Easy Deployment Research Articles

Robust fall detection in video surveillance based on weakly supervised learning

Fall event detection has been a research hotspot in recent years in the fields of medicine and health. Currently, vision-based fall detection methods have been considered the most promising methods due to their advantages of a non-contact characteristic and easy deployment. However, the existing vision-based fall detection methods mainly use supervised learning in model training and require much time and energy for data annotations. To address these limitations, this work proposes a detection method that uses a weakly supervised learning-based dual-modal network. The proposed method adopts a deep multiple instance learning framework to learn the fall events using weak labels. As a result, the proposed method does not require time-consuming fine-grained annotations. The final detection result of each video is obtained by integrating the information obtained from two streams of the dual-modal network using the proposed dual-modal fusion strategy. Experimental results on two public benchmark datasets and a proposed dataset demonstrate the superiority of the proposed method over the current state-of-the-art methods.

Enhancing BIM security in emergency construction projects using lightweight blockchain-as-a-service

Rapid design and construction of mobile cabin hospitals (MCHs) have become imperative in the COVID-19 response. However, due to unique design specifications (e.g., parallel design and model pre-revision), collaboration in emergency construction projects (ECPs) like MCHs presents data security vulnerabilities, including a lack of traceability and transparency. These hazards invariably reduce design effectiveness, leading to undesirable rework and project delay. Blockchain technology is a potential solution to address the aforementioned security issues in ECPs because it offers immutable and traceable data storage. Nevertheless, directly implementing blockchain in ECPs is impractical, for the blockchain has a complex deployment process and provides limited functions supporting BIM-based design. Therefore, this paper develops a lightweight blockchain-as-a-service (LBaaS) prototype to enhance the ECPs design efficiency by securing and automating information exchange while eliminating the difficulties of deploying and using blockchain. This paper contributes three elements: (1) Security vulnerabilities of design in ECP are identified. Taking an MCH in Hong Kong as an example, this paper investigates its design process and determines two design characteristics and associated security flaws. (2) Key technologies to support easy deployment and usage of blockchain in ECPs are developed. New technical elements, including a Multi-to-One mapping (MtOM) kit for easy blockchain registration, an integrated workflow retaining existing design practices, and smart contracts for secure interaction with blockchain, are developed to support LBaaS functionality. (3) An LBaaS prototype is validated and evaluated. The prototype is illustrated and evaluated using design examples based on actual MCH project data. Results show that the LBaaS is a feasible and secure approach for ECPs collaboration. This paper deepens the understanding of data security issues in ECPs and offers technical guidance in establishing blockchain solutions.

Abstract 2194: Performance of a 24-hour turnaround, guideline-complete NSCLC assay, optimized for sparse samples

Abstract Non-Small Cell Lung Cancer (NSCLC) is the most common type of lung cancer with 236,740 new cases diagnosed in 2022 in the United States. In this study, we demonstrate the performance of a digital PCR (dPCR) based assay that reports 15 (NCCN) biomarkers using only 1 slide of FFPE tissue. ChromaCode’s High Definition PCR (HDPCR™) NSCLC Tumor Profiling Panel (Research Use Only) detects guideline biomarkers including variants for EGFR, BRAF, KRAS, ERBB2, ALK, ROS1, RET, MET, and NTRK1-3 with a 24 hour turnaround time and a simple PCR workflow. A total of 34 NSCLC FFPE specimens were evaluated using the HDPCR NSCLC Panel. DNA and RNA were extracted from a single 10 µm section of specimen, with a sister section previously characterized with the Oncomine Precision Assay GX. The DNA/RNA eluates (20ng DNA per well, 20ng RNA) were run with the HDPCR NSCLC Panel on the Qiagen QIAcuity dPCR instrument. Data analysis was conducted using ChromaCloud™, which includes a proprietary algorithm that can detect and quantify 6 targets (5 targets and 1 internal control) for each of the 3 wells. DNA samples discordant with the comparator assay were evaluated using the Archer DNA VariantPlex panel, while RNA discordant samples were analyzed using the Archer RNA fusion plex panel. Of the 34 specimens evaluated, 16 SNV, 13 Insertion/Deletion, and 7 fusion positive specimens were identified using NGS. On the dPCR assay, the results show a high-level of agreement, after discordant resolution, of 95.8% across the panel. The minor allele fraction (MAF), also calculated using ChromaCode Cloud, ranged from 5% to 90%. With 20 ng input (Qubit), the average copy number of amplifiable DNA ranged from 791 copies (~2.3 ng) to 6583 copies (~19.9 ng) with a mean of 2410 copies (~7.43 ng). Here we demonstrate that the HDPCR NSCLC Tumor Profiling Panel provides comparable performance to validated NGS assays with a turnaround that enables adherence to guideline-recommended treatment, and a low sample requirement that maximizes the number of patients for molecular testing. The standard protocols used by the assay and the use of a cloud based analysis pipeline enables easy deployment of this assay in any lab. Citation Format: Kerri Cabrera, Jeff Gole, Bryan Leatham, Isabel Regoli, Tiffany Martinez, Andrew Richards, Leah Herdt, Juan Enciso, Paige Berroteran, Heather Carolan, Brad Brown. Performance of a 24-hour turnaround, guideline-complete NSCLC assay, optimized for sparse samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2194.

