This paper illustrates the pedagogical approach to teaching computational design and digital fabrication in the Master of Advanced Studies in Architecture and Digital Fabrication. It demonstrates how the introduction of computational design and digital fabrication methods foster a holistic approach to integrate novel material and constructive systems into the design process. Such an integration allows the students to combine digital fabrication techniques with sustainable material processes, taking into account the questions of reversibility, recycling and reuse, and thus designing for a more sustainable construction. In the presented paper, the structure and the curriculum of the MAS programme is introduced and the pedagogical approach of the Integrated Design and Robotic Fabrication Project is demonstrated through four case studies, highlighting their respective teaching strategies in combination with the learning experiences of the students. Digital technologies in architecture have changed the way we understand and work with materials. Complementing a digital design discussion on forms, a digital materiality evolves through the interplay between digital and material processes in design and construction . At the What does this new approach enabled by digital technology mean for the skill-sets of architects and for teaching architecture? In the nineteenth century, the emergence of the natural sciences, biology and chemistry, ended the dominance of a purely visual approach to understanding nature. Increasingly intricate theories and models were developed, describing nature as a set of dynamic and complex interrelated processes. Similarly, architecture in the digital age is described through new models of interrelated processes, through algorithms and digital code. The first and most basic skill for the contemporary architect is thus learning how to code. Code controls the robot, which manipulates the material accordingly. At the same time, the dynamic behaviour and characteristics of the material are reflected in the code itself or affect the encoded parameters directly through sensors. The second skill for the architect is thus to learn about material behaviour in relation to a constructive system and its properties. The third skill is then to be able to collaborate within and across disciplines, because material knowledge is far too complex to be entirely understood by a single individual. Finally, the fourth skill is simply to design, meaning the ability to synthesize and merge all the other skills into a productive and coherent process.Over the past six years, the Master of Advanced Studies in Architecture and Digital Fabrication at ETH Zurich  has deveThe curriculum of the MAS DFAB is structured in three trimesters that build upon one another. The first trimester Digital Foundations is dedicated to training skills in using digital fabrication tools and methods. As it is a post-graduate programme, courses are designed so that students with different backgrounds and levels of experience can follow and quickly build up knowledge in coding and digital fabrication while developing a conceptual understanding of and architectural sensibility for the technologies at hand.The second trimester is dedicated to the design and construction of a building scale structure. The Integrated Design and Robotic Fabrication Project enables students to apply the knowledge gained in the first trimester to a large-scale fabrication project, taking architectural, structural and logistic constraints into account. With the whole student group working together, emphasis is placed on collaboration and knowledge-exchange, fostering a culture of teamwork that allows them to move successfully from idea to realisation in just ten weeks.In the third trimester, students are required to develop an individual master thesis based on and in collaboration with ongoing research projects at the Institute.Each trimester has its distinct focus, pedagogical challenges, and learning goals, aimed at holistic teaching of advanced methods and technologies at the forefront of digital design and fabrication and their implementation in architecture and construction. While the first trimester builds a common ground of knowledge and skills, the second trimester focuses on real-world problems, bridging between academia and practice. Finally, the third trimester introduces specific research methodologies, allows for individual specialization and prepares students for a career in research. Overall the curriculum prioritises conceptual and technological “fluency” over a “skill based” approach following an understanding that the development of adaptive, foundational skills empowers students to manipulate the medium to their advantage and to handle unintended and unexpected problems when they arise . This paThe pedagogical approach in the Integrated Design and Robotic Fabrication Project pushes the boundaries of a design research methodology in architecture education on several levels. In the context of this paper the notion of design research builds upon the concept of “learning by doing” originally attributed to John Dewey which describes reflective thinking as an an act of search or investigation directed toward bringing to light further facts which serve to corroborate or to nullify the suggested belief . Here, tFirst, all projects start with a completely novel constructive system for which a proof of concept has been previously developed by the tutors. Rather than selecting an established fabrication process and calling for a design proposal, the Integrated Design and Robotic Fabrication Project focuses on the refinement and full-scale implementation of the proposed prototypical material and constructive system in a collaborative effort between students and tutors. This approach interweaves the design and fabrication process development, fostering a culture of creative problem-solving skills.Second, as students work on the parallel development of a computational design workflow and digital fabrication process, physical prototyping at full-scale, structural testing, and construction planning, they experience first hand the need for an integrative design approach, where interdependencies are reflected in the workflow. The different aspects of the project development inform one another, and certain design intentions can directly influence the development of the material aware fabrication process and its tools and vice versa. Findings from prototyping or material tests inform and define the design and feed directly back into the computational design setup. The role of the designer who exerts his own will is transformed into a role where the designer acts as an interpreter of this performative connection between design and material-technological development.Third, each project is aimed at a clearly scheduled delivery according to a project brief for a real-world design and building task, including the planning of resources and logistics that come with it. As such, during the design process, the knowledge and time of the collective group need to be effectively allocated and the material and machine availability taken into account. In the Integrated Design and Robotic Fabrication Project, this need is addressed by the development of a custom computational design model integrating the complete project chain, from the conceptual design, structural analysis, and generation of fabrication data to the construction segmentation and sequencing. Each project develops its own algorithmic design model and data that allow students to specify which interfaces are needed to efficiently integrate these different tasks into one parametric workflow.Lastly, the Integrated Design and Robotic Fabrication Project has a hybrid format, which fundamentally addresses questions regarding constructive design, detailing, and assembling, as well as sustainability and reusability. All in all, students learn to apply their conceptual and technical skills to a concrete building task, learning to deal with reality, resources and with the potentials and challenges of design development as collaborative team work. As such, it facilitates rapid project development and becomes itself a testbed for novel ideas and future research projects. Although each of the case studies presented below investigates a different material and fabrication system, they share a common methodology. By pointing out specific didactic methods and teaching strategies, we illustrate how the pedagogical approach evolves from one project to the next.The Brick Labyrinth is the first iteration of the teaching format of the Integrated Design and Robotic Fabrication Project and as such, a pedagogical experiment. Built as a temporary structure by a multi-robotic process to dry-stack bricks, the Brick Labyrinth is a large-scale, accessible construction providing a unique spatial experience for its visitors Fig. . The desWhile building on the experience of the previous robotic brick works at Gramazio Kohler Research , the BriThe design task is relatively open, as only the maximum amount of bricks and the construction area are given as constraints, but what makes the design process complex is the need to integrate fabrication, structural, and material parameters. This challenge is addressed by a performative collaborative approach, where the division of tasks and definition of interfaces between groups allows students to focus on specific topics. In a first phase, smaller teams are formed to explore different conceptual design approaches to the architectural typology of the labyrinth, which are then collectively evaluated by students and tutors for their spatial potential and feasibility. This process helps to define a common design strategy for the labyrinth: Two differentiated walls spiraling around and leaning towards each other create a series of highly articulated spaces along a path Fig. . MediateThe speed of a pick-and-place process decreases as a construction becomes larger, as each brick has to be carried for longer distances. Ultimately, however, the possible size and building time of the Brick Labyrinth directly depend on the speed of the fabrication process. Optimising the process speed thus becomes an important teaching goal that provides the students with the opportunity to develop both the concept and the physical implementation of a robotic process and tooling. Here, the inherently slow robotic pick-and-place procedure was made more efficient by the development of a new robotic end-effector, a brick magazine that can hold a stack of eight bricks at a time and dispense them one after the other Fig. . While tThe Brick Labyrinth demonstrates how a fully reversible digital fabrication process could be used for architectural design explorations in a prototypical setting at full-scale. Conceptually, the reuse of the bricks stands for a completely circular constructive system, where the sustainable use of materials is supported by the efficient and precise execution of the construction and deconstruction, brick by brick from “pallet-to-pallet”. On a didactic level, the design development is closely linked to the further refinement of the tooling, the fabrication process, and the structural performance. The results are evaluated through physical prototypes, and in this way the digital fabrication process itself informs the design idea. Finally, the fabrication process introduces the idea of different robotic systems collaborating with each other and with humans as a powerful alternative to the idea of pure industrial automation.This section discusses Gradual Assemblies Fig.  and Up S1Prefabricated in segments and assembled as a permanent large-scale structure, Gradual Assemblies applies the constructive system in the design of a sun-shading pergola on the terrace of the Istituto Svizzero in Rome Fig. . The desBuilding on the experience of previous robotic timber projects at Gramazio Kohler Research, the material and fabrication system of Timber Dowel Assemblies (TDA) is mainly inspired by two predecessors: The Sequential Roof  introducTimber Dowel Assemblies as a constructive system introduces novelties that not only expand the design space and geometric freedom but also relate on a conceptual level to the material behavior of wood and its re-usability. It is a digital approach to a new timber-only joint typology, which investigates contemporary forms of cooperative multi-robotic processes and human–robot collaboration. The cooperation of two robotic arms allows a timber slat to be positioned in space by one arm while the other arm drills the holes for the dowels at a defined angle Fig. . These hBoth Timber Dowel Assemblies projects involved working with external partners and clients, which introduces a real world context to the project and thus a real test for the application of advanced digital methods. Students have to consider a specific site and its constraints, a design brief, and institutional framework of the client, as well as the logistics involved in realizing the design and its segmentation, including transport, assembly, execution planning and delivery schedules. The design of the two large-scale projects presented here illustrates how the constructive system adapts to different contexts and constraints.In the Gradual Assemblies project, as the curve radius of the design differs on the inner and outer side of the structure, an algorithmic branching logic within the computational design model allows to geometrically remove or add elements at strategic moments and to optimise the distribution of all timber slats Fig. . While tIn the Up Sticks installation, the design is based on a continuous