Natural and Artificial Intelligence

Chapter 1-The Vision of VishwaGuru We live in an age of breathtaking technology and deep uncertainty. Artificial Intelligence (AI) can write, draw, speak, and reason faster than any human in history-yet the question remains: Are we becoming wiser? In the rush toward faster, smarter machines, something essential risks being left behind: the quiet, steady guidance of Natural Intelligence (NI)-the wisdom of nature, rhythm, and lived human experience. NI is not a product. It cannot be coded in a lab. It flows in the seasons, in the rise and fall of breath, and in the way we care for one another. The VishwaGuru App was born from this understanding. It is not just a tool on your phone. It is a companion-a quiet friend you can turn to at any moment, one who responds with clarity and kindness. Its promise is simple: One gentle question away from your next step. Behind this app is a living circle of contributors-thinkers, builders, and guides-including the daily insights of Guruji Vishwanathan, whose reflections on life, discipline, and selfless love have shaped its rhythm. These teachings are not stored as mere words. They are coded into the app's pulse, so that every response carries both practical guidance and a deeper alignment with what we call Yuga Dharma-the natural law of right timing and balance. VishwaGuru blends two worlds:-Timeless dharmic wisdom-the same principles that guided generations toward inner balance.-Practical, evidence-based tools-short, clear practices that can fit into even the busiest day. This is not an app to master; it is a companion to walk with. Whether you are a student preparing for exams, a researcher balancing deadlines, or simply someone navigating daily stress, the VishwaGuru App offers a way to pause, reset, and move forward with confidence. The journey begins with a single step. Or, in our case, a single gentle question. Chapter 2-The Roots of Natural Intelligence Natural Intelligence-or NI-is older than language, older than the written word, older even than human memory. It is the quiet order behind life itself. The tide knows when to rise, the moon knows when to wax, and the bird knows when to return in spring-not because of an instruction manual, but because of a rhythm woven into its very being.

The rapid expansion of wireless networks demands efficient spectrum allocation. Dynamic Spectrum Sharing (DSS) is a technology that allows multiple wireless networks to share the same frequency spectrum dynamically. It is an effective technique for optimizing spectrum use, particularly in heterogeneous environments where multiple wireless technologies with diverse spectrum access requirements coexist, often leading to interference challenges and increased spectrum competition. This research proposes an enhanced DSS technique based on Deep Reinforcement Learning (DRL). The proposed method enables an effective sharing of the available spectrum between two access technologies, namely Long Term Evolution (LTE) and Narrowband IoT (NB-IoT). The study optimizes throughput through DRL methods, including Deep Q-Networks (DQN), conducting experiments in three phases: LTE-DRL coexistence, NB-IoT-DRL coexistence, and LTE-NB-IoT coexistence. Results show that deep learning enhances the LTE-DRL system's convergence rate and throughput, achieving over 85% throughput with convergence times as low as 24 milliseconds (ms). The study highlights the trade-offs between parameters such as probabilities (arrival, successful transmission, and retransmission), packet expiry duration, learning rate, discount factor, fairness index, and the neural network architecture as well as the parameters' impact on the overall system throughput. NB-IoT coexistence with DRL shows similar results with a slight decrement in throughput and negligibly longer convergence rate, while the coexistence of LTE and NB-IoT results in throughput of around 70% for each of the LTE and NB-IoT systems due to increased spectrum competition and increased complexity of the operating environment. This work offers insights into optimizing spectrum sharing using DRL and underscores the balance between various parameters for efficient spectrum management.

This research investigates the organization and movement of root systems, emphasizing their ability to connect, adhere, and interact with organisms while maintaining self-awareness. Root movements depend on capillary adhesion, enabling the fixation and elevation of natural elements such as moss, spider webs, and raindrops. The research of biotectonics introduces an innovative approach to surface adaptation, where upper layers emerge from lower layers, facilitating root colonization through site-specific knowledge. This layered integration is not an imitation but a self-generating system, requiring collaboration with biological intelligence. Scale is not only a unit of measurement but a medium for interaction between natural and artificial systems. The project examines the adaptation of natural organisms to physical surfaces, analyzing their behavioral patterns and constructing multi-scaled prototypes to explore cross-scale interactions. The research integrates digital modeling and artificial intelligence to map invisible traces in physical models, encoding root dynamics within structured surfaces. Interactive architectural facades are designed by leveraging biological and structural properties of root systems, forming adaptive environments responsive to external conditions. By synthesizing biotectonic principles, the study explores the role of frequency and density in shaping surface formations that accommodate living organisms. Surfaces are reimagined as dynamic, bio-integrated structures that interact with environmental factors such as soil moisture, sunlight, and atmospheric changes. The ultimate goal is to redefine architecture through biologically driven design methodologies, creating living surfaces that evolve, respond, and grow, bridging the gap between built environments and natural ecosystems.

