Miracle-dz/UniFAR-FTUs huggingface dataset:
Preliminary results on crossbreeding Jerseys with Fleckvieh sires

In South Africa the Jersey breed is very popular especially among dairy farmers using cultivated pastures as a primary feed source. A major problem with Jerseys is that little income is generated by rearing bull calves for beef while the sale of cull cows (for beef) also do not contribute significantly to herd income. To improve this Jerseys have to be crossbred with other breeds with more beef potential.
Research on A Serious Game for Simulation of National Grid Operation

Serious Game for Simulation of National Grid Operation game is a game with education, which is mainly used to spread the knowledge of the operation process of the National Grid. The game shows the entire operation of the National Grid, including generation, transmission, distribution, and electricity sales. The game's database is designed by MySQL, and use Java achieve user management, simulation computing, animation with voice display, console interactive description, and so on. User management includes user's authority management and information management; Simulation calculation is to calculate the amount of data in the operation of the state grid; Animation with voice display is about the simple demonstration of the state grid operation of the four processes; Console interaction instructions can describe in detail the parameters involved in the operation process. By playing this game, the players can learn the related knowledge of state grid operation easily. The results show that the scheme design is feasible and the research method is effective.
ASHRAY: Enhancing Water-usage Comfort in Developing Regions using Data-driven IoT Retrofits

In developing countries, majority of the households use overhead water tanks to have running water. These water tanks are exposed to the elements, which usually render the tap water uncomfortable to use, given the extreme subtropical weather conditions. Externally weatherproofing these tanks to maintain the groundwater temperature is short-lived, and only results in a marginal (0.5°C–1°C) improvement in tap water temperature. We propose Ashray, an IoT-inspired, intelligent system to minimize the exposure of water to the elements thereby maintaining its temperature close to that of the groundwater. Ashray learns the water demand patterns of a household and pumps water into the overhead tank only when necessary. The predictive, machine learning based, approach of Ashray improves water comfort by up to 8°C in summers and 3°C in winters, on average. Ashray is retrofitted into existing infrastructure with a hardware prototyping cost of $27, whereas it can save up to 16% on water heating costs, through reduction in natural gas consumption, by leveraging groundwater temperature. Moreover, we also consider a transiently-powered Ashray, which uses the energy harvested from the ambient environment, and propose an intermittent data pipeline to improve its prediction accuracy. The transiently-powered Ashray is suitable for long-term deployment, requires minimal maintenance and delivers approximately the same performance. Ashray has the potential to improve the thermal comfort and reduce energy costs for millions of households in developing countries.
Analysing the Influence of Land Cover Type on the Performance of Large-scale Road Extraction

Large-scale road network data plays an important role in fields of traffic management, urban planning, automatic vehicle navigation and emergency management. In recent years, many deep learning methods have been applied on road extraction and improved the accuracy of road network data. However, most of the previous studies are on local or regional scale, and it is still a challenging task to accurately extract a large-scale road dataset. The main reason for it is the influence of land cover type diversity on the accuracy of road extraction. In this paper, Sentinel-2 data will be used for large-scale road extraction in One Belt and One Road area, and the influence of different land cover type (city, vegetation, bare soil) on road extraction is comprehensively analysed.
A 3D Lightweight Siamese Network for Hyperspectral Image Classification with Limited Samples

Hyperspectral remote image is a useful and important tool to analyze the ground object. With the development of hyperspectral imaging technology, more and more hyperspectral images can be accessed and need to be processed now. In the last few years, deep learning has gained a significant breakthrough in computer vision and natural language process, which is introduced into the hyperspectral image classification as well. And much literature about deep learning and hyperspectral image classification can be viewed now. However, there are no enough labeled samples in the hyperspectral image to train the deep learning model with massive parameters, which hinders its application in practice. Thus, a 3D lightweight Siamese network (3DLSN) is proposed to solve the above problem. It does not only reduce the trainable parameter effectively but also requires few labeled samples to train. The experimental result on two benchmark datasets demonstrates the effectiveness of the proposed model. For convenience reproducibility, the source code of the 3DLSN would be released at https://github.com/ShuGuoJ/3DLSN.git.
Support Vector Machine of Particle Swarm Optimization For Motor Imagery Recognition using Feature Fusion

