An Architecture for Dependable Connectivity in OSGi-Enabled Dynamic Distributed Systems

Clustering is a very popular network structuring technique which mainly\naddresses the issue of scalability in large scale Wireless Sensor Networks.\nAdditionally, it has been shown to improve the energy efficiency and prolong\nthe life of the network. The suggested protocols mostly base their clustering\ncriteria on some grouping attribute(s) of the nodes. One important attribute\nthat is largely ignored by most of the existing multi-hop clustering protocols\nis the reliability of the communication links between the nodes. In this paper,\nwe suggest an adaptive and completely distributed multi-hop clustering protocol\nthat incorporates different notions of reliability of the communication links,\namong other things, into a composite metric and uses it in all phases of the\nclustering process. The joining criteria for the nodes, which lie at one hop\nfrom the elected cluster heads, to a particular cluster not only consider the\nreliability of their communication link with their cluster head but also other\nimportant attributes. The nodes that lie outside the communication range of\ncluster heads become cluster members transitively through existing cluster\nmembers utilizing the end-to-end notion of link reliability, between the nodes\nand the cluster heads, along with other important attributes. Similarly,\ninter-cluster communication paths are selected using a set of criteria that\nincludes the end-to-end communication link reliability with the sink node along\nwith other important node and network attributes. We believe that incorporating\nlink reliability in all phases of clustering process results in an efficient\nmulti-hop communication hierarchy that has the potential of bringing down the\ntotal communication costs in the network.\n

Zhang, S.; Yang, W.; Xin, Y.; Wang, R.; Li, C.; Cai, D., and Gao H., 2018. Prototype system design of mooring buoy for seafloor observation and construction of its communication link. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 41–49. Coconut Creek (Florida), ISSN 0749-0208.A prototype system of mooring buoy for ocean seafloor observation is constructed, that, ocean buoy and underwater junction box are combined together with an Electro-Optical-Mechanical (EOM) cable. So, the seafloor observation data can transmit to the sea surface buoy through EOM cable, and finally, the data transmit to the shore through satellite and wireless communication. On the other side, sea surface buoy offers power with the wind and solar panel to underwater junction box. Compared to the traditional sea surface observational buoy, we redesign the communication model, so as to transmit the seafloor data to shore in the real time mode. The transmit communication is constructed as "Seafloor observation (underwater junction box) – Ethernet (underwater junction box) – Ethernet/Optical Switch – EOM cable – Optical/Ethernet Switch – Ethernet (buoy) – Beidou satellite/wireless communication (buoy)". According to the energy transmission property of EOM cable and the power supply demand of the seafloor, the wind/solar controller system and DC-DC convert modular is designed. We give the detail design and test result. In the test, we transmit the sea surface video and seafloor video in real time mode through wireless communication link. The reliability of the communication link is verified, and the feasibility of launch and recovery process is verified.

In this work we applied real-time classification of prosthetic fingers movements using surface electromyography (sEMG) data. We employed support vector machine (SVM) for classification of fingers movements. SVM has some benefits over other classification techniques e.g. 1) it avoids overfitting, 2) handles nonlinear data efficiently and 3) it is stable. SVM is employed on Raspberry pi which is a low-cost, credit-card sized computer with high processing power. Moreover, it supports Python which makes it easy to build projects and it has multiple interfaces available. In this paper, our aim is to perform classification of prosthetic hand relative to human fingers. To assess the performance of our framework we tested it on ten healthy subjects. Our framework was able to achieve mean classification accuracy of 78%.

The Raspberry Pi is being increasingly adopted as a suitable platform in both research and applications of the Internet of Things (IoT). This study presents a novel project-based teaching and learning approach devised in an Internet of Things course for undergraduate students in the computer science major, where the Raspberry Pi platform is used as an effective vehicle to greatly enhance students’ learning performance and experience. The devised course begins with learning simple hardware and moves to building a whole prototype system. This paper illustrates the outcome of the proposed approach by demonstrating the prototype IoT systems designed and developed by students at the end of one such IoT course. Furthermore, this study provides insights and lessons regarding how to facilitate the use of the Raspberry Pi platform to successfully achieve the goals of project-based teaching and learning in IoT.

