Field programmable gate array based sigmoid function implementation using differential lookup table and second order nonlinear function

Artificial neural network (ANN) is an established artificial intelligence technique that is widely used for solving numerous problems such as classification and clustering in various fields. However, the major problem with ANN is a factor of time. ANN takes a longer time to execute a huge number of neurons. In order to overcome this, ANN is implemented into hardware namely field-programmable-gate-array (FPGA). However, implementing the ANN into a field-programmable gate array (FPGA) has led to a new problem related to the sigmoid function implementation. Often used as the activation function for ANN, a sigmoid function cannot be directly implemented in FPGA. Owing to its accuracy, the lookup table (LUT) has always been used to implement the sigmoid function in FPGA. In this case, obtaining the high accuracy of LUT is expensive particularly in terms of its memory requirements in FPGA. Second-order nonlinear function (SONF) is an appealing replacement for LUT due to its small memory requirement. Although there is a trade-off between accuracy and memory size. Taking the advantage of the aforementioned approaches, this thesis proposed a combination of SONF and a modified LUT namely differential lookup table (dLUT). The deviation values between SONF and sigmoid function are used to create the dLUT. SONF is used as the first step to approximate the sigmoid function. Then it is followed by adding or deducting with the value that has been stored in the dLUT as a second step as demonstrated via simulation. This combination has successfully reduced the deviation value. The reduction value is significant as compared to previous implementations such as SONF, and LUT itself. Further simulation has been carried out to evaluate the accuracy of the ANN in detecting the object in an indoor environment by using the proposed method as a sigmoid function. The result has proven that the proposed method has produced the output almost as accurately as software implementation in detecting the target in indoor positioning problems. Therefore, the proposed method can be applied in any field that demands higher processing and high accuracy in sigmoid function outpu

Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models.

The realistic dynamics mathematical model of a system is very important for analyzing a system. The mathematical system model can be derived by applying physical, thermodynamic, and chemistry laws. But this method has some drawbacks, among which is difficult for complex systems, sometimes is untraceable for nonlinear behavior that almost all systems have in the real world, and requires much knowledge. Another method is system identification which is also called experimental modeling. System identification can be made offline, but this method has a disadvantage because the features of a dynamic system may change over time. The parameters may vary as environmental conditions change. It requires big data and consumes a long time. This research introduces a developed method for online system identification based on the Hammerstein and Wiener nonlinear block-oriented structure with the artificial neural networks (NN) advantages and recursive weighted least squares algorithm for optimizing neural network learning in real-time. The proposed method aimed to obtain a maximally informative mathematical model that can describe the actual dynamic behaviors of a system, using the DC motor as a case study. The goodness of fit validation based on the normalized root-mean-square error (NRMSE) and normalized mean square error, and Theil’s inequality coefficient are used to evaluate the performance of models. Based on experimental results, for best Wiener parallel NN model and series-parallel NN model are 93.7% and 89.48%, respectively. Best Hammerstein parallel NN polynomial based model and series-parallel NN polynomial model are 88.75% and 93.9% respectively, for best Hammerstein parallel NN sigmoid based model and series-parallel NN sigmoid based model 78.26% and 95.95% respectively, and for best Hammerstein parallel NN hyperbolic tangent based model and series-parallel NN hyperbolic tangent based model 70.7% and 96.4% respectively. The best model of the developed method outperformed the conventional NARX and NARMAX methods best model by 3.26% in terms of NRMSE goodness of fit

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction

Energy and Area Efficient Machine Learning Architectures using Spin-Based Neurons

Recently, spintronic devices with low energy barrier nanomagnets such as spin orbit torque-Magnetic Tunnel Junctions (SOT-MTJs) and embedded magnetoresistive random access memory (MRAM) devices are being leveraged as a natural building block to provide probabilistic sigmoidal activation functions for RBMs. In this dissertation research, we use the Probabilistic Inference Network Simulator (PIN-Sim) to realize a circuit-level implementation of deep belief networks (DBNs) using memristive crossbars as weighted connections and embedded MRAM-based neurons as activation functions. Herein, a probabilistic interpolation recoder (PIR) circuit is developed for DBNs with probabilistic spin logic (p-bit)-based neurons to interpolate the probabilistic output of the neurons in the last hidden layer which are representing different output classes. Moreover, the impact of reducing the Magnetic Tunnel Junction\u27s (MTJ\u27s) energy barrier is assessed and optimized for the resulting stochasticity present in the learning system. In p-bit based DBNs, different defects such as variation of the nanomagnet thickness can undermine functionality by decreasing the fluctuation speed of the p-bit realized using a nanomagnet. A method is developed and refined to control the fluctuation frequency of the output of a p-bit device by employing a feedback mechanism. The feedback can alleviate this process variation sensitivity of p-bit based DBNs. This compact and low complexity method which is presented by introducing the self-compensating circuit can alleviate the influences of process variation in fabrication and practical implementation. Furthermore, this research presents an innovative image recognition technique for MNIST dataset on the basis of p-bit-based DBNs and TSK rule-based fuzzy systems. The proposed DBN-fuzzy system is introduced to benefit from low energy and area consumption of p-bit-based DBNs and high accuracy of TSK rule-based fuzzy systems. This system initially recognizes the top results through the p-bit-based DBN and then, the fuzzy system is employed to attain the top-1 recognition results from the obtained top outputs. Simulation results exhibit that a DBN-Fuzzy neural network not only has lower energy and area consumption than bigger DBN topologies while also achieving higher accuracy

