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In this article, we discuss the recent developments of using radiofrequency (RF) and terahertz (THz) waves in the tracking the wellbeing of living organisms that include human beings and plants. We particularly focus on the accurate monitoring of human activity through RF waves found in the environment around us and sensing a plant's health by detecting the leaf water content using THz waves.
Qammer H Abbasi; Hasan T. Abbas; Syed A Shah; Adnan Zahid; Muhammad A Imran; Akram Alomainy. Microwave and Terahertz Sensing for Well Being. Reference Module in Biomedical Sciences 2021, 1 .
AMA StyleQammer H Abbasi, Hasan T. Abbas, Syed A Shah, Adnan Zahid, Muhammad A Imran, Akram Alomainy. Microwave and Terahertz Sensing for Well Being. Reference Module in Biomedical Sciences. 2021; ():1.
Chicago/Turabian StyleQammer H Abbasi; Hasan T. Abbas; Syed A Shah; Adnan Zahid; Muhammad A Imran; Akram Alomainy. 2021. "Microwave and Terahertz Sensing for Well Being." Reference Module in Biomedical Sciences , no. : 1.
Water is considered to be the most essential and vital resources to sustaining life. Ensuring its delivery to people with no intrusion of harmful impurities, safe, reliable, and in an affordable manner is one of huge challenge amid to the ongoing climate transformations. This demands to introduce a cost effective and notion of real-time monitoring system that can detect the microbiological contaminants in aqueous solutions in timely manner to protect the public and environment health. In this paper, the prospects of integrating non-invasive terahertz (THz) waves with machine learning (ML) enabled technique is studied. The research explores a method of using Fourier transform Infrared Spectroscopy (FTIR) system to observe the absorption spectra and characteristics of three solvents solution , including salt, sugar and glucose with various quantity in aqueous solutions in the frequency range of 1 THz to 20 THz. In this study, due to the different molecular configuration and vibration modes of substances, distinct absorption spectra peaks were achieved for different concentrations of solvent solutions at certain sensitive THz region. Moreover, using measurements observations data, meaningful features are extracted and incorporated four algorithms such as random forest (RF), support vector machine (SVM), decision tree (D-tree) and k-nearest neighbour (KNN). The results demonstrated that RF obtained a higher accuracy of 84.74% in identifying the substance in aqueous solutions. Moreover, it was also found that RF with 97.98%, outperformed other classifiers for estimation of salts concentration added in aqueous solutions. However, for sugar and glucose concentrations, SVM exhibited a higher accuracy of 93.11% and 96.88%, respectively, compared to other classifiers. Thus, proposed technique incorporating ML with THz waves, may be significant in providing an efficient, cost-effective and real-time monitoring for water quality detection system.
Adnan Zahid; Kia Dashtipour; Hasan Abbas; Aifeng Ren; David R.S. Cumming; James P. Grant; Muhammad Ali Imran; Qammer Hussain Abbasi; Ivonne E. Carranza1. Machine Learning Framework for the Detection of Anomalies in Aqueous Solutions Using Terahertz Waves. 2021, 1 .
AMA StyleAdnan Zahid, Kia Dashtipour, Hasan Abbas, Aifeng Ren, David R.S. Cumming, James P. Grant, Muhammad Ali Imran, Qammer Hussain Abbasi, Ivonne E. Carranza1. Machine Learning Framework for the Detection of Anomalies in Aqueous Solutions Using Terahertz Waves. . 2021; ():1.
Chicago/Turabian StyleAdnan Zahid; Kia Dashtipour; Hasan Abbas; Aifeng Ren; David R.S. Cumming; James P. Grant; Muhammad Ali Imran; Qammer Hussain Abbasi; Ivonne E. Carranza1. 2021. "Machine Learning Framework for the Detection of Anomalies in Aqueous Solutions Using Terahertz Waves." , no. : 1.