surface built up by segments that twist, align and connect to form tubular bridging structures and spatial enclosures Fig. , so the The performance of the joint and its geometric limitations play a central role in the constructive system of Timber Dowel Assemblies. As such, the type of joint becomes an important element, defining the design space by introducing structural as well as fabrication constraints. Therefore, the development of a performative joint typology becomes an important teaching goal that was addressed and iterated in each of the different teaching projects. The optimisation of the joint is a complex problem, as there are infinite possible combinations of dowel-slat connections, and every combination brings its own constraints. For each project, students thus have had the opportunity to weigh multiple objectives and to develop the joint typology in parallel with the design idea, the implementation of the fabrication process, and the evaluation of its structural performance Fig. .As such, a first joint type is explored in Gradual Assemblies, where each individual timber slat is directly connected to its neighbors by two oriented dowels. This facilitates the fabrication process as it reduces the number of dowels needed as well as their average length, allowing for precise in-place drilling and easy manual insertion of the dowels. Building upon this, another promising joint type is developed through an MAS Master Thesis. Here, the dowels extend to always connect three consecutive timber slats instead of only two, thus ensuring a better distribution of forces within the dowels and a stronger joint. This joint type is chosen for its structural performance, which is implemented and further developed in Up Sticks. In that project, the refined joint detail is combined with the closed ring geometries of the design, thus greatly increasing the overall stiffness of the structure. However, the fact that now each dowel has to connect three timber slats instead of two poses some challenges for the fabrication process. Instead of drilling the holes in-place, now all timber slats are robotically pre-drilled before being placed, requiring an extra level of precision in the placement to secure the alignment of three oriented holes for the insertion of the connecting dowels.Timber Dowel Assemblies demonstrates the development of a digital approach to timber-only connections, from first ideation to a performative system that can be further extended. Parts of the findings on the use of dowels for timber-timber joints was later applied to a large-scale construction project of Gramazio Kohler Research. The process itself challenges the classic divide between digital and analog processes, machine and craftsmanship. Lastly, given the limited resources on our planet, this project illustrates how contemporary knowledge in computation and digital fabrication technology can be combined with traditional knowledge about construction methods in order to create innovations in the construction sector.Rapid Clay Formations is a radical demonstration of how new construction methods based on natural material systems and robotic fabrication processes are developed from scratch through multiple teaching formats, gradually scaled and refined, and ultimately lead to the erection of a permanent building structure. Rapid Clay Formations explores a novel fabrication process for malleable materials, where soft clay cylinders are robotically picked, precisely positioned and pressed against each other to form a soft-bond through cohesion, plastic deformation and interlocking.Compared to previous Integrated Design and Robotic Fabrication Projects, this project presents two new challenges: First, using a non-homogeneous material system such as a clay-based mix in combination with digitally controlled design and fabrication, this project requires that students address both complex material behavior and the need for geometric precision. Here, it becomes even more essential to combine empiric testing and prototyping with the development of predictive design tools, introducing the need for an adaptive design and fabrication workflow up to and during the fabrication phase. Second, shifting from a pre-fabrication and assembly to an on-site semi-mobile fabrication method changes the robotic control requirements as well as the logistics for planning and executing the construction. In addition to posing these two concrete challenges, the project addresses current global challenges for which the use of alternative construction materials could promote a sustainable and reusable building practice.The concept of this constructive system originated with the Remote Material Deposition installation . In thatThe constructive system can be described as a contemporary reinterpretation of earthen construction, where unfired cylinders of clay, which we call soft-bricks, are robotically aggregated to form what we describe as a soft-bond Fig. . Each inThe initial design for the UIA 2020 Pavilion resulted in the proposal for an undulating clay wall with a length of approximately 21 m and up to two meters high. Findings on the material mix and processing, the soft-bond typology and its segmentation, as well as the control of the robotic fabrication process formed the base for the design development of the Clay Rotunda, a freestanding, single layer earth-based cylindrical structure constituting the outer, soundproof shell of a high-fidelity music auditorium built inside a former Brewery in Bern Fig. .Following an open design brief, the original proposal for the UIA pavilion envisioned a curvilinear wall that creates specific spatial conditions in relation to its site and context. For this purpose, students developed the design synchronously with the material and structural system through explorations at different scales. They developed a differentiated soft-bond, optimising the structural behaviour of the system so as to be able to build up to two meters in height. As the performance of the process is highly dependent on the material behavior, the design and robotic process are informed by the continuous refinement of a suitable material mix, which was optimized with the main goal of reducing the shrinkage and chemical cracks while still retaining enough plasticity to ensure proper bonding between the elements and reach considerable heights. The findings from the pavilion project were then integrated into the design development for the Clay Rotunda. The clay mix was further optimized to increase the structural capacity and bonding behaviour of the soft-bricks, which made possible the impressive scale of the structure. It features a diameter of almost 11 m and is 35 m in length, and it reaches a height of 5 m with a width of just 15 cm in unreinforced clay, with 30′000 clay cylinders in total.The development of a suitable soft-bond typology is key for the design explorations with Rapid Clay Formations. The design process has to pull together different aspects of the constructive system, including the material behaviour and its expressive aesthetic, the robotic fabrication process, and the structural capacity. Physical testing at different scales is necessary to understand the complex behaviour of the malleable material system in relation to the precise control by computational design and digital fabrication.The goal of a first short-term design research and fabrication exercise as part of the first trimester Digital Foundations curriculum was to explore the typological and structural potential of the soft-bond at small-scale. Topics included varying force applied to the soft-bricks to create different bonding behavior and thickness of the structure, varying soft-brick sizes (diameter and height) and their respective interlocking behavior, discretizing surfaces into single and multilayer high-resolution aggregations, and exploring the possibility of different soft-brick shapes Fig.  9]..9].For the first application at full architectural scale for the design proposal of the UIA Pavilion, the development of the soft-bond aimed to allow for differentiated expression and high structural performance while minimizing the material used. At the bottom part of the wall, the cylinders are assembled in a stacked-bond that doubles-up to provide the necessary structural width, while at the top section of the wall the topology transitions to a running-bond, creating a thin and elegant expression Fig. . MaintaiWhen the project was stopped due to the Covid-19 outbreak, the soft-bond concept was further developed for the subsequent realization of the Clay Rotunda, realised using the mobile robotic platform In-situ Fabricator Fig.  6, 15]., 15.6, 1The work on this project contributed to a successful application for a large research grant to continue our investigations beyond the teaching format and to the first large-scale implementation of the soft-bond in practice. Thus, the project overall has acted as a catalyst for knowledge transfer between research and industry. During these explorations, a smaller scale robotic setup allowed for quick prototyping at model scale to explore the typological and topological variations of soft-bonds and their design potential. At the same time, the large mobile robotic platform allowed us to build full-scale prototypes to ensure the scalability of the process and to investigate the material and structural performance of the system over time. Combining the traditional knowledge of clay constructions with contemporary digital design and fabrication processes, the robotic clay aggregation process increases the degree of control of both material and process, allowing complex structures to be built that go beyond what has traditionally been possible. The process pushes the usage of locally sourced clay, suggesting a meaningful and sustainable approach to robotic construction of earthen structures. Finally, as minimizing energy consumption and emissions becomes more important in the context of digital fabrication, Rapid Clay Formations proposes an entirely waste-free, fully reversible and recyclable constructive system, promoting the circular use of material from “pit-to-pit” Fig. .The Integrated Design and Robotic Fabrication Project is highly valuable not only as a teaching format but also as a catalyst to explore new research topics. While it may seem ambitious for the projects to design, develop, and deliver large-scale structures in an experimental setup in only 10 weeks, this short duration encourages a culture of intense collaboration and interdisciplinarity throughout the whole process. This fosters skills that cannot be taught ex-cathedra or in separate manner, but which students, tutors and project partners alike can acquire through this all-encompassing experience. We argue that understanding the building format as a laboratory for experimentation allows us to question and rethink conventional design approaches to material and constructive systems while at the same time critically assessing computational design and digital fabrication strategies — strategies that are often celebrated as innovative without further scrutiny. Although all case studies discussed relate to and build upon prior research investigations, they all take a radical stance towards topics such as alternative material systems, sustainability, reusability, detailing and construction planning. As such, many of the ideas tested within the MAS Integrated Design and Robotic Fabrication Project have opened up new research streams that have been continued in different formats. This close relationship of research and teaching topics is rooted in the understanding of the Integrated Design and Robotic Fabrication Project as a form of collaborative design research. From a pedagogical perspective, with the MAS being a post-graduate programme in the rather emerging field of digital fabrication, this integration of research within teaching allows students to develop as independent young professionals.In summary, the projects cannot be characterized as pure research demonstrators, large-scale prototypes or “student pavilions”. Instead, they reflect that we are addressing a larger ambition, namely the development of a new practice of Architecture. Shifting from the Albertian paradigm of the designer author as an idealist-intellectual, disconnected from the actual task of building , we emphDuring the past four years, the Integrated Design and Robotic Fabrication Project has evolved through the development of novel computational design and robotic fabrication techniques. The evolution went from the assembly of traditional construction materials following a conventional structural system (The Brick Labyrinth) to traditional materials fabricated into novel spatial construction systems (Timber Dowel Assemblies) and ventured most recently into the spatial aggregation of malleable materials with complex behaviour (Rapid Clay Formations). This rapid growth in complexity enabled the development of novel computational design methods, material simulation and adaptive robotic fabrication methods, but increased the necessary time for preparation. Here, we push for a conceptual change, where instead of developing novel processes, research topics which have reached a certain level of maturity are integrated as the base for the teaching agenda. The approach allows researchers, tutors and students to experience research as a form of architectural practice. Our project is based on the idea that students can be part of investigating how digital technologies will reform the way we design and build, and this participation forms the foundation of our pedagogy for training future architects.


