arXiv subjects

Ling Cheng

Publications and source records attributed to Ling Cheng.

At least 37 records · Page 2Linked to original sources

arXiv2021

On Decentralization of Bitcoin: An Asset Perspective

Since its advent in 2009, Bitcoin, a cryptography-enabled peer-to-peer digital payment system, has been gaining increasing attention from both academia and industry. An effort designed to overcome a cluster of bottlenecks inherent in existing centralized financial systems, Bitcoin has always been championed by the crypto community as an example of the spirit of decentralization. While the decentralized nature of Bitcoin's Proof-of-Work consensus algorithm has often been discussed in great detail, no systematic study has so far been conducted to quantitatively measure the degree of decentralization of Bitcoin from an asset perspective -- How decentralized is Bitcoin as a financial asset? We present in this paper the first systematic investigation of the degree of decentralization for Bitcoin based on its entire transaction history. We proposed both static and dynamic analysis of Bitcoin transaction network with quantifiable decentralization measures developed based on network analysis and market efficiency study. Case studies are also conducted to demonstrate the effectiveness of our proposed metrics.

cs.CR↗

arXiv2021

A Note on Slepian-Wolf Bounds for Several Node Grouping Configurations

The Slepian-Wolf bound on the admissible coding rate forms the most fundamental aspect of distributed source coding. As such, it is necessary to provide a framework with which to model more practical scenarios with respect to the arrangement of nodes in order to make Slepian-Wolf coding more suitable for multi-node Wireless Sensor Networks. This paper provides two practical scenarios in order to achieve this aim. The first is by grouping the nodes based on correlation while the second involves simplifying the structure using Markov correlation. It is found that although the bounds of these scenarios are more restrictive than the original Slepian-Wolf bound, the overall model and bound are simplified.

cs.IT↗

arXiv2021

Symbol-Level Synchronisation Channel Modelling With Real-World Application: From Davey-Mackay, Fritchman to Markov

Errors in realistic channels contain not only substitution errors, but synchronisation errors as well. Moreover, these errors are rarely statistically independent in nature. By extending on the idea of the Fritchman channel model, a novel error category-based methodology in determining channel characteristics is described for memory channels which contain insertion, deletion, and substitution errors. The practicality of such a methodology is reinforced by making use of real communication data from a visible light communication system. Simulation results show that the error-free and error runs using this new method of defining the channel clearly deviates from the Davey-Mackay synchronisation model which is memoryless in nature. This further emphasises the inherent memory in these synchronisation channels which we are now able to characterise. Additionally, a new method to determine the parameters of a synchronisation memory channel using the Levenshtein distance metric is detailed. This method of channel modelling allows for more realistic communication models to be simulated and can easily extend to other areas of research such as DNA barcoding in the medical domain.

cs.IT↗

arXiv2021

Self-Synchronising On-Off-Keying Visible Light Communication System For Intra and Inter-Vehicle Data Transmission

Visible Light Communication (VLC) is a current technology which allows data to be transmitted by modulating information onto a light source. It has many advantages over traditional radio frequency communication and up to 10,000 times larger bandwidth. Existing research in visible light communication assumes a synchronised channel, however, this is not always easily achieved. In this paper, a novel synchronised intra and inter-vehicle VLC system is proposed to ensure reliable communication in both inter and intra-vehicle communication for Infotainment Systems (IS). The protocol achieves synchronisation at the symbol level using the transistor-transistor logic protocol and achieves frame synchronisations with markers. Consequently, the deployment of the protocol in both inter and intra-vehicle communication presents numerous advantages over existing data transmission processes. A practical application, where VLC is used for media streaming is also previewed. In addition, various regions of possible data transmission are determined with the intention to infer forward error correction schemes to ensure reliable communication.

eess.SP↗

arXiv2020

Optimization Algorithms for Improving the Performance of Permutation Trellis Codes

In this paper, soft-decision (SD) decoders of permutation trellis code (PTC) with $M$-ary frequency shift keying are designed using three optimization algorithms and presented in four decoding schemes. In a concatenated code such as PTC, the Viterbi decoder for the outer convolutional code provides maximum likelihood decoding. Hence, the error correction performance is dependent on the decoding scheme used for the inner code. Due to the structure of the encoder with the modulation scheme, the channel output can be interpreted as an assignment problem. SD decoding can then be designed accordingly, using the presented, low-complexity optimization-based schemes. The bit error rate (BER) performance of the schemes are simulated in an additive white Gaussian noise (AWGN) and powerline communication (PLC) channel. The complexities of the schemes are also presented. The performance of the SD schemes are compared with the existing SD threshold detector, with BER results showing significant coding gain for certain codebooks. From the results, a reasonable trade-off between the complexity and coding gain is observed for a noisy channel such as the PLC channel.