With the popularity of smart wearable systems, sensor signal processing poses more challenges to machine learning in embedded scenarios. For example, traditional machine-learning methods for data classification, especially in real time, are computationally intensive. The deployment of Artificial Intelligence algorithms on embedded hardware for fast data classification and accurate fall detection poses a huge challenge in achieving power-efficient embedded systems. Therefore, by exploiting the associative memory feature of Hopfield Neural Network, a hardware module has been designed to simulate the Neural Network algorithm which uses sensor data integration and data classification for recognizing the fall. By adopting the Hebbian learning method for training neural networks, weights of human activity features are obtained and implemented/embedded into the hardware design. Here, the neural network weight of fall activity is achieved through data preprocessing, and then the weight is mapped to the amplification factor setting in the hardware. The designs are checked with validation scenarios, and the experiment is completed with a Hopfield neural network in the analog module. Through simulations, the classification accuracy of the fall data reached 88.9% which compares well with some other results achieved by the software-based machine-learning algorithms, which verify the feasibility of our hardware design. The designed system performs the complex signal calculations of the hardware’s feedback signal, replacing the software-based method. A straightforward circuit design is used to meet the weight setting from the Hopfield neural network, which is maximizing the reusability and flexibility of the circuit design.
Zheqi Yu; Adnan Zahid; Shuja Ansari; Hasan Abbas; Amir M. Abdulghani; Hadi Heidari; Muhammad A. Imran; Qammer H. Abbasi. Hardware-Based Hopfield Neuromorphic Computing for Fall Detection. Sensors 2020, 20, 7226 .
AMA StyleZheqi Yu, Adnan Zahid, Shuja Ansari, Hasan Abbas, Amir M. Abdulghani, Hadi Heidari, Muhammad A. Imran, Qammer H. Abbasi. Hardware-Based Hopfield Neuromorphic Computing for Fall Detection. Sensors. 2020; 20 (24):7226.
Chicago/Turabian StyleZheqi Yu; Adnan Zahid; Shuja Ansari; Hasan Abbas; Amir M. Abdulghani; Hadi Heidari; Muhammad A. Imran; Qammer H. Abbasi. 2020. "Hardware-Based Hopfield Neuromorphic Computing for Fall Detection." Sensors 20, no. 24: 7226.
The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocardiogram (ECG) can improve the early detection of diagnosis. In this paper, we have further developed a framework for processing the ECG signal in order to determine the AF episodes. We have implemented machine learning and deep learning algorithms to detect AF. Moreover, the experimental results show that better performance can be achieved with long short-term memory (LSTM) as compared to other algorithms. The initial experimental results illustrate that the deep learning algorithms, such as LSTM and convolutional neural network (CNN), achieved better performance (10%) as compared to machine learning classifiers, such as support vectors, logistic regression, etc. This preliminary work can help clinicians in AF detection with high accuracy and less probability of errors, which can ultimately result in reduction in fatality rate.
Sidrah Liaqat; Kia Dashtipour; Adnan Zahid; Khaled Assaleh; Kamran Arshad; Naeem Ramzan. Detection of Atrial Fibrillation Using a Machine Learning Approach. Information 2020, 11, 549 .
AMA StyleSidrah Liaqat, Kia Dashtipour, Adnan Zahid, Khaled Assaleh, Kamran Arshad, Naeem Ramzan. Detection of Atrial Fibrillation Using a Machine Learning Approach. Information. 2020; 11 (12):549.
Chicago/Turabian StyleSidrah Liaqat; Kia Dashtipour; Adnan Zahid; Khaled Assaleh; Kamran Arshad; Naeem Ramzan. 2020. "Detection of Atrial Fibrillation Using a Machine Learning Approach." Information 11, no. 12: 549.
This paper suggests an optimisation approach in heterogeneous computing systems to balance energy power consumption and efficiency. The work proposes a power measurement utility for a reinforcement learning (PMU-RL) algorithm to dynamically adjust the resource utilisation of heterogeneous platforms in order to minimise power consumption. A reinforcement learning (RL) technique is applied to analyse and optimise the resource utilisation of field programmable gate array (FPGA) control state capabilities, which is built for a simulation environment with a Xilinx ZYNQ multi-processor systems-on-chip (MPSoC) board. In this study, the balance operation mode for improving power consumption and performance is established to dynamically change the programmable logic (PL) end work state. It is based on an RL algorithm that can quickly discover the optimization effect of PL on different workloads to improve energy efficiency. The results demonstrate a substantial reduction of 18% in energy consumption without affecting the application’s performance. Thus, the proposed PMU-RL technique has the potential to be considered for other heterogeneous computing platforms.