Deep learning (DL)-based modulation recognition methods of underwater acoustic communication signals are mostly applied to a single hydrophone reception scenario. In this paper, we propose a novel end-to-end multihydrophone fusion network (MHFNet) for multisensory reception scenarios. MHFNet consists of a feature extraction module and a fusion module. The feature extraction module extracts the features of the signals received by the multiple hydrophones. Then, through the neural network, the fusion module fuses and classifies the features of the multiple signals. MHFNet takes full advantage of neural networks and multihydrophone reception to effectively fuse signal features for realizing improved modulation recognition performance. Experimental results on simulation and practical data show that MHFNet is superior to other fusion methods. The classification accuracy is improved by about 16%. Modulation recognition of underwater acoustic communication signals, which is the key link of underwater information acquisition, refers to the process of accurately classifying the signal modulation mode under the condition of a known modulation typeset. With the upgrade of hardware, modulation recognition based on sensor networks has become a common method of communication. In the field of radio modulation recognition, researchers agree that the modulation recognition performance of multiple sensors is better than that of a single sensor ,2,3,4. MIn recent years, deep learning (DL) has made major breakthroughs in the modulation recognition of underwater acoustic communication signals by virtue of its powerful feature extraction and computing capabilities. The current research based on DL can be divided into two categories—the modulation recognition method applied to a single hydrophone scenario and that applied to a multihydrophone scenario. Various network structures have been designed for the single hydrophone modulation recognition problem to realize the effective recognition of underwater acoustic communication signals. First, the received signal is preprocessed, and then different network structures are used to complete the feature extraction and classification of the signal. Common preprocessing includes noise reduction processing ,6, filteUnlike the single hydrophone scenario, multiple hydrophones can use different locations to obtain signals with different reception qualities. Then, a fusion strategy is adopted to improve the reliability of signal recognition. Due to the lack of literature on multihydrophone modulation recognition, we refer to the signal recognition method based on multi-antenna reception. Wang et al.  proposedIn this paper, by considering the combination of the actual underwater acoustic communication scenarios and the limitations of the fusion decision-making mechanism in the radio field, we introduce a multihydrophone fusion network (MHFNet) for reception modulation recognition. The specific contributions of the study are as follows.This study proposes a new network framework, including a feature extraction module and a fusion module, which successfully realizes effective identification in the multihydrophone reception scenarios of 2FSK, 4FSK, 8FSK, BPSK, LFM, and OFDM and other commonly used underwater acoustic communication signals.To extract the characteristics of the signals received by multiple hydrophones, this study uses a CNN to extract the characteristics of the signal time-domain sequence.To fully utilize different locations to obtain signals of different reception qualities, this study uses a neural network to construct a fusion module that can automatically evaluate the signal quality and assign the optimal weights to different hydrophones.Experimental results show that the proposed method is better than DV and DA in multiple hydrophone scenarios, showing an improvement of approximately 16% when the symbol signal-to-noise ratio is 10 dB.This study considers a multihydrophone receiver system, shown in In the signal receiving process, owing to the lack of a priori information of the underwater acoustic communication source, the location of hydrophones is random, and the relative distance and water environment are unknown. Therefore, the different underwater acoustic transmission channels are assumed to be independent of each other. The signal models received by different hydrophones are modeled asThe structure of MHFNet is shown in In this paper, the sample matrix composed of the received signals of multiple hydrophones is fed to the feature extraction module. The signal sample matrix The feature extraction module is composed of four convolution (conv) blocks and two pooling layers. The specific structure is shown in In the problem of modulation recognition of noncooperative underwater acoustic communication signals, because of the lack of prior knowledge, the quality of signals from different hydrophones is different. Because DV and DA ignore the analysis of the signal quality of different hydrophones, there is significant room for improvement in performance. The key concept of the fusion module is to use the self-learning ability and computing power of the neural network to automatically evaluate the signal quality from different hydrophones and assign the best weight to each hydrophone. Equivalently, a hydrophone with a high signal-to-noise ratio is assigned a large weight, while that with a low ratio is assigned a small weight. It makes the fused signal features more discriminative. The network structure of the fusion module is shown in The loss function is designed as follows:Information fusion methods include signal fusion, feature fusion and decision fusion. In this study, the feature fusion method is used to fuse multichannel received signals and then identify and classify them. To highlight the advantages of the algorithm, we assess the performance of each of the other fusion methods. The first method is signal fusion. In actual underwater acoustic communication, owing to the effect of multichannel underwater acoustics, the signals received by multiple hydrophones have a large time delay and severe synchronization errors. The signal fusion method is sensitive to the delay and synchronization errors of the signals received by multiple hydrophones; hence, it is not a feasible method. Next, we explore decision fusion. The decision fusion method includes voting and confidence decision methods. References ,15 propoThe signals received by different hydrophones are first subjected to preprocessing, then identified in the recognition module To compare the fusion performance of different methods and ensure fairness of the experiment, this study adopts a network structure similar to the MHFNet. The recognition module of this study adds two Fc to serve as classifiers based on the feature extraction module of MHFNet. The decision fusion method in this study includes voting fusion and confidence decision fusion, which are equivalent to DV and DA in , respectIn this section, we generate data for network training and testing based on Equation (1). The sampling rate of the signal is 48 kHz, the signal length In the single-input multiple-output (SIMO) system, depending on the receiving location, environment, and distance of different hydrophones, the transmitted signal reaches the receiving end passing through different channels. For a close representation of the actual application scenario, we choose the typical underwater acoustic channel hA–hF in  as the rIn the testing process, we select channels given in  as the tThe experiment in this paper is based on the PyTorch DL platform for training and testing in an environment with the following specifications: the CPU is Intel(R) Xeon(R) Silver 4110, the GPU is RTX2080Ti, and the memory is 128 GB. The training process uses the Adam optimizer, and the hyperparameters of MHFNet are set as follows: the learning rate is 0.0005, the batch size is 60 × First, the overall recognition performance of MHFNet is analyzed. When Next, to highlight the advantages of MHFNet, different fusion methods, including DV, DA, and AMC , where tIn addition, the recognition performance in different receiving scenarios is compared for more comprehensively highlighting the advantages of MHFNet. Finally, to test the performance of MHFNet in the actual underwater environment, we design an underwater acoustic communication experiment in the artificial lake. The weather conditions are as follows: cloudy day, northeast wind level 1, and 17 °C temperature. The layout of the experimental equipment is shown in terature , using 3This study proposed a modulation recognition method called MHFNet for the underwater SIMO system. MHFNet adopts a feature extraction module and a fusion module to perform feature extraction and fusion on the signals received by multiple hydrophones. The experimental results showed that the fusion mechanism of the proposed neural network is better than other fusion rules. In addition, MHFNet can not only be applied to underwater acoustic communication signal modulation recognition but also provide new ideas for classification problems in other multisensor receiving scenarios.