Several emerging medical applications require that a miniature data acquisition device be implanted into the head to extract and wirelessly communicate brain activity to other devices. Designing a reliable communication link for such an application requires a realistic model of the surrounding biological tissues. This paper exploits a realistic model of the biological channel to design a suitable wireless ultra wideband communication link in a brain monitoring application. Two scenarios for positioning the implanted transmitting antenna are considered. The 1(st) scenario places the antenna under the skull, whereas the 2(nd) scenario places the antenna under the skin, above the skull. The propagation characteristics of the signal through the tissues of the human head have been determined with full-wave electromagnetic simulation based on Finite Element Method. The implantable antenna and the external antenna are key components to establish an electromagnetic link between an implanted transmitter and an external receiver. The average specific absorption rate (ASAR) of the implantable antennas are evaluated and compared for the two proposed scenarios. Moreover, the maximum available power from the implanted antenna is evaluated to characterize the performance of the communication link established between the implantable antenna and the external antenna, with respect to spectrum and safety regulations. We show how sensitive the receiver must be in order to implement a reliable telemetry link based on the proposed model of the channel.

Cultural periodicals, as a specific part of the field of culture and publishing, can be studied in several directions: 1) by analysing the conceptual aspect of cultural periodicals, i.e., realisation of their cultural functions; 2) by taking into account the general models of publishing periodicals; 3) by analysing other aspects of publishing that are specific to cultural periodicals only, for example, the specific features of funding.
 This article analyses how the cultural functions of national periodicals were realised in the context of the virtualisation of cultural periodicals in the period under research (in 2000–2015). (In this article, the virtualisation of cultural publications is understood as the transfer of cultural periodicals (magazines, newspapers) into the virtual space or their creation in that space (blogs, portals, social networks)). Cultural periodicals as a part of the cultural field were relevant in the period under research: 1. It is relevant to transmit and preserve cultural information (in this case, the communicative link between the cultural message and the cultural space and the lasting value of cultural texts); 2. To form new addressees to receive the cultural message (each publication brings together its own audience and thus complements the overall cultural field, thus attracting new readers by disseminating cultural information); 3. To actualise the message being sent through more diverse channels (the importance of internationality), to expand the possibilities of accessibility and availability of cultural texts; changes in the media (social networks, portals, websites) have created a broader, more modern and more diverse cultural field of communication. Between the years 2000 and 2015, media change gradually turned into a necessity. First of all, it was undertaken as a way to reach more readers. Due to the specific content, cultural publications were moderate in their approach to the changes in form during the period under analysis: to maintain a high-quality printed version was a priority to them. On the other hand, cultural publications, although not always willingly, began to recognize the importance of the Internet and to consider it as a platform for disseminating cultural information. This aspect became an important turning point in the publishing of cultural periodicals.

In today's world, Internet of Things (IoT) technology has revolutionized various fields – including agriculture – by offering smart solutions that enhance crop yield and quality. One crucial aspect of smart farming is the application of IoT for disease detection and prevention. Crop diseases caused by bacteria and viruses can have devastating effects, leading to significant financial losses for farmers. Early detection plays a crucial role in managing these diseases, and IoT technology provides reliable and efficient methods to achieve this goal. To aid in the research on disease recognition in corn or maize plants, a large-scale dataset comprising 4188 images across four disease categories – namely, healthy, common rust, gray-leaf spot, and blight – is utilized. This dataset is used to train various machine learning algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), decision tree (DT), convolutional neural network (CNN), naïve Bayes (NB), gradient boosting trees (GBT), long short-term memory (LSTM), and the algorithms from recent literature such as genetic algorithm-based ensemble classifier, XGBoost, AdaBoost and XGBoost + KNN to accurately identify disease patterns in plants and is loaded into the Raspberry Pi. By interfacing a camera with a Raspberry Pi, leaf diseases can be detected, and the farm’s status can be sent to the farmer’s webpage using a Wi- Fi server. This information empowers farmers to take immediate action to control the disease such as activating sprinklers or applying pesticides. Through manually controllable on/off mechanisms, farmers can remotely manage these actions, ensuring the health and disease-free state of their crops. Among the selected algorithms, the XGBoost + KNN algorithm demonstrates the highest accuracy, reaching an impressive 98.77%.

