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  • 1.
    Arunachalam, Ajay
    Department of Computer Science, School of Applied Statistics, National Institute of Development Administration (NIDA), Bangkok, Thailand.
    Rock, Paper, Scissors Game Based Model for Content Discovery in P2P MANETs2020In: Wireless personal communications, ISSN 0929-6212, E-ISSN 1572-834XArticle in journal (Refereed)
    Abstract [en]

    Resource discovery is a key challenge in dynamic environment such as Peer-to-Peer (P2P) MANETs. To leverage the lookup costs and efficiently discover the resources, the peers in a P2P network communicate with each other forming one-or-more overlay structure. And, thus the peer’s connections in such overlay networks plays a crucial role. To harness this, we propose a model that focuses on the underlying network topology as each virtual link of the overlay network is supported by a path in the underlying physical network. Also, we design a new resource discovery algorithm that uses the famous Rock-Paper-Scis-sors (RPS)  game ideology. In this work, we present a Rock-Paper-Scissors-Game-Based (RPSGB) algorithm for content discovery in mobile peer-to-peer network. The proposed work focuses on providing efficient resource discovery scheme in such a dynamic network, using the game theory concepts. Our scheme is a light-weight model aimed to suit the unstructured architecture better. The major goal of this work, is to reduce the consumption of power, minimize the lookup cost, lower the bandwidth consumption, and increase the hit ratio. We compare our scheme with the traditional well-known benchmarked schemes. After evaluation, the  simulation results justify the effectiveness of the proposed protocol. The obtained results show that the proposed scheme substantially decreases the network traffic, lowers the battery power and bandwidth consumption, while having good search efficiency. Also, the search latency is minimized. The result justifies that RPSGB algo-rithm proposes to make resource searching much more efficient, and improves the statistics against the posed challenges.

  • 2.
    Arunachalam, Ajay
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    MSI-RPi: Affordable, Portable, and Modular Multispectral Imaging Prototype Suited to Operate in UV, Visible and Mid-Infrared Regions2022In: Journal of Mobile Multimedia, ISSN 1550-4646, E-ISSN 1550-4654, Vol. 18, no 3, p. 723-742Article in journal (Refereed)
    Abstract [en]

    Digital plant inventory provides critical growth insights, given the associated data quality is good. Stable & high-quality image acquisition is critical for further examination. In this work, we showcase an affordable, portable, and modular spectral camera prototype, designed with open hardware’s and open-source software’s. The image sensors used were color, and infrared Pi micro-camera. The designed prototype presents the advantage as being low-cost and modular with respect to other general commercial market available spectral devices. The micro-size connected sensors make it a compact instrument that can be used for any general spectral acquisition purposes, along with the provision of custom selection of the bands, making the presented prototype design a Plug-nd-Play (PnP) setup that can be used in different wide application areas. The images acquired from our custom-built prototype were back-tested by performing image analysis and qualitative assessments. The image acquisition software, and processing algorithm has been programmed, which is bundled with our developed system. Further, an end-to-end automation script is integrated for the users to readily leverage the services on-demand. The design files, schematics, and all the related materials of the spectral block design is open-sourced with open-hardware license & is made available at https://github.com/ajayarunachalam/Multi-Spectral-Imaging-RaspberryPi-Design. The automated data acquisition scripts & the spectral image analysis done is made available at https://github.com/ajayarunachalam/SI-RPi.

  • 3.
    Arunachalam, Ajay
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    RaspberryPi‐Arduino (RPA) powered smart mirrored and reconfigurable IoT facility for plant science research2022In: Internet Technology Letters, E-ISSN 2476-1508, Vol. 5, no 1, article id e272Article in journal (Refereed)
    Abstract [en]