This paper synthesizes a series of foundational studies proposing fractal-based frameworks for Artificial General Intelligence (AGI). By integrating theoretical models, optical hardware implementations, and modular computational architectures, we demonstrate how non-binary, fractal processing bridges the gap between biological cognition and machine intelligence. Central to this work is the complex wave function f(λ,t)=I0sin(ωt+αλ)⋅ ei[ωt-kλ+βλ2], which underpins both dynamic neural simulations and physical fractal processors. We unify these efforts into a cohesive paradigm, highlighting scalability, adaptability, and emergent behavior as critical pathways to AGI.

This paper introduces an innovative proposal for developing a fractal processor as the foundation for a natural Artificial General Intelligence (AGI), drawing on principles of self-similarity and nonlinear dynamics. The theoretical basis is the equation f(λ,t)=I0 sin(ω t+α λ) ei [ω t-k λ+β λ2], ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ which integrates quartz crystal oscillations and optical propagation to create a multiscale computational system. The text elaborates on the concept, the derivation of the equation, and its implications for constructing an AGI capable of emulating human cognitive complexity.

UnderWater (UW) Acoustic networks face unique challenges due to limited bandwidth, high latency, and dynamic channel conditions, necessitating adaptive communication protocols to optimize performance under strict energy constraints. Modulation schemes play a crucial role in determining the efficiency and reliability of these networks; dynamically adjusting modulation depending on channel conditions can significantly enhance network performance. While Machine Learning algorithms offer valuable solutions for real-time adaptation, many existing methods are based on deep learning, which often demands computational resources beyond the capabilities of typical UW devices. In contrast, Multi-Armed Bandit (MAB) algorithms offer a simpler yet effective solution, well-suited for environments with limited computational resources. In this paper, we present AMUSE, a scalable and efficient framework designed to leverage the MAB approach for dynamic modulation selection, while enabling the optimization of various key performance metrics. Specifically, to illustrate the high level of flexibility of AMUSE in addressing multi-objective optimization, we here focus on the trade-off of Packet Error Rate (PER) and energy consumption across changing conditions, so as to make both reliability and energy efficiency the basis of the modulation adaptation decision-making process. Through extensive simulation in the DESERT simulator, we evaluate AMUSE performance against other state-of-the-art algorithms based on Deep Reinforcement Learning (DRL). Despite its simple design, AMUSE proves to be more efficient and responsive than the baselines, making it a powerful solution for improving UW communication performance. The results show that, in spite of the lightweight nature of AMUSE, our framework is able to outperform the DRL baselines by achieving an improvement of up to 23.64% in the network PER, and up to 80.65% in energy saving.

Providing acceptable Quality of Experience (QoE) in Wireless Local Area Network (WLAN) is very difficult: home networks are managed by non-technical people, and the proprietary management solutions of enterprise networks usually do not incorporate QoE mechanisms. Due to these difficulties, automatic QoE management mechanisms are welcome. This paper presents control loops capable of changing the power and transmission channels in the WLAN, based on Software Defined Wireless Networks and reinforcement learning, in order to improve user satisfaction for Web applications. A prototype evaluates our proposal in three case studies with a web browsing application, in which several access points are controlled by a central controller or by independent controllers. Our results show that the control loop can improve the Mean Opinion Score (MOS) by at least 4 % in the worst case, and 167 % in the best case, thus benefiting the user. Further, the control loop also reduced in page load time by 25...

"The Performative Interface - Making Visible a Reality Beyond Appearance" by Monika Fleischmann and Wolfgang Strauss.
This article explores the transformative potential of digital technology and its interface with human perception and interaction. Reflecting on the evolution of media art since the early 1990s, the authors highlight missed opportunities for social empowerment with the rise of centralized platforms and corporate control. They confront pressing global crises such as social inequality, climate change, and mass migration, urging a reevaluation of societal values and a shift toward sustainable living. Amid the proliferation of artificial intelligence (AI), the essayists emphasize the importance of critical engagement and public discourse, and advocate media art as a means of exposing and revealing hidden truths. They introduce the concept of the "performative interface," which goes beyond traditional notions of interactivity to engage users in a cognitive and sensory encounter that invites participation in the unknown. Using examples such as their groundbreaking Semantic Map, Fleischmann and Strauss demonstrate the potential of the performative interface to reveal hidden realities and encourage collaborative thinking. They envision a future where natural and artificial intelligence coexist, creating augmented thinking spaces that empower humans to explore new meanings and create knowledge. See the article on page 91 in "Humanities and Artificial Intelligence" by Freddy Paul Grunert. The biography of Monika Fleischmann and Wolfgang Strauss highlights their pioneering work in digital media and their commitment to empowering individuals through interactive art experiences.

This publication has its origin in the keynotes and papers of the 5th International Conference for Biodigital Architecture & Genetics, curated by Alberto T. Estevez, that was held in Barcelona, 2023 March 30-31. Thus, the order of appearance of the texts is the same of the presentations in the Conference.

Arquitecturas genéticas, nuevas técnicas biológicas y digitales, ¡arquitectura biodigital! Se nos han dado las condiciones para una nueva arquitectura, que con el organicismo digital se ha convertido en la primera vanguardia del siglo XXI: definitivamente, la ciencia ha superado la ficción. Y la utopía de hoy es la realidad de mañana (y ¡hoy es mañana!). Alberto T. Estévez.

