Scheduling Domestic Shiftable Loads in Smart Grids: A Learning Automata-Based Scheme

In this paper, we consider the problem of scheduling shiftable loads, over multiple users, in smart grids. We approach the problem, which is becoming increasingly pertinent in our present energy-thirsty society, using a novel distributed game-theoretic framework. From a modeling perspective, the distributed scheduling problem is formulated as a game, and in particular, a so-called “Potential” game. This game has at least one pure strategy Nash Equilibrium (NE), and we demonstrate that the NE point is a global optimal point. The solution that we propose, which is the pioneering solution that incorporates the theory of Learning Automata (LA), permits the total supplied loads to approach the p…

MAC strategies for single rendezvous multi-hop cognitive radio networks

This paper presents two MAC strategies for multi-hop cognitive radio networks in single radio multi-channel cases. Both strategies use one of the idle multiple channels for communication among secondary users, and the network will leave the current channel and jump to another channel as a group if any primary user appears. The first strategy is based on a pre-defined pattern that will always tune to the next available channel when primary user emerges. The second one is based on the concept of connected dominating set in which a backbone is formed in the network in order to keep the continuity of the communication. The strategies are evaluated in both homogeneous and heterogeneous channels …

Face Inpainting via Nested Generative Adversarial Networks

Face inpainting aims to repaired damaged images caused by occlusion or cover. In recent years, deep learning based approaches have shown promising results for the challenging task of image inpainting. However, there are still limitation in reconstructing reasonable structures because of over-smoothed and/or blurred results. The distorted structures or blurred textures are inconsistent with surrounding areas and require further post-processing to blend the results. In this paper, we present a novel generative model-based approach, which consisted by nested two Generative Adversarial Networks (GAN), the sub-confrontation GAN in generator and parent-confrontation GAN. The sub-confrontation GAN…

The Regression Tsetlin Machine: A Tsetlin Machine for Continuous Output Problems

The recently introduced Tsetlin Machine (TM) has provided competitive pattern classification accuracy in several benchmarks, composing patterns with easy-to-interpret conjunctive clauses in propositional logic. In this paper, we go beyond pattern classification by introducing a new type of TMs, namely, the Regression Tsetlin Machine (RTM). In all brevity, we modify the inner inference mechanism of the TM so that input patterns are transformed into a single continuous output, rather than to distinct categories. We achieve this by: (1) using the conjunctive clauses of the TM to capture arbitrarily complex patterns; (2) mapping these patterns to a continuous output through a novel voting and n…

Optimizing channel selection for cognitive radio networks using a distributed Bayesian learning automata-based approach

Consider a multi-channel Cognitive Radio Network (CRN) with multiple Primary Users (PUs), and multiple Secondary Users (SUs) competing for access to the channels. In this scenario, it is essential for SUs to avoid collision among one another while maintaining efficient usage of the available transmission opportunities. We investigate two channel access schemes. In the first model, an SU selects a channel and sends a packet directly without Carrier Sensing (CS) whenever the PU is absent on this channel. In the second model, an SU invokes CS in order to avoid collision among co-channel SUs. For each model, we analyze the channel selection problem and prove that it is a so-called "Exact Potent…

Analysis on channel bonding/aggregation for multi-channel cognitive radio networks

Paper presented at the 2010 European Wireless Conference, Lucca. (c) 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Paper also available from the publisher: http://dx.doi.org/10.1109/EW.2010.5483492 Channel bonding/aggregation techniques, which assemble several channels together as one channel, could be used in cognitive radio networks for the purpose of achieving better bandwidth utilizatio…

The regression Tsetlin machine: a novel approach to interpretable nonlinear regression

Relying simply on bitwise operators, the recently introduced Tsetlin machine (TM) has provided competitive pattern classification accuracy in several benchmarks, including text understanding. In this paper, we introduce the regression Tsetlin machine (RTM), a new class of TMs designed for continuous input and output, targeting nonlinear regression problems. In all brevity, we convert continuous input into a binary representation based on thresholding, and transform the propositional formula formed by the TM into an aggregated continuous output. Our empirical comparison of the RTM with state-of-the-art regression techniques reveals either superior or on par performance on five datasets. Thi…

Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook

The deep learning revolution is touching all scientific disciplines and corners of our lives as a means of harnessing the power of big data. Marine ecology is no exception. These new methods provide analysis of data from sensors, cameras, and acoustic recorders, even in real time, in ways that are reproducible and rapid. Off-the-shelf algorithms can find, count, and classify species from digital images or video and detect cryptic patterns in noisy data. Using these opportunities requires collaboration across ecological and data science disciplines, which can be challenging to initiate. To facilitate these collaborations and promote the use of deep learning towards ecosystem-based management…

The Convolutional Tsetlin Machine

Convolutional neural networks (CNNs) have obtained astounding successes for important pattern recognition tasks, but they suffer from high computational complexity and the lack of interpretability. The recent Tsetlin Machine (TM) attempts to address this lack by using easy-to-interpret conjunctive clauses in propositional logic to solve complex pattern recognition problems. The TM provides competitive accuracy in several benchmarks, while keeping the important property of interpretability. It further facilitates hardware-near implementation since inputs, patterns, and outputs are expressed as bits, while recognition and learning rely on straightforward bit manipulation. In this paper, we ex…

User Grouping and Power Allocation in NOMA Systems: A Reinforcement Learning-Based Solution

In this paper, we present a pioneering solution to the problem of user grouping and power allocation in Non-Orthogonal Multiple Access (NOMA) systems. There are two fundamentally salient and difficult issues associated with NOMA systems. The first involves the task of grouping users together into the pre-specified time slots. The subsequent second phase augments this with the solution of determining how much power should be allocated to the respective users. We resolve this with the first reported Reinforcement Learning (RL)-based solution, which attempts to solve the partitioning phase of this issue. In particular, we invoke the Object Migration Automata (OMA) and one of its variants to re…

A Bayesian Learning Automata-Based Distributed Channel Selection Scheme for Cognitive Radio Networks

We consider a scenario where multiple Secondary Users SUs operate within a Cognitive Radio Network CRN which involves a set of channels, where each channel is associated with a Primary User PU. We investigate two channel access strategies for SU transmissions. In the first strategy, the SUs will send a packet directly without operating Carrier Sensing Medium Access/Collision Avoidance CSMA/CA whenever a PU is absent in the selected channel. In the second strategy, the SUs implement CSMA/CA to further reduce the probability of collisions among co-channel SUs. For each strategy, the channel selection problem is formulated and demonstrated to be a so-called "Potential" game, and a Bayesian Lea…

