This paper demonstrates how a combination of Business Intelligence (BI) energy analytics and Advanced Metering Infrastructure (AMI) data can serve as a better decision support platform for Electricity Distribution Companies in Nigeria (DISCOS) to compute and account for power flows in their electrical distribution networks. The authors analyzed a sample undisclosed Distribution Transformer (DT) metering dataset collected from the AMI cloud data of one of the Nigerian DISCOS using Microsoft Power BI. Results obtained have indicated that the analytic platform can serve as a decision support tool for DISCOs in Nigeria to account for energy flows in their networks and can also be combined with power flow tools to estimate energy losses in the network.
The ongoing integration of Internet of Things (IoT) within Fifth-generation (5G) mobile ecosystem promises to provide smart and intelligent services meant to foster vertical industry digitalization. 5G-IoT private networks have become a popular choice of adoption by industry verticals in accelerating digital transformation across various sectors, thereby culminating in Industry 4.0. Research and development on 5G-IoT private networks has mainly focused on the technical aspect with less attention on the business side. This paper discusses viable business model options for 5G-IoT private networks for applications in diverse industry sectors and the attainment of economic value in terms of revenue maximization, cost minimization and sustainability.
As businesses look to boost flexibility and cut costs, cloud computing is becoming more and more popular. There is always room for improvement, despite the fact that the major cloud service providers use a pay-as-you-go pricing model and provide consumers rapid scaling up and down. CPU and memory utilisation, which represents workload, frequently varies, which causes businesses to incur extra costs and has an adverse effect on the environment. To lessen the impact of this, a Random Forest algorithm which is a supervised machine learning model is proposed for resource scheduling in cloud computing environment. This proposed model is an ensemble algorithm for regression, classification and task decisions that deal with developing decision trees. The proposed strategy is compared to existing machine learning algorithms including XGBoost, Ridge and Lasso. Experimental results show that the proposed algorithm performs better than the compared algorithms in terms of the prediction accuracy of the CPU and memory. Experimental results conducted on go ogle colaboratory using Materna Dataset shows that the proposed algorithm achieved a prediction higher accuracy of 0.1 – 45.1 % higher than the compared algorithms in terms of the R-squared (R 2 ), the Root Mean Square Error (RMSE) and lower percentage errors for Mean Absolute Percentage Error (MAPE).
Utilities offer time-of-use tariff schemes as a form of demand-side management. In this tariff structure, the cost of electricity is high during peak periods when the power system is constrained. For many users, the cost of electricity during peak periods can be costly for their operations. In this work, a load management solution is proposed together with its associated hardware implementation. The load management solution requires users to classify their loads as either critical or non-critical and set a peak period energy limit for the control. The load management strategy is demonstrated through a case study to reduce overall energy consumption and the cost of energy.
Humans (and other types of primates for that matter) have a long history in different types of dispute resolution. The expression Alternative Dispute Resolution is used to describe types of non-violent dispute resolution which do not involve the court system. There are documented historical records of alternative dispute resolution systems being used in the ancient Greek city-states as early as 400 BC. This paper provides a brief history of some of these different types of alternative dispute resolution systems and then specifically looks at how alternative dispute resolution can be transformed using digital tools. The different types of alternative dispute resolution systems include negotiation, mediation, and arbitration. Online Dispute Resolution is a specific type of alternate dispute resolution which is conducted at a distance over the internet and may include a combination of negotiation, mediation, and arbitration. Online dispute resolution systems appeared on the internet at the same time as e-commerce systems started to appear. These online dispute resolution systems gave potential users of e-commerce systems the confidence that any dispute which may arise on the e-commerce system would be dealt with fairly. The use of digital tools can transform these processes of alternative dispute resolution systems.