Pallet racking is an essential element within warehouses, distribution centers, and manufacturing facilities. To guarantee its safe operation as well as stock protection and personnel safety, pallet racking requires continuous inspections and timely maintenance in the case of damage being discovered. Conventionally, a rack inspection is a manual quality inspection process completed by certified inspectors. The manual process results in operational down-time as well as inspection and certification costs and undiscovered damage due to human error. Inspired by the trend toward smart industrial operations, we present a computer vision-based autonomous rack inspection framework centered around YOLOv7 architecture. Additionally, we propose a domain variance modeling mechanism for addressing the issue of data scarcity through the generation of representative data samples. Our proposed framework achieved a mean average precision of 91.1%. Smart manufacturing, also known as Industry 4.0, refers to manufacturing functions that are integrated and collaborative, and process the ability to act on data in a timely manner. Focusing on the operation level, the computerization of certain tasks leads to data generation via communication protocols. The data are procured, processed, and utilized to improve the decision-making power. The acquisition of data and the ability to facilitate interconnectedness between various distributed operations within a manufacturing facility are succinctly seen as smart manufacturing.Pallet racking is one of the most ubiquitous infrastructures found within logistic/distribution centers, warehouses, and many other types of manufacturing facilities. Its purpose is to hold stock in a safe manner ready to be shipped when required. Depending on the nature and scale of operations, pallet racking can be densely populated, containing thousands of pallets of considerable monetary value.The structural safety of racking is of paramount importance in order to avoid damage accumulation resulting in collapsed racking, which costs the business financially and also puts human lives at risk. Collapsed racking can be a result of two factors. The first is incorrect installation and the second is the careless operation of forklifts near the racking whilst loading/off-loading. This research presents a framework for the automated detection and localization of racking defects via YOLOv7 architecture coupled with AprilTag-based localization.The current literature concerning the inspection of pallet racking suggests a shortage of active research for this area; there are only a couple of publications concerning pallet racking. Hence, we broadened our literature review to focus on structural defect detection.Farahnakian et al.  proposedDong et al.  presenteSimilarly, Zhu et al.  examinedWe have observed an increase in the implementation of CV-based applications across various sectors. This is a result of recent and continuous improvements in CV-based architectures along with the wide adoption of transfer learning.Around a decade ago in 2012, AlexNet  was intrThe architectures mentioned above are widely applied in industries such as medicine ,11, reneBased on the above premise, CV researchers are actively developing new lightweight architectures suitable for edge device deployment utilizing limited computational resources. The two most popular architectures in this regard are Single Shot Detector (SSD) MobileNet  and You Adibhatla et al.  implemenIn our previous work , we propSummarizing the literature, we have learnt that there is a dearth of research that focuses on architectural quantization for addressing deployment issues. For example, the subscription to the segmentation domain of the CV as opposed to object detection in  means thOur first contribution to automated pallet racking came in the form of a classifier detecting five different classes: horizontal, vertical, support, vertical damage, and support damage. We addressed the issue of data scarcity through selected image processing techniques appropriate for modelling the variance induced within various distribution centers, warehouses, and manufacturing facilities as a result of internal and external factors. The scaled dataset was used to train the recent YOLOv7 architecture with a focus on producing a highly generalized model that was able to differentiate between various racking states.Secondly, we benchmarked the trained architecture performance not only on architectural and computational performance but also on post-deployment metrics such as frame-per-second (FPS), demonstrating the realistic inference speed of the trained architecture. Additionally, we proposed a complete deployment framework and deployed the developed architecture onto a jetson device. We resolved the issue of determining the location of the damaged racking leg through the use of AprilTags and a positive-inference window (PiW) mechanism.To the best of our understanding, there is no open-source pallet racking dataset consisting of representative samples that can be used for developing automated defect detection classifiers for addressing damage detection in pallet racking. This potentially justifies the reason for the lack of active research in this field as data are the fundamental components for initiating any development work in AI-related applications. Extending our research from , we presAfter deriving the original dataset based on the data procurement strategy, the next step involved the annotating of the data. This was a fundamental step; its efficiency would have an impact on the performance of the model. If the bounding boxes around the classes of interest were too loosely defined, this could force the models to generalize on a false assumption. Conversely, very stringent bounding boxes could result in missing a section of the relevant class, again leading to the risk of false generalization during training. In order to balance the two risks, we decided to annotate based on close proximity. At the same time, due to stock being placed on the racking, certain images contained occlusions with respect to the class of interest. To address this, images containing an occlusion of a quarter of the class of interest were fully annotated, including the occlusion part A; with oIt was noticed during the frame splitting that many frames did not contain any pallet racking due to the movement of the smartphone around the warehouse. These images were filtered out of the dataset. The next phase within the data transformation involved the scaling of the dataset thorough representative augmentations in order to provide enough training data for the selected architecture to generalize on. The first type of variance modelling was based on capturing the variance that could be caused by the Jetson Nano used for capturing and inferencing the racking images. The Jetson Nano was located on the adjustable bracket of the forklift, as shown in Although the device was initially installed (with a magnetic mount) onto the adjustable bracket in a certain orientation, the orientation could be adjusted due to external factors such as stock off-setting the device position whilst loading/off-loading. Hence, to cater for this variance and enable the model to accurately inference regardless of the orientation, shift and rotation augmentations were applied. For this, a shift component was introduced denoting the shift factor in the form of  would be reached via the matrix:As result of pixel shifting, the variance caused by the off-setting of the hardware device was captured. However, this type of processing eliminated the damaged regions of the racking in a few cases (when rack damage was located at the edge of the image). To facilitate the preservation of damaged racking in the images, center-based rotations were introduced. Habitually, the rotation of an image with respect to an angle (centre is the rotation center (input image), θ is the rotation angle (degrees), and scale is the isotropic scale factor:However, the aim here was to reference the center of the image for the rotation:Additionally, the placement of the device on the adjustable brackets of the forklift enabled the strategic coverage of the particular racking on which the forklift was operating. However, this also resulted in a continuous dynamic state for the device to operate in; i.e., inferencing on the state of the racking. Depending on the speed of the forklift, the image data captured by the device could be blurry. To cater for this type of variance, Gaussian Blur was introduced. The pixel-wise blurring of certain images was aimed at introducing a representative variance; i.e., due to varying lighting conditions and the hardware specifications of camera device, the model was provided with a richer training dataset to assist with generalization. This architecture was aimed at pallet racking detection across a wide range of warehouses, distribution centers, and manufacturing facilities. It was plausible to expect that different locations would present varying external factors that the model-trained architecture would need to adapt to. For example, warehouse M may have an increased lux intensity (increased lighting) whereas warehouse B, located in another country, may have significantly less lux intensity. It is also possible that warehouse M may have variations in its lux intensity during the day–night shifts.To model the lux variations into the training dataset, a random brightness adjustment was implemented. This was achieved by varying the pixel intensity of a given image within a globally predefined intensity mechanism varying between −11 and +11%. The scaled dataset consisted of 2094 samples. There are many architectures available for training custom defect detection classifiers. We selected the most appropriate based on the following criteria. First, the research focused on defect detection without requiring pixel-level accuracy; hence, object detection was selected as opposed to segmentation. Second, object detection has many promising architectures; however, the benchmark here was real-time inference speed along with an acceptable accuracy. Based on the above two filters, the architecture search was narrowed down to MobileNet and the YOLO family of models. MobileNet was implemented in ; as an eYOLOv7 presents several architectural reforms aimed at improving the detection speed and accuracy. In general, all YOLO architectures consist of a backbone, head, and neck. The backbone is responsible for the grounding work, extracting essential features and feeding them through to the head via the neck component. YOLOv7 moves away from its predecessors when it comes to the backbone; that is, rather than utilizing the darknet, an extended efficient layer aggregation network (E-ELAN) is deployed as the computational block for the backbone. Although the E-ELAN paper has not been published yet, the concept is based on the use of expand, shuffle, and merge cardinality to continuously enhance the learning ability of the network without losing the original gradient path.Furthermore, to address the issue of model scaling for a specific device deployment, researchers generally utilize a Network/Neural Architecture Search (NAS) tool . NAS enaYOLOv7 also caters for re-parameterization planning (RP). RP is based on the concept of averaging various models to produce a final model that is robust in performance. Module-level re-parameterization has been an active area of research, where certain segments of the model have exclusive re-parameterization strategies. YOLOv7 utilizes the gradient flow propagation paths for determining the segments (modules) within the overall model that require re-parameterization. Finally, the head component of the architecture is based on the multihead concept. That is, the lead head is responsible for the final classification whilst the auxiliary heads assist with the training process in the middle layers. In order to initiate the training process, a set of hyperparameters had to be defined. Although the training could have been carried out via Google Colaboratory, the GPU allocation for the free tier is limited and, hence, timeout issues could result incomplete training. Hence, a standalone system was utilized for the complete training of the architecture. The system specifications along with the defined hyperparameters for guiding the training process are presented in Various metrics were utilized to comprehend the model performance from different perspectives and granularities. Along with the generic metrics of evaluation precision, recall, and F1 scores, the Intersection over Union (IoU) was specifically used because our application was based within the object detection realm as opposed to image classification or segmentation. The IoU, also referred to as the Jaccard Index, facilitated similarity quantifications between the ground truth i-th class and C equated to the number of classes:The training time coupled with the resultant performance was a manifestation of the effectiveness of our proposed data scaling mechanism. As presented in The support racking and damaged support racking saw a decrease in performance of 88.1% and 88.6%, respectively. This was due to the fact that the support racking was not as visually apparent as the vertical and horizontal racking as it was located on the sides of the vertical racking providing support; hence, it was usually obscured when loaded with stock. Thus, the limited class access to these support racking images could have resulted in a decrease in the generalizability of the model for this particular class. However, the overall performance of the architecture was still 91.1%.In order to evaluate the overall success of our research it was essential to provide a comparison against similar research conducted in present times. A prior work  focused First, our research objective was to train a lightweight architecture that could be deployed onto an edge device to provide a real-time inference. This could not be achieved using the approach taken by  as a MasWith respect to the data size,  only conAlthough our trained architecture had ostensibly the lowest performance 91.1%), the difference was minimal compared with .1%, the  and lessFinally, we also presented the FPS for our trained architecture, achieving a satisfactory performance of 19 FPS. The FPS coupled with the detection accuracy based on the five different classes testified to the success of our proposed framework.In conclusion, our research was successful in the development of the first multiclass pallet racking detection framework. The performance manifestation through the achieved MAP coupled with the real-time performance of the trained architecture (19 FPS) demonstrated that the proposed framework could be utilized to deploy the system within warehouses, distribution centers, and manufacturing facilities for the monitoring of pallet racking.Our solution presented a non-invasive approach to defect detection that was distinct from conventional racking monitoring products aimed at sensor-oriented solutions . These rFurthermore, the proposed framework could also be applied to various other domains where the objective may be to reduce the reliance on hardware-oriented data collection and inference such as PV fault detection and post-system deployment ,30.