Decoupled Downlink-Uplink Coverage Analysis with Interference Management for Enriched Heterogeneous Cellular Networks

Heterogeneous cellular networks (HetCNets) offer a promising solution to cope with the current cellular coverage crunch. Due to the large transmit power disparity, while following maximum power received (MPR) association scheme, a larger number of users are associated with macro-cell BS (MBS) than small-cell BSs (SBSs). Therefore, an imbalance load arrangement takes place across the HetCNets. Hence, using cell range expansion-based cell association, we can balance the load across the congested MBS. However, using MPR association scheme, users’ offloading leads to two challenges: 1) macro-cell interference , in which the MBS interferes with the offloaded users, and 2) coupled downlink-uplink…

Dynamic Channel Aggregation Strategies in Cognitive Radio Networks with Spectrum Adaptation

In cognitive radio networks, channel aggregation techniques which combine several channels together as one channel have been proposed in many MAC protocols. In this paper, spectrum adaptation is proposed in channel aggregation and two strategies which dynamically adjust channel occupancy of ongoing traffic flows are further developed. The performance of these strategies is evaluated using continuous time Markov chain models. Moreover, models in the quasi-stationary regime are analyzed and the closed-form capacity expression is derived in this regime. Numerical results demonstrate that the capacity of the secondary network can be improved by using channel aggregation with spectrum adaptation.

Dynamic Flow-Adaptive Spectrum Leasing with Channel Aggregation in Cognitive Radio Networks.

Cognitive radio networks (CRNs), which allow secondary users (SUs) to dynamically access a network without affecting the primary users (PUs), have been widely regarded as an effective approach to mitigate the shortage of spectrum resources and the inefficiency of spectrum utilization. However, the SUs suffer from frequent spectrum handoffs and transmission limitations. In this paper, considering the quality of service (QoS) requirements of PUs and SUs, we propose a novel dynamic flow-adaptive spectrum leasing with channel aggregation. Specifically, we design an adaptive leasing algorithm, which adaptively adjusts the portion of leased channels based on the number of ongoing and buffered PU …

A Data-Driven Architecture for Personalized QoE Management in 5G Wireless Networks

With the emergence of a variety of new wireless network types, business types, and QoS in a more autonomic, diverse, and interactive manner, it is envisioned that a new era of personalized services has arrived, which emphasizes users' requirements and service experiences. As a result, users' QoE will become one of the key features in 5G/future networks. In this article, we first review the state of the art of QoE research from several perspectives, including definition, influencing factors, assessment methods, QoE models, and control methods. Then a data-driven architecture for enhancing personalized QoE is proposed for 5G networks. Under this architecture, we specifically propose a two-ste…

Temperate Fish Detection and Classification: a Deep Learning based Approach

A wide range of applications in marine ecology extensively uses underwater cameras. Still, to efficiently process the vast amount of data generated, we need to develop tools that can automatically detect and recognize species captured on film. Classifying fish species from videos and images in natural environments can be challenging because of noise and variation in illumination and the surrounding habitat. In this paper, we propose a two-step deep learning approach for the detection and classification of temperate fishes without pre-filtering. The first step is to detect each single fish in an image, independent of species and sex. For this purpose, we employ the You Only Look Once (YOLO) …

Channel aggregation with guard-band in D-OFDM based CRNs: Modeling and performance evaluation

Channel aggregation (CA) techniques can offer flexible channel allocation and improve overall system performance in multi-channel cognitive radio networks (CRNs). Although many CA techniques have been proposed and studied, the impact of guard-band on CA for channel access has not been addressed in-depth. In this paper, we study the guard-band allocation mechanisms in discontinuous-orthogonal frequency division multiplexing (D-OFDM) based CRNs, and investigate the impact of guard-band sharing on SU flows when CA is enabled. Continuous time Markov chain (CTMC) based models have been developed in order to investigate the stochastic behavior of PU and SU flows. Based on our mathematical analysi…

Markov Chain Analysis of CA and CF with Multiple Types of Users

In the previous chapter, we studied in depth the impact of CA and CF on traffic flows in systems where there exists only one type of users with one type of flows. In this chapter, the influence of CA and CF is studied in a more complicated scenario, i.e., the one in which multiple users exist, and where users have different priorities in using channel resources. CRN is a typical example for such a system, and we study the CRNs where PUs and SUs have one type of flows for each.

A dynamic parallel-rendezvous MAC mechanism in multi-rate cognitive radio networks: Mechanism design and performance evaluation

Published version of an article published in the journal:Journal of Communications, © Academy Publisher Also available from publisher: http://dx.doi.org/10.4304/jcm.4.10.752-765 Parallel rendezvous multi-channel MAC mechanisms are regarded as an efficient method for media access control in cognitive radio networks since they do not need a control channel and use only one transceiver. However, existing parallel rendezvous MAC mechanisms assume that all channels have the same maximum capacity and channel availability for secondary users. In this paper, we propose a dynamic parallel rendezvous multi-channel MAC mechanism for synchronized multi-rate cognitive radio networks in which secondary u…

A novel faster failure detection strategy for link connectivity using Hello messaging in mobile ad hoc networks

Faster failure detection is one of the main steps responsible for efficient link connectivity in mobile ad hoc networks (MANETs). Under a random behaviour of network nodes and link/node failure, th...

A formal proof of the ε-optimality of absorbing continuous pursuit algorithms using the theory of regular functions

Published version of an article from the journal: Applied Intelligence. Also available on Springerlink: http://dx.doi.org/10.1007/s10489-014-0541-1 The most difficult part in the design and analysis of Learning Automata (LA) consists of the formal proofs of their convergence accuracies. The mathematical techniques used for the different families (Fixed Structure, Variable Structure, Discretized etc.) are quite distinct. Among the families of LA, Estimator Algorithms (EAs) are certainly the fastest, and within this family, the set of Pursuit algorithms have been considered to be the pioneering schemes. Informally, if the environment is stationary, their ε-optimality is defined as their abili…

Indoor Space Classification Using Cascaded LSTM

Author's accepted manuscript. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Indoor space classification is an important part of localization that helps in precise location extraction, which has been extensively utilized in industrial and domestic domain. There are various approaches that employ Bluetooth Low Energy (BLE), Wi-Fi, magnetic field, object detecti…

Greedy versus Dynamic Channel Aggregation Strategy in CRNs: Markov Models and Performance Evaluation