Fully Biodegradable and Long-Term Operational Primary Zinc Batteries as Power Sources for Electronic Medicine.

Given the advantages of high energy density and easy deployment, biodegradable primary battery systems remain as a promising power source to achieve bioresorbable electronic medicine, eliminating secondary surgeries for device retrieval. However, currently available biobatteries are constrained by operational lifetime, biocompatibility, and biodegradability, limiting potential therapeutic outcomes as temporary implants. Herein, we propose a fully biodegradable primary zinc-molybdenum (Zn-Mo) battery with a prolonged functional lifetime of up to 19 days and desirable energy capacity and output voltage compared with reported primary Zn biobatteries. The Zn-Mo battery system is shown to have excellent biocompatibility and biodegradability and can significantly promote Schwann cell proliferation and the axonal growth of dorsal root ganglia. The biodegradable battery module with 4 Zn-Mo cells in series using gelatin electrolyte accomplishes electrochemical generation of signaling molecules (nitric oxide, NO) that can modulate the behavior of the cellular network, with efficacy comparable with that of conventional power sources. This work sheds light on materials strategies and fabrication schemes to develop high-performance biodegradable primary batteries to achieve a fully bioresorbable electronic platform for innovative medical treatments that could be beneficial for health care.

Digital Twin Evolution for Hard-to-Follow Aeronautical Ad-Hoc Networks in Beyond 5G

The aircrafts were top of the places that disrupted the seamless connectivity requirement of 5G and beyond. The Aeronautical Ad-hoc Networks (AANETs) take the attention of both industry and academia to satisfy this connectivity requirement with the low cost, easy deployment, and continuous coverage features. On the other hand, the ultra-dynamic characteristics of AANET with unstructured topology make its environment hard-to-follow. Here, Artificial Intelligence (AI)-based methodologies have an essential role in handling the management complexity of this hard-to-follow environment. However, these methodologies increase the computational complexity of aircraft due to the continuous update, convergence time, and scalability issues. At that point, we propose the utilization of the Digital Twin (DT) technology to handle the management complexity of AANET while solving the main issues of AI-based methodologies on it. The DT can virtually replicate the physical AANET components through closed-loop feedback in real-time. Therefore, this work introduces the utilization of DT technology for the AANET orchestration and, accordingly, proposes a DT-enabled AANET (DT-AANET) topology management framework. This framework consists of the Physical AANET Twin and Controller, including Digital AANET Twin with Operational Module. Here, the Digital AANET Twin virtually represents the physical environment while the operational module executes the AI-based computations on them through unsupervised learning-based training or supervised learning-based prediction. Finally, we present a case study based on Learning Vector Quantization (LVQ) to show the usability of the proposed framework and support this through evaluation results.