This paper presents a novel adaptive algorithm for multicomponent signal decomposition from the time–frequency (TF) plane using the short-time Fourier transform (STFT). The approach is inspired by a common technique used within radar detection called constant false alarm rate (CFAR). The areas with the strongest magnitude are detected and clustered, allowing for TF mask creation and filtering only those signal modes that contribute the most. As a result, one can extract a particular component void of noise and interference regardless of the signal character. The superiority understood as an improved reconstructed waveform quality of the proposed method is shown using both simulated and real-life radar signals. Non-stationary and multicomponent signals are present in a variety of applications such as radar ,2, sonarA typical approach to component extracting is the TF processing due to the informative character of the result, the possibility to distinguish particular signal terms, and invertibility . In the This paper uses the 2D CFAR algorithm to adaptively detect TF distribution local maxima. The proposed approach is similar to that presented in , where tThe paper has the following structure: For the complex and continuous signal K is the number of frequency bins. This work assumes that the window length equals the Fourier transform size. For the Gaussian window, which theoretically has an infinite length, the assumption amounts to elongation of the window with samples very close to 0, which can be regarded as zero-padding. The discrete STFT variant of  from 3.Group the detected points into clusters using the DBSCAN algorithm.4.Sort clusters comprising their energy.5.For each desired component, spread the mask to the nearest zeros of the spectrogram 6.Apply the STFT masking.7.Retrieve the signal back in the time domain using the inverse STFT.The proposed algorithm can be divided into the following steps:The outcomes for the exemplified multicomponent signal with vertical, horizontal, and amplitude-modulated terms are presented in K stands for the known number of components The proposed method was compared to two other well-defined techniques in the literature. The first is the one proposed by Flandrin  with a d follows :(12)Sxh . The simIn order to define the reconstructed signal quality, the reconstruction quality factor (RQF) was used for all considered methods :(15)RQF=Signal decomposition is an issue in various applications, and the proposed algorithm can be used widely. As a representative example, frequency-modulated radar chirps were considered. The main goal was to extract only the direct pulse, whilst maintaining amplitude, phase, and frequency dependencies unaltered. In this case, a simple inverse STFT is useless. As a result, one would obtain the unmodified signal . Thus, the proposed algorithm can be practically used in such an application.The first signal originates from the ATC radar system located at Warsaw’s Chopin Airport, Poland. The sampling rate during the data collection was shown in a was proThe results clearly show differences between the retrieved pulses. After processing using the method proposed in this work, the entire waveform was extracted, including its linear and nonlinear terms, as shown in The reconstructed pulse can be used in a passive radar that uses another active radar as a source of illumination  or for sAs can be seen, the best capabilities were obtained for the proposed method since the side-lobes have the lowest value, while the peak is high and narrow. The main factor influencing the quality of the recovered pulse is the possibility of extracting vertical components at the ends of the transmitted signal. Only the proposed method allows complete pulse extraction, including the part with linear and nonlinear frequency modulation. Furthermore, resistance to local amplitude variation is achievable with the proposed approach. It should be noted that local amplitude deviations resulting from various factors such as the nonlinearity of the radio frequency chain, noise, and multipath propagation influence the amplitude distribution on the spectrogram. That has a crucial impact on the efficiency of methods known from the literature ,22, whicThe second signal was transmitted by the medium-range radar and recorded with a similar setup as in the NLFM case. The results are illustrated in This paper presented a novel signal decomposition approach to component retrieval from the STFT with examples supporting its effectiveness. The superiority of the presented finding relies on the unsupervised ability to extract differently oriented waveforms, e.g., bursts, transient, nonlinear chirps, harmonic terms, and signals with amplitude modulation. The proposed approach’s properties were confirmed by thorough numerical experiments using simulated signals and real-life radar pulses. Future research should cover the proposed algorithm’s extension to the analysis of multicomponent signals with crossing modes and rapidly oscillating instantaneous frequency. In another direction, work should involve CFAR algorithm parametrization comprising signal and processing parameters.


Wearable and implantable medical devices (IMDs) have come a long way in the past few decades and have contributed to the development of many personalized health monitoring and therapeutic applications. Sustaining these devices with reliable and long-term power supply is still an ongoing challenge. This review discusses the challenges and milestones in energizing wearable and IMDs using the RF energy harvesting (RFEH) technique. The review highlights the main integrating frontend blocks such as the wearable and implantable antenna design, matching network, and rectifier topologies. The advantages and bottlenecks of adopting RFEH technology in wearable and IMDs are reviewed, along with the system elements and characteristics that enable these devices to operate in an optimized manner. The applications of RFEH in wearable and IMDs medical devices are elaborated in the final section of this review. This article summarizes the recent developments in RFEH, highlights the gaps, and explores future research opportunities. Biomedical devices (wearable and implantable) have expanded in popularity in integrating digital health for a diversity of biomedical applications, which includes the sensing, tracking, and monitoring of vital signals for continuous monitoring, thus making it possible for early intervention. In the healthcare sector, the advancement in these technologies is a result of several factors such as the increasing population of people with chronic diseases (such as diabetes and obesity), the aging population, and the rising need for real-time health monitoring, including fitness and wellness . Recent The widespread availability of RF energy makes it suitable to supply sustainable energy for various biomedical devices when compared with other energy harvesting technologies such as thermal, solar, and vibration.Unlike solar, RFEH is a cost-effective technology and can be used to power the IMDs.In comparison with solar, kinetic, and thermal energy, RFEH does not depend on the light source, body motion, or temperature.The exponential growth in the broadcasting infrastructures and wireless communication with the advancement in the broadcasting technologies have led to an increase in the availability of ambient RF power density. The open-space medium is populated by sources of electromagnet signal such as wireless fidelity (Wi-Fi) signals ,9,10,11,A typical RFEH system comprises a receiving antenna, a matching circuit, a rectifier, a power management unit (PMU), and the load, as illustrated in The recently published review papers on energy harvesting for biomedical devices ,27,28,29RFEH is enabled by ambient, dedicated, or unidentified RF signal sources. The amount of energy harvested using RFEH is dependent on the transmission power, the RF signal’s wavelength, and the distance between the RF power source and the harvesting node. The harvested power at the receiving antenna can be computed using the Friis equation as given in (1) for a transmitter and receiver antenna with a line of sight propagation .(1)Pr=PFree space loss represents the loss in the signal strength and is computed by determining the distance between the transmitter and receiver, the transmitting frequency, and the antenna gain . The equDC output voltage, and The performance parameters of an energy harvester (based on different applications requirements) are power conversion efficiency (PCE), operation distance, output power, and sensitivity. The PCE measures the rectifier efficiency to convert the RF signal to DC. Equations (4) and (5) are used to compute the PCE of a rectenna system :(4)η=OutDC output power describes the output of the RFEH system. The output is technically described as the minimum and maximum load voltage and current of the power management system. Due to the dependency of the load voltage and the current on the load impedance, measurement in an open load condition will accurately describe the performance of the RFEH system. In some applications, such as the sensors, the load voltage is a more critical parameter than the current, but in others, such as the LED, the predominant parameter is current. For the RFEH system, the sensitivity can be described as the optimum input power required to operate. The threshold voltage of complementary metal–oxide–semiconductor (CMOS) technology impacts the sensitivity tradeoffs and in an advanced technology node, the leakage current adversely affects the efficiency.The operational distance of a harvester is reliant on the operating frequency. The K is essential in calculating the PCE [K [M is the mutual inductance, and The free space RF energy is divided into two categories: near field and far field. The frequency range of energy transfer is from 3 kHz to 3000 MHz. RFEH can be achieved in a near field by magnetic resonance coupling  or induc the PCE . The dise PCE [K .