Publishes version of a paper from the book: Networking 2011 Workshops. Also available f rom the publisher on SpringLink:http://dx.doi.org/10.1007/978-3-642-23041-7_3 In cognitive radio networks, channel aggregation techniques which aggregate several channels together as one channel have been proposed in many MAC protocols. In this paper, we consider elastic data traffic and spectrum adaptation for channel aggregation, and propose two new strategies named as Greedy and Dynamic respectively. The performance of channel aggregation represented by these strategies is evaluated using continuous time Markov chain models. Moreover, simulation results based on various traffic distributions are utili…

Medium access in cognitive radio networks: From single hop to multiple hops

Doktorgradsavhandling i informasjons- og kommunikasjonsteknologi, Universitetet i Agder, Grimstad, 2012 If channel assembling is enabled, this technique can be utilized for potential performance improvement in CRNs. Two use cases are envisaged for channel assembling. In the first use case, the system can accommodate parallel SU services in multiple channels, while in the second use case, the system allows only one SU service at a time. In the use case where parallel SU services are allowed, various channel assembling strategies are proposed and modeled in order to investigate their performance and to acquire better comprehension of the behavior of CRNs with channel assembling. Moreover, the…

The Regression Tsetlin Machine: A Tsetlin Machine for Continuous Output Problems

The recently introduced Tsetlin Machine (TM) has provided competitive pattern classification accuracy in several benchmarks, composing patterns with easy-to-interpret conjunctive clauses in propositional logic. In this paper, we go beyond pattern classification by introducing a new type of TMs, namely, the Regression Tsetlin Machine (RTM). In all brevity, we modify the inner inference mechanism of the TM so that input patterns are transformed into a single continuous output, rather than to distinct categories. We achieve this by: (1) using the conjunctive clauses of the TM to capture arbitrarily complex patterns; (2) mapping these patterns to a continuous output through a novel voting and n…

LCRT: A ToA Based Mobile Terminal Localization Algorithm in NLOS Environment

©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Article also available from publisher: http://dx.doi.org/10.1109/VETECS.2009.5073644 Non line-of-sight (NLOS) propagation in range measurement is a key problem for mobile terminal localization. This paper proposes a low computational residual test (LCRT) algorithm that can identify the number of line-of-sight (LOS) transmissions and reduce the computational com…

A Comprehensive Utility Function for Resource Allocation in Mobile Edge Computing

In mobile edge computing (MEC), one of the important challenges is how much resources of which mobile edge server (MES) should be allocated to which user equipment (UE). The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only. This paper presents a novel comprehensive utility function for resource allocation in MEC. The utility function considers the heterogeneous nature of applications that a UE offloads to MES. The proposed utility function considers all important parameters, including CPU, RAM, hard disk space, required time, and distance, to calculate a more realis…

Markov Chain Analysis of CA and CF with a Single Type of Users

In this chapter, we study the impact of CA and CF on traffic flows in the simplest system where there is one single type of flows generated by one single type of users. We will use CTMC to model the system, and the goal is to deliver the elementary concept of CTMC analysis for a system with CA and CF.

Positionless aspect based sentiment analysis using attention mechanism.

Abstract Aspect-based sentiment analysis (ABSA) aims at identifying fine-grained polarity of opinion associated with a given aspect word. Several existing articles demonstrated promising ABSA accuracy using positional embedding to show the relationship between an aspect word and its context. In most cases, the positional embedding depends on the distance between the aspect word and the remaining words in the context, known as the position index sequence. However, these techniques usually employ both complex preprocessing approaches with additional trainable positional embedding and complex architectures to obtain the state-of-the-art performance. In this paper, we simplify preprocessing by …

Word-level human interpretable scoring mechanism for novel text detection using Tsetlin Machines

Recent research in novelty detection focuses mainly on document-level classification, employing deep neural networks (DNN). However, the black-box nature of DNNs makes it difficult to extract an exact explanation of why a document is considered novel. In addition, dealing with novelty at the word-level is crucial to provide a more fine-grained analysis than what is available at the document level. In this work, we propose a Tsetlin machine (TM)-based architecture for scoring individual words according to their contribution to novelty. Our approach encodes a description of the novel documents using the linguistic patterns captured by TM clauses. We then adopt this description to measure how …

Deep Learning for Classifying Physical Activities from Accelerometer Data

Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening life expectancy. There are minimal medical care and personal trainers’ methods to monitor a patient’s actual physical activity types. To improve activity monitoring, we propose an artificial-intelligence-based approach to classify the physical movement activity patterns. In more detail, we employ two deep learning (DL) methods, namely a deep feed-forward neural network (DNN) and a deep recurrent neural network (RNN) for this purpose. We evaluate the proposed models on two phy…

Complexity analysis of spectrum access strategies with channel aggregation in CR networks

Cognitive radio has been introduced to increase spectrum utilization efficiency. To further improve bandwidth utilization of cognitive radio users, channel aggregation (CA) techniques can be adopted for spectrum access. In this paper, we analyze the complexity of three CA strategies, in terms of required amount of handshakes for channel adaptation due to primary and secondary user activities. Continuous time Markov chain models are developed to evaluate the total number of handshakes required per unit time by different CA strategies and the analytical results are validated by simulations. Numerical results reveal that the complexity of CA strategies depends on the capability and the design …

Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization With Medical Applications

Medical applications challenge today's text categorization techniques by demanding both high accuracy and ease-of-interpretation. Although deep learning has provided a leap ahead in accuracy, this leap comes at the sacrifice of interpretability. To address this accuracy-interpretability challenge, we here introduce, for the first time, a text categorization approach that leverages the recently introduced Tsetlin Machine. In all brevity, we represent the terms of a text as propositional variables. From these, we capture categories using simple propositional formulae, such as: if "rash" and "reaction" and "penicillin" then Allergy. The Tsetlin Machine learns these formulae from a labelled tex…

Neuroevolution of Actively Controlled Virtual Characters - An Experiment for an Eight-Legged Character

Physics-based character animation offers an attractive alternative for traditional animations. However, it is often strenuous for a physics-based approach to incorporate active user control of different characters. In this paper, a neuroevolutionary approach is proposed using HyperNEAT to combine individually trained neural controllers to form a control strategy for a simulated eight-legged character, which is a previously untested character morphology for this algorithm. It is aimed to evaluate the robustness and responsiveness of the control strategy that changes the controllers based on simulated user inputs. The experiment result shows that HyperNEAT is able to evolve long walking contr…

Measuring the Novelty of Natural Language Text Using the Conjunctive Clauses of a Tsetlin Machine Text Classifier

