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Articles 391 - 420 of 2088

Full-Text Articles in Systems and Communications

A Patient-Specific Algorithm For Lung Segmentation In Chest Radiographs, Manawaduge Supun De Silva, Barath Narayanan Narayanan, Russell C. Hardie Nov 2022

A Patient-Specific Algorithm For Lung Segmentation In Chest Radiographs, Manawaduge Supun De Silva, Barath Narayanan Narayanan, Russell C. Hardie

Electrical and Computer Engineering Faculty Publications

Lung segmentation plays an important role in computer-aided detection and diagnosis using chest radiographs (CRs). Currently, the U-Net and DeepLabv3+ convolutional neural network architectures are widely used to perform CR lung segmentation. To boost performance, ensemble methods are often used, whereby probability map outputs from several networks operating on the same input image are averaged. However, not all networks perform adequately for any specific patient image, even if the average network performance is good. To address this, we present a novel multi-network ensemble method that employs a selector network. The selector network evaluates the segmentation outputs from several networks; on …

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Deepdemod: Bpsk Demodulation Using Deep Learning Over Software-Defined Radio, Arhum Ahmad, Satyam Agarwal, Sam Darshi, Sumit Chakravarty Nov 2022

Deepdemod: Bpsk Demodulation Using Deep Learning Over Software-Defined Radio, Arhum Ahmad, Satyam Agarwal, Sam Darshi, Sumit Chakravarty

Open Access Publishing Fund Collection

In wireless communication, signal demodulation under non-ideal conditions is one of the important research topic. In this paper, a novel non-coherent binary phase shift keying demodulator based on deep neural network, namely DeepDeMod, is proposed. The proposed scheme makes use of neural network to decode the symbols from the received sampled signal. The proposed scheme is developed to demodulate signal under fading channel with additive white Gaussian noise along with hardware imperfections, such as phase and frequency offset. The time varying nature of hardware imperfections and channel poses a additional challenge in signal demodulation. In order to address this issue, …

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A Socio-Economic Analysis Of Automated Container Terminal (Act) Concept In Indonesia : Case Study : New Priok Container Terminal One, Febri Triana Hartami Siagian Oct 2022

A Socio-Economic Analysis Of Automated Container Terminal (Act) Concept In Indonesia : Case Study : New Priok Container Terminal One, Febri Triana Hartami Siagian

World Maritime University Dissertations

No abstract provided.

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Device Free Indoor Localization Of Human Target Using Wifi Fingerprinting, Prasanga Neupane Oct 2022

Device Free Indoor Localization Of Human Target Using Wifi Fingerprinting, Prasanga Neupane

LSU Master's Theses

Indoor localization of human objects has many important applications nowadays. Proposed here is a new device free approach where all the transceiver devices are fixed in an indoor environment so that the human target doesn't need to carry any transceiver device with them. This work proposes radio-frequency fingerprinting for the localization of human targets which makes this even more convenient as radio-frequency wireless signals can be easily acquired using an existing wireless network in an indoor environment. This work explores different avenues for optimal and effective placement of transmitter devices for better localization. In this work, an experimental environment is …

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Detection Of Data Over Wireless Mobile Channels Based On Maximum Likelihood Technique, Mohd Israil Oct 2022

Detection Of Data Over Wireless Mobile Channels Based On Maximum Likelihood Technique, Mohd Israil

Al-Bahir

Next generation wireless systems are characterized by very high transmission bit rates which gives rise to severe Intersymbol interference (ISI) and this makes the detection process very challenging. Hence, assessment of performance of near-optimal detectors like Near Maximum Likelihood Detectors (NMLD) over such channels assumes great importance. This paper deals with the detection of data in the presence of Noise and ISI with NMLD. Performance improvement of NMLD, as compared to nonlinear equalization, has been assessed in terms of BER versus SNR curves obtained through computer simulation. A number of different cases of mobile radio channels have been simulated in …

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A Design Flow For Additively Manufactured 3d Metasurface Antennas​, Justin Parkhurst Oct 2022

A Design Flow For Additively Manufactured 3d Metasurface Antennas​, Justin Parkhurst