(6)K=MLDue to varying rate of attenuation, the near-field inductively coupled power propagates for a shorter distance than the far field power. The attenuation rate of the far-field propagation is 20 dB/decade, which is lower in strength than the near-field energy . MultiplFar-field energy transfer is achieved when the distance of RF power transfer is more than coverage . Neverthcoverage . The maicoverage . As suchThe receiving antenna plays a pivotal role in the RFEH system. The antenna is responsible for capturing the received RF signal from the extended sources and significantly impacts the conversion efficiency. The optimization of an antenna to resonate can be at single or multiple frequency bands. A high gain receiving antenna design is required to meet the requirement of high-power input for standard applications such as recharging the mobile phone battery remotely, waking up sensors in a sleep mood, and energizing the wireless sensor network . The antThe integration of an antenna in wearable medical devices must be conformal and nonobstructive. The mechanical deformations of the wearable antennas affect the bandwidth and center frequency, especially when the antenna operates in a narrow band, where even a small change could detune the frequency of optimization. The wearable antenna must be isolated from the human body loading effect and absorptive losses by the ground plane. Different types of dielectric and conductive materials are used to construct wearable antennas. The selection of these materials is driven by their ability to offer a sufficient number of mechanical deformations  with little influence from various environmental factors , as well as adequate protection from EM radiation. Recently, several types of fabric and nonfabric materials have been used to fabricate wearable antennas; however, these fabric materials need proper characterization before being used for such purpose . Given tFurthermore, wearable antennas with potential use in RFEH must be stretchable to accommodate natural skin deformations or large human motions . The detThe safety of the wearable antenna must be taken into account during the design phase, in addition to ensuring that a wearable antenna can blend in with the body. The specific absorption rate (SAR) is used to evaluate the electromagnetic safety of wearable antennas. The International Commission on Non-Ionizing Radiation Protection and the Federal Communications Commission (FCC)/International Electrotechnical Commission (IEC) limits the maximum general exposure to SAR to 1.6 W/kg (SAR 1 g) or 2 W/kg (SAR 10 g), respectively. SAR is computed by averaging over a sample volume, which is typically 1 or 10 g (ICNIRP), respectively.Implantable antennas are an essential element in electromagnetic energy conversion, whether they are utilized for RFEH or data transmission in IMDs . Additiostudy by  describethors in  reportedor rates , but wheor rates , they reigned in  for far-orted by . The antw et al.  proposedSimilar to miniaturization, biocompatibility is another important aspect that should be considered during the design of implantable antennas. Biocompatibility can be defined as the capability of the material to work with proper host response in a particular application. Two methods have been proposed to ensure the biocompatibility of the implantable antennas. The first method is based on covering the implantable antenna with biocompatible materials (PDMS) to accomplish the desirable biocompatible specifications ,91. The Although different groups have determined SAR for implantable antennas using different phantoms, input powers, etc., the safe limits established by the IEEE, FCC, and ICNIRP remain unchanged. Implantable antennas have similar SAR values to wearable antennas; however, the depth of the implantable device inside the body is a further degree of freedom for SAR determination .A comparative study along with performative analysis of wearable and implantable antennas for RFEH are presented in The impedance matching network is employed to prevent power leakage, and it guarantees maximum power transmission between the source of energy and the load. To achieve maximum power transfer, the impedance of the output antenna should be identical to the load impedance. The incident wave at the load will be reflected when an impedance mismatch leads to reduced efficiency. In the RFEH system, the receiver antenna acts as an RF source, while the rectifier represents the load. The impedance matching network concurrently functions as a low-pass filter in suppressing higher-order harmonics generated by the rectifying circuit, which can be re-radiated by the antenna, causing additional loss . A perfeThe rectifier functions by converting the harvested RF signal by the receiving antenna to DC output voltage. The main design bottleneck in the rectifier’s design is to generate DC voltage from low power input. There are two types of rectifiers: diode-based rectifiers and metal–oxide–semiconductor field-effect transistor MOSFET rectifiers. When compared to MOSFET circuits, the diode-based rectifier has a lower forward voltage drop; hence, it is preferred in applications. The ability of low forward voltage diodes to achieve higher PCE makes it an optimal choice. For rectenna applications, Schottky barrier diodes are popularly adopted . The halApplications driven by high power need are enabled through a rectifier, known as the voltage multiplier, in boosting of AC to DC conversion. The voltage multiplier encompasses numerous single rectifiers connected in series . The CocThe widespread availability and the diversity of RF energy sources  and the exponential growth in Internet of Things (IoT) and wireless sensor nodes has encouraged recent reported research work to employ RFEH techniques to energize low-power wearable and implantable medical devices. Biomedical devices gained widespread application in healthcare sectors due to the favorable feature of continuous monitoring of vital signals from the human body or apparent spurs from the internal body organs. RF energy is a prominent integral block for wearable and IMDs in sustaining the lifetime of the battery in use for an extended period, relaxing the need to change or recharge the battery frequently. This is enabled through the development and integration of ultra-low-power electronics, which decreases the power consumption of the primary sensor components to less than a milliwatt . The curWearable medical devices contribute significantly to improving the current medical system and supporting physical activities due to their ability to offer extensive monitoring of different physiological functions, emergency alerting, and computer-aided rehabilitation. These devices can be used in the healthcare sector and military sector for evaluating the mental and physical health of soldiers in combat ,119. Thi–1.4 μW) ,123,124,μW) [μW) ,126,127.μW) [μW) ,129. RFEThe environmental friendliness and low-cost fabrication processes of additive manufacturing, such as 3D printing and inkjet printing, have increased its industrial relevance, as these emerging fabrication techniques are designed to significantly reduce the required number of manufacturing steps, such as the elimination of the etching processes, thereby improving the fabrication efficiency. Most of the time, wearable sensor devices are preferable in areas where disposable or single-time use is hygienically sorted, such as in hospital settings. This application type requires the creation of numerous circuit components within the device, using additive manufacturing to reduce the device cost. The improvements recorded recently in fabrication and performance have endeared inkjet printing technology to sensor and RF applications. In this regard, a design of enabling near-field RFEH for wearable sensors with an additional fabrication process was proposed in . The worTo achieve particular structures, traditional 3D printing technologies often demand a large amount of time and support material. Therefore, the work by  proposedWearable devices may benefit considerably from embedded tiny electronics and conductive materials to usher in the smart clothing paradigm . RectennNext, in , a desigTo increase the output power of a rectenna, numerous research efforts have been devoted to designing a textile rectenna array. The authors in  introduchi et al  presentehi et al  developehi et al  proposedDue to the frequency detuning caused by the mechanical deformations, such as stretching or bending, the performance of the wearable rectennas was significantly reduced. For the target frequency bands of ambient wireless energies, the antenna size within the rectenna is restricted to a specific range even though the miniaturized, thin-film rectifying circuit design and the matching network can offer a strong mechanical property. Furthermore, for low ambient RF energies (less than 1 mW), the conversion efficiency of the existing rectennas from any single frequency is dramatically lowered ,149,150,Numerous rectenna systems have been described with features such as flexibility, lightweight, compactness, and wideband operation. Palazzi et