Objectives/Aim: The present paper deals with building a smart assistant with the aim of assisting the visually impaired people in mobility with confidence by realizing the nearby obstacles and also implement image processing techniques to recognize people. Methods: We use Raspberry Pi 4 which has increased computing performance and is interfaced to the picamera, GPS and GSM modules. Arduino pro mini with buzzer, ultrasonic sensors and vibration motors are used for obstacle detection. Certain libraries important for image processing are used such as OpenCV, Dlib, face detection (Haar cascades, HOG + Linear SVM, or CNNs), espeak for converting text to speech. Programming was implemented through Python and Arduino compiler. Findings: Analysis was carried out using the proposed system for the blind and visually impaired people who could move around comfortably with confidence and were also able to detect objects and recognize people. Hence the proposed system removes the use for the cumbersome white cane in exchange for small and handy modules which can be used in the form of wearables mounted on shoulders, hands and legs to detect the obstacles from multiple directions and provide for a comfortable wear. It was determined that the Raspberry pi 4 was incapable of running CNN detection for which ideally computer GPU was required, so HOG detection method was used instead. Application/Improvements: This project can be implemented mainly in the commercial field of helping visually impaired people with poor eyesight or being completely blind. Industrial applications can be devised and enhanced like robots and machineries along with Security, Identifying and Tracking. Keywords: Face Recognition; Obstacle Detection; Open Source Computer Vision (OpenCV); Deep Learning; Histogram of Oriented Gradients (HOG); Support Vector Machine (SVM); Convolutional Neural Network (CNN)

Rice is a staple food in many countries. The price of rice depends on the qualities that are often quantified based on color, size, and presence of some regional color information. In the Philippines, the National Food Authority released the National Grain Standards for milled rice grains to facilitate the uniform classification of rice. The standards specify the grades: Premium and Grade 1-5 to grade milled rice grain samples based on the number of immature, red, fermented, chalky grains, and others, present in the sample. This study aimed to design and develop a standalone system capable of grading rice samples using grain validation, color and area analysis, and support vector machines with adaptive boosting. The image acquisition platform was created to provide a constant lighting setting and an enclosed staging platform capable of extracting an average of fifty grain images per sample. Seven support vector machine classifiers boosted with adaptive boosting, one chalky classifier, one grain size classifier, were created, trained, and tested. Feature vectors for the SVMs were histogram of gradients features and the color histogram properties: mean, skew, and dominant. The evaluation of the device resulted with an overall micro-average precision of 0.8667 and a micro-average recall of 0.8667 with an Fl-Score of 0.8667.

This work has been focused on the part of the population with hearing impairment who owns a dog and that worries about not listening the dog barks, specially when a risky situation is taking place at home. A survey was carried out on people with deafness problems to find out hazard situations which they are exposed at home. A system prototype was developed to be integrated as a component of ambient intelligence (AmI) for ambient assisted living (AAL) that serves to Hearing Impaired People (HIP). The prototype detects dog barks and notifies users through both a smart mobile app and a visual feedback. It consists of a connection between a Raspberry Pi 3 card and a ReSpeaker Mic Array v2.0 microphone array; a communication module with a smartphone was implemented, which displays written messages or vibrations when receiving notifications. The cylinder-shaped device was designed by the authors and sent it to 3D print with a resin material. The prototype recognized the barking efficiently by using a machine learning model based on Support Vector Machine technique. The prototype was tested with deaf people which were satisfied with precision, signal intensity, and activation of lights.

The network security is getting more important due to the wide-spread computer viruses and increasing network attacks. Nowadays, more and more security mechanisms, such as firewalls and intrusion detection systems (IDS), are introduced to protect the network from malicious attacks. This paper proposes an agent and service based intrusion detection and response system for active network. In contrast to a traditional passive network, an active network gives the nodes programmable ability to exercise various active network technologies. The intrusion response, service deployment, and service update mechanisms are centered on this technology. The proposed model of intrusion detection and response system (IDRS) catches network attacks and responses to stop the attacks at the first time to reduce the damage. Detecting, reporting, and responding capabilities are all embedded and integrated in the proposed system. A prototype system is developed using a novel data mining technology (the support vector machine) to enhance the detection function. In addition, several experiments were conducted to verify the system and results showed that the system was able to effectively identify the intrusions and respond promptly. Experiments also showed that the support vector machine outperforms the competitive neural networks in identifying the intrusions.

Hardware implementation of human detection is a challenging task for embedded designs. This paper presents a real-time image-based field-programmable gate array (FPGA) implementation of human detection. Our implementation is based on the histograms of oriented gradients (HOG) feature and linear support vector machine (SVM) classifier. The novelty of this work is that we replace normalization process of HOG with a modified binarization process. Therefore, during classification process with SVM classifier, all multiplication operations are replaced by addition operations. All these modifications result in reduction of hardware resource. Experimental evaluation reveals that 293 fps can be achieved on a low-end Xilinx Spartan-3e FPGA. Moreover, a detection accuracy of 1.97% miss rate and 1% false positive rate is achieved. For further demonstration, a prototype system is developed with an OV7670 camera device. Restricted to the speed of camera, a detection rate of 30 fps is achieved.