To solve the problem that a single feature is not adequate for the classification accuracy of the motor imagery electroencephalograph (EEG) signals, a multi-domain feature fusion method is proposed by using the wavelet packet transform, the common space pattern and the sample entropy of EEG signals. Moreover, the improved particle swarm optimization algorithm is developed through an decreasing inertia weight of the velocity. Another contribution of this paper is the support vector machine optimized by using the developed particle swarm algorithm for the motor imagery EEG signal classification. The average recognition accuracy of the proposed multi-domain feature fusion method is 92.92%. It is increased respectively by 14.7%, 12.68%, 9.58%, 6.2%, 7.12%, 4.6% compared with the recognition accuracy using the features of the wavelet packet transform, the common space pattern, the sample entropy, and the combinations of the two types of the features,respectively. The developed particle swarm optimization support vector machine method increases the recognition accuracy by 5.1%, 4.68%, 3.22%, respectively compared with the recognition accuracy using the traditional support vector machine, the genetic support vector machine, and the artificial bee colony support vector machine.
Few-shot Steel Surface Defect Detection Based on Meta Learning

The performance of the deep learning-based object detection algorithms mainly relies on the large-scale image sets. However, it is extremely difficult to collect the defect samples in the practical steel production. Few-shot object detection provides an ideal solution for the insufficient training sets. However, the efficient feature extractor is crucial for the few-shot object detection methods. In this paper, a novel few-shot defect detection method on Steel Surface is proposed based on the meta-learning technology. The detection model includes three main components: a meta feature learner, a feature matching module and a bounding box prediction module. The meta features are extracted from the base classes with large-scale sets using the meta feature learner, and it can be generalized to detect the novel steel surface defects. The feature matching module adopts a few support images from the base classes and the novel class to generalize a global feature vector which can be used to adjust the weights of meta features maps for detecting the corresponding defects. The bounding box prediction module is designed to conduct the defect detection on the adjusted feature maps. Experiments on the steel surface defect dataset demonstrate that our proposed method can efficiently localize the defects with only a few annotated samples, and outperform the well-established baseline models.
Polarization Gradient Histogram for Object Tracking in Infrared Polarization Imaging: A Feature Extraction Method for Polarization Mosaic Image

Infrared polarization feature improves the performance of anti-interference for object detection. Division-of-focal-plane polarization imagers (DPIs) make it possible for real-time object tracking in infrared polarization imaging. Output of DPIs is polarization mosaic image. Traditional processing way is combined by demosaicing and then computing polarization parameters for object detection and tracking, which is time consuming and subjected to demosaicing error. This paper proposes a histogram of infrared polarization mosaic gradient (HIPMG) for feature extraction in infrared polarization image. In our proposed algorithm, Polarization and Spatial Correlation Matrixes (PCM and SCM) are designed to obtain spatial and polarization correlation of the object. Based on PCM and SCM, two Polarization Filter Matrixes (PFM) are proposed to obtain spatial-polarization gradient maps (SPGMs). Then a HIPMG descriptor is designed from the intensity and direction of SPGMs. A polarization-track method is proposed by using HIPMG for object tracking. Experiment results demonstrate the effectiveness of proposed feature extraction method over the state-of-the-art methods.
Accelerating Finite Impulse Response Filtering Using Tensor Cores

This paper studies how to accelerate a single channel 2D image convolution using NVIDIA's Tensor Core. Tensor Core is a dedicated arithmetic unit for speeding up matrix products and was proposed to speed up the convolution in machine learning. From the Volta architecture of NVIDIA's GPU, Tensor Core is utilized for various applications. The Tensor Core is not limited to machine learning but various applications. This paper focus acceleration of image convolution of a single-channel filter of finite impulse response (FIR) filtering. Usually, Tensor Core is used for multi-channel convolution; thus, direct usage of Tensor Core cannot maximize the performance of the usual FIR filtering. Therefore, we propose three programming patterns for parallel processing of FIR filtering: kernel-loop unrolling, kernel-image-loop unrolling, and diagonal kernel-image-loop unrolling. Our experiments compare the proposed loop unrolling methods for linear time-invariant (LTI) and variant filters (LTV): Gaussian filtering and bilateral filtering. The results show that Tensor Core can accelerate the convolution for LTI filters but cannot accelerate linear LTV filters. Also, we compare the two GPU architectures of Turing and Ampere, and we can accelerate the LTI filter using TCs in both architectures.
Filter-free Band-limited Digital Predistortion of Power Amplifiers for 5G Wireless Transmitters