eess.SP↗

arXiv2020

Surface Damage Detection Scheme using Convolutional Neural Network and Artificial Neural Network

Surface damage on concrete is important as the damage can affect the structural integrity of the structure. This paper proposes a two-step surface damage detection scheme using Convolutional Neural Network (CNN) and Artificial Neural Network (ANN). The CNN classifies given input images into two categories: positive and negative. The positive category is where the surface damage is present within the image, otherwise the image is classified as negative. This is an image-based classification. The ANN accepts image inputs that have been classified as positive by the ANN. This reduces the number of images that are further processed by the ANN. The ANN performs feature-based classification, in which the features are extracted from the detected edges within the image. The edges are detected using Canny edge detection. A total of 19 features are extracted from the detected edges. These features are inputs into the ANN. The purpose of the ANN is to highlight only the positive damaged edges within the image. The CNN achieves an accuracy of 80.7% for image classification and the ANN achieves an accuracy of 98.1% for surface detection. The decreased accuracy in the CNN is due to the false positive detection, however false positives are tolerated whereas false negatives are not. The false negative detection for both CNN and ANN in the two-step scheme are 0%.

cs.CV↗

arXiv2020

Low-Complexity Detection of Multiweight Permutation Modulation Space-Time Block Codes for Indoor Visible Light Communication

In this paper, the spectral efficiency of permutation modulation-based multiple input multiple output (MIMO) visible light communication is improved using systematically designed, multiweight codeword matrices. Soft-decision, low-complexity detection schemes are then designed for the receiver and compared with the maximum likelihood (ML) detection method. Bit error rate (BER) results show the soft-decision detection algorithm is able to decode the transmitted information without knowledge of the channel state information. This enables the mobile receiver decode information while within the field of view of the transmitter unit. The BER results also show a close match with the ML detection in some codebooks and the performance of the soft-decision decoder is evaluated for different positions of the receiver in an indoor environment.

eess.SP↗

arXiv2020

Solving MKP Applied to IoT in Smart Grid Using Meta-heuristics Algorithms: A Parallel Processing Perspective

Increasing electricity prices in South Africa and the imminent threat of load shedding due to the overloaded power grid has led to a need for Demand Side Management (DSM) devices like smart grids. For smart grids to perform to their peak, their energy management controller (EMC) systems need to be optimized. Current solutions for DSM and optimization of the Multiple Knapsack Problem (MKP) have been investigated in this paper to discover the current state of common DSM models. Solutions from other NP-Hard problems in the form of the iterative Discrete Flower Pollination Algorithm (iDFPA) as well as possible future scalability options in the form of optimization through parallelization have also been suggested.

cs.NE↗

arXiv2020

Is Deep Learning a Renormalization Group Flow?

Although there has been a rapid development of practical applications, theoretical explanations of deep learning are in their infancy. Deep learning performs a sophisticated coarse graining. Since coarse graining is a key ingredient of the renormalization group (RG), RG may provide a useful theoretical framework directly relevant to deep learning. In this study we pursue this possibility. A statistical mechanics model for a magnet, the Ising model, is used to train an unsupervised restricted Boltzmann machine (RBM). The patterns generated by the trained RBM are compared to the configurations generated through an RG treatment of the Ising model. Although we are motivated by the connection between deep learning and RG flow, in this study we focus mainly on comparing a single layer of a deep network to a single step in the RG flow. We argue that correlation functions between hidden and visible neurons are capable of diagnosing RG-like coarse graining. Numerical experiments show the presence of RG-like patterns in correlators computed using the trained RBMs. The observables we consider are also able to exhibit important differences between RG and deep learning.

cs.LG↗

arXiv2020

Structured Light in Turbulence

Optical communication is an integral part of the modern economy, having all but replaced electronic communication systems. Future growth in bandwidth appears to be on the horizon using structured light, encoding information into the spatial modes of light, and transmitting them down fibre and free-space, the latter crucial for addressing last mile and digitally disconnected communities. Unfortunately, patterns of light are easily distorted, and in the case of free-space optical communication, turbulence is a significant barrier. Here we review recent progress in structured light in turbulence, first with a tutorial style summary of the core concepts, before highlighting the present state-of-the-art in the field. We support our review with new experimental studies that reveal which types of structured light are best in turbulence, the behaviour of vector versus scalar light in turbulence, the trade-off of diversity and multiplexing, and how turbulence models can be exploited for enhanced optical signal processing protocols. This comprehensive treatise will be invaluable to the large communities interested in free-space optical communication with spatial modes of light.