El iNSTITUTE FOR BiODiGITAL ARCHITECTURE & GENETICS (iBAG), en el seno de la UIC Barcelona (Universitat Internacional de Catalunya), tiene tres campos de acción: investigación, docencia y práctica profesional. Todo comenzó el año 2000, con la fundación del Genetic Architectures Research Group & Office (investigación y práctica profesional), que a su vez lleva el programa de Master in Biodigital Architecture (docencia), iniciado igual-mente el año 2000, en la ESARQ - UIC Barcelona, la Escuela de Arquitectura fundada el año 1996 por Alberto T. Estévez, su primer Director.

Básicamente se trata con todo ello de aplicar las nuevas técnicas biológicas y digitales a la arquitectura y al diseño. Esto llevó a crear el año 2000 el primer laboratorio de arquitectura genética del mundo, donde empezó a aplicarse la genética a la arquitectura (árboles bioluminiscentes, biolamps, bioprin-ting, microscopía electrónica, etc.), y el primer laboratorio de arquitectura digital sito en una escuela española (diseño y fabricación digital de arquitec-tura). Y todo organizando un grupo interdisciplinar de arquitectos, diseñado-res, artistas, ingenieros, genetistas y filósofos.

Biomiméticas en el Institute for Biodigital Architecture & Genetics Arquitecturas genéticas, nuevas técnicas biológicas y digitales, ¡arquitectura biodigital! Se nos han dado las condiciones para una nueva arquitectura, que con el organicismo digital se ha convertido en la primera vanguardia del siglo XXI: definitivamente, la

In recent years, the demand for new applications using various Internet of Things (IoT) devices has led to an increase in the number of devices connected to wireless networks. However, owing to the limitation of available frequency resources for IoT devices, the degradation of the communication quality caused by channel congestion is a practical problem in developing IoT technology. Many IoT devices have hardware and software limitations that prevent centralized channel allocation, and congestion is even more severe in massive IoT networks without a central controller. Therefore, developing a distributed and sophisticated channel selection algorithm is necessary. In previous studies, the channel selection of each IoT device was modeled as a multi-armed bandit (MAB) problem, and a wireless channel selection method based on the MAB algorithm, which is a simple reinforcement learning, was proposed. In particular, it has been shown that the MAB algorithm of tug-of-war (TOW) dynamics can efficiently select channels with much lower computational complexity and power compared with other reinforcement learning-based channelselection methods. This paper proposes a distributed channel selection method based on TOW dynamics in fully decentralized networks. We evaluate the effectiveness of the proposed method and other distributed channel-selection methods on the communication success rate in massive IoT networks by experiments and simulations. The results show that the proposed method improves the communication success rate more than other distributed channel selection methods even in a dense and dynamic network environment.

Non-Orthogonal Multiple Access is one of the most important technologies in 5G and Beyond 5G wireless communications, which improve system performance by power domain multiplexing. In realizing Non-Orthogonal Multiple Access, the pairing of multiple users is necessary where efficient principles are highly demanded in dynamically changing electromagnetic environments. In the meantime, ultrafast methods of solving multi-armed bandit problems have been developed using chaotic laser time series. In this paper, we consider the user pairing problem in Non-Orthogonal Multiple Access as a multi-armed bandit problem and propose an ultra-fast user pairing algorithm based on the laser chaos decision maker. We numerically demonstrate that the proposed scheme accomplishes higher throughputs compared with traditional user pairing algorithms, especially in cases with lower user density.

Non-terrestrial networks, composed of ground, air, and satellite communications, are considered one of the key components for the Beyond 5G/6G, and optical satellite communication is a fundamental technology to enable high-capacity communications. It is affected by interruptions of optical communications due to clouds on the communication link. A satellite can mitigate the interruption by switching its destination ground station to the other communication available station, though it brings additional delays in establishing optical links. In this study, we propose a ground station selection method using reinforcement learning algorithms to realize a fast and stable satellite-terrestrial optical communication system. We introduce two multi-armed bandit algorithms, Q-learning and Deep Q-learning, for the proposed method. We evaluate them using actual data of the optical satellite communication availability. Our simulation results show that the proposed method with deep Q-learning has the best average throughput. The proposed scheme efficiently follows changes in the state of communication links, and it becomes even better than fixed to ideal best link.

We propose a simple channel-allocation method based on tug-of-war (TOW) dynamics, combined with the time scheduling based on nonlinear oscillator synchronization to efficiently use of the space (channel) and time resources in wireless communications. This study demonstrates that synchronization groups, where each node selects a different channel, are non-uniformly distributed in phase space such that every distance between groups is larger than the area of influence. New type of self-organized spatiotemporal patterns can be formed for resource allocation according to channel rewards.

The wide spread of mobile communication devices has increased the opportunities to use wireless communication technologies, irrespective of one's geographical location. However communication quality deteriorates due to factors such as competition for scarce radio resources and interference among nearby devices. Cognitive radio technologies have been developed recently to conquer such difficulties. In this paper, we propose a wireless network optimization method using learning algorithms based on a control model known as cognitive cycle. We implement the proposed optimization method in wireless LANs and evaluate the throughput performance. The experimental results show the effectiveness of the proposed approach in a real environment.