    Continuous monitoring of crops is critical for the sustainability of agriculture. The effects of changes in temperature, light intensity, humidity, pH, soil moisture, gas intensities, etc. have an overall impact on the plant growth. Growth chambers are environmental controlled facilities which needs to be monitored round-the-clock. To improve both the reproducibility, and maintenance of such facilities, remote monitoring plays a very pivotal role. An automated re-configurable & persistent mirrored storage-based remote monitoring system is developed with low-cost open source hardwares and softwares. The system automates sensors deployment, storage (database, logs), and provides an elegant dashboard to visualize the real-time continuous data stream. We propose a new smart AGRO IoT system with robust data acquisition mechanism, and also propose two software component nodes, (i.e., Mirroring and Reconfiguration) running as an instance of the whole IoT facility. The former one is aimed to minimize/avoid the downtime, while the latter one is aimed to leverage the available cores, and better utilization of the computational resources. Our system can be easily deployed in growth chambers, greenhouses, CNC farming test-bed setup, cultivation plots, and further can be also extended to support large-farms with either using multiple individual standalone setup as heterogeneous instances of this facility, or by extending it as master-slave cluster configuration for communication as a single homogeneous instance. Our RaspberryPi-Arduino (RPA) powered solution is scalable, and provides stability for monitoring any environment continuously at ease.

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    RaspberryPi-Arduino (RPA) powered smart mirrored and reconfigurable IoT facility for plant science research
  • 4.
    Arunachalam, Ajay
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Real-time plant phenomics under robotic farming setup: A vision-based platform for complex plant phenotyping tasks2021In: Computers & electrical engineering, ISSN 0045-7906, E-ISSN 1879-0755, Vol. 92, article id 107098Article in journal (Refereed)
    Abstract [en]

    Plant phenotyping in general refers to quantitative estimation of the plant's anatomical, ontogenetical, physiological and biochemical properties. Analyzing big data is challenging, and non-trivial given the different complexities involved. Efficient processing and analysis pipelines are the need of the hour with the increasing popularity of phenotyping technologies and sensors. Through this work, we largely address the overlapping object segmentation & localization problem. Further, we dwell upon multi-plant pipelines that pose challenges as detection and multi-object tracking becomes critical for single frame/set of frames aimed towards uniform tagging & visual features extraction. A plant phenotyping tool named RTPP (Real-Time Plant Phenotyping) is presented that can aid in the detection of single/multi plant traits, modeling, and visualization for agricultural settings. We compare our system with the plantCV platform. The relationship of the digital estimations, and the measured plant traits are discussed that plays a vital roadmap towards precision farming and/or plant breeding.

    Download full text (pdf)
    Real-time plant phenomics under robotic farming setup: A vision-based platform for complex plant phenotyping tasks
  • 5.
    Arunachalam, Ajay
    et al.
    Örebro University, School of Science and Technology.
    Ravi, Vinayakumar
    Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia .
    Acharya, Vasundhara
    Manipal Institute of Technology (MIT), Manipal Academy of Higher Education (MAHE), Manipal, India.
    Pham, Tuan D.
    Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia .
    Toward Data-Model-Agnostic Autonomous Machine-Generated Data Labeling and Annotation Platform: COVID-19 Autoannotation Use Case2023In: IEEE transactions on engineering management, ISSN 0018-9391, E-ISSN 1558-0040, Vol. 70, no 8, p. 2695-2706Article in journal (Refereed)
    Abstract [en]

    Quick, early, and precise detection is important for diagnosis to control the spread of COVID-19 infection. Artificial Intelligence (AI) technology could certainly be used as a modulating tool to ease the detection, and help with the preventive steps further. Convolutional neural networks (CNNs) have achieved state-of-the-art performance in many visual recognition tasks. Nevertheless, most of these state-of-the-art networks highly rely on the availability of a high amount of labeled data, being an essential step in supervised machine learning tasks. Conventionally, this manual, mundane, and time-consuming process of annotating images is done by humans. Learning to localize or detect COVID-19 infection masks in our specific case study typically requires the collection of CT scan data that has been labeled with bounding boxes or similar annotations, which generally is limited. A technique that could perform such learning with much less annotations, and transfer the learned proposals that are algorithm-driven to generate more synthetic annotated samples would be helpful & quite valuable. We present such a technique inspired by weakly trained mask region based convolutional neural networks (R-CNN) architecture for localization, in which the number of images with their pixel-level masks can be a small proportion of the total dataset, and then further improvise CNNs by inversely generating dense annotations on-the-go using an algorithmic-based computational approach. We focus on alleviating the bottleneck associated with deep learning models needing annotated data for training in an intuitive reverse engineering fashion through this work. Our proposed solution can certainly provide the prospect of automated labeling on-the-fly, thereby reducing much of the manual work. As a result, one can quickly train a precise COVID-19 infection detector with the leverage of autonomous frame-by-frame machine generated annotations. The model achieved mean precision accuracy (%) of 0.99, 0.931, and 0.8 for train, validation, and test set, respectively. The results demonstrate that the proposed method can be adopted in a clinical setting for assisting radiologists, and also our fully autonomous approach can be generalized to any detection/recognition tasks at ease.