physics.optics↗

arXiv2020

A multivariate water quality parameter prediction model using recurrent neural network

The global degradation of water resources is a matter of great concern, especially for the survival of humanity. The effective monitoring and management of existing water resources is necessary to achieve and maintain optimal water quality. The prediction of the quality of water resources will aid in the timely identification of possible problem areas and thus increase the efficiency of water management. The purpose of this research is to develop a water quality prediction model based on water quality parameters through the application of a specialised recurrent neural network (RNN), Long Short-Term Memory (LSTM) and the use of historical water quality data over several years. Both multivariate single and multiple step LSTM models were developed, using a Rectified Linear Unit (ReLU) activation function and a Root Mean Square Propagation (RMSprop) optimiser was developed. The single step model attained an error of 0.01 mg/L, whilst the multiple step model achieved a Root Mean Squared Error (RMSE) of 0.227 mg/L.

cs.LG↗

arXiv2020

A Comprehensive Analysis of Correlated Source Compression Using Edge Computing in Distributed Systems

This paper examines the theory pertaining to lossless compression of correlated sources located at the edge of a network. Importantly, communication between nodes is prohibited. In particular, a method that combines correlated source coding and matrix partitioning is explained. This technique is then made more flexible, by restricting the method to operate on two distinct groups of nodes. As a result, this new method allows for more freedom in compression performance, with consequent trade-off in node integrity validation. Specifically, it provides 2-3 times the compression savings when using a Hamming(7,4) with 4 nodes. It also decreases the complexity with regard to managing the nodes as they join/leave the network, while retaining the range within which the information can be losslessly decoded.

cs.IT↗

arXiv2020

Optimal DG allocation and sizing in power system networks using swarm-based algorithms

Distributed generation (DG) units are power generating plants that are very important to the architecture of present power system networks. The benefit of the addition of these DG units is to increase the power supply to a network. However, the installation of these DG units can cause an adverse effect if not properly allocated and/or sized. Therefore, there is a need to optimally allocate and size them to avoid cases such as voltage instability and expensive investment costs. In this paper, two swarm-based meta-heuristic algorithms, particle swarm optimization (PSO) and whale optimization algorithm (WOA) were developed to solve optimal placement and sizing of DG units in the quest for transmission network planning. A supportive technique, loss sensitivity factors (LSF) was used to identify potential buses for optimal location of DG units. The feasibility of the algorithms was confirmed on two IEEE bus test systems (14- and 30-bus). Comparison results showed that both algorithms produce good solutions and they outperform each other in different metrics. The WOA real power loss reduction considering techno-economic factors in the IEEE 14-bus and 30-bus test system are 6.14 MW and 10.77 MW, compared to the PSOs' 6.47 MW and 11.73 MW respectively. The PSO has a more reduced total DG unit size in both bus systems with 133.45 MW and 82.44 MW compared to WOAs' 152.21 MW and 82.44 MW respectively. The paper unveils the strengths and weaknesses of the PSO and the WOA in the application of optimal sizing of DG units in transmission networks.

cs.NE↗

arXiv2020

Short sighted deep learning

A theory explaining how deep learning works is yet to be developed. Previous work suggests that deep learning performs a coarse graining, similar in spirit to the renormalization group (RG). This idea has been explored in the setting of a local (nearest neighbor interactions) Ising spin lattice. We extend the discussion to the setting of a long range spin lattice. Markov Chain Monte Carlo (MCMC) simulations determine both the critical temperature and scaling dimensions of the system. The model is used to train both a single RBM (restricted Boltzmann machine) network, as well as a stacked RBM network. Following earlier Ising model studies, the trained weights of a single layer RBM network define a flow of lattice models. In contrast to results for nearest neighbor Ising, the RBM flow for the long ranged model does not converge to the correct values for the spin and energy scaling dimension. Further, correlation functions between visible and hidden nodes exhibit key differences between the stacked RBM and RG flows. The stacked RBM flow appears to move towards low temperatures whereas the RG flow moves towards high temperature. This again differs from results obtained for nearest neighbor Ising.

cs.LG↗

arXiv2019

Trends in the optimal location and sizing of electrical units in smart grids using meta-heuristic algorithms