The parts produced by additive manufacturing are inherently subjected to discretization effects due to their layer-based addition. The stair-stepping effect on the surface quality is inevitable for most of the techniques and it becomes more dominant for the regions having small surface inclinations. The stair-stepping influences the mechanical properties as well as the aesthetic perception. Many researchers have been presented several approaches to overcome or minimize the stair-stepping effects and improve the surface quality of additively manufactured parts. One of these methods is non-planar slicing. The stair-stepping effect was significantly decreased by this method. The attempts have been made generally for the FDMprinted objects, however, there is no or fewer efforts have been made for parts of large-scale additive manufacturing (LSAM). Due to higher deposition rates and larger nozzle diameters (i.e. bead size), the discretization effect is more in large-scale additive manufacturing. In this paper, the presented methods to mitigate the stair-stepping effect and improving the surface quality of additive manufacturing are reviewed, and practicing in large-scale 3D printing is discussed. Moreover, a preliminary experimental study of 3D printing with a non-planar toolpath was carried out and the results were presented.

This paper examines how the evolution of architectural generative design processes aim to apply similar physical and geometrical principles of biological processes taking place during development and to translate them to fabrication processes. In analogy to the reaction-diffusion mechanism for biological pattern prediction, the logic of stripe is used as construction system and examined for its structural behaviour. Both, mesh relaxation processes and weighted mesh graphs representations are employed as design tools for the construction of a minimal thin shell structural skin with branching topologies. Eventually the design workflow is extended to engage also collaborative fabrication processes and to steer the design based on intuition, knowledge of the fabrication tools, properties of the materials, manufacturing simulations and logic of assemble. This approach could lead to the optimization of material usage and machine time and facilitate the assembly process of a physical object which integrates the whole process into its form. The outcomes have been used to fabricate a prototype, using three different materials and digital fabrication methods, to examine the stability and the mechanical connectivity by taking in count the tolerances. The paper argues that biological skin patterns and segmentation in fabrication open a new field of interdisciplinary investigation and architectural applications.

Non-orthogonal multiple access schemes with grant free access have been recently highlighted as a prominent solution to meet the stringent requirements of massive machine-type communications (mMTCs). In particular, the multi-user shared access (MUSA) scheme has shown great potential to grant free access to the available resources. For the sake of simplicity, MUSA is generally conducted with the successive interference cancellation (SIC) receiver, which offers a low decoding complexity. However, this family of receivers requires sufficiently diversified received user powers in order to ensure the best performance and avoid the error propagation phenomenon. The power allocation has been considered as a complicated issue especially for a decentralized decision with a minimum signaling overhead. In this paper, we propose a novel algorithm for an autonomous power decision with a minimal overhead based on a tight approximation of the bit error probability (BEP) while considering the error...

This paper examines how the evolution of architectural generative design processes aim to apply similar physical and geometrical principles of biological processes taking place during development and to translate them to fabrication processes. In analogy to the reaction-diffusion mechanism for biological pattern prediction, the logic of stripe is used as construction system and examined for its structural behaviour. Both, mesh relaxation processes and weighted mesh graphs representations are employed as design tools for the construction of a minimal thin shell structural skin with branching topologies. Eventually the design workflow is extended to engage also collaborative fabrication processes and to steer the design based on intuition, knowledge of the fabrication tools, properties of the materials, manufacturing simulations and logic of assemble. This approach could lead to the optimization of material usage and machine time and facilitate the assembly process of a physical object which integrates the whole process into its form. The outcomes have been used to fabricate a prototype, using three different materials and digital fabrication methods, to examine the stability and the mechanical connectivity by taking in count the tolerances. The paper argues that biological skin patterns and segmentation in fabrication open a new field of interdisciplinary investigation and architectural applications.

Multi-armed bandit (MAB) models are a viable approach to describe the problem of best wireless network selection by a multi-Radio Access Technology (multi-RAT) device, with the goal of maximizing the quality perceived by the final user. The classical MAB model does not allow, however, to properly describe the problem of wireless network selection by a multi-RAT device, in which a device typically performs a set of measurements in order to collect information on available networks, before a selection takes place. The MAB model foresees in fact only one possible action for the player, which is the selection of one among different arms at each time step; existing arm selection algorithms thus mainly differ in the rule according to which a specific arm is selected. This work proposes a new MAB model, named measure-use-MAB (muMAB), aiming at providing a higher flexibility, and thus a better accuracy in describing the network selection problem. The muMAB model extends the classical MAB model in a twofold manner; first, it foresees two different actions: to measure and to use; second, it allows actions to span over multiple time steps. Two new algorithms designed to take advantage of the higher flexibility provided by the muMAB model are also introduced. The first one, referred to as measure-use-UCB1 (muUCB1) is derived from the well known UCB1 algorithm, while the second one, referred to as Measure with Logarithmic Interval (MLI), is appositely designed for the new model so to take advantage of the new measure action, while aggressively using the best arm. The new algorithms are compared against existing ones from the literature in the context of the muMAB model, by means of computer simulations using both synthetic and captured data. Results show that the performance of the algorithms heavily depends on the Probability Density Function (PDF) of the reward received on each arm, with different algorithms leading to the best performance depending on the PDF. Results highlight, however, that as the ratio between the time required for using an arm and the time required to measure increases, the proposed algorithms guarantee the best performance, with muUCB1 emerging as the best candidate when the arms are characterized by similar mean rewards, and MLI prevailing when an arm is significantly more rewarding than others. This calls thus for the introduction of an adaptive approach capable of adjusting the behavior of the algorithm or of switching algorithm altogether, depending on the acquired knowledge on the PDF of the reward on each arm.