Measurements, Models, Systems and Design

531 s.

Remote-controlled ambidextrous robot hand actuated by pneumatic muscles: from feasibility study to design and control algorithms

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThis thesis relates to the development of the Ambidextrous Robot Hand engineered in Brunel University. Assigned to a robotic hand, the ambidextrous feature means that two different behaviours are accessible from a single robot hand, because of its fingers architecture which permits them to bend in both ways. On one hand, the robotic device can therefore behave as a right hand whereas, on another hand, it can behave as a left hand. The main contribution of this project is its ambidextrous feature, totally unique in robotics area. Moreover, the Ambidextrous Robot Hand is actuated by pneumatic artificial muscles (PAMs), which are not commonly used to drive robot hands. The type of the actuators consequently adds more originality to the project. The primary challenge is to reach an ambidextrous behaviour using PAMs designed to actuate non-ambidextrous robot hands. Thus, a feasibility study is carried out for this purpose. Investigating a number of mechanical possibilities, an ambidextrous design is reached with features almost identical for its right and left sides. A testbench is thereafter designed to investigate this possibility even further to design ambidextrous fingers using 3D printing and an asymmetrical tendons routing engineered to reduce the number of actuators. The Ambidextrous Robot Hand is connected to a remote control interface accessible from its website, which provides video streaming as feedback, to be eventually used as an online rehabilitation device. The secondary main challenge is to implement control algorithms on a robot hand with a range twice larger than others, with an asymmetrical tendons routing and actuated by nonlinear actuators. A number of control algorithms are therefore investigated to interact with the angular displacement of the fingers and the grasping abilities of the hand. Several solutions are found out, notably the implementations of a phasing plane switch control and a sliding-mode control, both specific to the architecture of the Ambidextrous Robot Hand. The implementation of these two algorithms on a robotic hand actuated by PAMs is almost as innovative as the ambidextrous design of the mechanical structure itself

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section

Adaptive torque-feedback based engine control

The aim of this study was to develop a self-tuning or adaptive SI engine controller using torque feedback as the main control variable, based on direct/indirect measurement and estimation techniques. The indirect methods include in-cylinder pressure measurement, ion current measurement, and crankshaft rotational frequency variation. It is proposed that torque feedback would not only allow the operating set-points to be monitored and achieved under wider conditions (including the extremes of humidity and throttle transients), but to actively select and optimise the set-points on the basis of both performance and fuel economy. A further application could allow the use of multiple fuel types and/or combustion enhancing methods to best effect. An existing experimental facility which comprised a Jaguar AJ-V8 SI engine coupled to a Heenan-Froude Dynamatic GVAL (Mk 1) dynamometer was adopted for this work, in order to provide a flexible distributed engine test system comprising a combined user interface and cylinder pressure monitoring system, a functional dynamometer controller, and a modular engine controller which is close coupled to an embedded PC has been created. The considerable challenges involved in creating this system have meant that the core research objectives of this project have not been met. Nevertheless, an open-architecture software and hardware engine controller and independent throttle controller have been developed, to the point of testing. For the purposes of optimum ignition timing validation and combustion knock detection, an optical cylinder pressure measurement system with crank angle synchronous sampling has been developed. The departure from the project’s initial aims have also highlighted several important aspects of eddy-current dynamometer control, whose closed-loop behaviour was modelled in Simulink to study its control and dynamic response. The design of the dynamometer real-time controller was successfully implemented and evaluated in a more contemporary context using an embedded digital controller.EThOS - Electronic Theses Online ServiceSchool of Mechanical & Systems EngineeringNewcastle UniversityGBUnited Kingdo