Dynamic load allowance of long-span modular steel bridges

Modular steel bridges are structures whose construction is based on regular prefabricated truss units. This presents several advantages, such as rapid and easy deployment, high adaptability to the terrain and reduced construction costs. However, they generally face operational restrictions for span lengths greater than 60m. Recent technological innovations search to overcome these limitations and develop modular structures with larger spans. Hence, the main objective of this work is to evaluate the dynamic effects on long-span modular steel bridges. The present contribution provides a study on two modular bridge typologies, considering different span lengths from 120 to 140m. A 3D coupled vehicle–bridge model is used to analyse the vehicle–bridge interaction and the dynamic load allowance of the structures. The vehicle is represented as a multibody truck system and the bridges are modelled with the finite element method. Several types of randomly-generated road irregularities are considered on the bridge deck. The effect of each type of irregularity is evaluated on the dynamic load allowance of the bridges. The results obtained reveal the notable influence of road irregularities that involve abrupt vertical displacements that excite the vehicle mode shapes. In addition, it is observed that dynamic load allowance indices tend to decrease with longer spans and higher speeds, except when a resonance is produced.

Improved traffic sign recognition algorithm based on YOLOv4-tiny

This study offers an enhanced yolov4-tiny traffic sign identification method for easy deployment on mobile or embedded devices to address the difficulties of a high number of parameters, low recognition accuracy, and poor real-time performance of traffic sign recognition models in complex scenarios. The yolov4-tiny network serves as the model’s foundation. To begin, Octave Convolution is incorporated into the backbone network to eliminate low-frequency feature redundancy, lowering the number of parameters in the model and enhancing computational efficiency. Second, the convolutional block attention module is employed to improve the recognition accuracy of small and medium-sized targets by strengthening the weights of traffic sign regions and suppressing the weights of invalid features. Finally, in the feature fusion stage, the Feature Pyramid Networks structure is replaced with the Simplified Path Aggregation Network structure to improve the fusing of shallow feature information with deep semantic knowledge and lower the miss detection rate even more On the TT100K data set as well as on CCTSDB dataset, the experimental results suggest that our technique can achieve good recognition performance. With a 16MB model size, our solution improves the mean average precision by 3.5 percent and the Frame Per Second by 12.5 f/s when compared to the yolov4-tiny algorithm. Our method outperforms yolov4-tiny in terms of recognition accuracy and detection speed, and it can easily meet the real-time requirements for traffic sign recognition.

RPL objective function for multihop PLC network

The increasing number of devices in the Internet of things (IoT) is beginning to exhaust the wireless channel. As an alternative, power line communication (PLC) appears promising and has the potential of providing easy deployment at low cost. On one hand, PLC resembles wireless low-power and lossy networks (LLN). Its performance is time-varying and sensitive to noise, interference, and asymmetry owing to coexisting electronic appliances. However, when running the standard IPv6 routing protocol for LLN (RPL) on PLC, the network suffers significant reliability loss because RPL does not reflect the unique characteristics of PLC. To address this problem, we propose PLC-OF, an objective function (OF) for RPL over PLC. PLC-OF exploits PLC's physical layer (PHY) diversity as a routing metric distinct from other approaches in wireless. PLC-OF finds the most suitable path to prepare for sudden interference or congestion in power lines. Evaluation results from real testbed experiments show that PLC-OF better tolerates noisy medium compared to existing objective functions. Particularly, PLC-OF has better reliability (≈10%) and robustness with less channel usage (≈30%) than that of ETX-based MRHOF in highly congested environments.

Petri Nets-Based Modeling Solution for Cyber–Physical Product Control Considering Scheduling, Deployment, and Data-Driven Monitoring

For a complex electromechanical product that is a cyber–physical system (CPS), its dynamic behaviors are embodied in the closed-loop control between the logic process in its cyber component and actual actuators/sensors in its physical component, and thus, a well-defined model of the control is important to create a digital twin that acts as much like the real machine as possible. This article proposes a Petri nets (PNs)-based modeling solution that employs hybrid PNs (HPNs) for physics and system of sequential systems with shared resources (S4R) nets for logic in building a hierarchical control model. We also present PNs technologies for implementing a smooth transition and bidirectional mapping from the virtual prototype to the real machine. These technologies involve a PNs integration of a reinforcement learning (RL) method for generating a workflow scheduling agent in design, an extension of PNs definitions that is compatible with the microcontroller for easy deployment in manufacturing, and an architecture of PNs execution recording for data-driven monitoring in service. A software kit is provided for the solution that includes an integrated development environment of PNs, tools for quickly building a virtual prototype, and a monitor server for remote data-driven monitoring. This solution is successfully applied in the development of a typical cyber–physical product case, namely, the chemiluminescence immunoassay (CLIA) analyzer.