Most supervised text classification approaches assume a closed world, counting on all classes being present in the data at training time. This assumption can lead to unpredictable behaviour during operation, whenever novel, previously unseen, classes appear. Although deep learning-based methods have recently been used for novelty detection, they are challenging to interpret due to their black-box nature. This paper addresses \emph{interpretable} open-world text classification, where the trained classifier must deal with novel classes during operation. To this end, we extend the recently introduced Tsetlin machine (TM) with a novelty scoring mechanism. The mechanism uses the conjunctive clau…

Analysis of load balancing and interference management in heterogeneous cellular networks

To meet the current cellular capacity demands, proactive offloading is required in heterogeneous cellular networks (HetCNets) comprising of different tiers of base stations (BSs), e.g., small-cell BSs (sBSs) and conventional macro-cell BSs (mBSs). Each tier differs from the others in terms of BS transmit power, spatial density, and association bias. Consequently, the coverage range of each tier BSs is also different from others. Due to low transmit power, a fewer number of users are associated to an sBS as compared with mBS. Thus, inefficient utilization of small-cell resources occurs. To balance the load across the network, it is necessary to push users to the underloaded small cells from …

Interpretability in Word Sense Disambiguation using Tsetlin Machine

Domestic load forecasting using neural network and its use for missing data analysis

Domestic demand prediction is very important for home energy management system and also for peak reduction in power system network. In this work, active and reactive power consumption prediction model is developed and analysed for a typical Southern Norwegian house for hourly power (active and reactive) consumptions and time information as inputs. In the proposed model, a neural network is adopted as a main technique and historical domestic load data of around 2 years are used as input. The available data has some measurement errors and missing segments. Before using the data for training purpose, missing and inaccurate data are considered and then it is used for testing the model. It is ob…

A Conclusive Analysis of the Finite-Time Behavior of the Discretized Pursuit Learning Automaton

Author's accepted version (post-print). © 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Available from 20/03/2021. This paper deals with the finite-time analysis (FTA) of learning automata (LA), which is a topic for which very little work has been reported in the literature. This is as opposed to the asymptotic steady-state analysis for which there are, probabl…

The Hierarchical Discrete Learning Automaton Suitable for Environments with Many Actions and High Accuracy Requirements

Author's accepted manuscript Since its early beginning, the paradigm of Learning Automata (LA), has attracted much interest. Over the last decades, new concepts and various improvements have been introduced to increase the LA’s speed and accuracy, including employing probability updating functions, discretizing the probability space, and implementing the “Pursuit” concept. The concept of incorporating “structure” into the ordering of the LA’s actions is one of the latest advancements to the field, leading to the ϵ-optimal Hierarchical Continuous Pursuit LA (HCPA) that has superior performance to other LA variants when the number of actions is large. Although the previously proposed HCPA is …

Modelling of Compressors in an Industrial CO $$_2$$ -Based Operational Cooling System Using ANN for Energy Management Purposes

Large scale cooling installations usually have high energy consumption and fluctuating power demands. There are several ways to control energy consumption and power requirements through intelligent energy and power management, such as utilizing excess heat, thermal energy storage and local renewable energy sources. Intelligent energy and power management in an operational setting is only possible if the time-varying performance of the individual components of the energy system is known. This paper presents an approach to model the compressors in an industrial, operational two-stage cooling system, with CO\(_2\) as the working fluid, located in an advanced food distribution warehouse in Norw…

Distributed routing and channel allocation in multi-channel multi-hop ad hoc networks

In this paper, we propose a novel routing protocol which is integrated with channel assignment for multi-channel multi-hop wireless ad hoc networks. In such a network, each node is equipped with three transceivers. One is always tuned on a control channel which is responsible for control and broadcast messages, and the other two perform as transmitter and receiver respectively for traffic flows on different data channels. The routing protocol works in an on-demand manner, and the proposed routing discovery process selects a path that potentially traverses nodes with lighter traffic load and lower number of carried flows. With a given number of non-overlapping channels, the optimal solution …

Using the Theory of Regular Functions to Formally Prove the ε-Optimality of Discretized Pursuit Learning Algorithms

Learning Automata LA can be reckoned to be the founding algorithms on which the field of Reinforcement Learning has been built. Among the families of LA, Estimator Algorithms EAs are certainly the fastest, and of these, the family of Pursuit Algorithms PAs are the pioneering work. It has recently been reported that the previous proofs for e-optimality for all the reported algorithms in the family of PAs have been flawed. We applaud the researchers who discovered this flaw, and who further proceeded to rectify the proof for the Continuous Pursuit Algorithm CPA. The latter proof, though requires the learning parameter to be continuously changing, is, to the best of our knowledge, the current …

User grouping and power allocation in NOMA systems: a novel semi-supervised reinforcement learning-based solution

Author's accepted manuscript In this paper, we present a pioneering solution to the problem of user grouping and power allocation in non-orthogonal multiple access (NOMA) systems. The problem is highly pertinent because NOMA is a well-recognized technique for future mobile radio systems. The salient and difcult issues associated with NOMA systems involve the task of grouping users together into the prespecifed time slots, which are augmented with the question of determining how much power should be allocated to the respective users. This problem is, in and of itself, NP-hard. Our solution is the frst reported reinforcement learning (RL)-based solution, which attempts to resolve parts of thi…

Power allocation in multi-channel cognitive radio networks with channel assembling

Accepted version of a paper in the book: 2011 IEEE 12th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). Published version available from the IEEE:http://dx.doi.org/10.1109/SPAWC.2011.5990485 Consider power allocation for Secondary User (SU) packet transmissions over multiple channels with variable Primary User (PU) arrival rates in cognitive radio networks. Two problems are studied in this paper: The first one is to minimize the collision probability with PUs and the second one is to maximize the data rate while keeping the collision probability bounded. It is shown that the optimal solution for the first problem is to allocate all power onto the bes…

On the Convergence of Tsetlin Machines for the XOR Operator.