Zheqi Yu; Pedro Machado; Adnan Zahid; Amir Abdulghani; Kia Dashtipour; Hadi Heidari; Muhammad Imran; Qammer Abbasi. Energy and Performance Trade-Off Optimization in Heterogeneous Computing via Reinforcement Learning. Electronics 2020, 9, 1812 .
AMA StyleZheqi Yu, Pedro Machado, Adnan Zahid, Amir Abdulghani, Kia Dashtipour, Hadi Heidari, Muhammad Imran, Qammer Abbasi. Energy and Performance Trade-Off Optimization in Heterogeneous Computing via Reinforcement Learning. Electronics. 2020; 9 (11):1812.
Chicago/Turabian StyleZheqi Yu; Pedro Machado; Adnan Zahid; Amir Abdulghani; Kia Dashtipour; Hadi Heidari; Muhammad Imran; Qammer Abbasi. 2020. "Energy and Performance Trade-Off Optimization in Heterogeneous Computing via Reinforcement Learning." Electronics 9, no. 11: 1812.
Precision Agriculture (PA) and Agriculture 4.0 (A4.0) have been widely discussed as a medium to address the challenges related to agricultural production. In this research, we present a Systematic Literature Review (SLR) supported by a Bibliometric Performance and Network Analysis (BPNA) of the use of A4.0 technologies and PA techniques in the coffee sector. To perform the SLR, 87 documents published since 2011 were extracted from the Scopus and Web of Science databases and processed through the Preferred Reporting Items for Systematic reviews and Meta-Analyzes (PRISMA) protocol. The BPNA was carried out to identify the strategic themes in the field of study. The results present 23 clusters with different levels of development and maturity. We also discovered and presented the thematic network structure of the most used A4.0 technologies in the coffee sector. Our findings shows that Internet of Things, Machine Learning and geostatistics are the most used technologies in the coffee sector, we also present the main challenges and trends related to technological adoption in coffee systems. We believe that the demonstrated results have the potential to be considered by researchers in future works and decision making related to the field of study.
Michele Kremer Sott; Leonardo Bertolin Furstenau; Liane Mahlmann Kipper; Faber D. Giraldo; Jose Ricardo Lopez-Robles; Manuel J. Cobo; Adnan Zahid; Qammer H. Abbasi; Muhammad Ali Imran. Precision Techniques and Agriculture 4.0 Technologies to Promote Sustainability in the Coffee Sector: State of the Art, Challenges and Future Trends. IEEE Access 2020, 8, 149854 -149867.
AMA StyleMichele Kremer Sott, Leonardo Bertolin Furstenau, Liane Mahlmann Kipper, Faber D. Giraldo, Jose Ricardo Lopez-Robles, Manuel J. Cobo, Adnan Zahid, Qammer H. Abbasi, Muhammad Ali Imran. Precision Techniques and Agriculture 4.0 Technologies to Promote Sustainability in the Coffee Sector: State of the Art, Challenges and Future Trends. IEEE Access. 2020; 8 (99):149854-149867.
Chicago/Turabian StyleMichele Kremer Sott; Leonardo Bertolin Furstenau; Liane Mahlmann Kipper; Faber D. Giraldo; Jose Ricardo Lopez-Robles; Manuel J. Cobo; Adnan Zahid; Qammer H. Abbasi; Muhammad Ali Imran. 2020. "Precision Techniques and Agriculture 4.0 Technologies to Promote Sustainability in the Coffee Sector: State of the Art, Challenges and Future Trends." IEEE Access 8, no. 99: 149854-149867.
