Miracle-dz/UniFAR-FTUs huggingface dataset:
Localization of Small Neoplastic Lesions in Colonoscopy by Estimating Edge, Texture and Motion Saliency

Early screening in Colorectal Cancer consists in finding and removing small precancerous masses or neoplastic lesions developed from the mucosa, usually lesions smaller than 10 mm. Localization of small neoplastic lesions is a very challenging task since colon exploration is highly dependent on the expert training and colon preparation. Several strategies have attempted to locate neoplasias, but usually they are huge lesions that a trained gastroenterologist could hardly miss. This work presents a saliency-based strategy to localize polypoid and non-polypoid neoplastic lesions smaller than 10 mm in colonoscopy videos by combining spatio-temporal descriptors. For doing so, a per-frame-multi-scale representation is computed and edge, texture and motion features are extracted. Each of these features is used to construct a primary saliency map which are then combined to obtain a coarse saliency map. Finally, the neoplasia is localized as the bounding box of a circular region, approximated by the Hough transform, with the largest salience. The proposed approach was evaluated in 8 short colonoscopy videos obtaining an average of Annotated Area Covered of 0.75 and a precision of 0.82.
Future Internet: The Internet of Things-A Literature Review

Internet today is the backbone of virtual communication worldwide. It is expanding its horizon with a fast pace. Internet is rightly defined as “network of networks”. It is, in most simple terms, a means of establishing a connection between your computer and any other computer globally through servers and some dedicated routers. The major form of communication of current Internet is among humans (i.e. human-to-human). The next upcoming form of communication that uses Internet as the underlying technology is The Internet of Things (IoT). IoT extends the capabilities of Internet to enable machine-to-machine communication (M2M). Therefore, IoT can be said to provide communication among everyone and everything. IoT enables us to embed some sort of intelligence in the objects that are or can be connected to the Internet in order to exchange information, hence communicate, invoke certain actions based on inputs, take decisions and provide useful services. In this paper, the basic underlying workflow of IoT is discussed and the architecture of IoT is explained. The paper also highlights the most useful technologies of these days, which employ IoT for their functioning. Further, the applications and features of IoT are mentioned in the paper. Lastly, the issues and challenges in implementing the IoT are briefly discussed. The IoT today is gaining a lot of recognition due to its potential that is yet to be extracted by the industry, academia and government as well. These sectors can bring huge economic and professional benefits by employing IoT in the right manner.
Reconstructing Degree of Forearm Rotation from Imagined movements for BCI-based Robot Hand Control

Brain-computer interface (BCI) is an important tool for rehabilitation and control of an external device (e.g., robot arm or home appliances). Fully reconstruction of upper limb movement from brain signals is one of the critical issues for intuitive BCI. However, decoding of forearm rotation from imagined movements using electroencephalography (EEG) is difficult to decode degree of rotation accurately. In this paper, we reconstructed imagined forearm rotation from low- frequency (0.3-3 Hz) of EEG signals. We selected 20 EEG channel on motor cortex for analysis. Ten healthy subjects participated in our experiment. The subjects performed actual and imagined forearm rotation to reach different targets. We trained a reconstruction decoder which used the EEG signals measured from actual movements and the kinematic information only. Additionally, we applied a long short-term memory (LSTM) network to enhance decoding performances. As a result, we achieved the high correlation performance (Average: 0.67) to decode imagined forearm rotation angle. This result has demonstrated that the reconstruction decoder which is trained by the EEG data from actual movement has effective to decode robustly for the imagined forearm rotation angle.
Bangladeshi License Plate Recognition Using Adaboost Classifier

License plate recognition (LPR) is a technology for the authentication of a vehicle by locating and recognizing the license plate number in an image through computer vision techniques and machine learning models. To develop intelligent traffic management such as vehicle monitoring, LPR is a key component. However, due to the diversity of layouts and characters of plates, universal solution is not possible. So, this research focuses on development of an algorithm for the recognition of license plate of Bangladesh by using image processing's and machine learning model. This algorithm executes in three steps: detection of the plate with shape verification, tilt correction and recognition of the number. For detection, RGB color space, median filtering, binarization, morphological analysis, region properties for filtering are applied. To discard noisy object, shape verification is done through robust distances to borders vectors. Before character segmentation, horizontal tilt correction is applied. Then, characters are extracted by using bounding box parameters from the extracted plate. Finally, the recognition is implemented by using the blending of Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) features and adaptive boosting (Adaboost) classifier is used to categorize the characters. The proposed algorithm is simulated on the images which are captured from different roads of Bangladesh. The experimental result shows that the detection and recognition accuracy is noteworthy.
Decoding Motor Imagery Movements using Area of 2-D Phase Space Reconstruction