The Tsetlin Machine (TM) is a novel machine learning algorithm with several distinct properties, including transparent inference and learning using hardware-near building blocks. Although numerous papers explore the TM empirically, many of its properties have not yet been analyzed mathematically. In this article, we analyze the convergence of the TM when input is non-linearly related to output by the XOR-operator. Our analysis reveals that the TM, with just two conjunctive clauses, can converge almost surely to reproducing XOR, learning from training data over an infinite time horizon. Furthermore, the analysis shows how the hyper-parameter T guides clause construction so that the clauses c…

A Deep Learning Approach for Energy Efficient Computational Offloading in Mobile Edge Computing

Mobile edge computing (MEC) has shown tremendous potential as a means for computationally intensive mobile applications by partially or entirely offloading computations to a nearby server to minimize the energy consumption of user equipment (UE). However, the task of selecting an optimal set of components to offload considering the amount of data transfer as well as the latency in communication is a complex problem. In this paper, we propose a novel energy-efficient deep learning based offloading scheme (EEDOS) to train a deep learning based smart decision-making algorithm that selects an optimal set of application components based on remaining energy of UEs, energy consumption by applicati…

A multi-step finite-state automaton for arbitrarily deterministic Tsetlin Machine learning

A general framework for group authentication and key exchange protocols

Published version of a chapter in the book: Foundations and Practice of Security. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-319-05302-8_3 In this paper, we propose a novel framework for group authentication and key exchange protocols. There are three main advantages of our framework. First, it is a general one, where different cryptographic primitives can be used for different applications. Second, it works in a one-to-multiple mode, where a party can authenticate several parties mutually. Last, it can provide several security features, such as protection against passive adversaries and impersonate attacks, implicit key authentication, forward and backward securi…

Learning Automata Based Q-learning for Content Placement in Cooperative Caching

An optimization problem of content placement in cooperative caching is formulated, with the aim of maximizing sum mean opinion score (MOS) of mobile users. Firstly, a supervised feed-forward back-propagation connectionist model based neural network (SFBC-NN) is invoked for user mobility and content popularity prediction. More particularly, practical data collected from GPS-tracker app on smartphones is tackled to test the accuracy of mobility prediction. Then, a learning automata-based Q-learning (LAQL) algorithm for cooperative caching is proposed, in which learning automata (LA) is invoked for Q-learning to obtain an optimal action selection in a random and stationary environment. It is p…

The Hierarchical Discrete Pursuit Learning Automaton: A Novel Scheme With Fast Convergence and Epsilon-Optimality

Author's accepted manuscript © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Since the early 1960s, the paradigm of learning automata (LA) has experienced abundant interest. Arguably, it has also served as the foundation for the phenomenon and field of reinforcement learning (RL). Over the decades, new concepts and fundamental principles have been introduced t…

Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling

Using logical clauses to represent patterns, Tsetlin Machines (TMs) have recently obtained competitive performance in terms of accuracy, memory footprint, energy, and learning speed on several benchmarks. Each TM clause votes for or against a particular class, with classification resolved using a majority vote. While the evaluation of clauses is fast, being based on binary operators, the voting makes it necessary to synchronize the clause evaluation, impeding parallelization. In this paper, we propose a novel scheme for desynchronizing the evaluation of clauses, eliminating the voting bottleneck. In brief, every clause runs in its own thread for massive native parallelism. For each training…

Analysis of interference avoidance with load balancing in heterogeneous cellular networks

In heterogeneous cellular networks (HCNets) smallcells are overlaid with macro-cells to handle heavy cellular data traffic in an efficient way. To achieve fast and reliable access to data with a better coverage it is necessary to offload users to the underutilized small-cells from the congested macro-cells. Sharing of the same licensed frequency spectrum by smallcells and macro-cells results in heavy cross-tier interference which significantly affects the downlink SINR. In this paper, we investigate and analyze a joint frequency-division duplex based cross-tier complementary spectrum sharing technique which is also regarded as reverse frequency allocation (RFA) scheme with load balancing. T…

Explainable Tsetlin Machine framework for fake news detection with credibility score assessment

The proliferation of fake news, i.e., news intentionally spread for misinformation, poses a threat to individuals and society. Despite various fact-checking websites such as PolitiFact, robust detection techniques are required to deal with the increase in fake news. Several deep learning models show promising results for fake news classification, however, their black-box nature makes it difficult to explain their classification decisions and quality-assure the models. We here address this problem by proposing a novel interpretable fake news detection framework based on the recently introduced Tsetlin Machine (TM). In brief, we utilize the conjunctive clauses of the TM to capture lexical and…

Domestic demand predictions considering influence of external environmental parameters

A precise prediction of domestic demand is very important for establishing home energy management system and preventing the damage caused by overloading. In this work, active and reactive power consumption prediction model based on historical power usage data and external environment parameter data (temperature and solar radiation) is presented for a typical Southern Norwegian house. In the presented model, a neural network is adopted as a main prediction technique and historical domestic load data of around 2 years are utilized for training and testing purpose. Temperature and global irradiation (which illustrates the solar radiation level quantitatively) are employed as external parameter…

Modeling and Performance Analysis of Channel Assembling in Multichannel Cognitive Radio Networks With Spectrum Adaptation

[EN] To accommodate spectrum access in multichannel cognitive radio networks (CRNs), the channel-assembling technique, which combines several channels together as one channel, has been proposed in many medium access control (MAC) protocols. However, analytical models for CRNs enabled with this technique have not been thoroughly investigated. In this paper, two representative channel-assembling strategies that consider spectrum adaptation and heterogeneous traffic are proposed, and the performance of these strategies is evaluated based on the proposed continuous-time Markov chain (CTMC) models. Moreover, approximations of these models in the quasistationary regime are analyzed, and closed-fo…

Image Colorization Method Using Texture Descriptors and ISLIC Segmentation

We present a new colorization method to assign color to a grayscale image based on a reference color image using texture descriptors and Improved Simple Linear Iterative Clustering (ISLIC). Firstly, the pixels of images are classified using Support Vector Machine (SVM) according to texture descriptors, mean luminance, entropy, homogeneity, correlation, and local binary pattern (LBP) features. Then, the grayscale image and the color image are segmented into superpixels, which are obtained by ISLIC to produce more uniform and regularly shaped superpixels than those obtained by SLIC, and the classified images are further post-processed combined with superpixles for removing erroneous classific…

Performance analysis of user-centric SBS deployment with load balancing in heterogeneous cellular networks: A Thomas cluster process approach

Abstract In conventional heterogeneous cellular networks (HCNets), the locations of user equipments (UEs) and base stations (BSs) are modeled randomly using two different homogeneous Poisson point processes (PPPs). However, this might not be a suitable assumption in case of UE distribution because UE density is not uniform everywhere in HCNets. Keeping in view the existence of nonuniform UEs, the small base stations (SBSs) are assumed to be deployed in the areas with high UE density, which results in correlation between UEs and BS locations. In this paper, we analyse the performance of HCNets with nonuniform UE deployment containing a union of clustered and uniform UE sets. The clustered UE…

Computational Offloading in Mobile Edge with Comprehensive and Energy Efficient Cost Function: A Deep Learning Approach