  • 6.
    Arunachalam, Ajay
    et al.
    Örebro University, School of Science and Technology.
    Ravi, Vinayakumar
    Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia.
    Krichen, Moez
    Faculty of CSIT, Al-Baha University, Saudi Arabia ReDCAD Laboratory, University of Sfax, Tunisia.
    Alroobaea, Roobaea
    Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia.
    Alqurni, Jehad Saad
    Department of Education Technologies, College of Education, Imam Abdulrahman Bin Faisal University, Saudi Arabia .
    Analytical Comparison of Resource Search Algorithms in Non-DHT Mobile Peer-to-Peer Networks2021In: Computers, Materials and Continua, ISSN 1546-2218, E-ISSN 1546-2226, Vol. 68, no 1, p. 983-1001Article in journal (Refereed)
    Abstract [en]

    One of the key challenges in ad-hoc networks is the resource discovery problem. How efficiently & quickly the queried resource/object can be resolved in such a highly dynamic self-evolving network is the underlying question? Broadcasting is a basic technique in the Mobile Ad-hoc Networks (MANETs), and it refers to sending a packet from one node to every other node within the transmission range. Flooding is a type of broadcast where the received packet is retransmitted once by every node. The naive flooding technique floods the network with query messages, while the random walk scheme operates by contacting subsets of each node's neighbors at every step, thereby restricting the search space. Many earlier works have mainly focused on the simulation-based analysis of flooding technique, and its variants, in a wired network scenario. Although, there have been some empirical studies in peer-to-peer (P2P) networks, the analytical results are still lacking, especially in the context of mobile P2P networks. In this article, we mathematically model different widely used existing search techniques, and compare with the proposed improved random walk method, a simple lightweight approach suitable for the non-DHT architecture. We provide analytical expressions to measure the performance of the different flooding-based search techniques, and our proposed technique. We analytically derive 3 relevant key performance measures, i.e., the avg. number of steps needed to find a resource, the probability of locating a resource, and the avg. number of messages generated during the entire search process.

  • 7.
    Arunachalam, Ajay
    et al.
    Örebro University, School of Science and Technology.
    Ravi, Vinayakumar
    Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia.
    Krichen, Moez
    Faculty of CSIT, Al-Baha University, Saudi Arabia ReDCAD Laboratory, University of Sfax, Tunisia.
    Alroobaea, Roobaea
    Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia.
    Rubaiee, Saeed
    Department of Industrial and Systems Engineering, College of Engineering, University of Jeddah, Jeddah, Saudi Arabia.
    Mathematical Model Validation of Search Protocols in MP2P Networks2021In: Computers, Materials and Continua, ISSN 1546-2218, E-ISSN 1546-2226, Vol. 68, no 2, p. 1807-1829Article in journal (Refereed)
    Abstract [en]

    Broadcasting is a basic technique in Mobile ad-hoc network (MANET), and it refers to sending a packet from one node to every other node within the transmission range. Flooding is a type of broadcast where the received packet is retransmitted once by every node. The naive flooding technique, floods the network with query messages, while the random walk technique operates by contacting the subsets of every node's neighbors at each step, thereby restricting the search space. One of the key challenges in an ad-hoc network is the resource or content discovery problem which is about locating the queried resource. Many earlier works have mainly focused on the simulation-based analysis of flooding, and its variants under a wired network. Although, there have been some empirical studies in peer-to-peer (P2P) networks, the analytical results are still lacking, especially in the context of P2P systems running over MANET. In this paper, we describe how P2P resource discovery protocols perform badly over MANETs. To address the limitations, we propose a new protocol named ABRW (Address Broadcast Random Walk), which is a lightweight search approach, designed considering the underlay topology aimed to better suit the unstructured architecture. We provide the mathematical model, measuring the performance of our proposed search scheme with different widely popular benchmarked search techniques. Further, we also derive three relevant search performance metrics, i.e., mean no. of steps needed to find a resource, the probability of finding a resource, and the mean no. of message overhead. We validated the analytical expressions through simulations. The simulation results closely matched with our analyticalmodel, justifying our findings. Our proposed search algorithm under such highly dynamic self-evolving networks performed better, as it reduced the search latency, decreased the overall message overhead, and still equally had a good success rate. 