The increasing number of studies that underline the relationship between industry 4.0 and sustainability shows that sustainability is one of the pillars of smart factories. Through a bibliometric performance and network analysis (BPNA), this research describes the existing relationship between industry 4.0 and sustainability, the strategic themes from 2010 to March 2019, as well as the research gaps for proposing future work. With this goal in mind, 894 documents and 5621 keywords were included for bibliometric analysis, which were treated with the support of Science Mapping Analysis Software Tool (SciMAT). The bibliometric performance analysis presented the number of publications over time and the most productive journals. The strategic diagram shown 12 main research clusters, which were measured according to bibliometric indicators. Moreover, the network structure of each cluster was depicted, and the patterns found were discussed based on the documents associated to the network. Our findings show the scientific efforts are focused to enhance economic and environmental aspects and highlights a lack of effort relating the social sphere. Finally, the paper concludes the challenges, perspectives, and suggestions for the potential future work in the field of study relating to industry 4.0 and sustainability.
Leonardo Bertolin Furstenau; Michele Kremer Sott; Liane Mahlmann Kipper; Ênio Leandro Machado; Jose Ricardo Lopez-Robles; Michael S. Dohan; Manuel J. Cobo; Adnan Zahid; Qammer H. Abbasi; Muhammad Ali Imran. Link Between Sustainability and Industry 4.0: Trends, Challenges and New Perspectives. IEEE Access 2020, 8, 140079 -140096.
AMA StyleLeonardo Bertolin Furstenau, Michele Kremer Sott, Liane Mahlmann Kipper, Ênio Leandro Machado, Jose Ricardo Lopez-Robles, Michael S. Dohan, Manuel J. Cobo, Adnan Zahid, Qammer H. Abbasi, Muhammad Ali Imran. Link Between Sustainability and Industry 4.0: Trends, Challenges and New Perspectives. IEEE Access. 2020; 8 (99):140079-140096.
Chicago/Turabian StyleLeonardo Bertolin Furstenau; Michele Kremer Sott; Liane Mahlmann Kipper; Ênio Leandro Machado; Jose Ricardo Lopez-Robles; Michael S. Dohan; Manuel J. Cobo; Adnan Zahid; Qammer H. Abbasi; Muhammad Ali Imran. 2020. "Link Between Sustainability and Industry 4.0: Trends, Challenges and New Perspectives." IEEE Access 8, no. 99: 140079-140096.
Parkinson’s disease (PD) is a progress and neurodegenerative condition causing motor impairments. One of the major motor related impairments that present biggest challenge is freezing of gait (FOG) in Parkinson’s patients. In FOG episode, the patient is unable to initiate, control or sustain a gait that consequently affects the Activities of Daily Livings (ADLs) and increases the occurrence of critical events such as falls. This paper presents continuous monitoring ADLs and classification freezing of gait episodes using Wi-Fi and radar imaging. The idea is to exploit the multi-resolution scalograms generated by channel state information (CSI) imprint and micro-Doppler signatures produced by reflected radar signal. A total of 120 volunteers took part in experimental campaign and were asked to perform different activities including walking fast, walking slow, voluntary stop, sitting down & stand up and freezing of gait. Two neural networks namely Autoencoder and a proposed enhanced Autoencoder were used classify ADLs and FOG episodes using data fusion process by combining the images acquired from both sensing techniques. The Autoencoder provided overall classification accuracy of ~87% for combined datasets. The proposed algorithm provided significantly better results by presenting an overall accuracy of ~98% using data fusion..
Syed Aziz Shah; Ahsen Tahir; Jawad Ahmad; Adnan Zahid; Haris Pervaiz; Aboajeila Milad Abdulhadi Ashleibta; Aamir Hasanali; Shadan Khattak; Qammer Hussain Abbasi. Sensor Fusion for Identification of Freezing of Gait Episodes Using Wi-Fi and Radar Imaging. IEEE Sensors Journal 2020, 20, 14410 -14422.
AMA StyleSyed Aziz Shah, Ahsen Tahir, Jawad Ahmad, Adnan Zahid, Haris Pervaiz, Aboajeila Milad Abdulhadi Ashleibta, Aamir Hasanali, Shadan Khattak, Qammer Hussain Abbasi. Sensor Fusion for Identification of Freezing of Gait Episodes Using Wi-Fi and Radar Imaging. IEEE Sensors Journal. 2020; 20 (23):14410-14422.