In mobile edge computing (MEC), partial computational offloading can be intelligently investigated to reduce the energy consumption and service delay of user equipment (UE) by dividing a single task into different components. Some of the components execute locally on the UE while the remaining are offloaded to a mobile edge server (MES). In this paper, we investigate the partial offloading technique in MEC using a supervised deep learning approach. The proposed technique, comprehensive and energy efficient deep learning-based offloading technique (CEDOT), intelligently selects the partial offloading policy and also the size of each component of a task to reduce the service delay and energy …

External parameters contribution in domestic load forecasting using neural network

Domestic demand prediction is very important for home energy management system and also for peak reduction in the power system network. In this work, for precise prediction of power demand, external parameters, such as temperature and solar radiation, are considered and included in the prediction model for improving prediction performance. Power prediction models for coming hours' power demand estimation are built using neural network based on hourly power consumptions data with / without ambient temperature data and global solar irradiation (GSI) data respectively. In this work, a typical Southern Norwegian household demand has been considered. As a result, both ambient temperature and GSI…

A Single radio based channel datarate-aware parallel rendezvous MAC protocol for cognitive radio networks

Channel hopping based parallel rendezvous multichannel MAC protocols have several advantages since they do not need a control channel, require only one transceiver, and produce higher system capacity. However, channel hopping sequences in existing parallel rendezvous MAC protocols have been designed as irrelevant to channel datarates, leading to under-utilization of channel resources in multi-rate multi-channel networks. Considering that datarates among channels may be different, we propose a dynamic parallel rendezvous multichannel MAC protocol for synchronized cognitive radio networks in which the secondary users adjust their own distinct hopping sequences according to the datarates of th…

A formal proof of the e-optimality of discretized pursuit algorithms

Learning Automata (LA) can be reckoned to be the founding algorithms on which the field of Reinforcement Learning has been built. Among the families of LA, Estimator Algorithms (EAs) are certainly the fastest, and of these, the family of discretized algorithms are proven to converge even faster than their continuous counterparts. However, it has recently been reported that the previous proofs for ??-optimality for all the reported algorithms for the past three decades have been flawed. We applaud the researchers who discovered this flaw, and who further proceeded to rectify the proof for the Continuous Pursuit Algorithm (CPA). The latter proof examines the monotonicity property of the proba…

The Hierarchical Continuous Pursuit Learning Automation for Large Numbers of Actions

Part 10: Learning - Intelligence; International audience; Although the field of Learning Automata (LA) has made significant progress in the last four decades, the LA-based methods to tackle problems involving environments with a large number of actions are, in reality, relatively unresolved. The extension of the traditional LA (fixed structure, variable structure, discretized, and pursuit) to problems within this domain cannot be easily established when the number of actions is very large. This is because the dimensionality of the action probability vector is correspondingly large, and consequently, most components of the vector will, after a relatively short time, have values that are smal…

ANN modelling of CO2 refrigerant cooling system COP in a smart warehouse

Author's accepted manuscript Industrial cooling systems consume large quantities of energy with highly variable power demand. To reduce environmental impact and overall energy consumption, and to stabilize the power requirements, it is recommended to recover surplus heat, store energy, and integrate renewable energy production. To control these operations continuously in a complex energy system, an intelligent energy management system can be employed using operational data and machine learning. In this work, we have developed an artificial neural network based technique for modelling operational CO2 refrigerant based industrial cooling systems for embedding in an overall energy management s…

Field Measurements and Parameter Calibrations of Propagation Model for Digital Audio Broadcasting in Norway

During 2017, digital audio broadcasting (DAB) replaces frequency modulation (FM) broadcasting and becomes the only technology for national terrestrial audio broadcasting services in Norway. As Norway is the first country that replaces FM completely with DAB, it is of great importance to measure the signal strength of such a technology in massive deployments and to tune a simulation model as a reference for future studies. Therefore, field measurements of received signal strength are carried out in a typical Norwegian area in this work. Based on the data obtained from the measurements, a simulator with a recent empirical propagation model, namely, ITU-R P.1546-5, has been calibrated. The fin…

Safeguarding the Ultra-dense Networks with the aid of Physical Layer Security: A review and a case study

In the wake of the extensive application of the fourth generation system, investigations of new technologies have been moving ahead vigorously to embrace the next generation communications in 2020. Thereinto, the technique of ultra-dense networks (UDNs) serves as a key enabler in meeting the roaring mobile traffic demands. With the prevalence of mobile Internet services especially those involve the mobile payment, security has gained an unprecedented amount of attention and become a highlighted feature for the fifth generation. Resource allocation, one of the most significant tools on getting over the obstacle of ubiquitous interference as well as elevating the spectrum/energy efficiency, h…

A Student's t‐based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Performance analysis of underlay two-way relay cooperation in cognitive radio networks with energy harvesting

Abstract Cognitive radio and energy harvesting are two important approaches to solve the problem of spectrum scarcity and energy constraint in wireless communications. In this work, we study a two-way relay cooperation scheme in underlay cognitive radio networks (CRNs) with energy harvesting in which two secondary users exchange information via an energy harvesting relay node. Since the relay node collects energy from the received signals and utilizes it to forward the information, the secondary transmission power can be markedly reduced. Therefore the interference of the secondary network to the primary network can be substantially reduced. We derive the outage probability of the secondary…

Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation

Our understanding and ability to effectively monitor and manage coastal ecosystems are severely limited by observation methods. Automatic recognition of species in natural environment is a promising tool which would revolutionize video and image analysis for a wide range of applications in marine ecology. However, classifying fish from images captured by underwater cameras is in general very challenging due to noise and illumination variations in water. Previous classification methods in the literature relies on filtering the images to separate the fish from the background or sharpening the images by removing background noise. This pre-filtering process may negatively impact the classificat…

Object Migration Automata for Non-equal Partitioning Problems with Known Partition Sizes

Part 4: Automated Machine Learning; International audience; Solving partitioning problems in random environments is a classic and challenging task, and has numerous applications. The existing Object Migration Automaton (OMA) and its proposed enhancements, which include the Pursuit and Transitivity phenomena, can solve problems with equi-sized partitions. Currently, these solutions also include one where the partition sizes possess a Greatest Common Divisor (GCD). In this paper, we propose an OMA-based solution that can solve problems with both equally and non-equally-sized groups, without restrictions on their sizes. More specifically, our proposed approach, referred to as the Partition Siz…

On the Performance of Channel Assembling and Fragmentation in Cognitive Radio Networks

