arXiv subjects

Justin Lipman

Publications and source records attributed to Justin Lipman.

9 recordsLinked to original sources

arXiv2025

Prediction of Herd Life in Dairy Cows Using Multi-Head Attention Transformers

Dairy farmers should decide to keep or cull a cow based on an objective assessment of her likely performance in the herd. For this purpose, farmers need to identify more resilient cows, which can cope better with farm conditions and complete more lactations. This decision-making process is inherently complex, with significant environmental and economic implications. In this study, we develop an AI-driven model to predict cow longevity using historical multivariate time-series data recorded from birth. Leveraging advanced AI techniques, specifically Multi-Head Attention Transformers, we analysed approximately 780,000 records from 19,000 unique cows across 7 farms in Australia. The results demonstrate that our model achieves an overall determination coefficient of 83% in predicting herd life across the studied farms, highlighting its potential for practical application in dairy herd management.

cs.LG↗

arXiv2024

A Data-Driven Review of Remote Sensing-Based Data Fusion in Precision Agriculture from Foundational to Transformer-Based Techniques

This review explores recent advancements in data fusion techniques and Transformer-based remote sensing applications in precision agriculture. Using a systematic, data-driven approach, we analyze research trends from 1994 to 2024, identifying key developments in data fusion, remote sensing, and AI-driven agricultural monitoring. While traditional machine learning and deep learning approaches have demonstrated effectiveness in agricultural decision-making, challenges such as limited scalability, suboptimal feature extraction, and reliance on extensive labeled data persist. This study examines the comparative advantages of Transformer-based fusion methods, particularly their ability to model spatiotemporal dependencies and integrate heterogeneous datasets for applications in soil analysis, crop classification, yield prediction, and disease detection. A comparative analysis of multimodal data fusion approaches is conducted, evaluating data types, fusion techniques, and remote sensing platforms. We demonstrate how Transformers outperform conventional models by enhancing prediction accuracy, mitigating feature redundancy, and optimizing large-scale data integration. Furthermore, we propose a structured roadmap for implementing data fusion in agricultural remote sensing, outlining best practices for ground-truth data selection, platform integration, and fusion model design. By addressing key research gaps and providing a strategic framework, this review offers valuable insights for advancing precision agriculture through AI-driven data fusion techniques.

cs.LG↗

arXiv2024

Efficient Dual-Band Single-Port Rectifier for RF Energy Harvesting at FM and GSM Bands

This paper presents an efficient dual-band rectifier for radiofrequency energy harvesting (RFEH) applications at FM and GSM bands. The single-port rectifier circuit, which comprises a 3-port network, optimized T-matching circuits and voltage doubler, is designed, simulated and fabricated to obtain a high RF-to-DC power conversion efficiency (PCE). Measurement results show PCE of 26% and 22% at -20 dBm, and also 58% and 51% at -10 dBm with a maximum amount of 69% and 65% at -2.5 dBm and -5 dBm, with single tone at 95 and 925 MHz, respectively. Besides, the fractional bandwidth of 21% at FM and 11% at GSM band is achieved. The measurement and simulation results are in good agreement. Consequently, the proposed rectifier can be a potential candidate for ambient RF energy harvesting and wireless power transfer (WPT). It should be noted that a 3-port network as a duplexer is designed to be integrated with single-port antennas which cover both FM and GSM bands as a low-cost solution. Moreover, based on simulation results, PCE has small variations when the load resistor varies from 10 to 18 k$\Omega$. Therefore, this rectifier can be utilized for any desired resistance within the range, such as sensors and IoT devices.

eess.SY↗

arXiv2024

Highly Sensitive Differential Microwave Sensor Using Enhanced Spiral Resonators for Precision Permittivity Measurement

This paper presents a highly sensitive microwave sensor for dielectric sensing. One of the main disadvantages of microwave resonant-based sensors is cross-sensitivity originated by time-dependent uncontrolled environmental factors such as temperature that affect the material under test (MUT) behavior, leading to undesirable frequency shifts and, hence, lower accuracy. However, this work eliminates the unwanted errors using the differential measurement technique by comparing two transmission resonance frequencies during a unit test setup to measure the permittivity of MUT over time. The proposed structure comprises a spiral resonator with an extended horizontal microstrip line (EH-ML) coupled to a microstrip transmission line (MTL). Creating EH-ML within the structure comprises two primary contributions: enhanced sensitivity resulting from stronger fringing fields generated by increasing the effective area and improved resolution due to higher resonance frequencies caused by a lower total capacitive coupling effect. The proposed sensor is fabricated and tested using MUTs with a permittivity of less than 80 to verify the performance. In this regard, a frequency detection resolution (FDR) of 44MHz and a sensitivity of 0.85% are achieved at a maximum permittivity of 78.3. The results of theoretical analysis, simulation, and measurement are in relatively good agreement. Consequently, the proposed highly sensitive microwave sensor offers significant advantages, such as low complexity in design and fabrication. It also offers high resolution and precision in a wide range of permittivity, which can be an attractive candidate for dielectric sensing in health, chemical and agriculture applications.

