Paul O. Oladele

Bio: Paul O. Oladele is an academic researcher from Luleå University of Technology. The author has contributed to research in topics: Smart grid & Digital signal processor. The author has an hindex of 2, co-authored 4 publications receiving 14 citations.

Towards statistical machine learning for edge analytics in large scale networks: Real-time Gaussian function generation with generic DSP

Abstract: The smart grid (SG) is a large-scale network and it is an integral part of the Internet of Things (IoT). For a more effective big data analytic in large-scale IoT networks, reliable solutions are being designed such that many real-time decisions will be taken at the edge of the network close to where data is being generated. Gaussian functions are extensively applied in the field of statistical machine learning, pattern recognition, adaptive algorithms for function approximation, etc. It is envisaged that soon, some of these machine learning solutions and other gaussian function based applications that have low computation and low-memory footprint will be deployed for edge analytics in large-scale IoT networks. Hence, it will be of immense benefit if an adaptive, low-cost, method of designing gaussian functions becomes available. In this paper, gaussian distribution functions are designed using C28x real-time digital signal processor (DSP) that is embedded in the TMS320C2000 modem designed for powerline communication (PLC) at the low voltage distribution end of the smart grid, where numerous devices that generate massive amount of data exist. Open-source embedded C programming language is used to program the C28x for real-time gaussian function generation. The designed gaussian waveforms are stored in lookup tables (LUTs) in the C28x embedded DSP, and could be deployed for a variety of applications at the edge of the SG and IoT network. The novelty of the design is that the gaussian functions are designed with a generic, low-cost, fixed-point DSP, different from state of the art in which gaussian functions are designed using expensive arbitrary waveform generators and other specialized circuits. C28x DSP is selected for this design since it is already existing as an embedded DSP in many smart grid applications and in other numerous industrial systems that are part of the large scale IoT network, hence it is envisaged that integration of any gaussian function based solution using this DSP in the smart grid and other IoT systems may not be too challenging.

Smart grid communication over DC powerline: Evaluation of powerline communication OFDM PAPR for new types of destabilizing electrical loads

Abstract: In this paper, the channel peak-to-average-power-ratio (PAPR) of powerline communication (PLC) for smart grid and industrial Internet of Things (IoT) communication is evaluated in view of new types of destabilizing, non-stable, nonlinear electrical loads recently reported in literature. These types of loads may occur due to faults or varying bandwidth of controllers on the powerline and the presence of such loads always leads to spurious group delay, increasing channel distortion, noise and very high PAPR on the PLC channel. This work is an attempt towards better understanding of the impact of such load types on powerline communication over a direct current (DC) powerline channel. The effect of having such loads on powerline channel implementing Orthogonal Frequency Division Multiplexing (OFDM) scheme is also examined with the aid of Matlab. It is discovered that while PLC amplifiers normally introduce high PAPR, having first order lag loads will exacerbate this problem. The result of this study will be useful for more effective modulation, coding and signal processing for PLC. It will also be useful to other researchers working on designing more effective transmission channel's group delay equalizers, channel digital to analogue converters (DACs) and to standard bodies such as the US National Institute of Standards and Technology (NIST).

Advanced Signal Processing for Communication Networks and Industrial IoT Machines Using Low-Cost Fixed-Point Digital Signal Processor

Abstract: In this paper, the uses and functions of an existing, widely available digital signal processor (DSP) is extended to include using it to generate needed waveforms that could be used for computing, filtering, modulation and noise removal applications at the edges of communication networks that supports Industrial Internet of Things (IIoT) and Cyber Physical Systems (CPS). Such waveforms could also be used to optimize performances of IIoT communication networks such as wireless and powerline communication networks. The C28x digital signal processor (DSP), manufactured by Texas Instruments is used as a case study, and it is deployed in this paper as a mother wavelet generator by programming it to generate needed wavelets using embedded C programming language. Our implementation is the first known application of the C28x as a basic mother wavelet generator. Advanced signal processing method which include signal clipping, sequencing and concatenation are used with embedded C to program the C28x DSP. Wavelets generated with the C28x DSP are found to satisfy the wavelet admissibility condition. The open-loop voltage signal of an IIoT machine, generated at an IIoT network edge, and sent across a powerline communication (PLC) channel is used to evaluate the performance of the constructed Mexican Hat wavelet. The Mexican Hat wavelet constructed using the C28x DSP is used to remove noise from the machine signal corrupted with noise when the signal is transmitted over a PLC channel. Denoised signal using the DSP based Mexican Hat wavelet shows high correlation when compared to the original transmitted signal, while communication channel noise is successfully removed from the original signal.

Arbitrary Waveform Generation for IoT and Cyber Physical Systems Communication Networks Using C28x TMS320C2000 Digital Signal Processor