Doctoral Dissertations and Master's Theses

Metasurface (MTS) antennas are complex arrays consisting of hundreds or thousands of individual elements that each exert their own influence on the performance of the antenna. Due to this, the process of designing and developing a MTS antenna can be intensive in terms of both the time to understand the how these antennas operate and time running calculations that achieve optimal performance. Through the use of automation for geometry creation in ANSYS HFSS, the work involved in making a MTS antenna can be greatly simplified. The overall objective of this thesis is to reduce the burden of constructing a MTS …

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Hardware Security For Wireless Communications Systems Using Antenna-Based Radio Frequency Fingerprint Engineering, Noemi Miguelez Gomez Oct 2022

Hardware Security For Wireless Communications Systems Using Antenna-Based Radio Frequency Fingerprint Engineering, Noemi Miguelez Gomez

Doctoral Dissertations and Master's Theses

The design and application of novel physical-layer security techniques have been increasing in the last decades as means to enhance the security that more traditional techniques provide to wireless communications systems. Well-known hardware security techniques, such as radio frequency fingerprinting, use unintended manufacturing process variations and unique hardware structures in the semiconductors for applications such as identification and classification of the source of different transmitted signals, and detection of hardware modifications. The uniqueness of the features that two different modules present, even maintaining the same design, can be used for modules characterization at a lower cost and complexity than other …

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Evaluating Large Delay Estimation Techniques For Assisted Living Environments, Swarnadeep Bagchi, Ruairí De Fréin Sep 2022

Evaluating Large Delay Estimation Techniques For Assisted Living Environments, Swarnadeep Bagchi, Ruairí De Fréin

Articles

Abstract Phase wraparound due to large inter-sensor spacings in multi-channel demixing limits the range of relative delays that many time–frequency relative delay estimators can estimate. The performance of a large relative delay estimation method, called the elevatogram, is evaluated in the presence of significant phase wraparound. This paper compares the elevatogram with the popular relative delay estimator used in DUET and the brute-force approach in D-AdRess and analyses its computational efficiency. The elevatogram can accurately estimate relative delays of speech signals of up to 800 samples, whereas DUET and D-AdRess were limited to delays of 7 and 35 samples, given …

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An Empirical Comparison Of The Security And Performance Characteristics Of Topology Formation Algorithms For Bitcoin Networks, Muntadher Sallal, Ruairí De Fréin, Ali Malik, Benjamin Aziz Sep 2022

An Empirical Comparison Of The Security And Performance Characteristics Of Topology Formation Algorithms For Bitcoin Networks, Muntadher Sallal, Ruairí De Fréin, Ali Malik, Benjamin Aziz

Articles

There is an increasing demand for digital crypto-currencies to be more secure and robust to meet the following business requirements: (1) low transaction fees and (2) the privacy of users. Nowadays, Bitcoin is gaining traction and wide adoption. Many well-known businesses have begun accepting bitcoins as a means of making financial payments. However, the susceptibility of Bitcoin networks to information propagation delay, increases the vulnerability to attack of the Bitcoin network, and decreases its throughput performance. This paper introduces and critically analyses new network clustering methods, named Locality Based Clustering (LBC), Ping Time Based Approach (PTBC), Super Node Based Clustering …

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Studying Routing Issues In Vanets Using Ns-3 And Sumo, Mohammad Mahmoud Abdellatif, Omar E. Aly Sep 2022

Studying Routing Issues In Vanets Using Ns-3 And Sumo, Mohammad Mahmoud Abdellatif, Omar E. Aly

Electrical Engineering

ehicular Ad-hoc Networks VANETs are normally sparse, highly dense, and highly mobile with many different and ever-changing topologies. These characteristics impose a challenge on finding a routing algorithm that fits the requirements of such network. The aim of this work is to study the performance issues of VANETs under different scenarios using realistic mobility models. In this paper, a comparative study is done among Ad-hoc On- Demand Distance Vector (AODV), Optimized Link State Routing (OLSR) and position-based routing protocols, namely Greedy Perimeter stateless routing (GPSR), and Max duration Min angle GPSR (MMGPSR). The comparison is done using key quality of …

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Development Of A Security-Focused Multi-Channel Communication Protocol And Associated Quality Of Secure Service (Qoss) Metrics, Paul M. Simon Sep 2022

Development Of A Security-Focused Multi-Channel Communication Protocol And Associated Quality Of Secure Service (Qoss) Metrics, Paul M. Simon