Intelligent Reflecting Surface Aided Cloud Access Networks with Federated Learning

Compared with traditional cellular network architecture, cloud access network has some significant advantages in spectrum utilization, energy consumption and network construction cost. However, a high-quality forward link is required between the baseband processing unit pool and remote radio head (RRH) in the cloud access network, which results in limited RRH deployment and affects user access link transmission and coverage. To solve this issue, this paper introduces the intelligent reflector technology into the cloud access network as a solution with low energy consumption, low cost and easy deployment to deal with the existing bottlenecks. Firstly, an efficient channel information acquisition strategy based on federated learning is designed for the smart reflector to enhance the user access link, so as to achieve a compromise between the channel estimation accuracy and cost. On this basis, a robust beamforming design and optimization method of the compression mechanism of the forward link are proposed for the smart reflector to enhance the user access link and the wireless forward link, so as to improve the system transmission performance. Finally, we explore the joint resource allocation method of intelligent reflector assisted cloud access network, and improve the system energy efficiency through the collaborative configuration of intelligent reflector and cloud access network communication resources. The research of this paper will provide an important theoretical basis for the application of intelligent reflectors in cloud access networks, especially for the federated learning.

Extending and Programming the NVMe I/O Determinism Interface for Flash Arrays

Predictable latency on flash storage is a long-pursuit goal, yet unpredictability stays due to the unavoidable disturbance from many well-known SSD internal activities. To combat this issue, the recent NVMe IO Determinism (IOD) interface advocates host-level controls to SSD internal management tasks. Although promising, challenges remain on how to exploit it for truly predictable performance. We present IODA , 1 an I/O deterministic flash array design built on top of small but powerful extensions to the IOD interface for easy deployment. IODA exploits data redundancy in the context of IOD for a strong latency predictability contract. In IODA , SSDs are expected to quickly fail an I/O on purpose to allow predictable I/Os through proactive data reconstruction. In the case of concurrent internal operations, IODA introduces busy remaining time exposure and predictable-latency-window formulation to guarantee predictable data reconstructions. Overall, IODA only adds five new fields to the NVMe interface and a small modification in the flash firmware while keeping most of the complexity in the host OS. Our evaluation shows that IODA improves the 95–99.99 th latencies by up to 75×. IODA is also the nearest to the ideal, no disturbance case compared to seven state-of-the-art preemption, suspension, GC coordination, partitioning, tiny-tail flash controller, prediction, and proactive approaches.

A Near-Optimal Energy Management Mechanism Considering QoS and Fairness Requirements in Tree Structure Wireless Sensor Networks.

The rapid development of AIOT-related technologies has revolutionized various industries. The advantage of such real-time sensing, low costs, small sizes, and easy deployment makes extensive use of wireless sensor networks in various fields. However, due to the wireless transmission of data, and limited built-in power supply, controlling energy consumption and making the application of the sensor network more efficient is still an urgent problem to be solved in practice. In this study, we construct this problem as a tree structure wireless sensor network mathematical model, which mainly considers the QoS and fairness requirements. This study determines the probability of sensor activity, transmission distance, and transmission of the packet size, and thereby minimizes energy consumption. The Lagrangian Relaxation method is used to find the optimal solution with the lowest energy consumption while maintaining the network's transmission efficiency. The experimental results confirm that the decision-making speed and energy consumption can be effectively improved.

Design and analysis of lightweight anonymity protocol for host- and AS-level anonymity

Lightweight anonymity protocols in the network layer are promising in terms of high throughput and low latency. The realistic and weak assumption of local adversaries deletes the need for hop-by-hop encryption of onion routing, and this contributes to high throughput and low latency. Among them, PHI and its successor dPHI are promising due to the fact that path-setup packets for anonymous paths are forwarded according to IP routing. This feature enables easy deployment of the protocol on the Internet infrastructure. However, PHI and dPHI are vulnerable to adversaries such as malicious ASes who compromise servers for path setup, called helpers, and malicious ASes who leverage topological information and routing policies of the IP network. The paper resolves the vulnerabilities in the two steps. In the first step, we design attacks that leverage the vulnerabilities to break anonymity. In the second step, we extend dPHI to mitigate such attacks by adding a new server called a guard. The extended protocol is called gPHI, and gPHI is resilient against adversaries who perform such attacks. This paper designs gPHI and validates its resilience through formal proof and simulation.