Chicago/Turabian StyleSyed Aziz Shah; Ahsen Tahir; Jawad Ahmad; Adnan Zahid; Haris Pervaiz; Aboajeila Milad Abdulhadi Ashleibta; Aamir Hasanali; Shadan Khattak; Qammer Hussain Abbasi. 2020. "Sensor Fusion for Identification of Freezing of Gait Episodes Using Wi-Fi and Radar Imaging." IEEE Sensors Journal 20, no. 23: 14410-14422.
Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular movements such as falls, gait and breathing disorders. This can allow people to live more independent lifestyles and still have the safety of being monitored if more direct care is needed. At present wearable devices can provide real-time monitoring by deploying equipment on a person’s body. However, putting devices on a person’s body all the time makes it uncomfortable and the elderly tend to forget to wear them, in addition to the insecurity of being tracked all the time. This paper demonstrates how human motions can be detected in a quasi-real-time scenario using a non-invasive method. Patterns in the wireless signals present particular human body motions as each movement induces a unique change in the wireless medium. These changes can be used to identify particular body motions. This work produces a dataset that contains patterns of radio wave signals obtained using software-defined radios (SDRs) to establish if a subject is standing up or sitting down as a test case. The dataset was used to create a machine learning model, which was used in a developed application to provide a quasi-real-time classification of standing or sitting state. The machine-learning model was able to achieve 96.70% accuracy using the Random Forest algorithm using 10 fold cross-validation. A benchmark dataset of wearable devices was compared to the proposed dataset and results showed the proposed dataset to have similar accuracy of nearly 90%. The machine-learning models developed in this paper are tested for two activities but the developed system is designed and applicable for detecting and differentiating x number of activities.
William Taylor; Syed Aziz Shah; Kia Dashtipour; Adnan Zahid; Qammer H. Abbasi; Muhammad Ali Imran. An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare. Sensors 2020, 20, 2653 .
AMA StyleWilliam Taylor, Syed Aziz Shah, Kia Dashtipour, Adnan Zahid, Qammer H. Abbasi, Muhammad Ali Imran. An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare. Sensors. 2020; 20 (9):2653.
Chicago/Turabian StyleWilliam Taylor; Syed Aziz Shah; Kia Dashtipour; Adnan Zahid; Qammer H. Abbasi; Muhammad Ali Imran. 2020. "An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare." Sensors 20, no. 9: 2653.
Aifeng Ren; Adnan Zahid; Ahmed Zoha; Syed Aziz Shah; Muhammad Ali Imran; Akram Alomainy; Qammer H. Abbasi. Machine Learning Driven Approach Towards the Quality Assessment of Fresh Fruits Using Non-Invasive Sensing. IEEE Sensors Journal 2019, 20, 2075 -2083.
AMA StyleAifeng Ren, Adnan Zahid, Ahmed Zoha, Syed Aziz Shah, Muhammad Ali Imran, Akram Alomainy, Qammer H. Abbasi. Machine Learning Driven Approach Towards the Quality Assessment of Fresh Fruits Using Non-Invasive Sensing. IEEE Sensors Journal. 2019; 20 (4):2075-2083.
Chicago/Turabian StyleAifeng Ren; Adnan Zahid; Ahmed Zoha; Syed Aziz Shah; Muhammad Ali Imran; Akram Alomainy; Qammer H. Abbasi. 2019. "Machine Learning Driven Approach Towards the Quality Assessment of Fresh Fruits Using Non-Invasive Sensing." IEEE Sensors Journal 20, no. 4: 2075-2083.