Theses and Dissertations

The threat of eavesdropping, and the challenge of recognizing and correcting for corrupted or suppressed information in communication systems is a consistent challenge. Effectively managing protection mechanisms requires an ability to accurately gauge the likelihood or severity of a threat, and adapt the security features available in a system to mitigate the threat. This research focuses on the design and development of a security-focused communication protocol at the session-layer based on a re-prioritized communication architecture model and associated metrics. From a probabilistic model that considers data leakage and data corruption as surrogates for breaches of confidentiality and integrity, a set …

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Resource Allocation, User Association And Placement For Uav-Assisted Communications, Shuai Zhang Aug 2022

Resource Allocation, User Association And Placement For Uav-Assisted Communications, Shuai Zhang

Dissertations

In the past few years, unmanned aerial vehicle (UAV)-assisted heterogeneous network has attracted significant attention due to its wide range of applications, such as disaster rescue and recovery, ground macro base station (MBS) traffic offloading, communications for temporary events, and data collection for further processing in Internet of Things (IoT). A UAV can act as a flying base station (BS) to quickly recover the communication coverage in the disaster area when the regular terrestrial infrastructure is malfunctioned. The UAV-assisted heterogeneous network can effectively provision line of sight (LoS) communication links and therefore can mitigate potential signal shadowing and blockage. The …

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28 Ghz Patch Antenna Array With Reduced Mutual Coupling For 5g Communications Systems, Rahabu F. Mwang’Amba, Hashimu Uledi Iddi Aug 2022

28 Ghz Patch Antenna Array With Reduced Mutual Coupling For 5g Communications Systems, Rahabu F. Mwang’Amba, Hashimu Uledi Iddi

Tanzania Journal of Engineering and Technology (TJET)

A 28 GHz patch antenna array with reduced mutual coupling for 5G communication systems is presented in this paper. Two elements antenna array was simulated with a periodic boundary to represent an infinity array. The antenna array is attached with a pair of the coupled directional coupler with a coupling value of -3.47 dB, and transmission lengths of 3.40 mm and 7.62 mm depending on the antenna array's magnitude and phase coefficient were designed and simulated. A reduced mutual coupling of -31.86 dB compared to -10.75 dB for an array without a decoupling network was observed. The wide scanning angle …

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Towards A Low-Cost Solution For Gait Analysis Using Millimeter Wave Sensor And Machine Learning, Mubarak A. Alanazi, Abdullah K. Alhazmi, Osama Alsattam, Kara Gnau, Meghan Brown, Shannon Thiel, Kurt Jackson, Vamsy P. Chodavarapu Aug 2022

Towards A Low-Cost Solution For Gait Analysis Using Millimeter Wave Sensor And Machine Learning, Mubarak A. Alanazi, Abdullah K. Alhazmi, Osama Alsattam, Kara Gnau, Meghan Brown, Shannon Thiel, Kurt Jackson, Vamsy P. Chodavarapu

Electrical and Computer Engineering Faculty Publications

Human Activity Recognition (HAR) that includes gait analysis may be useful for various rehabilitation and telemonitoring applications. Current gait analysis methods, such as wearables or cameras, have privacy and operational constraints, especially when used with older adults. Millimeter-Wave (MMW) radar is a promising solution for gait applications because of its low-cost, better privacy, and resilience to ambient light and climate conditions. This paper presents a novel human gait analysis method that combines the micro-Doppler spectrogram and skeletal pose estimation using MMW radar for HAR. In our approach, we used the Texas Instruments IWR6843ISK-ODS MMW radar to obtain the micro-Doppler spectrogram …

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Glaciernet2: A Hybrid Multi-Model Learning Architecture For Alpine Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Michael P. Bishop, Jeffrey S. Kargel, Theus Aspiras Aug 2022

Glaciernet2: A Hybrid Multi-Model Learning Architecture For Alpine Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Michael P. Bishop, Jeffrey S. Kargel, Theus Aspiras

Electrical and Computer Engineering Faculty Publications

In recent decades, climate change has significantly affected glacier dynamics, resulting in mass loss and an increased risk of glacier-related hazards including supraglacial and proglacial lake development, as well as catastrophic outburst flooding. Rapidly changing conditions dictate the need for continuous and detailed ob-servations and analysis of climate-glacier dynamics. Thematic and quantitative information regarding glacier geometry is fundamental for understanding climate forcing and the sensitivity of glaciers to climate change, however, accurately mapping debris-cover glaciers (DCGs) is notoriously difficult based upon the use of spectral information and conventional machine-learning techniques. The objective of this research is to improve upon an …