Amyotrophic lateral sclerosis (ALS) is a common neurological disorder where voluntary muscle movements of the patient stop functioning and resulting paralyzed person. One of the solution of ALS is motor imagery (MI) based brain computer interface (BCI) which helps motor disabled patient to interact with the external world through their brain signal. But it has limited real-time applications due to its lower performance. The performance depends on feature extraction technique which extract relevant feature related to MI movements. The extraction of significant feature is challenging task in MI based BCI system. To improve the performance, this paper introduces an efficient feature extraction technique known as phase space reconstruction (PSR) for decoding various MI movements. First, filter bank technique was applied to MI signal and sets of sub-bands were generated. To study MI activities effectively, PSR was applied to each sub-band. The features (area of 2-D PSR pattern) of all sub-bands were combined and the significant features (p <0.05) were extracted using one-way analysis of variance (ANOVA). The significant features were fed into multi-class support vector machine (SVM) for decoding MI movements. The proposed method and classifier were tested on BCI competition 2008 dataset-II-a. The performance of proposed method was based on Cohen’s kappa coefficient (K). The results show that the SVM improved mean kappa coefficient (K=0.60) and outperformed existing methods presented in the literature.
Cross Entropy Profiling to Test Pattern Synchrony in Short-Term Signals

Examining nonlinear bi-variate time series for pattern synchrony has been largely carried out by the cross sample entropy measure, X-SampEn, which is highly bound by parametric restrictions. Threshold parameter r is the one that limits X-SampEn estimations most adversely. An inappropriate r choice leads to erroneous synchrony detection, even for the case of X-SampEn analysis on simple synthetically generated signals like the MIX(P) process. This gives us an intimation of how difficult it would be for such synchrony measures to handle the more complex physiologic data. The recently introduced concept of entropy profiling has been proved to release such measures from the clutches of r dependence. In this study, we demonstrate how entropy profiling with respect to r can be implemented on cross entropy analysis, particularly X-SampEn. We have used different sets of simple MIX(P) processes for the purpose and validated the impact of X-SampEn profiling over X-SampEn estimation, with a special focus on short-term data. From results, we see that X-SampEn profiling alone can accurately classify MIX(P) signals based on pattern synchrony. Here, X-SampEn estimation fails undoubtedly, even at the higher data lengths where traditional SampEn estimation is known to perform with good accuracy.
3D-Printed Whole Prostate Models with Tumor Hotspots Using Dual-Extruder Printer

3D printing has emerged as a popular technology in various biomedical applications. Physical models of anatomical structures concretize the digital representations and can be used for teaching and analysis. In this study we combine 3D histology with 3D printing, creating realistic physical models of tissues with hotspots of interest. As an example we use mouse prostates containing tumors. Surface meshes are created from binary masks of HE-stained serial sections of mouse prostates and manually annotated tumor areas. Sections are interpolated to expand sparse image stacks for smoother results. Fiji, Meshlab and Tinkercad are used for mesh creation and processing. Objects are printed with Prusa-based dual-extruder printer enabling different colors for tumors and the surrounding prostate tissue. Our 3D-printed mouse prostates appear realistic and tumors located at the edges of the organ are clearly visible. When transparent filament is used, the tumor hotspots are visible even when they are inside the prostate.
Feature Ranking using Robust Fuzzy Score Function for Gene Expression Data

Feature engineering plays a vital role in selecting relevant features that have maximum predictive value. In this paper, we have proposed a Gaussian fuzzy score function to rank the features in descending order of their score values. The mean and variance of the Gaussian fuzzy score function are determined using mean of k-middle. The mean of k-middle plays an important role to determine the complementary information of the features in the dataset. The features selected using proposed feature ranking method are fed to four widely used classifiers, i.e., linear kernel support vector machine, radial basis function kernel support vector machine, random forest and softmax classifier respectively. To show the effectiveness of the proposed approach, we compared its performance with that of state-ofthe-art methods on five large-scale gene expression datasets.
Right Ventricular Segmentation from MRI Using Deep Convolutional Neural Networks

The assessment of right ventricular (RV) function is essential in the diagnosis of many cardiac diseases. Magnetic resonance imaging (MRI) offers an excellent solution to image right ventricle non-invasively with high contrast and temporal resolution. Manual assessment of the RV function from MRI sequences is tedious and time-consuming and automating the process is of great interest. This study proposes a convolutional neural network-based machine learning approach to automate the delineation of the RV from a sequence of MRI. The architecture of the neural network differs from that of a widely-known U-Net approach. Additionally, the proposed approach used image concatenation to create and utilize 3D spatial information in the segmentation process. Quantitative evaluations were performed over 256 images acquired from 16 patients in publicly available data in comparison to manual delineations. Comparisons with the results by U-Net demonstrated that the proposed method outperforms the prior state-of-the-art method.
Energy Scheduling of a Household Considering Different Dynamic Pricing Schemes