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    Mathematical Model Validation of Search Protocols in MP2P Networks
  • 8.
    Arunachalam, Ajay
    et al.
    Department of Computer Science, Graduate School of Applied Statistics, National Institute of Development Administration, Thailand.
    Sornil, Ohm
    Department of Computer Science, Graduate School of Applied Statistics, National Institute of Development Administration, Thailand.
    A broadcast based random query gossip algorithm for resource search in non-DHT mobile Peer-to-Peer networks2017In: Diànnǎo xuékān (Journal of Computers), ISSN 1991-1599, Vol. 28, no 1, p. 209-223Article in journal (Refereed)
    Abstract [en]

    This paper presents a resource discovery scheme for decentralized non-DHT Mobile Peer-to-Peer (MP2P) networks. In a mobile environment, the energy of mobile device is very critical. The aim of the proposed technique is to reduce the network overhead, lower battery power consumption and minimize query delay while improving the chance to resolve the query at every successive stage. Peer-to-Peer applications have gained a lot of attention in past years due to its decentralized nature. Resource searching algorithms are one of the major focuses of P2P network. Mobile Ad hoc Network (MANET) with its changing topology further poses additional challenges and thus increasing the search effort. Methods like flooding, random walk and probabilistic forwarding techniques are good candidates to run over such dynamic network. In this work, we study the flooding, random walk and gossip based resource discovery protocols on a P2P Mobile Ad hoc Network. We observed that the classic gossip algorithm does not work well under MANET as in the case of a wired network. We focus to improve the algorithm to suit and work better under such dynamic network scenario. The proposed system presents a light weight resource discovery design to suit the mobility requirement of ad hoc networks to optimize the search performance while at the same time minimize the extra usage of mobile and network resources. For quick and energy efficient search scheme, we explore a novel addressed jumping approach. Our algorithm is entirely distributed, and hence will scale well even to the growing size of the network. The efficiency of our proposed algorithm is validated through extensive NS-2 simulations. The results show that our proposed scheme gives better performance than the widely used techniques. We also validate through statistical hypothesis testing of simulation data.

  • 9.
    Arunachalam, Ajay
    et al.
    Department of Computer Science, National Institute of Development Administration (NIDA), Thailand.
    Sornil, Ohm
    Department of Computer Science, National Institute of Development Administration (NIDA), Thailand.
    An Analysis of the Overhead and Energy Consumption in Flooding, Random Walk and Gossip Based Resource Discovery Protocols in MP2P Networks2015In: 2015 Fifth International Conference on Advanced Computing & Communication Technologies, ISSN 2327-0632, p. 292-297Article in journal (Refereed)
    Abstract [en]

    All major mobile communication architectures are mainly centralized. When the mobile devices are switched on it will search for nearby base station or access point. The content being searched is mostly stored in a centralized directory manner. Peer-to-Peer platform can be one of the best possibilities to overcome the restrictions and resolve issues incurred due to centralization. Mobile environment poses additional challenges on such P2P networks - due to limited resources, dynamic and wireless network characteristics, heterogeneity of nodes, limitations on processing power and wireless bandwidth. Hence resource discovery becomes further challenging. Even today mostly the traditional methods like flooding, random walk or gossip based forwarding methods have to be considered along with major limitations and drawbacks. Further in Mobile Peer-to-Peer (MP2P) system the energy aspect is very crucial with regards to the participation of nodes in the system. The search failure rate may increase if a mobile device uses all its energy and hence not participate in the resource discovery process. In this paper, we simulate the existing standard flooding, random walk and gossip based resource discovery algorithms on a P2P Mobile Adhoc Network (MANET) and studied their performance under such highly dynamic mobile network scenario. The efficiency of the resource discovery protocols are validated through extensiveNS-2 simulations.