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A Memory-Centric Customizable Domain-Specific Fpga Overlay For Accelerating Machine Learning Applications, Atiyehsadat Panahi Aug 2022

A Memory-Centric Customizable Domain-Specific Fpga Overlay For Accelerating Machine Learning Applications, Atiyehsadat Panahi

Graduate Theses and Dissertations

Low latency inferencing is of paramount importance to a wide range of real time and userfacing Machine Learning (ML) applications. Field Programmable Gate Arrays (FPGAs) offer unique advantages in delivering low latency as well as energy efficient accelertors for low latency inferencing. Unfortunately, creating machine learning accelerators in FPGAs is not easy, requiring the use of vendor specific CAD tools and low level digital and hardware microarchitecture design knowledge that the majority of ML researchers do not possess. The continued refinement of High Level Synthesis (HLS) tools can reduce but not eliminate the need for hardware-specific design knowledge. The designs …

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Snap : A Software-Defined & Named-Data Oriented Publish-Subscribe Framework For Emerging Wireless Application Systems, Manveen Kaur Aug 2022

Snap : A Software-Defined & Named-Data Oriented Publish-Subscribe Framework For Emerging Wireless Application Systems, Manveen Kaur

All Dissertations

The evolution of Cyber-Physical Systems (CPSs) has given rise to an emergent class of CPSs defined by ad-hoc wireless connectivity, mobility, and resource constraints in computation, memory, communications, and battery power. These systems are expected to fulfill essential roles in critical infrastructure sectors. Vehicular Ad-Hoc Network (VANET) and a swarm of Unmanned Aerial Vehicles (UAV swarm) are examples of such systems. The significant utility of these systems, coupled with their economic viability, is a crucial indicator of their anticipated growth in the future. Typically, the tasks assigned to these systems have strict Quality-of-Service (QoS) requirements and require sensing, perception, and …

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Deep Learning Based Localization Of Zigbee Interference Sources Using Channel State Information, Dylan Kensler Aug 2022

Deep Learning Based Localization Of Zigbee Interference Sources Using Channel State Information, Dylan Kensler

All Theses

As the field of Internet of Things (IoT) continues to grow, a variety of wireless signals fill the ambient wireless environment. These signals are used for communication, however, recently wireless sensing has been studied, in which these signals can be used to gather information about the surrounding space. With the development of 802.11n, a newer standard of WiFi, more complex information is available about the environment a signal propagates through. This information called Channel State Information (CSI) can be used in wireless sensing. With the help of Deep Learning, this work attempts to generate a fingerprinting technique for localizing a …

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An Evaluation Of Wi-Fi 802.11b Backscatter, Anthony Chen Aug 2022

An Evaluation Of Wi-Fi 802.11b Backscatter, Anthony Chen

All Theses

Internet of Things (IoT) devices are in need of low-power communications systems with longevity and reliability. With the use of backscatter technology, IoT devices can communicate at the cost of almost no power and can last for up to a decade. Furthermore, backscatter technology is compatible with everyday wireless signals such as Wi-Fi and Bluetooth, allowing for easy communication without specific hardware constraints. This thesis aims to evaluate a Wi-Fi backscatter system and analyze its ease in triggering off of such ambient signals and sources. The system will utilize Wi-Fi 802.11b as a backscatter source to trigger the backscatter system …

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Algorithm Optimization And Hardware Acceleration For Machine Learning Applications On Low-Energy Systems, Jianchi Sun Aug 2022

Algorithm Optimization And Hardware Acceleration For Machine Learning Applications On Low-Energy Systems, Jianchi Sun

All Dissertations

Machine learning (ML) has been extensively employed for strategy optimization, decision making, data classification, etc. While ML shows great triumph in its application field, the increasing complexity of the learning models introduces neoteric challenges to the ML system designs. On the one hand, the applications of ML on resource-restricted terminals, like mobile computing and IoT devices, are prevented by the high computational complexity and memory requirement. On the other hand, the massive parameter quantity for the modern ML models appends extra demands on the system's I/O speed and memory size. This dissertation investigates feasible solutions for those challenges with software-hardware …

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Reduced Fuel Emissions Through Connected Vehicles And Truck Platooning, Paul D. Brummitt Aug 2022

Reduced Fuel Emissions Through Connected Vehicles And Truck Platooning, Paul D. Brummitt