An increasing global aridification due to climate change has made the health monitoring of vegetation indispensable to maintaining the food supply chain. Cost-effective and smart irrigation systems are required not only to ensure the efficient distribution of water, but also to track the moisture of plant leaves, which is an important marker of the overall health of the plant. This paper presents a novel electromagnetic method to monitor the water content (WC) and characterisation in plant leaves using the absorption spectra of water molecules in the terahertz (THz) frequency for four consecutive days. We extracted the material properties of leaves of eight types of pot herbs from the scattering parameters, measured using a material characterisation kit in the frequency range of 0.75 to 1.1 THz. From the computed permittivity, it is deduced that the leaf specimens increasingly become transparent to the THz waves as they dry out with the passage of days. Moreover, the loss in weight and thickness of leaves were observed due to the natural evaporation of leaf moisture cells and change occurred in the morphology of fresh and water-stressed leaves. It is also illustrated that loss observed in WC on day 1 was in the range of 5% to 22%, and increased from 83.12% to 99.33% on day 4. Furthermore, we observed an exponential decaying trend in the peaks of the real part of the permittivity from day 1 to 4, which was reminiscent of the trend observed in the weight of all leaves. Thus, results in paper demonstrated that timely detection of water stress in leaves can help to take proactive action in relation to plants health monitoring, and for precision agriculture applications, which is of high importance to improve the overall productivity.
Adnan Zahid; Hasan T. Abbas; Muhammad A. Imran; Khalid A. Qaraqe; Akram Alomainy; David R. S. Cumming; Qammer H. Abbasi. Characterization and Water Content Estimation Method of Living Plant Leaves Using Terahertz Waves. Applied Sciences 2019, 9, 2781 .
AMA StyleAdnan Zahid, Hasan T. Abbas, Muhammad A. Imran, Khalid A. Qaraqe, Akram Alomainy, David R. S. Cumming, Qammer H. Abbasi. Characterization and Water Content Estimation Method of Living Plant Leaves Using Terahertz Waves. Applied Sciences. 2019; 9 (14):2781.
Chicago/Turabian StyleAdnan Zahid; Hasan T. Abbas; Muhammad A. Imran; Khalid A. Qaraqe; Akram Alomainy; David R. S. Cumming; Qammer H. Abbasi. 2019. "Characterization and Water Content Estimation Method of Living Plant Leaves Using Terahertz Waves." Applied Sciences 9, no. 14: 2781.
An increasing global aridification due to climate change has made the health monitoring of vegetation indispensable to maintaining the food supply chain. Cost-effective and smart irrigation systems are required not only to ensure the efficient distribution of water, but also to track the moisture of plant leaves, which is an important marker of the overall health of the plant. This paper presents a novel electromagnetic method to monitor the water content (WC) and characterization in plant leaves utilizing the absorption spectra of water molecules in the terahertz (THz) frequency for four consecutive days. We extracted the material properties of leaves of eight types of pot herbs from the scattering parameters, measured using a material characterization kit in the frequency range of 0.75 to 1.1 THz. From the computed permittivity, it is deduced that the leaf specimens increasingly become transparent to the THz waves as they dry out with the passage of days. Moreover, the loss in weight and thickness of leaves were observed due to the natural evaporation of leaf moisture cells and change occurred in the morphology of fresh and water-stressed leaves. It is also illustrated that loss observed in WC on day 1 was in the range of 5\% to 22\%, and increased from 83.12\% to 99.33\% on day 4. Furthermore, we observed an exponential decaying trend in the peaks of the real part of the permittivity from day 1 to 4, which was reminiscent of the trend observed in the weight of all leaves. Thus, results in paper demonstrated that timely detection of water stress in leaves can help to take proactive action in relation to plants health monitoring, and for precision agriculture applications, which is of high importance to improve the overall productivity.
Adnan Zahid; Hasan T. Abbas; Muhammad A. Imran; Khalid A. Qaraqe; Akram Alomainy; David R. S. Cumming; Qammer H. Abbasi. Characterization and Water Content Estimation Method of Living Plant Leaves Using Terahertz Waves. 2019, 1 .
AMA StyleAdnan Zahid, Hasan T. Abbas, Muhammad A. Imran, Khalid A. Qaraqe, Akram Alomainy, David R. S. Cumming, Qammer H. Abbasi. Characterization and Water Content Estimation Method of Living Plant Leaves Using Terahertz Waves. . 2019; ():1.
Chicago/Turabian StyleAdnan Zahid; Hasan T. Abbas; Muhammad A. Imran; Khalid A. Qaraqe; Akram Alomainy; David R. S. Cumming; Qammer H. Abbasi. 2019. "Characterization and Water Content Estimation Method of Living Plant Leaves Using Terahertz Waves." , no. : 1.