  • 10.
    Arunachalam, Ajay
    et al.
    Department of Computer Science, National Institute of Development Administration (NIDA), Bangkok, Thailand.
    Sornil, Ohm
    Department of Computer Science, National Institute of Development Administration (NIDA), Bangkok, Thailand.
    Issues of Implementing Random Walk and Gossip Based Resource Discovery Protocols in P2P MANETs & Suggestions for Improvement2015In: Procedia Computer Science, E-ISSN 1877-0509, Vol. 57, p. 509-518Article in journal (Refereed)
    Abstract [en]

    Wireless multi-hop networks attracted much attention in recent years. Mobile Ad-hoc Network (MANET) being one of such networks has its own limitations in terms of resource discovery with unstable topology and paths through the networks. So eventually traditional searching techniques are still widely used. Peer-to-Peer (P2P) model is the major candidate for the internet traffic mainly due to its decentralized nature. This article evaluates classic flooding, random walk and gossip based resource discovery algorithms under mobile peer-to-peer (MP2P) networks and studied their performance. Further we suggest way to improve these algorithms to suit and work better under MANET. We compare the performance in terms of success rate, query response time, network overhead, battery power consumed, overall dropped packets, MAC load, network bandwidth, packet delivery ratio, network routing load and end to end delay. The experiments are validated through NS-2 simulations.

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    Issues of Implementing Random Walk and Gossip Based Resource Discovery Protocols in P2P MANETs & Suggestions for Improvement
  • 11.
    Arunachalam, Ajay
    et al.
    Department of Computer Science, Graduate School of Applied Statistics, National Institute of Development Administration, Thailand.
    Sornil, Ohm
    Department of Computer Science, Graduate School of Applied Statistics, National Institute of Development Administration, Thailand.
    Minimizing Redundant Messages and Improving Search Efficiency under Highly Dynamic Mobile P2P Network2016In: Journal of Engineering Science and Technology Review, ISSN 1791-9320, E-ISSN 1791-2377, Vol. 9, no 1, p. 23-35Article in journal (Refereed)
    Abstract [en]

    Resource Searching is one of the key functional tasks in large complex networks. With the P2P architecture, millions of peers connect together instantly building a communication pattern. Searching in mobile networks faces additional limitations and challenges. Flooding technique can cope up with the churn and searches aggressively by visiting almost all the nodes. But it exponentially increases the network traffic and thus does not scale well. Further the duplicated query messages consume extra battery power and network bandwidth. The blind flooding also suffers from long delay problem in P2P networks. In this paper, we propose optimal density based flooding resource discovery schemes. Our first model takes into account local graph topology information to supplement the resource discovery process while in our extended version we also consider the neighboring node topology information along with the local node information to further effectively use the mobile and network resources. Our proposed method reduces collision at the same time minimizes effect of redundant messages and failures. Overall the methods reduce network overhead, battery power consumption, query delay, routing load, MAC load and bandwidth usage while also achieving good success rate in comparison to the other techniques. We also perform a comprehensive analysis of the resource discovery schemes to verify the impact of varying node speed and different network conditions.

  • 12.
    Arunachalam, Ajay
    et al.
    Department of Computer Science, School of Applied Statistics, National Institute of Development Administration, Bangkok, Thailand.
    Sornil, Ohm
    Department of Computer Science, School of Applied Statistics, National Institute of Development Administration, Bangkok, Thailand.
    Reducing Routing Overhead in random walk protocol under MP2P Network2016In: International Journal of Electrical and Computer Engineering, ISSN 2088-8708, Vol. 6, no 6, p. 3121-3130Article in journal (Refereed)
    Abstract [en]

    Due to network dynamics in self-organizing networks the resource discovery effort increases. To discover objects in unstructured peer-to-peer network, peers rely on traditional methods like flooding, random walk and probabilistic forwarding methods. With inadequate knowledge of paths, the peers have to flood the query message which creates incredible network traffic and overhead. Many of the previous works based on random walk were done in wired network. In this context random walk was better than flooding. But under MANETs random walk approach behaved differently increasing the overhead, due to frequent link failures incurred by mobility. Decentralized applications based on peer-to-peer computing are best candidates to run over such dynamic network. Issues of P2P service discovery in wired networks have been well addressed in several earlier works. This article evaluates the performance of random walk based resource discovery protocol over P2P Mobile Adhoc Network (MP2P) and suggests an improved scheme to suit MANET. Our version reduces the network overhead, lowers the battery power consumption, minimizes the query delay while providing equally good success rate. The protocol is validated through extensive NS-2 simulations. It is clear from the results that our proposed scheme is an alternative to the existing ones for such highly dynamic mobile network scenario.