al.  presenteA flexible RFEH system’s low efficiency can be solved by introducing multipath in the environment while quasi-omnidirectional RF reception can be achieved using antenna diversity; this increases the chances of receiving energy in a realistic environment. The study by  proposed work by  presenteThe previous section explored various ways of designing RFEH systems for powering a range of low-power wearable medical devices. RFEH technology is an appropriate solution for powering wearable medical devices in order to maintain power autonomy and ensure maintenance-free batteries. There are some issues associated with the design of a complete wearable rectenna, such as the reliability of the connectivity between rigid and flexible system components, optimizing the power conversion performance to enhance transfer range, maintaining efficiency and pattern of radiation on the human body, and the performance fluctuation caused by the bending effect, particularly for the antenna. Therefore, advancements in the design of lightweight, compact, and flexible rectennas capable of harvesting the maximum amount of RF energy from different RF sources randomly distributed in space are still required. One of the possible solutions to achieve this is by designing a flexible and lightweight rectenna array arranged in different orthogonal orientations. On the other hand, future advancement in the design of stretchable rectennas can be achieved by integrating or using the metasurface ground ,155 or sThe efficiency of the system determined by the suitable range and operating frequency;The design and integration of an appropriate rectifier circuit by evaluating the required output power and input sensitivity;The integration of a compact matching network for maximum power transfer independent of input power, frequency, or load change;The received signal strength is dependent on the design of an antenna with high gain and wide bandwidth;The design of a flexible and lightweight rectenna, capable of harvesting RF power from numerous RF sources, distributed randomly in the space;The antenna used in the wearable rectenna design should withstand various mechanical deformations such as stretching, bending, rolling, and twisting.The performance of the wearable RFEH system is affected by several technological challenges, such as the decrease in available energy density as propagation distance increases; impedance mismatch that is caused as a result of differences in the input resistance and reactance between the antenna and rectifier circuit; decreasing the rectenna size while improving the PCE; the dependency of the PCE on the enhancement of the RFEH circuit sensitivity. To overcome these challenges and improve the performance of the wearable RFEH system, several considerations need to be accounted for, such as the following:The highlight of IMDs is in the continuous monitoring of human body biological signals to enhance healthcare quality. Due to the favorable inherent low power consumption of IMDs, the goal of recent research works is to extend the battery lifetime of the devices in use. Many of these devices are enabled through a battery that needs frequent replacement, and in the case of invasive devices, surgical intervention is necessary which affects the comfort of patients. Supplying the IMDs with stable and permanent renewable energy sources remains an ongoing challenge. Ambient RFEH is considered as an appropriate solution in addressing this bottleneck. RFEH for IMDs can be achieved in the near-field or far-field scavenging. Some works have also reported midfield energy harvesting ,165,166.In the conventional near-field inductive and resonant coupling ,168,169,orted by  proposedorted by  proposedorted by  used an Magnetic resonant coupling is another type of near-field WPT technology that is often used to power IMDs. This method offers high PTE and has been used to power ventricular assist devices; the method used implantable wire coils  for energy delivery ,171. HowOn the other hand, the study by  experimeThe midfield WPT method is an integration of near-field inductive and far-field radiative methods at a frequency range of sub-GHz–GHz. This method was presented to eliminate the limitation of the traditional WPT methods ,185,186.The efficiency of the WPT system is reliant on the RF-to-DC rectifier since efficient rectification and effective RF power contribute significantly to a successful midfield WPT. Hence, a rectifier can achieve wide operating bandwidth and high conversion efficiency to compensate for the frequency shifts due to differences in the electrical potentials of the body tissues . HoweverAs the far-field radiative charging is more resistant to changes in antenna location, orientation, and environment, it is more suitable in the use for higher implantable depth. It is adoptable for high-frequencies’ operation, given that a small supporting antenna can be used to reduce the implanted devices’ size. Hence, the proposed solution is implanted deep inside a patient’s body without much discomfort as the user’s mobility is not restricted while the device is charging (inductive charging is not affected by the position or location of the device). Additionally, RF power transfer is significantly safer than inductive charging because of the rigorous power density regulation for the power transmitter.Reported research works have investigated RFEH in far field; for instance, the feasibility of implementing far-field RF powering enabled by the RFEH technique was investigated by  using ang et al.  proposedAnalyses of the SA, SAR, and increase in temperature were reported by ; the stuented by . A chip In the former section of this paper, the IMDs whose power needs meet with RFEH technology were reviewed. Wireless Power Transfer Methods: The near-field inductive coupling WPT method transfers a large amount of power to IMDs with a depth of approximately 10–30 mm. However, the performance of the link in this method deteriorates significantly due to high-frequency losses of the human tissues [ tissues . Further tissues . On the Midfield regime power transfer methods have a wider transmission range, but their efficiency is less than that of inductive coupling methods . HoweverIn the far-field WPT method, the misalignment sensitivity is lower, and the power transfer distance between the power source Tx and the Rx implant is extended to a few meters, which is more convenient for patients. Furthermore, this method can be combined with other RFEH technologies to power the IMDs . HoweverIn conclusion, the midfield power transfer method, which permits focusing on the energy hotspot and overcome the shortcomings of the near-field and far-field methods seems to be the best benchmarked option. Far-field methods are mostly considered when the mobility of patients is the priority at the expense of the efficiency and power level of the link.Safety Regulations: Specific conditions must be fulfilled to design a safe and efficient RFEH system for IMDs. The safety regulations of ICNIRP and IEEE involve general exposure only; the potential field produced by IMDs is described as an untested case [ted case . Therefoted case . Both elted case . When itted case . Moreoveted case , the conted case  should bted case ,214.Packaging Materials: The packaging material for IMDs is an important factor to assure biosafety. The recommended metals for IMDs could have negative consequences once implanted. To avoid these undesirable consequences, the entire system must be encapsulated in biocompatible material [material ,216. Thematerial . Howevermaterial .A review on enabling wearable and implantable medical devices with RF energy is presented in this work. The principles in the construction of the RF energy system are discussed, along with the antenna design and characterization, matching network, and rectifier topologies. Multiple wearable medical devices enabled through RF energy were also analyzed. Notably, RF energy is a reliable and desirable energy source for wearable medical devices; however, implementing the RF harvester in wearable medical devices faces several challenges. For example, the reliability of connectivity between rigid and flexible system components, optimizing the power conversion performance to enhance transfer range, maintaining efficiency and pattern of radiation on the human body, and the performance fluctuation caused by mechanical deformations, particularly for the antenna. Therefore, advancements in the design of lightweight, compact, and flexible rectennas capable of harvesting the maximum amount of RF energy from different RF sources randomly distributed in space are still required. The RF power transfer of implantable medical devices in the near field, midfield, and far field was also presented, with an analysis of the recently published research works that addressed the targeted fields. Midfield power transfer methods for implantable medical devices, which permit focusing on the energy hotspot, seem to be the best choice since the near-field method suffers from a limited performance range while the far-field method suffers from deterioration of the link efficiency. In fact, most of the RFEH devices are not ready for clinical study since they require time, resources, and compliance with strict safety standards for completion. Future advancements in applying the RF harvester in wearable and implantable medical devices are achievable through extensive cooperation between academia and industry to produce an enhanced system that can provide reliable and constant power supplies.