VERDA: A Multisampler Tool for Mesopelagic Nets

Different types and systems to discriminate plankton samples at different strata in the water column have been developed in recent decades. For sampling at sufficient depth, opening and closing zooplankton multinets are ideal because there is no contamination of one sample with organisms of the previous one. However, for bigger nets, such as those used to collect micronektonic organism, it is difficult to use multiple net units, and multiple cod ends are preferred because of their simplicity, but with the problem of sample contamination from having a common net passageway. We present here a cod-end Multisampler design, VERDA, that uses a carrousel-like system. Similar to some sediment traps, the system works like a revolver with six or eight compartments whose turning mechanism is triggered when the net arrives to a programmed depth level. This prototype was built with inexpensive and recycled components and electronics similar to Arduino® and Teensy PCB to carry out electronic control. The net we used for testing the equipment was a mid-size midwater trawl of ca. 30 m2 and total length of 58 m that works with a single towing cable and no doors. The overall system is useful for all type of ships, due to the relatively easy deployment operations and because the Multisampler does not need electrical cable or acoustics. In our case, we used a Marport® (Reykjavik Iceland) and Scanmar (Åsgårdstrand, Norway) sensors for real-time depth monitoring and opening distance.

Bolts looseness detection in construction machinery

Bolt connections have increasingly wide applications in the mechanical industry, such as civil construction, construction machinery, etc. However, along with the vibration of the mechanical operation, bolt looseness is unavoidable and thus can lead to severe consequences. Therefore, the detection of loose bolts deserves an in-depth study. However, conventional bolt looseness detection techniques require a lot of time and labor, and direct contact sensor-based detection techniques are sensitive to interference from the surroundings. To tackle these challenges mentioned above, this work proposes a vision-based bolt looseness detection method, with the advantages of low cost, easy deployment, and non-contact. Specifically, an innovative attention mechanism is introduced into the Yolov4 framework to improve the accuracy and speed of bolt detection. Each bolt image is cropped by the recognition result of the detector, then the bolt contour is extracted by edge detection, and the bolt is determined to be loose or not based on the angle of bolt rotation. Comprehensive experiments in real-world settings verify the effectiveness and efficiency of the proposed method.

Modeling of optical wave propagation through turbulent atmosphere using fractional approach for FSO wireless communication

Free space optical (FSO) wireless communication has emerged as a viable alternative to the existing fiber optics and radio frequency (RF) communications due to its ability to operate in unlicensed spectrum, offering huge data handling capacities and being cost effective with easy deployment. The underlying communication mechanism in the FSO link is based upon the laser beam propagation model. The propagating free space optical beam undergoes signal degradation due to refraction and diffraction caused by atmospheric turbulence measured by refractive index structure parameter (C n 2). In this work, a novel, robust analytical model for propagating Gaussian-beam through atmospheric turbulence is presented, by solving the space-fractional paraxial wave equation. We report analytical expressions for the intensity and long-term beam spreading of a Gaussian beam in terms of space-fractional parameter D, for the range 2 < D ≤ 3. This range of parameter D, defines the effective number of euclidean space corresponding to atmospheric turbulence levels faced by the propagating Gaussian beam in the FSO link. The results of the proposed fractional model and the existing models agreed well, therefore the D-dimensional parameter can be used to effectively express the value of refractive index structure parameter (C n 2). The classical values of C n 2 ranges form 10−13 to 10−16 for strong to weak fluctuations respectively. However, in fractional-dimension scale, these fluctuations can be express as D = 2.668 (strong fluctuations) to D = 2.999 (weak fluctuations). The ideal case of free space beam propagation can be expressed as D = 3, with no turbulence. Moreover, we have studied the fractional-dimension model performance for varying wavelengths. Further, based upon a practical example, we proposed a self-sustainable FSO link architecture to efficiently minimize the effect of weak and strong fluctuations on the propagating optical beam, based upon the D-dimension fractional parameter, hence ensuring reliability of FSO link.