Electronic Theses and Dissertations

Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication enable the sharing, in real time, of vehicular locations and speeds with other vehicles, traffic signals, and traffic control centers. This shared information can help traffic to better traverse intersections, road segments, and congested neighborhoods, thereby reducing travel times, increasing driver safety, generating data for traffic planning, and reducing vehicular pollution. This study, which focuses on vehicular pollution, used an analysis of data from NREL, BTS, and the EPA to determine that the widespread use of V2V-based truck platooning—the convoying of trucks in close proximity to one another so as to reduce air drag …

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Optimized Three Deep Learning Models Based-Pso Hyperparameters For Beijing Pm2.5 Prediction, Andri Pranolo, Yingchi Mao, Aji Prasetya Wibawa, Agung Bella Putra Utama, Felix Andika Dwiyanto Jul 2022

Optimized Three Deep Learning Models Based-Pso Hyperparameters For Beijing Pm2.5 Prediction, Andri Pranolo, Yingchi Mao, Aji Prasetya Wibawa, Agung Bella Putra Utama, Felix Andika Dwiyanto

Knowledge Engineering and Data Science

Deep learning is a machine learning approach that produces excellent performance in various applications, including natural language processing, image identification, and forecasting. Deep learning network performance depends on the hyperparameter settings. This research attempts to optimize the deep learning architecture of Long short term memory (LSTM), Convolutional neural network (CNN), and Multilayer perceptron (MLP) for forecasting tasks using Particle swarm optimization (PSO), a swarm intelligence-based metaheuristic optimization methodology: Proposed M-1 (PSO-LSTM), M-2 (PSO-CNN), and M-3 (PSO-MLP). Beijing PM2.5 datasets was analyzed to measure the performance of the proposed models. PM2.5 as a target variable was affected by dew point, pressure, …

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Load-Adjusted Prediction For Proactive Resource Management And Video Server Demand Profiling, Obinna Izima, Ruairí De Fréin Jul 2022

Load-Adjusted Prediction For Proactive Resource Management And Video Server Demand Profiling, Obinna Izima, Ruairí De Fréin

Articles

To lower costs associated with providing cloud resources, a network manager would like to estimate how busy the servers will be in the near future. This is a necessary input in deciding whether to scale up or down computing requirements. We formulate the problem of estimating cloud computational requirements as an integrated framework comprising of a learning and an action stage. In the learning stage, we use Machine Learning (ML) models to predict the video Quality of Delivery (QoD) metric for cloud-hosted servers and use the knowledge gained from the process to make resource management decisions during the action stage. …

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Big Data Analytics Framework For Effective Higher Education Institutions, George Matto Jul 2022

Big Data Analytics Framework For Effective Higher Education Institutions, George Matto

Tanzania Journal of Engineering and Technology (TJET)

There has been an increased dependency on Information and Communication Technologies (ICTs) in undertaking various activities in Higher Education Institutions (HEIs) ecosystems. Because of that, huge volumes of data have increasingly been generated. There have been, for instance, considerable amounts of data generated through electronic platforms involved in students’ admission and registration process, students’ academic records management, teaching and learning data, curriculum related data, and several other administrative data. Analysis of data generated from these platforms stands to give students, lecturers, HEIs Management, policy makers and implementers, and other stakeholders useful insights that would help in improving HEIs’ effectiveness. Unfortunately, …

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An Iot Based Worker Safety Helmet Using Cloud Computing Technology, Yuthriya Iddi Khamis, Mahmoud Alawi, Waheed Muhammad Sanya Jul 2022

An Iot Based Worker Safety Helmet Using Cloud Computing Technology, Yuthriya Iddi Khamis, Mahmoud Alawi, Waheed Muhammad Sanya

Tanzania Journal of Engineering and Technology (TJET)

Miner safety and security is a major challenge around the world due to the exposure to toxic gases that are frequently released in underground mines. Miners' health is adversely affected primarily by toxic gases, which endanger the workers' lives. Furthermore, human sensory abilities do not detect these dangerous gases. As a result, this paper proposes a safety monitoring system that includes a temperature sensor, humidity sensor, and gas sensors to detect harmful gases and alert miners to those harmful gases using the smart helmet they wear. These gases are transmitted to the control station via the cloud using Internet of …