The knowledge of when the amorphous masses of cells organize themselves into a first structural level is relevant for the architect. The architecture must also attend structural and economic stresses, following efficiency, as living beings do: something that we can learn from nature ("biolearning"), although that level can only be reached with microscope. So this becomes a useful tool for architectural research. Examples of this are presented here, seeking first to discover, analyze and evaluate microscopic structures of plants and animals. On that knowledge we can make emerge the digital design of real architectural projects, using computational strategies, also morphogenetic.

An automated method for the generation of curved layer toolpaths is demonstrated to produce 3D printed components with improved aesthetic and structural properties using fused filament fabrication printing. Three case studies are shown, which demonstrate the ability of the G-code generating algorithm to resolve concave and convex structures. The combination of conventionally printed layers and curved layers within a single print is also demonstrated by producing double skin curved layer sandwich structures with static z printed cores. Clear improvements in the surface finish of printed components using curved layer fused filament fabrication are shown visually.

Long-Range (LoRa) devices have been deployed in many Internet of Things (IoT) applications due to their ability to communicate over long distances with low power consumption. The scalability and communication performance of the LoRa systems are highly dependent on the spreading factor (SF) and channel allocations. In particular, it is important to set the SF appropriately according to the distance between the LoRa device and the gateway since the signal reception sensitivity and bit rate depend on the used SF, which are in a trade-off relationship. In addition, considering the surge in the number of LoRa devices recently, the scalability of LoRa systems is also greatly affected by the channels that the LoRa devices use for communications. It was demonstrated that the lightweight decentralized learning-based joint channel and SF-selection methods can make appropriate decisions with low computational complexity and power consumption in our previous study. However, the effect of the lo...

Long-Range (LoRa) devices have been deployed in many Internet of Things (IoT) applications due to their ability to communicate over long distances with low power consumption. The scalability and communication performance of the LoRa systems are highly dependent on the spreading factor (SF) and channel allocations. In particular, it is important to set the SF appropriately according to the distance between the LoRa device and the gateway since the signal reception sensitivity and bit rate depend on the used SF, which are in a trade-off relationship. In addition, considering the surge in the number of LoRa devices recently, the scalability of LoRa systems is also greatly affected by the channels that the LoRa devices use for communications. It was demonstrated that the lightweight decentralized learning-based joint channel and SF-selection methods can make appropriate decisions with low computational complexity and power consumption in our previous study. However, the effect of the location situation of the LoRa devices on the communication performance in a practical larger-scale LoRa system has not been studied. Hence, to clarify the effect of the location situation of the LoRa devices on the communication performance in LoRa systems, in this paper, we implemented and evaluated the learning-based joint channel and SF-selection methods in a practical LoRa system. In the learning-based methods, the channel and SF are decided only based on the ACKnowledge information. The learning methods evaluated in this paper were the Tug of War dynamics, Upper Confidence Bound 1, and 𝜖
-greedy algorithms. Moreover, to consider the relevance of the channel and SF, we propose a combinational multi-armed bandit-based joint channel and SF-selection method. Compared with the independent methods, the combinations of the channel and SF are set as arms. Conversely, the SF and channel are set as independent arms in the independent methods that are evaluated in our previous work. From the experimental results, we can see the following points. First, the combinatorial methods can achieve a higher frame success rate and fairness than the independent methods. In addition, the FSR can be improved by joint channel and SF selection compared to SF selection only. Moreover, the channel and SF selection dependents on the location situation to a great extent.

Unmanned aerial vehicles (UAVs) are becoming an integral part of numerous commercial and military applications. In many of these applications, the UAV is required to self-navigate in highly dynamic urban environments. This means that the UAV must have the ability to determine its location in an autonomous and real time manner. Existing localization techniques rely mainly on the Global Positioning System (GPS) and do not provide a reliable real time localization solution, particularly in dense urban environments. Our objective is to propose an effective alternative solution to enable the UAV to autonomously determine its location independent of the GPS and without message exchanges. We therefore propose utilizing the existing 5G cellular infrastructure to enable the UAV to determine its 3-D location without the need to interact with the cellular network. We formulate the UAV localization problem to minimize the error of the RSSI measurements from the surrounding cellular base stations. While exact optimization techniques can be applied to accurately solve such a problem, they cannot provide the real time calculation that is needed in such dynamic applications. Machine learning based techniques are strong candidates to provide an attractive alternative to provide a near-optimal localization solution with the needed practical real-time calculation. Accordingly, we propose two machine learning-based approaches, namely, deep neural network and reinforcement learning based approaches, to solve the formulated UAV localization problem in real time. We then provide a detailed comparative analysis for each of the proposed localization techniques along with a comparison with the optimization-based techniques as well as other techniques from the literature.