In the era of modern technology, object detection using the Gray Level Co-occurrence Matrix (GLCM) extraction method plays a crucial role in object recognition processes. It finds applications in real-time scenarios such as security surveillance and autonomous vehicle navigation, among others. Computational efficiency becomes a critical factor in achieving real-time object detection. Hence, there is a need for a detection model with low complexity and satisfactory accuracy. This research aims to enhance computational efficiency by selecting appropriate features within the GLCM framework. Two classification models, namely K-Nearest Neighbours (K-NN) and Support Vector Machine (SVM), were employed, with the results indicating that K-Nearest Neighbours (K-NN) outperforms SVM in terms of computational complexity. Specifically, K-NN, when utilizing a combination of Correlation, Energy, and Homogeneity features, achieves a 100% accuracy rate with low complexity. Moreover, when using a combination of Energy and Homogeneity features, K-NN attains an almost perfect accuracy level of 99.9889%, while maintaining low complexity. On the other hand, despite SVM achieving 100% accuracy in certain feature combinations, its high or very high complexity can pose challenges, particularly in real-time applications. Therefore, based on the trade-off between accuracy and complexity, the K-NN model with a combination of Correlation, Energy, and Homogeneity features emerges as a more suitable choice for real-time applications that demand high accuracy and low complexity. This research provides valuable insights for optimizing object detection in various applications requiring both high accuracy and rapid responsiveness.

Traditional Physical Unclonable Function (PUF)-based authentication protocols are vulnerable to machine learning attacks and evolving cyber threats. Moreover, these protocols lack suitability for resource-constrained IoT devices due to the involvement of heavy cryptographic primitives, error correction modules, and significant computational overhead. This article proposes a mutual authentication protocol and session key agreement utilizing commercial-off-the-shelf (COTS) SRAM integrated circuits (ICs) to extract a secret key. We introduce a block-based lightweight fuzzy extractor to minimize the overhead associated with error correction modules on IoT devices. Our protocol relies only hash, XOR and masking functions for the identity verification for both parties and stores only one challenge-response pair (CRP) on the server, reducing memory overhead on the authentication server. In addition, we perform a rigorous informal security analysis against well-known attacks and formal security analysis using Verifpal tool considering an active attacker in the communication link. Furthermore, the performance evaluation and comparative analysis indicate that the proposed protocol significantly outperforms the state-of-the-art protocols in terms of communication, computational overhead, storage overhead, and energy consumption by up to 69%, 81%, 87.5% and 76.6% respectively. We have implemented the proposed authentication protocol on ESP32 and Raspberry Pi 3 boards to show its applicability and scalability in a real-world IoT framework.

Due to modern microcomputers and platforms based on microprocessors such as, for example, Raspberry Pi, Orange Pi, Nano Pi, Rock Pi, Banana Pi, Asus Tinker Board – the development of prototypes of em-bedded systems is possible in a «design» mode. The software part is implemented on the basis of operat-ing systems and standard technologies based on well-known programming languages such as C / C++, Python, C#, Java, etc. In such case the control channel for the embedded system can be either imple-mented via a web service separated by a communication channel or controlled independently. It is im-portant to understand that creating an embedded system on a standard platform is much more expensive than buying a ready-made mass-produced device with the same functionality. Therefore, it makes sense to use platforms like the Raspberry Pi mainly for individual artificial devices. If it is necessary to build a project of embedded systems and there is a problem with choosing a hardware platform for the client side, then currently there is a wide range of boards and solutions for building an efficient and inexpen-sive system using ready-made modules. The number of expansion cards and various sensors, video cam-eras, internet connection via Ethernet, Wi-Fi and Bluetooth provides a wide range of opportunities for building almost any solution based on this component base. The foundation can be made within a small budget, with minimal time spent, using large blocks and ready-made libraries for programming embed-ded systems. This article presents the results of research and development work on the creation of a software and hardware infrastructure of a terrestrial platform with the elements of artificial intelligence. Based on the actual results of the research, a deployment diagram and a component diagram of such an infrastructure have been constructed.