  • 13.
    Herdenstam, Anders P. F.
    et al.
    Örebro University, School of Hospitality, Culinary Arts & Meal Science.
    Kurtser, Polina
    Örebro University, School of Science and Technology.
    Swahn, Johan
    Örebro University, School of Hospitality, Culinary Arts & Meal Science.
    Arunachalam, Ajay
    Örebro University, School of Science and Technology.
    Nature versus machine: A pilot study using a semi-trained culinary panel to perform sensory evaluation of robot-cultivated basil affected by mechanically induced stress2022In: International Journal of Gastronomy and Food Science, ISSN 1878-450X, E-ISSN 1878-4518, Vol. 29, article id 100578Article in journal (Refereed)
    Abstract [en]

    In this paper we present a multidisciplinary approach combining technical practices with sensory data to optimize cultivation practices for production of plants using sensory evaluation and further the how it affects nutritional content. We apply sensory evaluation of plants under mechanical stress, in this case robot cultivated basil. Plant stress is a research field studying plants' reactions to suboptimal conditions leading to effects on growth, crop yield, and resilience to harsh environmental conditions. Some of the effects induced by mechanical stress have been shown to be beneficial, both in futuristic commercial growing paradigms (e.g., vertical farming), as well as in altering the plant's nutritional content. This pilot study uses established sensory methods such as Liking, Just-About-Right (JAR) and Check-All-That-Apply (CATA) to study the sensory effect of mechanical stress on cropped basil induced by a specially developed robotic platform. Three different kinds of cropped basil were evaluated: (a) mechanically stressed-robot cultivated, (b) non-stressed -robot cultivated from the same cropping bed (reference); and (c) a commercially organic produced basil. We investigated liking, critical attributes, sensory profile, and the use of a semi-trained culinary panel to make any presumptions on consumer acceptance. The semi-trained panel consisted of 24 culinary students with experience of daily judging sensory aspects of specific food products and cultivated crops. The underlying goal is to assess potential market aspects related to novel mechanical cultivation systems. Results shows that basil cropped in a controlled robot cultivated platform resulted in significantly better liking compared to commercially organic produced basil. Results also showed that mechanical stress had not negatively affected the sensory aspects, suggesting that eventual health benefits eating stressed plants do not come at the expense of the sensory experience.

  • 14.
    Herdenstam, Anders P. F.
    et al.
    Örebro University, School of Hospitality, Culinary Arts & Meal Science.
    Kurtser, Polina
    Örebro University, School of Science and Technology.
    Swahn, Johan
    Örebro University, School of Hospitality, Culinary Arts & Meal Science.
    Arunachalam, Ajay
    Örebro University, School of Science and Technology.
    Edberg, Karl-Magnus
    Örebro University, School of Hospitality, Culinary Arts & Meal Science.
    Nature versus machine: Sensory evaluation of robot-cultivated basil affected by mechanically induced stress2022Conference paper (Other academic)
    Download full text (pdf)
    Nature versus machine: Sensory evaluation of robot-cultivated basil affected by mechanically induced stress
  • 15.
    Kurtser, Polina
    et al.
    Örebro University, School of Science and Technology.
    Castro Alves, Victor
    Örebro University, School of Science and Technology.
    Arunachalam, Ajay
    Örebro University, School of Science and Technology.
    Sjöberg, Viktor
    Örebro University, School of Science and Technology.
    Hanell, Ulf
    Örebro University, School of Science and Technology.
    Hyötyläinen, Tuulia
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Development of novel robotic platforms for mechanical stress induction, and their effects on plant morphology, elements, and metabolism2021In: Scientific Reports, E-ISSN 2045-2322, Vol. 11, no 1, article id 23876Article in journal (Refereed)
    Abstract [en]

    This research evaluates the effect on herbal crops of mechanical stress induced by two specially developed robotic platforms. The changes in plant morphology, metabolite profiles, and element content are evaluated in a series of three empirical experiments, conducted in greenhouse and CNC growing bed conditions, for the case of basil plant growth. Results show significant changes in morphological features, including shortening of overall stem length by up to 40% and inter-node distances by up to 80%, for plants treated with a robotic mechanical stress-induction protocol, compared to control groups. Treated plants showed a significant increase in element absorption, by 20-250% compared to controls, and changes in the metabolite profiles suggested an improvement in plants' nutritional profiles. These results suggest that repetitive, robotic, mechanical stimuli could be potentially beneficial for plants' nutritional and taste properties, and could be performed with no human intervention (and therefore labor cost). The changes in morphological aspects of the plant could potentially replace practices involving chemical treatment of the plants, leading to more sustainable crop production.