The fifth generation mobile networks (5G) envisions to interconnect massive numbers of devices with a wide range of characteristics and demands for 2020 and beyond. With more radio-frequency (RF) bands to support multiple frequency transmission, separate antennas and RF chips may be required for widely separated frequency bands. The development of cognitive radio systems in which a wireless transceiver automatically adapts its communication parameters to network and user demands, is therefore crucial to the successful roll-out of the 5G. This paper introduces an innovative CR system that performs spectrum sensing for joint energy harvesting and channel access. A prototype testbed is employed to detect and convert RF signals from different bands to a DC voltage for powering a sensor board that communicates over another channel to an access point. The system's objective is to use the harvested energy for concurrent data transmission. We model the selection of channels to maximize this objective as a Multi-Armed Bandit (MAB) problem. Depending on the type of the MAB problem, three spectrum sensing strategies are developed for joint energy harvesting and channel access. The first one, applicable for stochastic MAB, conceptualizes some formulations of the exploration/exploitation balancing technique of the available frequency bands. The second strategy is an opportunistic sensing framework for Markovian MAB when the state of the underlying Markov process is partially observed. The third strategy is based on the Gittins index allocation framework for fully observed Markovian MAB. The simulation results show that lower regrets can be obtained with more information on the underlying Markov process of the MAB.

The anticipated increase in the count of IoT devices in the coming years motivates the development of efficient algorithms that can help in their effective management while keeping the power consumption low. In this paper, we propose an intelligent multi-channel resource allocation algorithm for dense LoRa networks termed as LoRaDRL and provide a detailed performance evaluation. Our results demonstrate that the proposed algorithm not only significantly improves LoRaWAN's packet delivery ratio (PDR) but is also able to support mobile end-devices (EDs) while ensuring lower power consumption hence increasing both the lifetime and capacity of the network. Most previous works focus on proposing different MAC protocols for improving the network capacity, i.e., Lo-RaWAN, delay before transmit etc. We show that through the use of LoRaDRL, we can achieve the same efficiency with ALOHA compared to LoRaSim, and LoRa-MAB while moving the complexity from EDs to the gateway thus making the EDs simpler and cheaper. Furthermore, we test the performance of LoRaDRL under large-scale frequency jamming attacks and show its adaptiveness to the changes in the environment. We show that LoRaDRL's output improves the performance of state-of-the-art techniques resulting in some cases an improvement of more than 500% in terms of PDR compared to learning-based techniques.

Construction materials and techniques have witnessed major advancements due to the application of digital tools in the design and fabrication processes, leading to a wide array of possibilities, especially in additive digital manufacturing tools and 3D printing techniques, scales, and materials. However, possibilities carry responsibilities with them and raise the question of the sustainability of 3D printing applications in the built environment in terms of material consumption and construction processes: how should one use digital design and 3D printing to achieve minimum material use, minimum production processes, and optimized application in the built environment? In this work, we propose an optimized formal design of “Biodigital Barcelona Clay Bricks” to achieve sustainability in the use of materials. These were achieved by using a bottom-up methodology of biolearning to extract the formal grammar of the bricks that is suitable for their various applications in the built enviro...

Full-duplex relaying is an enabling technique of sixth generation (6G) mobile networks, promising tremendous rate and spectral efficiency gains. In order to improve the performance of full-duplex communications, power control is a viable way of avoiding excessive loop interference at the relay. Unfortunately, power control requires channel state information of both source-relay and relay-destination channels, as well as of the loop interference channel, thus resulting in increased overheads. Aiming to offer a low-complexity alternative for power control in full-duplex relay networks, we adopt reward-based learning in the sense of multi-armed bandits. More specifically, we provide bandit-based power control algorithms, relying on acknowledgements/negative-acknowledgements observations by the relay. The proposed algorithms avoid the need for channel state information acquisition and exchange, and can be employed in a distributed manner. Performance evaluation results in terms of outage probability, average throughput and accumulated regret over time highlight an interesting performance-complexity trade-off compared to optimal power control with full channel knowledge and significant performance gains over the cases without power control and random power level selection. Index Terms-Full-duplex relaying, power control, machine learning, multi-armed bandits.

