Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Geometric Modeling of Cellular Materials for Additive Manufacturing in Biomedical Field: A Review

Advances in additive manufacturing technologies facilitate the fabrication of cellular materials that have tailored functional characteristics. The application of solid freeform fabrication techniques is especially exploited in designing scaffolds for tissue engineering. In this review, firstly, a classification of cellular materials from a geometric point of view is proposed; then, the main approaches on geometric modeling of cellular materials are discussed. Finally, an investigation on porous scaffolds fabricated by additive manufacturing technologies is pointed out. Perspectives in geometric modeling of scaffolds for tissue engineering are also proposed

Perspective study: governance for C2C

This perspective study will serve as frame of reference for follow-up activities and exchanges both within and outside the Cradle to Cradle Network (C2CN) and it aims to reflect the current challenges and opportunities associated with implementing a Cradle to Cradle approach. In total, four perspective studies have been written, in the areas on industry, area spatial development, governance and on the build theme

Living IoT: A Flying Wireless Platform on Live Insects

Sensor networks with devices capable of moving could enable applications ranging from precision irrigation to environmental sensing. Using mechanical drones to move sensors, however, severely limits operation time since flight time is limited by the energy density of current battery technology. We explore an alternative, biology-based solution: integrate sensing, computing and communication functionalities onto live flying insects to create a mobile IoT platform. Such an approach takes advantage of these tiny, highly efficient biological insects which are ubiquitous in many outdoor ecosystems, to essentially provide mobility for free. Doing so however requires addressing key technical challenges of power, size, weight and self-localization in order for the insects to perform location-dependent sensing operations as they carry our IoT payload through the environment. We develop and deploy our platform on bumblebees which includes backscatter communication, low-power self-localization hardware, sensors, and a power source. We show that our platform is capable of sensing, backscattering data at 1 kbps when the insects are back at the hive, and localizing itself up to distances of 80 m from the access points, all within a total weight budget of 102 mg.Comment: Co-primary authors: Vikram Iyer, Rajalakshmi Nandakumar, Anran Wang, In Proceedings of Mobicom. ACM, New York, NY, USA, 15 pages, 201

Scaling Reversible Adhesion in Synthetic and Biological Systems

Geckos and other insects have fascinated scientists and casual observers with their ability to effortlessly climb up walls and across ceilings. This capability has inspired high capacity, easy release synthetic adhesives, which have focused on mimicking the fibrillar features found on the foot pads of these climbing organisms. However, without a fundamental framework that connects biological and synthetic adhesives from nanoscopic to macroscopic features, synthetic mimics have failed to perform favorably at large contact areas. In this thesis, we present a scaling approach which leads to an understanding of reversible adhesion in both synthetic and biological systems over multiple length scales. We identify, under various loading scenarios, how geometry and material properties control adhesion, and we apply this understanding to the development of high capacity, easy release synthetic adhesive materials at macroscopic size scales. Starting from basic fracture mechanics, our generalized scaling theory reveals that the ratio of contact area to compliance in the loading direction, A/C, is the governing scaling parameter for the force capacity of reversible adhesive interfaces. This scaling theory is verified experimentally in both synthetic and biological adhesive systems, over many orders of magnitude in size and adhesive force capacity (Chapter 2). This understanding is applied to the development of gecko-like adhesive pads, consisting of stiff, draping fabrics incorporated with thin elastomeric layers, which at macroscopic sizes (contact areas of 100 cm2) exhibit force capacities on the order of 3000 N. Significantly, this adhesive pad is non-patterned and completely smooth, demonstrating that fibrillar features are not necessary to achieve high capacity, easy release adhesion at macroscopic sizes and emphasizing the importance of subsurface anatomy in biological adhesive systems (Chapter 2, Chapter 3). We further extend the utility of the scaling theory under shear (Chapter 4) and normal (Chapter 5) loading conditions and develop simple expressions for patterned and non-patterned interfaces which describe experimental force capacity data as a function of geometric parameters such as contact area, aspect ratio, and contact radius. These studies provide guidance for the precise control of adhesion with enables the development of a simple transfer printing technique controlled by geometric confinement (Chapter 6). Force capacity data from each chapter, along with various literature data are collapsed onto a master plot described by the A/C scaling parameter, with agreement over 15 orders of magnitude in adhesive force capacity for synthetic and biological adhesives, demonstrating the generality and robustness of the scaling theory (Chapter 7)