The development of smart grids has effectively transformed the traditional grid system. This promises numerous advantages for economic values and autonomous control of energy sources. In smart grids development, there are various objectives such as voltage stability, minimized power loss, minimized economic cost and voltage profile improvement. Thus, researchers have investigated several approaches based on meta-heuristic optimization algorithms for the optimal location and sizing of electrical units in a distribution system. Meta-heuristic algorithms have been applied to solve different problems in power systems and they have been successfully used in distribution systems. This paper presents a comprehensive review on existing methods for the optimal location and sizing of electrical units in distribution networks while considering the improvement of major objective functions. Techniques such as voltage stability index, power loss index, and loss sensitivity factors have been implemented alongside the meta-heuristic optimization algorithms to reduce the search space of solutions for objective functions. However, these techniques can cause a loss of optimality. Another perceived problem is the inappropriate handling of multiple objectives, which can also affect the optimality of results. Hence, a recent method such as Pareto fronts generation has been developed to produce non-dominating solutions. This review shows a need for more research on (i) the effective handling of multiple objective functions, (ii) more efficient meta-heuristic optimization algorithms and/or (iii) better supporting techniques.

eess.SY↗

arXiv2019

Stack-VS: Stacked Visual-Semantic Attention for Image Caption Generation

Recently, automatic image caption generation has been an important focus of the work on multimodal translation task. Existing approaches can be roughly categorized into two classes, i.e., top-down and bottom-up, the former transfers the image information (called as visual-level feature) directly into a caption, and the later uses the extracted words (called as semanticlevel attribute) to generate a description. However, previous methods either are typically based one-stage decoder or partially utilize part of visual-level or semantic-level information for image caption generation. In this paper, we address the problem and propose an innovative multi-stage architecture (called as Stack-VS) for rich fine-gained image caption generation, via combining bottom-up and top-down attention models to effectively handle both visual-level and semantic-level information of an input image. Specifically, we also propose a novel well-designed stack decoder model, which is constituted by a sequence of decoder cells, each of which contains two LSTM-layers work interactively to re-optimize attention weights on both visual-level feature vectors and semantic-level attribute embeddings for generating a fine-gained image caption. Extensive experiments on the popular benchmark dataset MSCOCO show the significant improvements on different evaluation metrics, i.e., the improvements on BLEU-4/CIDEr/SPICE scores are 0.372, 1.226 and 0.216, respectively, as compared to the state-of-the-arts.

cs.CV↗

arXiv2019

Modelling the Memory of Turbulence-Induced Beam Wander

One of the major challenges for long range, high speed Free-Space Optical (FSO) communication is turbulence induced beam wander. Beam wander causes fluctuations in the received intensity as well as crosstalk in mode division multiplexed systems. Existing models for beam wander make use of probability distributions and long term averages and are not able to accurately model time-dependent intensity fluctuations such as deep fading, where the received intensity is too low to maintain reliable communication for an extended period of time. In this work we present an elegant new memory model which models the behaviour of beam wander induced intensity fluctuations with the unique capability to accurately simulate deep fading. This is invaluable for the development of optimised error correction coding and digital signal processing in order to improve the throughput and reliability of FSO systems.

eess.SP↗

arXiv2019

Long-Bone Fracture Detection using Artificial Neural Networks based on Contour Features of X-ray Images

The following paper proposes two contour-based fracture detection schemes. The development of the contour-based fracture is based on the line-based fracture detection schemes proposed in arXiv:1902.07458. Existing Computer Aided Diagnosis (CAD) systems commonly employs Convolutional Neural Networks (CNN), although the cost to obtain a high accuracy is the amount of training data required. The purpose of the proposed schemes is to obtain a high classification accuracy with a reduced number of training data through the use of detected contours in X-ray images. There are two contour-based fracture detection schemes. The first is the Standard Contour Histogram Feature-Based (CHFB) and the second is the improved CHFB scheme. The difference between the two schemes is the removal of the surrounding detected flesh contours from the leg region in the improved CHFB scheme. The flesh contours are automatically classified as non-fractures. The contours are further refined to give a precise representation of the image edge objects. A total of 19 features are extracted from each refined contour. 8 out of the 19 features are based on the number of occurrences for particular detected gradients in the contour. Moreover, the occurrence of the 0-degree gradient in the contours are employed for the separation of the knee, leg and foot region. The features are a summary representation of the contour, in which it is used as inputs into the Artificial Neural Network (ANN). Both Standard CHFB and improved CHFB schemes are evaluated with the same experimental set-ups. The average system accuracy for the Standard CHFB scheme is 80.7%, whilst the improved CHFB scheme has an average accuracy of 82.98%. Additionally, the hierarchical clustering technique is adopted to highlight the fractured region within the X-ray image, using extracted 0-degree gradients from fractured contours.

cs.CV↗