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A Stochastic Spectrum Trading And Resource Allocation Framework For Opportunistic Dynamic Spectrum Access Networks, Mohamed Abdelraheem, Mohammad Mahmoud Abdellatif Jul 2022

A Stochastic Spectrum Trading And Resource Allocation Framework For Opportunistic Dynamic Spectrum Access Networks, Mohamed Abdelraheem, Mohammad Mahmoud Abdellatif

Electrical Engineering

In this article, the spectrum trading problem between primary users and secondary networks is investigated. The secondary network requests multiple channels with the targeted availability to satisfy its users’ demands. Due to the uncertainty about the channels availability, stochastic optimization techniques are adopted to find the optimal set of channels for each secondary network for the lowest cost. Two different constraints on the secondary demand are defined. The first one is when the throughput has to be fully satisfied for a certain percentage of time, and the second one is when the expected value of the throughput has to exceed …

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Neural Networks For Interference Mitigation In Satellite Communication Systems, Martha E. Cash Jul 2022

Neural Networks For Interference Mitigation In Satellite Communication Systems, Martha E. Cash

LSU Master's Theses

The objective of this thesis is to utilize the power of machine learning to develop a neural network-aided receiver in a DVBS-2X satellite communication system to improve the downlink transmission quality in the presence of interference. An emphasis is placed on mitigating the effects caused by non-linear distortions, carrier frequency offset, and additive white Gaussian noise. This thesis proposes a feed-forward neural network with two hidden layers to compensate for the distortions in the received signal. The proposed system model uses 16-APSK modulation scheme. The neural network is tested under varying degrees of non-linear distortion, frequency offsets, and varying levels …

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Credit Card Fraud Detection Using Machine Learning Techniques, Nermin Samy Elhusseny, Shimaa Mohamed Ouf, Amira M. Idrees Ami Jul 2022

Credit Card Fraud Detection Using Machine Learning Techniques, Nermin Samy Elhusseny, Shimaa Mohamed Ouf, Amira M. Idrees Ami

Future Computing and Informatics Journal

This is a systematic literature review to reflect the previous studies that dealt with credit card fraud detection and highlight the different machine learning techniques to deal with this problem. Credit cards are now widely utilized daily. The globe has just begun to shift toward financial inclusion, with marginalized people being introduced to the financial sector. As a result of the high volume of e-commerce, there has been a significant increase in credit card fraud. One of the most important parts of today's banking sector is fraud detection. Fraud is one of the most serious concerns in terms of monetary …

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Measuring The Rol Of Digital Engineering: It's A Journey, Not A Number, Tom Mcdermott, Kaitlin Henderson, Eileen Van Aken, Alejandro Salado, Joseph Bradley Jul 2022

Measuring The Rol Of Digital Engineering: It's A Journey, Not A Number, Tom Mcdermott, Kaitlin Henderson, Eileen Van Aken, Alejandro Salado, Joseph Bradley

Engineering Management & Systems Engineering Faculty Publications

Systems engineering as a discipline has long had difficulty providing quantifiable evidence of its value (Honour 2004); DE transformation provides an opportunity to better measure its value. Transitioning from a document-based to a model-based approach is expensive, and organizations want to know if the effort and cost to adopt MBSE is worth it.

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Non-Gaussian Analysis Of Herbarium Specimen Damageto Optimize Specimen Collection Management, Aris Yaman, Yulia Aris Kartika, Ariani Indrawati, Zaenal Akbar, Lindung P. Manik, Wita Wardani, Tutie Djarwaningsih, Taufik Mahendra, Dadan R. Saleh Jul 2022

Non-Gaussian Analysis Of Herbarium Specimen Damageto Optimize Specimen Collection Management, Aris Yaman, Yulia Aris Kartika, Ariani Indrawati, Zaenal Akbar, Lindung P. Manik, Wita Wardani, Tutie Djarwaningsih, Taufik Mahendra, Dadan R. Saleh

Knowledge Engineering and Data Science

Damage to specimen collections occurs in practically every herbarium across the world. Hence, some precautions must be taken, such as investigating the factors that cause specimen damage in their collections and evaluating their herbarium collection handling and usage policy. However, manual investigation of the causes of herbarium collection damage requires a lot of effort and time. Only a few studies have attempted to investigate the causes of herbarium collection damage. So far, the non-gaussian approach to detecting the causes of damage to herbarium specimens has not been studied before. This study attempted to explore the effect of species type, time, …

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