Abstract: Arbitrary waveform generators (AWGs) are very expensive instruments useful for generating complex signals and waveforms needed as communication and test signals for state of the art communication, Internet of Things (IoT), and Cyber Physical Systems (CPS) devices. In recent years, research has been directed towards making powerline communication (PLC) feasible as a last mile communication network for IoT, smart grid (SG) and CPS. This paper present results of using a low-cost PLC modem (Texas Instrument's TMS320C2000 C28x) and embedded C programming language as an AWG to generate test signals for PLC, SG, IoT and CPS research purposes. Our implementation is the first known application of the TMS320C2000 C28x as an AWG. Using embedded C language makes the waveforms generated platform independent, and thus, avoids the use of platform dependent hexadecimal assembly languages. This method also overcomes the rigid amplitude problem of the Direct Digital Synthesis (DDS) technique. The core embedded signal processor used in this paper is the low-cost TMS320C2000 C28x which is widely deployed in many IoT, CPS, industrial systems and communication networks devices worldwide. It has 16-bit resolution at 100 kHz bandwidth. Several examples of industrial grade arbitrary waveforms were constructed for the TMS320C2000 C28x with the embedded C programming technique. Hence arbitrary signals generated using the C28x will be useful in testing many state of the art and legacy communication, IoT, SG, and CPS networks and devices worldwide. In addition to signal generation, examples are shown of using the arbitrary waveforms generated with TMS320C2000 to implement amplitude modulation (AM) and pulse amplitude modulation (PAM) schemes for CPS, IoT and PLC communication networks.

Transmission lines

Abstract: Describing methods whereby high quality sound reproduction in auditory perspective can be accomplished over long distances, this discussion centers largely upon a description of the exact technique employed in providing communication transmission circuits for the Philadelphia-Washington demonstration. Problems that might be involved in carrying out such transmission on a more widespread scale also are touched upon.

A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

Abstract: In industrial environments, over several decades, Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) have served to improve efficiencies of intralogistics and material handling tasks. However, for system integrators, the choice and effective deployment of improved, suitable and reliable communication and control technologies for these unmanned vehicles remains a very challenging task. Specifics of communication for AGVs and AMRs imposes stringent performance requirements on latency and reliability of communication links which many existing wireless technologies struggle to satisfy. In this paper, a review of latest AGVs and AMRs research results in the past decade is presented. The review encompasses results from different past and present research domains of AGVs. In addition, performance requirements of communication networks in terms of their latencies and reliabilities when they are deployed for AGVs and AMRs coordination, control and fleet management in smart manufacturing environments are discussed. Integration challenges and limitations of present state-of-the-art AGV and AMR technologies when those technologies are used for facilitating AGV-based smart manufacturing and factory of the future applications are also thoroughly discussed. The paper also present a thorough discussion of areas in need of further research regarding the application of 5G networks for AGVs and AMRs fleet management in smart manufacturing environments. In addition, novel integration ideas by which tactile Internet, 5G network slicing and virtual reality applications can be used to facilitate AGV and AMR based factory of the future (FoF) and smart manufacturing applications were motivated.

DEW: A New Edge Computing Component for Distributed Dynamic Networks

Abstract: The paper proposes the introduction of a new component in the family of distributed computer networks, with specific functions for edge computing devices. The DEW component is a network layer located between Fog and Edge, a position currently attributed to the Mist concept. Specifically, DEW is designed as a Mist counterpart that can be used in Distributed Dynamical Networks (DDN). The DEW characteristics and the associated specific algorithms are discussed on a particular type of DDN - microgrids and microgrids clusters.

Real-Time Distributed Computing at Network Edges for Large Scale Industrial IoT Networks

Abstract: Low-cost, real-time digital signal processors (DSPs) embedded in generic Internet of Things (IoT) edge devices can make significant contributions to distributed edge computing for industrial IoT (IIoT) networks. The DSP considered in this paper is the Texas Instruments (TI) TMS320C28x DSP (C28x). At the edge of the network, the C28x is programmed using low-level Embedded C programming language to construct the Morlet wavelet. Our implementation at this layer is the first known construction of the Morlet wavelet for C28x DSP using Embedded C. At the fog layer, near the edge of the IoT network, where more computing resources exist, the wavelet is then convolved with healthcare (electrocardiogram) and electrical network signals, using Matlab to reduce signal noise, and to identify important parts of examined signals. Convolution results indicates that the distributed computing approach for low-cost generic devices considered in this paper is suitable for use in large IIoT networks

Osmotic Collaborative Computing for Machine Learning and Cybersecurity Applications in Industrial IoT Networks and Cyber Physical Systems with Gaussian Mixture Models

Abstract: To implement machine learning algorithms and other useful algorithms in industrial Internet of Things (IIoT), new computing approaches are needed to prevent costs associated with having to install state of the art edge analytic devices. A suitable approach may include collaborative edge computing using available, resource-constrained IoT edge analytic hardware. In this paper, collaborative computing method is used to construct a popular and very useful waveform for IoT analytics, the Gaussian Mixture Model (GMM). GMM parameters are learned in the cloud, but the GMMs are constructed at the IIoT edge layer. GMMs are constructed using C28x, a ubiquitous, low-cost, embedded digital signal processor (DSP) that is widely available in many pre-existing IIoT infrastructures and in many edge analytic devices. Several GMMs including 2-GMM and 3-GMMs are constructed using the C28x DSP and Embedded C to show that GMM designs could be achieved in form of an osmotic microservice from the IIoT edge to the IIoT fog layer. Designed GMMs are evaluated using their differential and zero-crossings and are found to satisfy important waveform design criteria. At the fog layer, constructed GMMs are then applied for novelty detection, an IIoT cybersecurity and fault-monitoring application and are found to be able to detect anomalies in IIoT machine data using Hampel identifier, 3-Sigma rule, and the Boxplot rule. The osmotic collaborative computing method advocated in this paper will be crucial in ensuring the possibility of shifting many complex applications such as novelty detection and other machine learning based cybersecurity applications to edges of large scale IoT networks using low-cost widely available DSPs.