In this paper we present two different, software and reconfigurable hardware, open architecture approaches to the PUMA 560 robot controller implementation, fully document them and provide the full design specification, software code and hardware description. Such solutions are necessary in today’s robotics and industry: deprecated old control units render robotic installations useless and allow no upgrades, advancements, or innovation in an inherently innovative ecosystem. For the sake of simplicity, just the first robot axis is considered. The first approach described is a PC solution with data acquisition I/O board (Humusoft MF634). This board is supported with Matlab Real-Time Windows Toolbox for real-time applications and thus whole controller was designed in Matlab environment. The second approach is a robot controller developed on field programmable gate arrays (FPGA) board. The complexity of FPGA design can be overcome by using a third party software package, such as self-developed Matlab FPGA Real Time Toolbox. In both cases, parameters of motion controller are calculated by using simulation of the PUMA 560 robot first axis motion. Simulations were conducted in Matlab/Simulink using Robotics Toolbox.

Quantum computing has the power to break current cryptographic systems, disrupting online banking, shopping, data storage and communications. Quantum computing also has the power to support stronger more resistant technologies. In this paper, we describe a digital cash scheme created by Dmitry Gavinsky, which utilises the capability of quantum computing. We contribute by setting out the methods for implementing this scheme. For both the creation and verification of quantum coins we convert the algebraic steps into computing steps. As part of this, we describe the methods used to convert information stored on classical bits to information stored on quantum bits.

This paper presents the first comprehensive tutorial on a promising research field located at the frontier of two well-established domains: Neurosciences and wireless communications, motivated by the ongoing efforts to define how the sixth generation of mobile networks (6G) will be. In particular, this tutorial first provides a novel integrative approach that bridges the gap between these two, seemingly disparate fields. Then, we present the state-of-the-art and key challenges of these two topics. In particular, we propose a novel systematization that divides the contributions into two groups, one focused on what neurosciences will offer to 6G in terms of new applications and systems architecture (Neurosciences for Wireless), and the other focused on how wireless communication theory and 6G systems can provide new ways to study the brain (Wireless for Neurosciences). For the first group, we concretely explain how current scientific understanding of the brain would enable new application for 6G within the context of a new type of service that we dub braintype communications and that has more stringent requirements than human- and machine-type communication. In this regard, we expose the key requirements of brain-type communication services and we discuss how future wireless networks can be equipped to deal with such services. Meanwhile, for the second group, we thoroughly explore modern communication system paradigms, including Internet of Bio-nano Things and chaosbased communications, in addition to highlighting how complex systems tools can help bridging 6G and neuroscience applications. Brain-controlled vehicles are then presented as our case study. All in all, this tutorial is expected to provide a largely missing articulation between these two emerging fields while delineating concrete ways to move forward in such an interdisciplinary endeavor.

Classification of biological neuron types and networks poses challenges to the full understanding of the brain's organisation and functioning. In this paper, we develop a novel objective classification model of biological neuronal types and networks based on the communication metrics of neurons. This presents advantages against the existing approaches since the mutual information or the delay between neurons obtained from spike trains are more abundant data compare to conventional morphological data. We firstly designed two open-access supporting computational platforms of various neuronal circuits from the Blue Brain Project realistic models, named Neurpy and Neurgen. Then we investigate how the concept of network tomography could be achieved with cortical neuronal circuits for morphological, topological and electrical classification of neurons. We extract the simulated data to many different classifiers (including SVM, Decision Trees, Random Forest, and Artificial Neuron Networks) classifying the specific cell type (and sub-group types) achieving accuracies of up to 70\%. Inference of biological network structures using network tomography reached up to 65\% of accuracy. We also analysed recall, precision and F1score of the classification of five layers, 25 cell m-types, and 14 cell e-types. Our research not only contributes to existing classification efforts but sets the road-map for future usage of cellular-scaled brain-machine interfaces for in-vivo objective classification of neurons as a sensing mechanism of the brain's structure.