The increases of signal bandwidth in wireless communication systems pose new challenges to the digital predistortion (DPD) for the linearization of power amplifiers (PAs). To reduce the baseband bandwidth in the transmitter chain and the feedback sampling rate in the feedback loop, a band-limited DPD (BL-DPD) was proposed to linear the broadband PA output when sampling rates are insufficient. Most of the methods are based on the BL-DPD that adopts an analog filter in the feedback loop to limit the baseband bandwidth and feedback sampling rate of the DPD. However, the analog filter leads to the PA’s strong memory effect to increase the number of model coefficients of the DPD. In this paper, a novel filter-free band-limited DPD (FFBL-DPD) method, which combines oversampling method with an undersampling method to alleviate the PA’s nonlinear behavior with weak memory effect model, is proposed for the linearization of the broadband PA. First, a low speed analog-to-digital converter (ADC) will capture the PA output to obtain the low sampling rate PA output signal. Then, the DPD model with low sampling rate will be identified and constructed with the captured low sampling rate PA output signal and the original signal. It will be used to generate a low sampling rate predistorted signal. Theoretical analysis shows that most of the nonlinear compensation terms, which the conventional full sampling rate DPD model can obtain, can be generated by employing the DPD model with low sampling rate. The FFBL-DPD method can compromise a small part of the linearization performance. So it can significantly reduce the bandwidth requirements of the baseband, transmitter chain and feedback loop and the ADC/digital-to-analog converter (DAC) sampling rate. Finally, the low sampling rate predistorted signal will be oversampled to feed into the PA. To validate the proposed method, the simulation experiments are carried out with a 100-MHz OF DM signal. Experimental results show that excellent linearization performance can be obtained with fewer parameters at the limited available sampling rate than the existing band-limited DPD. Besides, it presents much lower complexity that will reduce expenses on hardware, which is very suitable for 5G broadband applications.
On Speech Sparsity for Computational Efficiency and Noise Reduction in Hearing Aids

Beamforming techniques are widely used in hearing aids to improve the signal-to-noise ratio. In a multi-speaker sce-nario, it is common to assume that the speech signals associated with each speaker do not overlap in the time-frequency domain. This so-called W-disjoint orthogonality assumption allows us to reduce the complexity of the beamforming algorithm. However, its validity decreases in presence of more than two speakers. In this study, we propose a beamforming algorithm relying on a less restrictive assumption regarding the sparsity of speech signals in the time-frequency domain. Its implications over the noise reduction performance and the computational complexity are discussed and compared with the Linearly Constrained Minimum Variance (LCMV) and the Minimum Variance Distortionless Response (MVDR) beamformers. We show that the proposed algorithm improves the noise reduction performance and reduces the computational cost compared to the LCMV beamformer without increasing the artifacts amount unlike the MVDR beam-former.
A Study of Salient Modulation Domain Features for Speaker Identification

This paper studies the ranges of acoustic and modulation frequencies of speech most relevant for identifying speakers and compares the speaker-specific information present in the temporal envelope against that present in the temporal fine structure. This study uses correlation and feature importance measures, random forest and convolutional neural network mod-els, and reconstructed speech signals with specific acoustic and/or modulation frequencies removed to identify the salient points. It is shown that the range of modulation frequencies associated with the fundamental frequency is more important than the 1–16 Hz range most commonly used in automatic speech recognition, and that the 0 Hz modulation frequency band contains significant speaker information. It is also shown that the temporal envelope is more discriminative among speakers than the temporal fine structure, but that the temporal fine structure still contains useful additional information for speaker identification. This research aims to provide a timely addition to the literature by identifying specific aspects of speech relevant for speaker identification that could be used to enhance the discriminant capabilities of machine learning models.
Improving the Performance of Ambulance Emergency Service Using Smart Health Systems

Smart health is a new paradigm that can significantly improve the healthcare systems. In smart health, novel sensing, computing and communication technologies are integrated in healthcare to improve the quality of service. In this paper, we use the smart health to improve the performance of ambulance service. In particular, we use the real-time traffic information and hospital waiting time to minimize the ambulance response time, ambulance travel time to hospitals, and waiting time at hospitals. Results indicate that the use of smart health improves the performance significantly especially with non-uniform hospital capacity and non-uniform traffic conditions.
On Improving the Accuracy of Object Detection for High Resolution Images Based on SSD

In this paper, we consider improving the accuracy of object detection using SSD (Single Shot multibox Detector). SSD is well known that it can execute the searching of region candidate and classifying the objects in a single process. One of the problems of the SSD is that if the object is not larger than a certain size, the accuracy will decrease. To solve this problem, we propose a method to improve the accuracy of the object detection by extending a conventional method. Although the proposed method applies the SSD after the segmentation of the image as in the conventional one, the difference is that SSD is performed using only partial information from the network. This allows us to detect small objects that could not be detected using the standard SSD. Then, region metrics are corrected using the result of detection without image segmentation and the result after image segmentation. The effectiveness of the proposed method is shown through the computer simulations.
Constrained Maximum Directivity Beamformers Based on Uniform Linear Acoustic Vector Sensor Arrays