eess.SP↗

arXiv2024

Dual-Band, Slant-Polarized MIMO Antenna Set for Vehicular Communication

Slant-polarized Multi Input Multi Output (MIMO) antennas are able to improve the performance of mobile communication systems in terms of channel capacity. Especially, the implementation of MIMO configurations for automotive applications requires to consider high gain, wideband, low-profile and affordable antennas in the communication link. In this work design, simulation and measurement of a new dual-band slant-polarized MIMO antenna with HPBW (Half Power Beam Width) of around 900 are presented. Then, four replicas of the proposed antenna set are placed at four different poles (North, South, West and East) to cover 3600 around the vehicle as an omni-directional pattern. In the real world scenario, the proper antenna set is selected to communicate with the intended user. Each slant MIMO antenna set consists of two inclined (450) low band (LB: 700 to 900 MHz) and two inclined high band (HB: 1.7 to 2.7 GHz) log-periodic antennas. The measured gain of LB and HB antennas are 7 dBi and 8 dBi, respectively. Great agreement between simulation and measurement results confirms the accuracy of the design and simulation procedures of antenna system using optimization algorithm (Genetic method). The proposed antenna is also measured in the field for industrial applications.

eess.SY↗

arXiv2024

Highly Sensitive Differential Microwave Sensor for Soil Moisture Measurement

This paper presents a highly sensitive differential soil moisture sensor (DSMS) using a microstrip line loaded with triangular two turn resonator(T2 SR) and complementary of the rectangular two turn spiral resonator(CR2 SR),simultaneously.Volumetric Water Content (VWC) or permittivity sensing is conducted by loading the T2 SR side with dielectric samples.Two transmission notches are observed for identical loads relating to T2 SR and CR2 SR.The CR2 SR notch at 4.39 GHz is used as a reference for differential permittivity measurement method.Further, the resonance frequency of T2 SR is measured relative to the reference value. Based on this frequency difference,the permittivity of soil is calculated which is related to the soil VWC.Triangular two turn resonator (T2 SR) resonance frequency changes from 4 to 2.38 GHz when VWC varies 0 percent to 30 percent.The sensor's operation principle is described through circuit model analysis and simulations.To validate the differential sensing concept, prototype of the designed 3 cell DSMS is fabricated and measured.The proposed sensor exhibits frequency shift of 110 MHz for 1 percent change at the highest soil moisture content (30 percent) for sandy type soil.This work proves the differential microwave sensing concept for precision agriculture.

physics.ins-det↗

arXiv2024

Multidirectional Pixelated Cubic Antenna with Enhanced Isolation for Vehicular Applications

This paper presents a pixelated cubic antenna design with enhanced isolation and diverse radiation pattern for vehicular applications. The design consists of four radiating patches to take advantage of a nearly omnidirectional radiation pattern with enhanced isolation and high gain. The antenna system with four patches has been pixelated and optimized simultaneously to achieve desired performance and high isolation at 5.4 GHz band. The antenna achieved measured isolation of more than -34 dB between antenna elements. The overall isolation improvement obtained by the antenna is about 18 dB compared to a configuration using standard patch antennas. Moreover, isolation improvement is achieved through patch pixelization without additional resonators or elements. The antenna achieved up to 6.9 dB realized gain in each direction. Additionally, the cubic antenna system is equipped with an E-shaped GPS antenna to facilitate connectivity with GPS satellite. Finally, the antenna performance has been investigated using a simulation model of the vehicle roof and roof rack. The reflection coefficient, isolation and radiation patterns of the antenna remains unaffected. The antenna prototype has been fabricated on Rogers substrate and measured to verify the simulation results. The measured results correlate well with the simulation results. The proposed antenna features low-profile, simple design for ease of manufacture, good radiation characteristics with multidirectional property and high isolation, which are well-suited to vehicular applications in different environments.

eess.SY↗

arXiv2022

ProML: A Decentralised Platform for Provenance Management of Machine Learning Software Systems

Large-scale Machine Learning (ML) based Software Systems are increasingly developed by distributed teams situated in different trust domains. Insider threats can launch attacks from any domain to compromise ML assets (models and datasets). Therefore, practitioners require information about how and by whom ML assets were developed to assess their quality attributes such as security, safety, and fairness. Unfortunately, it is challenging for ML teams to access and reconstruct such historical information of ML assets (ML provenance) because it is generally fragmented across distributed ML teams and threatened by the same adversaries that attack ML assets. This paper proposes ProML, a decentralised platform that leverages blockchain and smart contracts to empower distributed ML teams to jointly manage a single source of truth about circulated ML assets' provenance without relying on a third party, which is vulnerable to insider threats and presents a single point of failure. We propose a novel architectural approach called Artefact-as-a-State-Machine to leverage blockchain transactions and smart contracts for managing ML provenance information and introduce a user-driven provenance capturing mechanism to integrate existing scripts and tools to ProML without compromising participants' control over their assets and toolchains. We evaluate the performance and overheads of ProML by benchmarking a proof-of-concept system on a global blockchain. Furthermore, we assessed ProML's security against a threat model of a distributed ML workflow.

cs.SE↗

arXiv2022

Intelligent Spatial Interpolation-based Frost Prediction Methodology using Artificial Neural Networks with Limited Local Data

The weather phenomenon of frost poses great threats to agriculture. As recent frost prediction methods are based on on-site historical data and sensors, extra development and deployment time are required for data collection in any new site. The aim of this article is to eliminate the dependency on on-site historical data and sensors for frost prediction methods. In this article, a frost prediction method based on spatial interpolation is proposed. The models use climate data from existing weather stations, digital elevation models surveys, and normalized difference vegetation index data to estimate a target site's next hour minimum temperature. The proposed method utilizes ensemble learning to increase the model accuracy. Climate datasets are obtained from 75 weather stations across New South Wales and Australian Capital Territory areas of Australia. The results show that the proposed method reached a detection rate up to 92.55%.

cs.LG↗