The anticipated increase in the count of IoT devices in the coming years motivates the development of efficient algorithms that can help in their effective management while keeping the power consumption low. In this paper, we propose an intelligent multi-channel resource allocation algorithm for dense LoRa networks termed as LoRaDRL and provide a detailed performance evaluation. Our results demonstrate that the proposed algorithm not only significantly improves LoRaWAN's packet delivery ratio (PDR) but is also able to support mobile end-devices (EDs) while ensuring lower power consumption hence increasing both the lifetime and capacity of the network. Most previous works focus on proposing different MAC protocols for improving the network capacity, i.e., Lo-RaWAN, delay before transmit etc. We show that through the use of LoRaDRL, we can achieve the same efficiency with ALOHA compared to LoRaSim, and LoRa-MAB while moving the complexity from EDs to the gateway thus making the EDs simpler and cheaper. Furthermore, we test the performance of LoRaDRL under large-scale frequency jamming attacks and show its adaptiveness to the changes in the environment. We show that LoRaDRL's output improves the performance of state-of-the-art techniques resulting in some cases an improvement of more than 500% in terms of PDR compared to learning-based techniques.

Bluetooth Low Energy (BLE) has been applied to a variety of IoT applications due to the spread of Bluetooth-enabled devices and their low power consumption. However, further low power consumption can be achieved by efficient selection of advertisement intervals and channels. We propose an efficient selection method based on the Multi-Armed Bandit (MAB) algorithm, which takes the wireless environment into account for the channel and the advertising interval. In this paper, we evaluate the proposed method by simulations under various environments. The results show that the proposed method can reduce power consumption according to the wireless environment without significant loss of reliability. Furthermore, we confirmed that the proposed method works with actual BLE devices.

The knowledge of when the amorphous masses of cells organize themselves into a first structural level is relevant for the architect. The architecture must also attend structural and economic stresses, following efficiency, as living beings do: something that we can learn from nature ("biolearning"), although that level can only be reached with microscope. So this becomes a useful tool for architectural research. Examples of this are presented here, seeking first to discover, analyze and evaluate microscopic structures of plants and animals. On that knowledge we can make emerge the digital design of real architectural projects, using computational strategies, also morphogenetic.

We introduce a method to analyze and modify a shape to make it manufacturable for a given additive manufacturing (AM) process. Different AM technologies, process parameters, or materials introduce geometric constraints on what is manufacturable or not. Given an input 3D model and minimum printable feature size dictated by the manufacturing process characteristics and parameters, our algorithm generates a corrected geometry that is printable with the intended AM process. A key issue in model correction for manufacturability is the identification of critical features that are affected by the printing process. To address this challenge, we propose a topology aware approach to construct the allowable space for a print head to traverse during the 3D printing process. Combined with our build orientation optimization algorithm, the amount of modifications performed on the shape is kept at minimum while providing an accurate approximation of the as-manufactured part. We demonstrate our meth...

Access point (AP) coordinated spatial reuse with Q-learning enables efficient spectrum utilization [1]. Although sharing of transmission schedules among APs is necessary for coordination, there is no mechanism to identify the APs with which the schedules are to be shared, resulting in excess information being shared among APs. In this study, we propose a scheme to identify the interference-free APs that are not required for sharing of information by comparing Q-values. A simple simulation demonstrates that this scheme successfully reduces shared information without throughput degradation.

This paper examines the evolution of a teaching methodology and agenda of a series of intense workshops deployed and evolved with master students of architectural school. Especially, having the objective of digital manufacturing, merging in one single worklow, designing, materials and CNC methods; Milling, 3D printing and laser cutting. By simulating multiple aspects of developed processes with a bottom up approach the goal was to achieve the establishment of a unique panel based on the knowledge of the tool. The geometrical conigurations require also to take in account tolerances together with machine procedures and material behaviour. The adopted approach implements material and tool intuition into a learning by doing process to steer the design based on material and fabrication methods.

This paper examines how the evolution of architectural generative design processes aim to apply similar physical and geometrical principles of biological processes taking place during development and to translate them to fabrication processes. In analogy to the reaction-diffusion mechanism for biological pattern prediction, the logic of stripe is used as construction system and examined for its structural behaviour. Both, mesh relaxation processes and weighted mesh graphs representations are employed as design tools for the construction of a minimal thin shell structural skin with branching topologies. Eventually the design workflow is extended to engage also collaborative fabrication processes and to steer the design based on intuition, knowledge of the fabrication tools, properties of the materials, manufacturing simulations and logic of assemble. This approach could lead to the optimization of material usage and machine time and facilitate the assembly process of a physical object which integrates the whole process into its form. The outcomes have been used to fabricate a prototype, using three different materials and digital fabrication methods, to examine the stability and the mechanical connectivity by taking in count the tolerances. The paper argues that biological skin patterns and segmentation in fabrication open a new field of interdisciplinary investigation and architectural applications.