This paper studies the problem of beamforming with linear arrays consisting of acoustic vector sensors for speech signal acquisition. It presents a beamforming method, which maximizes the directivity factor (DF) in two steps. In the first step, the same weighting filter is applied to each vector sensor so that all the vector sensors in the lin-ear array have the same spatial response, which is steered to a look direction in the three-dimensional space. Then, a beamformer is de-signed in the second step to maximize the DF while constraining the gain of the beampattern to be maximum at the look direction. The resulting maximum DF (MDF) beamformer has many advantages over the traditional superdirective beamformer with linear arrays of omnidirectional sensors including, but not limited to: 1) it has better steering flexibility and its mainlobe can be steered to any direction in the three-dimensional space, while the traditional superdirective beamformer generally has its mainlobe placed in the endfire directions; 2) it is able to achieve a high DF with the same level of white noise gain. Simulations validate the theoretical study and demon-strate the properties of the presented method.
Utilizing Machine Learning to Reconnect Potential Customers of the E-Commerce Industry

In competitive markets, it is costly to attract new customers in the business as they already have a wide customer base; that being the case, businesses spend a healthy budget to reconnect with customers they have had transactions with in the past. As we are approaching towards post-pandemic, there is a need to increase the profitability of the businesses and retain their customer base. To increase customer turnover and re-engagement, this paper focuses on the implementation of intelligent business practices using machine learning and neural networks. The paper focuses on analyzing the customer behavior based on their purchase behavior and purchase transactional values for customer retention. The division is done to identify the customers who are profitable to the business and customers who are likely to churn for a number of reasons. In this paper, we proposed a Multi-layer perceptron (MLP) to segment the customers according to Recency, Frequency, and Monetary Value (RFM) methodology and customer profitability index. The result of MLP was compared with K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) as the later models have been widely used. Additionally, Bidirectional Long Short-Term Memory (Bi-LSTM) models have been implemented for primary customer classification and sales prediction. The prediction model is an attempt to reduce the financial loss on marketing campaigns for re-engagement of customers in the business. The dataset used was retrieved from Cardinal Care BD.
End-to-End Speaker Age and Height Estimation using Attention Mechanism and Triplet Loss

Automatic age and height estimation of speakers using acoustic features is widely used for the purpose of human-computer interaction, forensics, etc. In this work, we study end-to-end framework for age and height estimation. We first propose a novel attention mechanism, named cross-attention. Different from conventionally used attention, which calculates context vector as the sum of attention only across timeframes, the proposed approach introduces a modified context vector which takes into account total attention across both time-frames and encoder units. We further propose using triplet loss to enhance the discriminative power of the encoder. We evaluate the Root Mean Square Error (RMSE) of pro-posed approaches on the TIMIT corpus. The proposed cross-attention outperforms the conventional counterpart for both age and height estimation while the triplet loss brings 8% relative improvement for age estimation. We obtain RMSE of 6.92/6.42 cm for male/female height estimation and 7.20/7.10 years for male/female age estimation which outperforms all the previous baselines achieving state-of-the-art performance for age estimation on TIMIT dataset. By tracking the attention weights allocated to different phones, we find that Vowel phones are most important while Stop and Fricative phones are least important for the estimation task.
Scour Monitoring Technology Implementation

Bridge scour is the removal of sediment around bridge foundations and can result in the failure of the bridge. Scour monitoring is performed to identify unacceptable scour on bridges considered to be scour critical and determine when scour reaches elevations that could cause potential bridge failure. Two types of monitoring are available: portable monitoring and fixed monitoring. Prior to this project, the Minnesota Department of Transportation (MnDOT) was only using portable monitoring devices, which requires the deployment of personnel to make physical measurements of scour depths. For some scour critical bridges, especially during high-water events, fixed instrumentation capable of continuous scour monitoring was preferred, but MnDOT lacked the experience or expertise to install this type of equipment. This project installed fixed monitoring equipment at two bridge sites and monitored them for three years to determine the effectiveness and reliability of fixed scour monitoring deployments. Several device options were installed to allow MnDOT to analyze the installation and performance of different types of sensors. Both systems operated for the three years with some outages due to various causes but overall performance was acceptable. The outages were mostly related to power issues and communication issues. Valuable lessons were learned through the deployment, which may be applied to future installations. The deployment executed in this project has provided the confidence to deploy other fixed scour monitoring equipment at key bridges around the state of Minnesota. In addition, the data collected during deployment of the scour monitoring equipment have been stored and provide insight into scour processes. These data can be used by other research groups for design or research purposes.