  • 16.
    Paul, Satyam
    et al.
    Department of Engineering Design and Mathematics, University of the West of England, Bristol, United Kingdom.
    Arunachalam, Ajay
    Örebro University, School of Science and Technology.
    Khodadad, Davood
    Department of Applied Physics and Electronics, Umeå University, Umeå, Sweden.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Rubanenko, Olena
    Regional Innovational Center for Electrical Engineering, Faculty of Electrical Engineering, University of West Bohemia, Pilsen, Czech Republic.
    Fuzzy Tuned PID Controller for Envisioned Agricultural Manipulator2021In: International Journal of Automation and Computing, ISSN 1476-8186, E-ISSN 1751-8520, Vol. 18, no 4, p. 568-580Article in journal (Refereed)
    Abstract [en]

    The implementation of image-based phenotyping systems has become an important aspect of crop and plant science research which has shown tremendous growth over the years. Accurate determination of features using images requires stable imaging and very precise processing. By installing a camera on a mechanical arm driven by motor, the maintenance of accuracy and stability becomes non-trivial. As per the state-of-the-art, the issue of external camera shake incurred due to vibration is a great concern in capturing accurate images, which may be induced by the driving motor of the manipulator. So, there is a requirement for a stable active controller for sufficient vibration attenuation of the manipulator. However, there are very few reports in agricultural practices which use control algorithms. Although, many control strategies have been utilized to control the vibration in manipulators associated to various applications, no control strategy with validated stability has been provided to control the vibration in such envisioned agricultural manipulator with simple low-cost hardware devices with the compensation of non-linearities. So, in this work, the combination of proportional-integral-differential (PID) control with type-2 fuzzy logic (T2-F-PID) is implemented for vibration control. The validation of the controller stability using Lyapunov analysis is established. A torsional actuator (TA) is applied for mitigating torsional vibration, which is a new contribution in the area of agricultural manipulators. Also, to prove the effectiveness of the controller, the vibration attenuation results with T2-F-PID is compared with conventional PD/PID controllers, and a type-1 fuzzy PID (T1-F-PID) controller. 

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    Fuzzy Tuned PID Controller for Envisioned Agricultural Manipulator
  • 17.
    Paul, Satyam
    et al.
    Department of Engineering Design and Mathematics, University of the West of England, Bristol, United Kingdom.
    Arunachalam, Ajay
    Örebro University, School of Science and Technology.
    Khodadad, Davood
    Department of Applied Physics and Electronics, Umeå University, Umeå, Sweden.
    Rubanenko, Olena
    Regional Innovational Center for Electrical Engineering, Faculty of Electrical Engineering, University of West Bohemia, Pilsen, Czech Republic.
    Fuzzy Tuned PID Controller for Vibration Control of Agricultural Manipulator2020In: HORA 2020 - 2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications: Proceedings, IEEE, 2020, p. 166-170, article id 9152848Conference paper (Refereed)
    Abstract [en]

    Image-based phenotyping systems have evolved over the years, and become an integral part of crop and plant science research. Phenotyping systems provide great potential to deliver critical insights, than the conventional destructive field methods. Stable image acquisition and processing is very important to accurately determine the characteristics in general, which further becomes very challenging and non-trivial when mounted over an motor mechanised arm. To address the near associated problems, we investigate on the possibility of applying the Proportional–Integral–Derivative (PID) control algorithm to the present manual setup with an aim to reduce vibration. This study focused towards investigating the active control and stabilization of the external camera shake, that may be induced by the driving motor. Nonetheless, very few researchers have focused on application of control algorithms for agriculture related practices. We validate the active control, and justify the need for the same.Type-2 fuzzy logic is combined with the PID control for better effectiveness. The non-linearity associated with the system is compensated by the type-2 fuzzy logic. The results shows that the active control has been achieved, and the vibration is minimized.

