs by the thermal degradation of plastic in lack of oxygen. The interesting fact about pyrolysis-based plastic recycling is that the plastic wastes are converted to liquid fuel and char with minimal amount of gas emissions, rather than getting turned into mere ashes of high toxicity. However, the liquid fuel viscosity, the fuel yield and the quantities of recoverable gases or char depends on the pyrolysis of Polystyrene (PS) and low-density polyethylene (LDPE), the primary thermoplastic polymers found in Mixed Plastic Waste (MPW). Hence, research on pyrolysis of PS and LDPE is required to adjust the liquid fuel yield/viscosity and to increase gas recovery or char for commercial or industrial utilization. Paschal Chibuike Abugu, Eberechukwu Kelvin Oguguo, Evan Anukwam, Uchechi Perpetual Anyanwu, Jacob Happy Ineh and Innocent Chimezie Madufor from the Federal University of Technology, Nigeria, have compared the by-products from pyrolyzing PS, LDPE and a fifty-fifty PS-LDPE mixture under nitrogen. As per their research in Journal of Engineering in Industrial Research, vol. 6(3), the researchers have found PS to support fuel applications, LDPE to align with diesel characteristics and PS-LDPE mixture to give insights to MPW processing, eventually finding ways to dispose plastic wastes and to increase the circular economy in Nigeria. However, fuel selectivity, undesired byproduct removal and increased energy recovery to achieve self-sustained reactors or to meet the growing energy demand needs further research in future with new catalysts or optimal reactor designs. Additionally, as per the Nigerian researchers, the char of the LDPE-PE blend can serve more than a fuel, if its physicochemical characteristics are studied. They pinpoint the usability of LDPE-PE blend as corrosion treating agent in industries or a functional material for environmental restoration as well. So, researching on plastic waste removal using pyrolysis can possibly change an environmental burden into beneficial by-products or aid in resource recovery. Image courtesy: www.vecteezy.com
Disclaimer
The discussions in 'Trending Research' are purely based on the already-published articles received from the respective authors for the easy portrayal of trending research themes to young researchers
This magazine does not accept or publish any kind of novel research and it does not create copyright infringements to the discussed journal articles
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