Searching Page-Images of Early Music Scanned with OMR: A Scalable Solution Using Minimal Absent Words

We define three retrieval tasks requiring efficient search of the musical content of a collection of ~32k page images of 16th-century music to find: duplicates; pages with the same musical content; pages of related music. The images are subjected to Optical Music Recognition (OMR), introducing inevitable errors. We encode pages as strings of diatonic pitch intervals, ignoring rests, to reduce the effect of such errors. We extract indices comprising lists of two kinds of ‘word’. Approximate matching is done by counting the number of common words between a query page and those in the collection. The two word-types are (a) normal ngrams and (b) minimal absent words (MAWs). The latter have three important properties for our purpose: they can be built and searched in linear time, the number of MAWs generated tends to be smaller, and they preserve the structure and order of the text, obviating the need for expensive sorting operations. We show that retrieval performance of MAWs is comparable with ngrams, but with a marked speed improvement. We also show the effect of word length on retrieval. Our results suggest that an index of MAWs of mixed length provides a good method for these tasks which is scalable to larger collections

Biomedical Applications of Protein Films and Polymeric Nanomaterials

Biomaterials are widely applied for the diagnosis and treatment of numerous diseases. In addition to fulfilling specific biological functions, biomaterials must also be non-toxic, biocompatible, and sterilizable to be regarded as safe-for-use. Polymers are excellent candidates for fabricating functional biomaterials due to their wide availability and varied properties and may be natural or synthetic. Polymer precursors are fabricated into coatings, foams, scaffolds, gels, composites, and nanomaterials for several biomedical applications. This dissertation focuses on two types of polymeric biomaterials – protein-based materials and synthetic polymeric nanoparticles. Proteins are biopolymers that naturally occur with a variety of structural and functional properties. However, the fabrication of protein-based materials is challenging due to their aqueous and mechanical instability. In this work we highlighted the development of an additive-free, thermal treatment approach that relies on heat-curing protein films in fluorous media (fluorous-curing). In doing so, we are able to minimize protein denaturation and retain surface properties. Charged protein films were utilized to prepare antimicrobial coatings and size-sorting devices. We also demonstrated the utility of fluorous-curing to enhance mechanical and enzymatic stability of collagen films with minimal denaturation. In the latter part of this work, we utilized ultrasound treatment to enhance the activity of biomaterials. Ultrasound is gaining interest as a tool used in combination with biomaterials for applications such as enhanced penetration of therapeutics into tissue, regulating drug release through ultrasound-responsive scaffolds, and sonodynamic therapy. However, these developments are limited and delayed due to the lack of effective in vitro models that prevent uncontrolled cell lysis during ultrasound. We developed 2D and 3D cell cultures for ultrasound treatment using collagen-based materials. We hypothesized that collagen would act as a support for the cells and absorb the energy exerted by ultrasound, thereby protecting the cells. We then utilized ultrasound in combination with antimicrobial polymeric nanomaterials for the synergistic eradication of bacterial biofilms. Antimicrobial polymer nanoparticles are an alternative to traditional antibiotics that prevent development of drug resistance. However, longer incubation durations and higher concentrations are required to allow for penetration into the bacterial biofilms which results in toxicity to mammalian cells. Ultrasound enhances the penetration of these nanoparticles into the biofilm EPS thereby reducing the incubation time and enhancing antimicrobial activity, with minimal toxicity to mammalian cells. Overall, this dissertation discusses significant developments in polymeric materials for varied potential applications as diagnostic sensors, antimicrobial materials, wound-healing, tissue engineering, and drug delivery applications