  • 18.
    Ravi, Vinayakumar
    et al.
    Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia.
    Alazab, Mamoun
    College of Engineering, IT and Environment, Charles Darwin University, Darwin NT, Australia.
    Srinivasan, Sriram
    Center for Computational Engineering and Networking, Amrita School of Engineering, Coimbatore Amrita Vishwa Vidyapeetham, Coimbatore, India.
    Arunachalam, Ajay
    Örebro University, School of Science and Technology.
    Soman, KP
    Center for Computational Engineering and Networking, Amrita School of Engineering, Coimbatore Amrita Vishwa Vidyapeetham, Coimbatore, India.
    Adversarial Defense: DGA-Based Botnets and DNS Homographs Detection Through Integrated Deep Learning2023In: IEEE transactions on engineering management, ISSN 0018-9391, E-ISSN 1558-0040, Vol. 70, no 1, p. 249-266Article in journal (Refereed)
    Abstract [en]

    Cybercriminals use domain generation algorithms (DGAs) to prevent their servers from being potentially blacklisted or shut down. Existing reverse engineering techniques for DGA detection is labor intensive, extremely time-consuming, prone to human errors, and have significant limitations. Hence, an automated real-time technique with a high detection rate is warranted in such applications. In this article, we present a novel technique to detect randomly generated domain names and domain name system (DNS) homograph attacks without the need for any reverse engineering or using nonexistent domain (NXDomain) inspection using deep learning. We provide an extensive evaluation of our model over four large, real-world, publicly available datasets. We further investigate the robustness of our model against three different adversarial attacks: DeepDGA, CharBot, and MaskDGA. Our evaluation demonstrates that our method is effectively able to identify DNS homograph attacks and DGAs and also is resilient to common evading cyberattacks. Promising results show that our approach provides a more effective detection rate with an accuracy of 0.99. Additionally, the performance of our model is compared against the most popular deep learning architectures. Our findings highlight the essential need for more robust detection models to counter adversarial learning.

  • 19.
    Srinivasan, Sriram
    et al.
    Amrita Vishwa Vidyapeetham, Center for Computational Engineering and Networking, Coimbatore, India.
    Vinayakumar, R
    Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, USA.
    Arunachalam, Ajay
    Örebro University, School of Science and Technology.
    Alazab, Mamoun
    Charles Darwin University, Darwin Northern Territory, Australia.
    DURLD: Malicious URL Detection Using Deep Learning-Based Character Level Representations2020In: Malware Analysis Using Artificial Intelligence and Deep Learning / [ed] Mark Stamp, Mamoun Alazab, Andrii Shalaginov, Springer, 2020, p. 535-554Chapter in book (Refereed)
  • 20.
    Sureshkumar, Vidhushavarshini
    et al.
    Computer Science and Engineering, Sona College of Technology, Salem, India.
    Balasubramaniam, Sathiyabhama
    Computer Science and Engineering, Sona College of Technology, Salem, India.
    Ravi, Vinayakumar
    Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia.
    Arunachalam, Ajay
    Örebro University, School of Science and Technology.
    A hybrid optimization algorithm-based feature selection for thyroid disease classifier with rough type-2 fuzzy support vector machine2022In: Expert systems (Print), ISSN 0266-4720, E-ISSN 1468-0394, Vol. 39, no 1, article id e12811Article in journal (Refereed)
    Abstract [en]

    Thyroid hormones are essential for all the metabolic and reproductive activities with significance to growth, and neuron development in the human body. The thyroid hormone dysfunction has many ill consequences, affecting the human population; thereby being a global epidemic. It is noticed that every one in 10 persons suffer from different thyroid disorders in India. In recent years, many researchers have implemented various disease predictive models based on Information and Communications Technology (ICT). Increasing the accuracy of disease classification is a critical and challenging task. To increase the accuracy of classification, in this paper, we propose a hybrid optimization algorithm-based feature selection design for thyroid disease classifier with rough type-2 fuzzy support vector machine. This work uses the hybrid optimization algorithm, which combines the firefly algorithm (FA) and butterfly optimization algorithm (BOA) to select the top-n features. The proposed hybrid firefly butterfly optimization-rough type-2 fuzzy support vector machine (HFBO-RT2FSVM) is evaluated with several key metrics such as specificity, accuracy, and sensitivity. We compare our approach with well-known benchmark methods such as improved grey wolf optimization linear support vector machine (IGWO Linear SVM) and mixed-kernel support vector machine (MKSVM) methods. From the experimental evaluations, we justify that our technique improves the accuracy by large thereby precise in identifying the thyroid disease. HFBO-RT2FSVM model attained an accuracy of 99.28%, having specificity and sensitivity of 98 and 99.2%, respectively.

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