Task Offloading in Multi-Access Edge Computing Enabled UAV-Aided Emergency Response Operations

In emergency response operations, using uncrewed aerial vehicles (UAVs) has recently become a promising solution due to their flexibility and easy deployment. However, tasks performed by the UAVs, e.g., object detection and human pose recognition, usually require a high computation capacity and energy supply. Furthermore, offloading tasks to the edge server-equipped base stations may not always be possible because of a lack of infrastructure or distance. Therefore, UAV-aided edge servers can be deployed near UAV scouts to provide computing services. However, a UAV can not perform all types of tasks since it has limitations on memory, available software, central processing unit (CPU), and graphics processing unit (GPU) capacity. Therefore, this study focuses on task offloading (TO), power, and computation resource allocation (PRA) problems in a multi-layer MEC-enabled UAV network while taking into account CPU and GPU requirements of tasks, the capacity of the devices (i.e., computational resources, power, and energy), and limitations on the type of tasks a UAVs can perform. The problem is formulated as a non-convex mixed-integer nonlinear problem to minimize the weighted sum of the maximum energy consumption ratio in the network and total task execution latency ratio, and then decomposed and converted into an integer and a convex problem. A messy genetic algorithm (mGA)-based TO and PRA strategy (mGA-TPR) is proposed to solve the problem, where two PRA strategies are based on the Karush–Kuhn–Tucker conditions used to solve the PRA problem. Simulation results verify that the proposed scheme can outperform the baseline methods.

Connectivity Guarantee Within UAV Cluster: A Graph Coalition Formation Game Approach

Unmanned Aerial Vehicle (UAV) clustering is promising for performing large-scale missions because of high mobility and easy deployment. However, the connectivity problems caused by the high mobility of UAVs and the interferences are mostly ignored in existing UAV mission executions. Rapid restoration of UAV network connectivity is essential for preventing communication disruption and improving the overall network performance. Thus, a graph coalition formation game that integrates the UAV time-varying topology graph with the coalition formation game is proposed to rapidly restore the connectivity of the UAV network in need. The method improves the utility of UAV clustering and ensures the network connectivity. Then, a graph coalition formation game algorithm based on the shortest path tree (SPT-GCF) rapidly forms an approximately optimal coalition structure. The simulation results show that the proposed approach increases the average utility of UAV clustering by 6.5% and 14.5% respectively when compared with the existing non-overlapping coalition formation game (NOCFG) and the coalition formation game without considering the cluster connectivity.

Integrated Security Information and Event Management (SIEM) with Intrusion Detection System (IDS) for Live Analysis based on Machine Learning

This research builds Security Information & Event Management (SIEM) based on live analysis using machine learning on Intrusion Detection System (IDS). To implement a live analysis technique on IDS using machine learning that is integrated with SIEM, we need a combined system with many processes and services. All processes and services must be orchestrated and combined into one system to make live analysis work. Selection of the right components to be combined into a system that can build live analysis techniques on IDS using machine learning that are integrated with SIEM is needed. In addition, an open-source system for easy deployment is needed in the industrial application. Therefore, this research tries to build the system using most common open-source components for cyberattack live analysis, detection, and monitoring. This research uses a combination of Elastic (ELK) Stack, Slips, and Zeek IDS to build the system. To ensure that the components selected are correct, robust, and reliable, it is necessary to measure the performance of the combined system. Measurement focuses on measuring the performance of resource consumption (CPU and RAM). The proposed system is testing using Denial of Service (DoS) test with 344.1/sec packet. The performance testing shows Elasticsearch is the most component that uses CPU and RAM consumption with 78% of CPU usage and 2300 Mb of RAM usage. The least CPU and RAM consumption is Zeek with 3.5% CPU usage and 225 Mb RAM usage. The proposed system also worked for detecting DoS attacks on the network.

ARGUS: a pole climbing surveillance robot

Due to the high prevalence and unpredictability of violent protest action in South Africa, a need has arisen for rapidly deployable surveillance. This paper proposes an Automated Robotic Guardian for Urban Surveillance (ARGUS) platform, a wheeled, pole climbing robot as a potential solution. The platform is designed to attach to and traverse up existing cylindrically shaped infrastructure, such as light posts, enabling easy deployment in urban environments. The robot is intended for various surveillance needs, such as public safety at events, and periods of unrest or protest action. Following a detailed concept design stage, simulated results are presented for the proposed robot. This includes comprehensive CAD modelling, static force and torque calculations of the pole climbing robot, and finite element analysis of the component stresses while positioned on the pole. The robotic platform is currently in production and future work will include experimental validation of the simulated system.