[EN] Flexible channel allocation may be applied to multi-channel cognitive radio networks (CRNs) through either channel assembling (CA) or channel fragmentation (CF). While CA allows one secondary user (SU) occupy multiple channels when primary users (PUs) are absent, CF provides finer granularity for channel occupancy by allocating a portion of one channel to an SU flow. In this paper, we investigate the impact of CF together with CA for SU flows by proposing a channel access strategy which activates both CF and CA and correspondingly evaluating its performance. In addition, we also consider a novel scenario where CA is enabled for PU flows. The performance evaluation is conducted based on…

A Deep Reinforcement Learning scheme for Battery Energy Management

Deep reinforcement learning is considered promising for many energy cost optimization tasks in smart buildings. How-ever, agent learning, in this context, is sometimes unstable and unpredictable, especially when the environments are complex. In this paper, we examine deep Reinforcement Learning (RL) algorithms developed for game play applied to a battery control task with an energy cost optimization objective. We explore how agent behavior and hyperparameters can be analyzed in a simplified environment with the goal of modifying the algorithms for increased stability. Our modified Deep Deterministic Policy Gradient (DDPG) agent is able to perform consistently close to the optimum over multi…

Capacity driven small cell deployment in heterogeneous cellular networks : Outage probability and rate coverage analysis

Author's accepted manuscript. This is the peer reviewed version of the following article: Ullah, A., Haq Abbas, Z., Muhammad, F., Abbas, G. & Lei, J. (2020). Capacity driven small cell deployment in heterogeneous cellular networks: Outage probability and rate coverage analysis. Transactions on Emerging Telecommunications Technologies, 31(6): e3876, which has been published in final form at https://doi.org/10.1002/ett.3876. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.

Directive local color transfer based on dynamic look-up table

Abstract Color transfer in image processing usually suffers from misleading color mapping and loss of details. This paper presents a novel directive local color transfer method based on dynamic look-up table (D-DLT) to solve these problems in two steps. First, a directive mapping between the source and the reference image is established based on the salient detection and the color clusters to obtain directive color transfer intention. Then, dynamic look-up tables are created according to the color clusters to preserve the details, which can suppress pseudo contours and avoid detail loss. Subjective and objective assessments are presented to verify the feasibility and the availability of the…

Performance comparison of residual related algorithms for ToA positioning in wireless terrestrial and sensor networks

©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." Article also available from publisher: http://dx.doi.org/10.1109/WIRELESSVITAE.2009.5172462 Time of Arrival (ToA) is a popular technique for terrestrial positioning. This paper presents a comparison of ToA based residual related positioning algorithms in wireless terrestrial and sensor networks in both long range outdoor and short range indoor environments. Us…

Robust Interpretable Text Classification against Spurious Correlations Using AND-rules with Negation

The state-of-the-art natural language processing models have raised the bar for excellent performance on a variety of tasks in recent years. However, concerns are rising over their primitive sensitivity to distribution biases that reside in the training and testing data. This issue hugely impacts the performance of the models when exposed to out-of-distribution and counterfactual data. The root cause seems to be that many machine learning models are prone to learn the shortcuts, modelling simple correlations rather than more fundamental and general relationships. As a result, such text classifiers tend to perform poorly when a human makes minor modifications to the data, which raises questi…

Greedy versus Dynamic Channel Aggregation Strategy in CRNs: Markov Models and Performance Evaluation

Part 1: - PE-CRN 2011 Workshop; International audience; In cognitive radio networks, channel aggregation techniques which aggregate several channels together as one channel have been proposed in many MAC protocols. In this paper, we consider elastic data traffic and spectrum adaptation for channel aggregation, and propose two new strategies named as Greedy and Dynamic respectively. The performance of channel aggregation represented by these strategies is evaluated using continuous time Markov chain models. Moreover, simulation results based on various traffic distributions are utilized in order to evaluate the validity and preciseness of the mathematical models.

Analysis on channel bonding/aggregation for multi-channel cognitive radio networks

Channel bonding/aggregation techniques, which assemble several channels together as one channel, could be used in cognitive radio networks for the purpose of achieving better bandwidth utilization. In existing work on this topic, channel bonding/aggregation is focused on the cases when primary channels are time slotted or stationary as compared with secondary users' activities. In this paper, we analyze the performance of channel bonding/aggregation strategies when primary channels are not time slotted and the time scale of primary activities is at the same level as the secondary users', given that spectrum handover is not allowed. Continuous time Markov chain models are built in order to a…

A Learning-Automata Based Solution for Non-equal Partitioning: Partitions with Common GCD Sizes

The Object Migration Automata (OMA) has been used as a powerful tool to resolve real-life partitioning problems in random Environments. The virgin OMA has also been enhanced by incorporating the latest strategies in Learning Automata (LA), namely the Pursuit and Transitivity phenomena. However, the single major handicap that it possesses is the fact that the number of objects in each partition must be equal. Obviously, one does not always encounter problems with equally-sized groups (When the true underlying problem has non-equally-sized groups, the OMA reports the best equally-sized solution as the recommended partition.). This paper is the pioneering attempt to relax this constraint. It p…

Robust Transmission for Reconfigurable Intelligent Surface Aided Millimeter Wave Vehicular Communications With Statistical CSI

The integration of reconfigurable intelligent surface (RIS) into millimeter wave (mmWave) vehicular communications offers the possibility to unleash the potential of future proliferating vehicular applications. However, the high-mobility-induced rapidly varying channel state information (CSI) has been making it challenging to obtain the accurate instantaneous CSI (I-CSI) and to cope with the incurable high signaling overhead. The situation may become worse when the RIS with a large number of passive reflecting elements is deployed. To overcome this challenge, we investigate in this paper a robust transmission scheme for the time-varying RIS-aided mmWave vehicular communications, in which, s…

Channel selection in Cognitive Radio Networks: A Switchable Bayesian Learning Automata approach

We consider the problem of a user operating within a Cognitive Radio Network (CRN) which involves N channels each associated with a Primary User (PU). The problem consists of allocating a channel which, at any given time instant is not being used by a PU, to a Secondary User (SU). Within our study, we assume that a SU is allowed to perform “channel switching”, i.e., to choose an alternate channel S times (where S +1 ≤ N) if the previous choice does not lead to a channel which is vacant. The paper first presents a formal probabilistic model for the problem itself, referred to as the Formal Secondary Channel Selection (FSCS) problem, and the characteristics of the FSCS are then analyzed. Ther…

UDP flows in Cognitive Radios with Channel Aggregation and Fragmentation: A Test-bed Based Evaluation

Channel aggregation (CA) and channel fragmentation (CF) have been studied extensively in cognitive radios (CRs) for many years. However, a test-bed evaluation for such techniques at flow level is still open. In this study, employing National Instruments devices, a test-bed is set up to evaluate the performance of UDP flows for CRs with CA and CF, considering the aspects of blocking, preemption, and throughput in probability. The measurements clearly show that there are performance improvements in applying CA and CF in CRs for UDP flows.