Technology is advancing ever so steadily and all efforts are geared towards the automation of processes to make human life easier. The fourth industrial revolution is driving the use of technology in production processes and organizations such as health, agriculture and manufacturing among others to improve the functionalities of said processes. Machine learning and artificial intelligence, as well as the Internet of Things and cloud computing, are some of the current hot technologies. Agriculture is one of the most crucial areas in human survival and has therefore seen more technological resources directed towards improving production while minimizing costs. The process of realizing this vision however hasn't been a hurdle free and this paper strives to take a peek into the situation of technology use (Internet of Things) in agriculture to understand some of the successes, challenges and gaps that need filling. To effectively shed some light on the situation this paper reviews research work from scholars in the field of precision agriculture with a focus on their proposed solutions, Equipment and technologies employed such as (ZigBee, Low PAN, Bluetooth, GSM, Wi-Fi, AI and Cloud computing) and compares between solutions offered by the researchers by identifying gaps
Ultrasound imaging uses reflected echo signals from high-frequency sound waves transmitted into a body with high water content to create images of the internal soft tissue, bone, and blood flow. Different kinds of noise are introduced during the signal acquisition and processing stages. Ultrasound images are mostly affected by speckle noise. High-quality images can improve the observational accuracy of diagnostic exams. Speckle noise degrades an image's quality, making it difficult to recognize, analyze, and measure. To reduce speckle noise in ultrasound images and improve diagnostic capability, an investigation and comparison study of the different image filters currently available was done. Thereafter a hybrid filter combination of speckle, gaussian, median and average filter was used to remove speckle noise in ultrasound images. The proposed hybrid filters outperformed other filters in terms of PSNR, SNR, SSIM and MSE.
The global market for 5G technology has been experiencing significant growth and is expected to continue expanding rapidly in the foreseeable future. In 2020 alone, more than 225 million 5G smartphones were sold worldwide. This trend urges network operators to investigate and assess the readiness of their existing and planned 5G network infrastructure to effectively serve the increasing number of 5G subscribers. Among the various components of a 5G mobile network, the User Plane Function (UPF) within the core network plays a crucial role in determining its performance. As a result, it is essential for both network equipment vendors and operators to evaluate the performance of their UPF ecosystem. This research paper takes an initial step towards profiling UPF performance by employing stress testing procedures. The primary performance metric measured in this study is the CPU utilization of a UPF Network Function under different levels of network traffic. We leverage open-source 3GPP-compliant 5G New Radio stack by UERANSIM and a Core Network implementation by the Open5GS project. Our results show that the Open5Gs UPF implementation can achieve up to 200 concurrent data connections using only 17 % of the virtual machine's CPUs and 200 control traffic connections using only 15% of the same CPUs.
In this paper, infrastructure-based two-way relaying with single-active orbital angular momentum index modulation (SA-OAM-IM), a special case of OAM-IM, is proposed for radio frequency line-of-sight communication. In the first time slot or transmission phase, two source/destination nodes simultaneously transmit information blocks to a relay node. After decoding, in the second time slot or transmission phase, the relay forms a single information block via network coding, which is then forwarded to the source/destination nodes. The information block is then decoded at the respective destination node via network coding. Hence, exchange of information blocks is achieved between the source/destination nodes. The scheme demonstrates a significantly improved bit error performance compared to its one-way relaying counterpart. The theoretical average bit error probability at the destination node is formulated for the proposed scheme and validated by Monte Carlo simulation results. A low-complexity near-maximum likelihood detector for SA-OAM-IM is further investigated.
Underwater data centers have been recognized to be suitable future computing platforms due to the benefits of low cooling. In addition, underwater computing platforms reduce content access latency for coastal subscribers. The performance of underwater data centers is influenced by different system configuration parameters and different events such as ocean warming that occurs in the underwater environment. In addition, the capabilities of different underwater computing platforms to keep functioning given the occurrence of ocean warming events should also be considered. This challenge can be addressed by defining a tiering system for underwater computing platforms. The presented research proposes a four-tier system for underwater computing platforms. The four-tier system describes the different capabilities and operational contexts of underwater computing platforms enabling their continued functionality in the event of the occurrence of marine heat waves and ocean warming events.
The increasing penetration of renewable energy generation, the emergence of new type of loads, and the deregulation of energy markets have made it more challenging to operate power systems securely. The frequent changes in operating conditions require fast and flexible control of power flows and voltage. This paper presents a method for placing and configuring unified power flow controller (UPFC) in power systems. The UPFC is placed based on the line overload-voltage deviation index (LOVDI), and its settings are determined using a hybrid genetic-simulated annealing (HGSA) algorithm. The goal of the proposed method is to maximize the static security of the power system by minimizing the composite security index (CSI). The CSI measures the severity of both line overloading and voltage violations under a single branch outage. The proposed technique has been evaluated on one area of RTS-GMLC network under peak load operating scenario. The technique achieves remarkable improvement on the static security of the test system by reducing the line overloads, voltage deviations and composite security index, and shifting the operating state from insecure to alarm for most component outages.