Reversible computation has been recognized as a potential solution to the technological bottleneck in the future of computing machinery. Rolf Landauer determined the lower limit for power dissipation in computation and noted that dissipation happens when information is lost, that is, when a bit is erased. This meant that with reversible computation, conserving information conserves energy as well, and as such can operate on arbitrarily small power. There were only a few applications and use cases of reversible computing hardware. Here, we present a novel reversible computation architecture for time reversal of waves, with an application to sound wave communications. This energy-efficient design is also a natural one, and it allows the use of the same hardware for transmission and reception at the time reversal mirror.

This letter presents a topology inference technique for neuronal networks of the cortex of the human brain based on network tomography theory. We envision that this technique will be used for high-resolution and high-precision brain tissue tomography and imaging using principles of the Internet of Bio-Nano Things. Our network tomography solution relies on the classification of processed data of spike delay and synaptic weight functions of neuronal network activity. For a 6-layer cortical neural network, we achieved 99.27% of accuracy using the Decision Tree machine learning technique for individual neurons, 2-leaf and 4-leaf star topologies of neuronal networks.

Distributed antenna selection for distributed massive multiple input multiple output (MIMO) communication systems reduces computational complexity compared to centralized approaches, and provides high fault tolerance while retaining diversity and spatial multiplexity. We propose a novel distributed algorithm for antenna selection and show its advantage over existing centralized and distributed solutions. The proposed algorithm is shown to perform well with imperfect channel state information, and to execute a small number of simple computational operations per node, converging fast to a steady state. We base it on reversing Petri nets, a variant of Petri nets inspired by reversible computation, capable of both forward and backward execution while obeying conservation laws.

Dynamical systems are no strangers in wireless communications. Our story will necessarily involve chaos, but not in the terms in which secure chaotic communications have introduced it: we will look for the chaos, complexity, and dynamics that already exist in everyday wireless communications.

Dynamical systems are by no means strangers in wireless communications, but we sometimes tend to forget how rich their behavior can be and how useful general methods for dynamical systems can be. Our story will necessarily involve chaos, but not in the terms secure chaotic communications have introduced it: we will look for the chaos, complexity and dynamics that already exist in everyday wireless communications. We present a short overview of dynamical systems and chaos before focusing on the applications of dynamical systems theory to wireless communications in the past 30 years, ranging from the modeling on the physical layer to different kinds of self-similar traffic encountered all the way up to the network layer. The examples of past research and its implications are grouped and mapped onto the media layers of ISO OSI model to show just how ubiquitous dynamical systems theory can be and to trace the paths that may be taken now. When considering the future paths, we argue that the time has come for us to revive the interest of the research community to dynamical systems in wireless communications. We might have avoided those paths earlier because of the big question: can we afford observing systems of our interest as dynamical systems and what are the trade-offs? The answers to these questions are dynamical systems of its own: they change not only with the modeling context, but also with time. In the current moment, we argue, the available resources allow such approach and the current demands ask for it. The results we obtained standing on the shoulders of dynamical systems theory suggest the necessity for its inclusion in the wireless toolbox for the highly dynamical world of 5G and beyond.

Science fiction is immensely popular, particularly over the last two decades where over half of the top domestic grossing movies of the 2010s were science fiction. Many scientists also share this enthusiasm for Sci-fi, including the authors, so why not apply various research techniques to different Sci-fi worlds? We want to create a Sci-fi computational community, where we can explore different worlds and understand how different phenomena emerge in these worlds. Sci-Fi Agent-based Modeling Anthology is an open-source and fully volunteer-based project; thus, we also hope to find new collaborators who can take on different stories with their unique approach. The participants explore their favorite science fiction stories with new mediums like agent-based modeling or even differential equations. We believe that this project will get the attention of a new audience and bring them into the science fiction genre. Here, we explore our favorite science fiction stories (i.e., Fahrenheit 451 and 1984) with new mediums and hope to bring them to a new audience. We hope that the application of computational complexity science to these Sci-fi dystopian worlds will help us to learn more about our own world, our own histories, or even future potential trajectories. For example, how easily could our society turn into a 1984 world or vice versa? These models give you a sense of control, where you can change the variables. Therefore, you are no longer passively watching science fiction, but you are actively engaging in it, you are changing it, seeking to understand something meaningful.