To improve the user’s positioning accuracy of a Wi-Fi fingerprint-based positioning algorithm, this study proposes a graph optimization model based on the framework of  With increasing demand for location-based services (LBSs), indoor positioning has become a research topic of much interest . In receWi-Fi technology is widely used in indoor target intrusion detection and localization ,9. The WMulti-sensor fusion positioning is a common method to improve positioning accuracy. Fusing BLE and Wi-Fi for positioning inherits their advantages in low costs and the widespread deployment of basic equipment. Bluetooth Low Energy (BLE) products can run for months on a battery with low-power communication. Its transmission distance of up to 50–100 m meets the requirements of indoor positioning for wireless signals ,12. BLE Multiple users can use smart devices to obtain initial positioning coordinates by the Wi-Fi fingerprint method. Because BLE signals from other devices are available everywhere, the physical distance between users can be measured by BLE ranging technology and can act as additional constraints to adjust user coordinates.We propose a graph optimization model combining a Wi-Fi fingerprint and BLE ranging technology to improve indoor positioning accuracy. The general process is as follows. The problem can be considered a nonlinear least-squares optimization problem that a graph can represent, where users are regarded as nodes, and our designed error functions between users are edges. We obtain the initial coordinates of nodes based on the Wi-Fi fingerprint method and aggregate all edges to a total error function. The information matrix, edge selection principle, and Huber kernel function are used to control the measurement error of wireless sensors. The Levenberg–Marquardt (LM) algorithm can solve the total error function to obtain the optimized coordinates of the nodes, and the affine transformation estimation can solve the drifting problem of optimized nodes.The remainder of this paper is organized as follows. Many well-known indoor positioning systems have been developed based on the Wi-Fi fingerprint method, such as RADAR , Nibble The Stg algorithm  selects Neural network and deep-learning technologies are also applied in Wi-Fi fingerprint positioning ,22, but kth fingerprint at In the offline phase, a certain number of APs are deployed, several reference points (RPs) are set up evenly in the indoor environment, and the RSSIs from APs at the RPs are collected to establish a fingerprint database. A fingerprint consists of the physical coordinates and Wi-Fi signal fingerprints of the RPs. Suppose the number of APs involved in positioning is If we collect fingerprints In the online phase, we estimate the positions of users by collecting the RSSIs of different APs in real-time and applying the fingerprint-matching algorithm.Common propagation path-loss models include the free space propagation and logarithmic distance path-loss models . The latMany robotics and computer vision studies involve the minimization of nonlinear error functions, a typical example being simultaneous localization and mapping (SLAM) ,27,28. Tproblems , which cGraph optimization based on Wi-Fi fingerprint positioning can provide position results at the current moment that are not dependent on the previous one, so there is no cumulative error as the user moves. However, coordinates obtained in this way are not accurate enough due to noise. To optimize the positioning results of a Wi-Fi fingerprint, physical distances between users measured by BLE ranging technology are added to establish redundant constraints. Then, graph optimization based on We define the users as nodes of the graph and obtain their initial positioning coordinates by the construction of an offline Wi-Fi fingerprint database and online fingerprint matching. Users receive BLE RSSIs from other users’ devices and input them to the BLE ranging model to obtain physical distances to other users. We define the error function and regard it as the edges between pairs of nodes. The total error function is constructed by all edges and grows in size with the number of users. The optimal solution of the total error function is obtained by the solver, i.e., the LM algorithm and the drifting solution introduced in There will inevitably be several edges with large relative errors in the graph that affect optimization. The robust kernel function, the method of reconstructing the information matrix introduced in When we experimented with the proposed model, we used a laptop  running Windows 10 as the central server for user positioning calculation. The users need to provide the RSSIs received from APs to the central server, and the initial coordinates of the users can be obtained based on the Wi-Fi fingerprint positioning. The RSSIs received from other users’ devices with BLE function also need to be input into server to run the graph optimization model, and users can obtain the optimized position from the server.Constructing a reasonable total error function is the key to solving the graph optimization problem. As the number of user nodes in the graph increases, the number of variables Suppose there are Since the distance between users measured by the BLE ranging model adds constraints on nodes, in our proposed model, The total error function of the entire graph is composed of all of the error functions  between pairs of nodes. It can be defined asAfter defining the nodes and edges, we can define the size of the information matrix as Previous experiments with the BLE ranging model have shown that the RSSI of BLE can be approximated to a Gaussian distribution for simplicity . As the The experiments of For simplicity of notation, in the rest of this paper, we will express the error function in a new form:The error function can be approximated by its first-order Taylor expansion around the current initial guess function  is introWhen the error is small, the error function is approximated as a quadratic function with faster growth. When the error is large, it is approximated as a linear function with slower growth, which still ensures that the function is continuous and differentiable. The Huber kernel function isTo preserve the characteristics of the original total error function and the calculated parameters, we can simplify the processing of the Huber kernel function by adjusting the information matrix,Substituting the updated information matrix in the original total error function, we obtain a new total error function with the original form:Substituting Equation (13) in Equation (19), For simplicity of notation, we set The solution is obtained by adding the increments Algorithm 1: Pseudo code of LM algorithmInput: A total error function HF(p) and an initial vector wp   Output: A vector p* minimizing HF(p)    The popular LM algorithm iterates the linearization in Equation (20), the solution in Equation (21), and the update step in Equation (22). In every iteration, the previous solution is used as the linearization point and the initial guess until a given termination criterion is met. The derivation of the LM algorithm is not the work of this paper and is not repeated here. The implementation process of the LM algorithm is shown in Algorithm 1.The optimized graph has a better structure, but the nodes may drift together because of the missing absolute coordinate constraints. Since the affine transformation estimation can relatively better preserve the optimized structure, we use it to solve the drifting problem. We define the nodes calculated by the LM algorithm as During actual signal collection, the received RSSI from the BLE beacon fluctuates in the indoor environment. BLE ranging can affect the accuracy of the edges in the graph; thus, BLE ranging errors should be controlled within a certain range to build the network structure of nodes in the graph. In our experiments, the transmit power of the BLE beacon was +4 dBm, and its broadcast period was set to 80 ms. Since the broadcast period of the BLE beacon was set to 80 ms, the average number of RSSIs that could be collected every second was 12.5. To balance the positioning time and a reasonable filter window size, we adopted mean filtering and set the filter window to 10, that is, 10 raw BLE RSSIs were processed each time before input to the BLE ranging model. Obviously, this strategy can effectively reduce the signal fluctuation, as shown in This paper uses the simplified BLE ranging model (Equation (4)) and estimates the values of Then, we can calculate the distance between user nodes based on this mode,Through the linear fitting to the SD of the RSSI at different distances, we can obtain the signal propagation model applicable to our experiments,Our experiments used the underground parking lot of North China Electric Power University as a testbed, where the Wi-Fi fingerprint database was constructed. The testbed was 58 m long and 42 m wide, and Wi-Fi APs were installed on the wall 2 m high relative to the parking lot surface. Our device was set 1 m high relative to the parking lot surface, collecting Wi-Fi data at each RP. We collected 10 sets of Wi-Fi data of 10 APs continuously at each RP to construct the Wi-Fi fingerprint database, and we used the Wi-Fi fingerprint-matching algorithm to obtain the initial location of each TP. To verify the validity of the proposed model and control the cost, we conducted experiments with simulated BLE ranging data and randomly selected points from 84 TPs to perform the graph optimization.Edges with larger errors in the graph are often formed by two user nodes that are far apart. Giving these edges a reasonable information matrix can weaken the influence of errors. However, when we calculate the information matrix As the distance increases, the error and SD of ranging generally show a linear upward trend. The greater the distance, the more frequent the fluctuations of the error and SD. Experiments show that most BLE ranging errors within 15 m can be stably concentrated within 5 m, and more are concentrated within 2 m. The Huber kernel function can reduce the effect of large error edges in the graph. If the value of The experimental design of this part was as follows. We obtained the initial coordinates of 84 TPs by KNN matching algorithm and set the Huber kernel function threshold In the experiment, we obtained the initial coordinates of 84 TPs based on the KNN algorithm and set the Huber kernel function threshold When the number of nodes reaches 19, the reduction of the mean error and 75th percentile error is less than 2 cm for every two additional nodes. When the number of nodes is 7, the average distance between nodes is often greater than the threshold of 15 m, and too few edges can be added to the graph. Therefore, insufficient constraints and large errors will cause the failure of the total error function to converge and an improvement of positioning accuracy that is not obvious. When the number of nodes reaches 9, the total error function can converge in most cases, further improving the positioning accuracy. The experimental results show that under the KNN algorithm, we can obtain relatively stable positioning results when the number of nodes reaches 19 and It can be observed that the spacing between the curves decreases as the number of nodes increases, and the curves with 19, 21, and 23 nodes are tightly packed together. The gap between the curves is especially obvious at 2–5 m, indicating that our graph optimization model can significantly improve the positioning accuracy at 2–5 m as the number of nodes increases.The 84 TPs were labeled in U-shaped order with markers ranging from 1 to 84, which were sequentially divided into three groups. Each group contained 28 TPs, representing one of the three passages in the experimental area. We defined a good distribution as a random selection of nodes from only one of the three groups for graph optimization, and a bad distribution as a random and even selection of nodes from each of the three groups for graph optimization. In the experiment, we set the Huber kernel function threshold The experimental results show that the error tends to level off with the node increase under both good and bad distributions, and the errors of a good distribution tend to level off faster than those of a bad distribution. This shows that our graph optimization model is influenced by the node distribution. We find that the better the node distribution, the higher the positioning accuracy for the same number of nodes. The positioning accuracy of a good distribution is more likely to achieve relatively stable optimization results.The proposed graph optimization model can be used to optimize Wi-Fi fingerprint-matching algorithms other than the KNN algorithm. In the experiments, we constructed the user graph with 19 random nodes and set the Huber kernel function threshold The cumulative error probability of different algorithms under a fixed accuracy limit is shown in From the experimental results, it can be seen that the more accurate the initial positioning results output by Wi-Fi positioning, the more accurate the optimized positioning results, which indicates that the positioning results of our graph optimization model are sensitive to the initial positioning.We integrated Wi-Fi fingerprint positioning and BLE ranging technology to locate users in indoor environments based on a graph optimization model. After deriving the relationship between the physical distance and the RSSI of BLE, a BLE ranging simulation was carried out based on the actual RSSI standard deviation. We obtained initial positioning results through Wi-Fi fingerprint positioning, built a graph of users, added edges between user nodes based on BLE ranging data, and summarized them in a large-scale total error function, which we solved to obtain a better structure of nodes. A drifting solution based on the affine transformation was applied to obtain the final optimized position of the users. The experiments showed that the proposed graph optimization model has a relatively good positioning result when the kernel function threshold Although the proposed model can improve the positioning accuracy of traditional Wi-Fi fingerprint positioning, there is room for improvement. The experiments showed that the positioning accuracy rankings of other Wi-Fi algorithms before and after optimization are basically the same, and they verified the effect of the initial positioning results on the optimization results. In addition, we hypothesize that the accuracy of ranging between the user nodes in the graph can affect the final positioning results. Therefore, our next step will be to go beyond the use of BLE beacons for ranging. For example, wireless sensors with higher accuracy can be used to construct more reliable edges of the user graph.