This paper presents low-complexity block-based encoding and decoding algorithms for short block length channels. In terms of the precise use-case, we are primarily concerned with the baseline 3GPP Short block transmissions in which payloads are encoded by Reed-Muller codes and paired with orthogonal DMRS. In contemporary communication systems, the short block decoding often employs the utilization of DMRS- based least squares channel estimation, followed by maximum likelihood decoding. However, this methodology can incur substantial computational complexity when processing long bit length codes. We propose an innovative approach to tackle this challenge by introducing the principle of block/segment encoding using First-Order RM Codes which is amenable to low-cost decoding through block-based fast Hadamard transforms. The Block-based FHT has demonstrated to be cost-efficient with regards to decoding time, as it evolves from quadric to quasilinear complexity with a manageable decline in performance. Additionally, by incorporating an adaptive DMRS/data power adjustment technique, we can bridge/reduce the performance gap and attain high sensitivity, leading to a good trade-off between performance and complexity to efficiently handle small payloads.
We are in an era when the diagnosis of diseases benefits greatly from digitizing their control. On the other hand, malaria is a long-standing yet one of the deadliest diseases. Its diagnosis processes still use old mechanisms, even when we have embarked on the Fourth Industrial Revolution. It is, therefore, more important than ever to digitize its control to enhance its diagnosis. This paper studies a digital data management model for malaria control in the context of a typical African country, Rwanda. Existing data management models are revisited, exploited, and contextualized to suit the Rwandan scenario, with the aim of their adoption. The proposed model in this study is the first of its kind for the developing world.
The current energy mix model of South Africa is centred around coal-fired power plants which make up over 80% of the total power generation capacity in South Africa. In recent years, the performance of coal-fired power plants has deteriorated drastically; hence some are in the process of being decommissioned. This has led to roll-out load shedding to manage the energy supply and demand, protecting the grid from collapsing. To address this supply-demand inequality, this paper explores the feasibility of re-purposing the existing coal-fired power plants targeted for possible decommissioning for deploying Small Modular Reactors (SMRs). The SMRs are attractive because of their size and footprint; they could be placed in brownfield sites as replacements for decommissioned coal-fired power plants. Presented in this paper is a preliminary assessment of the existing framework, geographical information system, available scientific data, infrastructure requirements, industrial capacity and the current suitability gaps in coal-fired power station sites that could inhibit the deployment of modular reactors. When selecting sites for retired coal-fired power stations, it is essential to follow a consistent approach that aligns with generic nuclear regulations considering site qualifications and sensitivity criteria.
Electricity technology advancement in the world increases energy demand. The pollution problems caused by non-renewable fuels have accelerated the use of distributed energy resources (DERs) in all industries. Microgrids have been used in many industries to improve resiliency. Energy Management Systems (EMS) can help enhance microgrids' robustness, ensuring that the grid remains stable and operational long when energy is minimal. EMS allows for centralised monitoring and control of energy use across building systems. It is developed to record, store, and process power consumption data of every major appliance in the house and industries. An Energy Management System consists of different components seen as operational units. Operational units are responsible for measurement, communication, decision-making, and power supply switching control to manipulate the power output to meet the energy demands. If hierarchical control is used on a DC microgrid, efficiency can be improved by introducing a load-shedding concept, minimising downtime. This research aims to model and develop an energy management system in PV/Composite Storage DC microgrid system. An agent-based approach is used in the implementation of the EMS.
In recent years, many studies have failed to implement an effective palm print recognition system for high-security applications. This study focuses on developing a novel palm print recognition system using novel data processing techniques. The study proposes an embedded zero-tree wavelet (EZW) and principal component analysis (PCA) feature extraction technique concerning palm print recognition. The database contains palm print image samples from right and left palm images. 200 images of 5 people were captured with each person, and 40 shots were used. 150 images were used in the SVM training, and 50 images were used in the SVM testing. The spectral feature extraction of the palm print image is processed by the EZW. The spatial feature extraction of the palm print image is processed by PCA. The minimum distance classifier is used for the comparison of results. Finally, the palm print images are trained and classified with Support Vector Machine (SVM). The researcher concluded that, when compared to the other evaluated approaches and classifiers, the palm print recognition system that combines EZW and PCA as a method of feature extraction is the most accurate. The overall testing results show that the proposed approach yields a maximum of 90.4% recognition accuracy.