Test-Bed Evaluation of CA and CF via a Software Defined Radio

In Chaps. 3 and 4, we presented the analytical models and simulation approaches to study the impact of CA and CF on traffic flows in the single-flow single-user and the single-flow multi-user systems. In this chapter, we investigate the impact of CA and CF in a test-bed. We employ a software defined radio (SDR) from National Instruments (NI) to evaluate the performance of a CR system with user datagram protocol (UDP) flows. The adopted SDR is based on LTE protocol stack with additional functionalities that can support CA and CF, and accommodating PUs. By conducting measurements based on the test-bed system, we will be able to confirm that performance improvement can indeed be obtained by ap…

Channel Assembling with Priority-Based Queues in Cognitive Radio Networks: Strategies and Performance Evaluation

[EN] With the implementation of channel assembling (CA) techniques, higher data rate can be achieved for secondary users in multi-channel cognitive radio networks. Recent studies which are based on loss systems show that maximal capacity can be achieved using dynamic CA strategies. However the channel allocation schemes suffer from high blocking and forced termination when primary users become active. In this paper, we propose to introduce queues for secondary users so that those flows that would otherwise be blocked or forcibly terminated could be buffered and possibly served later. More specifically, in a multi-channel network with heterogeneous traffic, two queues are separately allocate…

Capacity Upper Bound of Channel Assembling in Cognitive Radio Networks with Quasistationary Primary User Activities

In cognitive radio networks (CRNs) with multiple channels, various channel-assembling (ChA) strategies may be applied to secondary users (SUs), resulting in different achieved capacity. However, there is no previous work on determining the capacity upper bound (UB) of ChA for SUs under given system configurations. In this paper, we derive the maximum capacity for CRNs with ChA through Markov chain modeling, considering that primary user (PU) activities are relatively static, compared with SU services. We first deduce a closed-form expression for the maximum capacity in a dynamic ChA strategy and then demonstrate that no other ChA strategy can provide higher capacity than that achieved by th…

Interpretable Tsetlin Machine-based Premature Ventricular Contraction Identification

Neural network-based models have found wide use in automatic long-term electrocardiogram (ECG) analysis. However, such black box models are inadequate for analysing physiological signals where credibility and interpretability are crucial. Indeed, how to make ECG analysis transparent is still an open problem. In this study, we develop a Tsetlin machine (TM) based architecture for premature ventricular contraction (PVC) identification by analysing long-term ECG signals. The architecture is transparent by describing patterns directly with logical AND rules. To validate the accuracy of our approach, we compare the TM performance with those of convolutional neural networks (CNNs). Our numerical …

Network Slicing Enabled Resource Management for Service-Oriented Ultra-Reliable and Low-Latency Vehicular Networks

Network slicing has been considered as a promising candidate to provide customized services for vehicular applications that have extremely high requirements of latency and reliability. However, the high mobility of vehicles poses significant challenges to resource management in such a stochastic vehicular environment with time-varying service demands. In this paper, we develop an online network slicing scheduling strategy for joint resource block (RB) allocation and power control in vehicular networks. The long-term time-averaged total system capacity is maximized while guaranteeing strict ultra-reliable and low-latency requirements of vehicle communication links, subject to stability const…

On Using the Theory of Regular Functions to Prove the ε-Optimality of the Continuous Pursuit Learning Automaton

Published version of a chapter in the book: Recent Trends in Applied Artificial Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-38577-3_27 There are various families of Learning Automata (LA) such as Fixed Structure, Variable Structure, Discretized etc. Informally, if the environment is stationary, their ε-optimality is defined as their ability to converge to the optimal action with an arbitrarily large probability, if the learning parameter is sufficiently small/large. Of these LA families, Estimator Algorithms (EAs) are certainly the fastest, and within this family, the set of Pursuit algorithms have been considered to be the pioneering schemes. The…

The Hierarchical Continuous Pursuit Learning Automation: A Novel Scheme for Environments With Large Numbers of Actions.

Although the field of learning automata (LA) has made significant progress in the past four decades, the LA-based methods to tackle problems involving environments with a large number of actions is, in reality, relatively unresolved. The extension of the traditional LA to problems within this domain cannot be easily established when the number of actions is very large. This is because the dimensionality of the action probability vector is correspondingly large, and so, most components of the vector will soon have values that are smaller than the machine accuracy permits, implying that they will never be chosen . This paper presents a solution that extends the continuous pursuit paradigm to …

Markov Chain and Stationary Distribution

MC has been a valuable tool for analyzing the performance of complex stochastic systems since it was introduced by the Russian mathematician A. A. Markov (1856–1922) in the early 1900s. More and more system analyses have been carried out by using MC, including the analysis on CA and CF. In this chapter, we will briefly review the essential ingredients of MC that are necessary for the performance analysis presented in this book. A more comprehensive introduction of MC and its applications can be found in Nelson (2013, Probability, stochastic processes, and queueing theory: the mathematics of computer performance modeling).

A Learning Automaton-based Scheme for Scheduling Domestic Shiftable Loads in Smart Grids

In this paper, we consider the problem of scheduling shiftable loads, over multiple users, in smart electrical grids. We approach the problem, which is becoming increasingly pertinent in our present energy-thirsty society, using a novel distributed game-theoretic framework. In our specific instantiation, we consider the scenario when the power system has a local-area Smart Grid subnet comprising of a single power source and multiple customers. The objective of the exercise is to tacitly control the total power consumption of the customers’ shiftable loads, so to approach the rigid power budget determined by the power source, but to simultaneously not exceed this threshold. As opposed to the…

On the Convergence of Tsetlin Machines for the IDENTITY- and NOT Operators

The Tsetlin Machine (TM) is a recent machine learning algorithm with several distinct properties, such as interpretability, simplicity, and hardware-friendliness. Although numerous empirical evaluations report on its performance, the mathematical analysis of its convergence is still open. In this article, we analyze the convergence of the TM with only one clause involved for classification. More specifically, we examine two basic logical operators, namely, the "IDENTITY"- and "NOT" operators. Our analysis reveals that the TM, with just one clause, can converge correctly to the intended logical operator, learning from training data over an infinite time horizon. Besides, it can capture arbit…