In recent years, the demand for new applications using various Internet of Things (IoT) devices has led to an increase in the number of devices connected to wireless networks. However, owing to the limitation of available frequency resources for IoT devices, the degradation of the communication quality caused by channel congestion is a practical problem in developing IoT technology. Many IoT devices have hardware and software limitations that prevent centralized channel allocation, and congestion is even more severe in massive IoT networks without a central controller. Therefore, developing a distributed and sophisticated channel selection algorithm is necessary. In previous studies, the channel selection of each IoT device was modeled as a multi-armed bandit (MAB) problem, and a wireless channel selection method based on the MAB algorithm, which is a simple reinforcement learning, was proposed. In particular, it has been shown that the MAB algorithm of tug-of-war (TOW) dynamics can efficiently select channels with much lower computational complexity and power compared with other reinforcement learning-based channel- selection methods. This paper proposes a distributed channel selection method based on TOW dynamics in fully decentralized networks. We evaluate the effectiveness of the proposed method and other distributed channel-selection methods on the communication success rate in massive IoT networks by experiments and simulations. The results show that the proposed method improves the communication success rate more than other distributed channel selection methods even in a dense and dynamic network environment.

Distributed power control by beamforming approach in cognitive radio networks requires a precise analysis of the impacts of the transmission parameters, tolerable interference and guarantees the quality of service of both the primary users and secondary users. In this paper, we propose an improved performance to solve the constrained nonlinear multi-object optimization and coherent power assignment by beamforming problem based on particle swarm optimization. This method is invoked to solve the constrained nonlinear optimization problem in order to reach to maximum capacity. A numerical study is performed to show the convergence behavior of the proposed algorithm and the efficiency of the proposed technique with a dynamic cost function.

For Massive IoT systems, various Low Power Wide Area (LPWA) systems have been developed and deployed, i.e., LoRa, SigFox, etc. In this paper, to avoid destructive collisions when multiple IoT LoRa signals simultaneously received in the same channel, we propose a Successive Interference Cancellation (SIC) based collision avoidance mechanism by accessing channel using reinforcement learning for distributed massive IoT systems. Simulation results show the effectiveness of our proposed mechanism in terms of Frame Success Rate (FSR).

The petroleum sector has undoubtedly contributed substantially to Nigerian economy since its discovery in 1956 and more especially, since 1970 when its price has been on the upward   trend. It is important to note that for a country to attain the growth and development level which Nigeria requires; its economy has to be diversified. With a fall in the price of oil in the international market, it has become very evident to Nigeria that the only option left to sustain her economy is looking outwards and looking away from oil as the only source of improving its GDP. Nigeria is a mono product nation and this mono productivism has put the nation in an economic quagmire. A strong growing sustain able economy is the goal of every nation. Mono product economy needs to give way to the productive development of various sectors of the economy. The study f ocused on economic diversification and the role of agricultural export therein. Most studies have concentrated on diversification of agricul...

For Massive IoT systems, various Low Power Wide Area (LPWA) systems have been developed and deployed, i.e., LoRa, SigFox, etc. In this paper, to avoid destructive collisions when multiple IoT LoRa signals simultaneously received in the same channel, we propose a Successive Interference Cancellation (SIC) based collision avoidance mechanism by accessing channel using reinforcement learning for distributed massive IoT systems. Simulation results show the effectiveness of our proposed mechanism in terms of Frame Success Rate (FSR).

The wide spread of mobile communication devices has increased the opportunities to use wireless communication technologies, irrespective of one's geographical location. However communication quality deteriorates due to factors such as competition for scarce radio resources and interference among nearby devices. Cognitive radio technologies have been developed recently to conquer such difficulties. In this paper, we propose a wireless network optimization method using learning algorithms based on a control model known as cognitive cycle. We implement the proposed optimization method in wireless LANs and evaluate the throughput performance. The experimental results show the effectiveness of the proposed approach in a real environment.

This paper examines how the evolution of architectural generative design processes aim to apply similar physical and geometrical principles of biological processes taking place during development and to translate them to fabrication processes. In analogy to the reaction-diffusion mechanism for biological pattern prediction, the logic of stripe is used as construction system and examined for its structural behaviour. Both, mesh relaxation processes and weighted mesh graphs representations are employed as design tools for the construction of a minimal thin shell structural skin with branching topologies. Eventually the design workflow is extended to engage also collaborative fabrication processes and to steer the design based on intuition, knowledge of the fabrication tools, properties of the materials, manufacturing simulations and logic of assemble. This approach could lead to the optimization of material usage and machine time and facilitate the assembly process of a physical object which integrates the whole process into its form. The outcomes have been used to fabricate a prototype, using three different materials and digital fabrication methods, to examine the stability and the mechanical connectivity by taking in count the tolerances. The paper argues that biological skin patterns and segmentation in fabrication open a new field of interdisciplinary investigation and architectural applications.

Additive manufacturing has widely been spread in the digital fabrication and design fields, allowing designers to rapidly manufacture complex geometry. In the additive process of Fused Deposition Modelling (FDM), machine movements are provided in the form of Gcode A language of spatial coordinates controlling the position of the 3D printing extruder. Slicing software use closed mesh models to create Gcode from planar contours of the imported mesh, which raises limitations in the geometry types accepted by slicing software as well as machine control freedom. This paper presents a framework that makes full use of three degrees of freedom of Computer Numerically Controlled (CNC) machines through the generation of Gcode in the Rhino and Grasshopper environment. Eliminating the need for slicing software, Gcode files are generated through user-defined toolpaths that allow for higher levels of control over the CNC machine and a wider range of possibilities for non-conventional 3D printing ...