The successful use of deep learning (DL) algorithms in a variety of applications is conceptually based on convolutions. Though convolution is a simple operation, it suffers from severe performance degradation when implemented in software. Recently, with the advancement of CMOS technology, the convolution operation in DL algorithms has been accelerated by being delegated to specialized hardware platforms such as Field Programmable Gate Array (FPGA) devices. On hardware platforms, the convolution operation can be implemented on a synthesizable processor core or custom hardware accelerators based on systolic arrays (SA). Choosing an optimal hardware implementation should not be done analytically but instead employ the use of tools for fast and accurate estimation of metrics such as execution cycles, hardware resource utilization, and power consumption. This work evaluates the efficiency of implementing the convolution operation on various SA dimensions (8 × 8, 16 × 16, and 32 × 32) on the open-source Gemmini DL hardware accelerator with a comparison to the synthesizable RISC-V Rocket processor core. In terms of execution cycles the 8 × 8, 16 × 16, and 32 × 32 Gemmini configurations offer speedups of 323×, 249×, and 204× relative to the Rocket core. This work shows that, unlike the General Matrix to Matrix Multiplication (GEMM), the performance of the convolution operation degrades by an average factor of 2 when the Gemmini SA is doubled. In terms of hardware resource utilization on the Zynq Ultrascale+ ZCU104 FPGA evaluation board, the area and power consumption increased by 3.1× and 2.7× when the Gemmini SA dimension is doubled. Overall, the 8 × 8 Gemmini SA dimension recorded the highest performance-per-area metric making it the most efficient for a popular convolution configuration.
Agriculture is one of the sectors that contribute to the development of the country as it is also a basic source of livelihood for people in rural areas. With an increase in the population, the demand for food also increases. As a result, agriculture is unable to meet this demand because of climate change affecting productivity. Climate change which is a long-term shift in weather pattern impact on the environment has made it difficult to produce enough to accommodate the growing population. To overcome this problem smart Agriculture is adopted. Smart agriculture is making use of IoT devices. The Internet of Things (IoT) plays an important role in weather prediction since accuracy is crucial to produce crops. However, the adoption of smart agriculture is still in the very early stages, especially in the rural areas of developing nations. Despite some level of awareness about climate issues regarding agriculture especially in the developed countries, in the rural areas of developing nations, the small-scale farmers have little or no knowledge about climate change which impacts their productivity. This article presents a general review of the innovation and technologies in smart agriculture using IoT. This paper also discusses the IoT-based commercial solutions developed for smart farming. Based on the review of these existing works and commercial products, a conclusion can be drawn that key challenges and future scope of research in this domain are found. One of the findings is that all the existing solutions or the proposed solutions cater to developed nations and financially stable and big farmers less to rural small-scale farmers. As the methods that they are currently using to predict the weather condition due to recent climate change, these methods' prediction accuracy is very low. Hence, there is a need to further explore other approaches that can be adopted by small-scale farmers for highly accurate weather prediction which this study aims to achieve.
While several standards guide the design and applications of solar systems, there is none that is specific to telecommunications environment. The industry combines these generic standards with best practice among peers and operators to get a solution that is tailor made for the specific country or region of operation. This paper uses Africa near-equator and regional behavior as additional inputs to optimize solar solution in telecom environment. This leads to additional push for reduced carbon emissions. Every watt counts! There should be a balance between capex and projected nopex at design stage, that is, increasing diesel costs versus maturity in Lithium Energy Storage. The results show that there are changing climate patterns which need additional consideration at design stage. Continuous power supply is possible with good storage even during low insolation and there is opportunity to extend back up duration by using smart power distribution integrated with user behavioral patterns leading to idle wireless resources. It research recommends disconnection of mid band at night since there is low generation. The low band comes into play in this case. Additional study on impact of artificial intelligence is required to hasten transition from traditional backup. Additionally, the use of software defined circuit breakers and authorization which is used to keep transmission systems live is necessary.
In the world of technology, data have been available easily and in huge amounts. Because of the large amounts of data, Educational Data Mining (EDM) is increasingly gaining more importance. Educational data mining is trending as it is the analysis method for analyzing educational data. It involves checking the relationship between the characteristics of students and which features affect their final grades the most. It can also involve predictive modeling by predicting the final grades of students in the future to help educational institutes rescue failing students before they actually fail. Predictive analysis was the main focus in past years where most researchers targeted predicting grades of students. However, not all educational institutes are able to collect the amount of data suitable for machine learning models to achieve good accuracy. That is why the main target of the work presented in this paper is to develop an interactive interface that gives the ability to educational institutes to check by themselves the relation between different factors using correlation mining. The output of the tool is visual with message boxes to make it understandable by users. That is the best way to discover hidden patterns and trends without the need for a large amount of data in addition to removing the cost of deploying machine learning models. The second goal of the work is to give teachers the ability to check the quality of the content of their slides to help them provide a better learning process.