Demand side management methods are used to flattening of load curve, to reduce stress on the power system during peak loads and to decrease electricity bills of the customer. Pricing scheme applied by utility has impact on energy scheduling of user. In this paper, we took four appliances generally used in a home and energy scheduling is done according to customer's choice. Pricing schemes time-of-Use, critical peak pricing and real time pricing are applied for scheduling a fixed load along with four appliances as variable load and comparison is done in the paper. Mixed linear integer programming method in association with CPLEX solver of GAMS software is used. The model results are verified with simulation results.
Using In-Vivo Audio Feedback to Improve Cycling Performance

iv Chapter One: Introduction . 1 Chapter Two: Methods. 6 Participants . 6 Setting . 7 Materials . 7 Target Behavior and Data Collection . 8 Interobserver Agreement (IOA) . 9 Experimental Design . 9 Procedures . 9 Baseline/Standard . 9 In-Vivo Audio Feedback.10 Treatment Integrity . 11 Social Validity . 11 Chapter Three: Results . 12 Chapter Four: Discussion. 15
Clustering of Microbiome Data: Evaluation of Ensemble Design Approaches

The research focus on the human microbiome is moving towards uncovering its association with the overall wellbeing and using this knowledge in personalized medicine and connected health. Driven by more affordable highthroughput sequencing, microbiome data generation rate has increased, enabling an efficient implementation of data-driven algorithms. This study evaluates the possibilities to identify clusters in a human microbiome data based on taxonomic profiles, relying on 24 different $\beta $diversity measures, individual and ensemble clustering approaches. The influence of ensemble creation techniques and parameter selection to the robustness and quality of consensus partition was explored. Furthermore, we have evaluated changes in the clustering performance after dimensionality reduction. The results indicate that careful selection of the algorithm parameters and ensemble design are needed to ensure the stable consensus partition. Reduction in the number of input features using kernel principal component analysis is accompanied with loss of discrimination potential.
An Acoustic Model for English Speech Recognition Based on Deep Learning

As one of the core modules of English speech recognition system, the performance of acoustic model directly affects the recognition effect of the final system. Aiming at the problems of acoustic modeling, model optimization and training efficiency in English speech recognition system, this paper focuses on the acoustic modeling method based on deep learning technology. By analyzing the basic theory of deep neural network and HMM, the structure and parameter configuration of DNN-HMM are intensively studied. A new hybrid network model is proposed by using clustered state instead of single factor state as the output unit of the neural network, and it is applied to the acoustic model of English speech recognition. The simulation results show that the improved scheme can get better recognition effect by increasing the enhancement of speech features under the condition of three-phoneme structure. In addition, the experimental results also verify that the acoustic modeling method based on DNN-HMM is superior to traditional GMM-HMM method.
A Sustainable Vehicle Parking using IoT

We present a highly efficient method for vehicle parking management in large scale parking lots based on IoT. The proposed system can be installed in a Raspberry pi module which will help us in solving the current problems in vehicle parking management. The system consists of an on-site deployment of an IoT module that is used to monitor and signalize the state of availability of each single parking slot. This can be done on a mobile phone using android application. This application can be managed by an administrator at the malls. This project primarily makes finding of parking systems more efficient and less time-consuming, and also eliminates the need of staff to be employed for such purpose of these places, thus increasing profit margins. The system helps a user know the availability of parking slots on a real time basis. Android mobile application is used to display the total parking slots in the parking lot, number of parking slots available, occupied parking slots and the reserved parking slots. To book the parking slot at that time your information are encoded to the QR code. This makes our parking system more secured. Once you park the vehicle in the slot, the timer will start to monitor the parking time. If time exceeds alert message is sent to the user's mobile.
Group decision making techniques for risk assessment: a literature review and research directions

Risk assessment is part of a decision making process that help organizations to achieve intended objectives. For this purpose, the literature presents several studies that propose computing with words (CW) combined with decision making techniques. It is common sense that in the organizational context, risk assessment should be reached by a group of experts. However, it was not found attempts of a literature review for group decision making techniques that support risk assessment. Therefore, this paper presents a review of 87 studies since 1996 that propose group decision making techniques to risk assessment. It presents a bibliometric analysis considering their year of publication, country, journal and subject. Also, the reviewed studies were classified according to the inputs (crisp or soft), linguistic representations and group decision methods. The results showed a predominance of Multicriteria Decision Making (MCDM) methods and an increase of fuzzy sets generalization proposals. In addition, it was found that the majority of the studies apply aggregation methods considering the individual experts judgments instead of consensus. Finally, this paper suggests some research directions for further studies.
An overview on Change Detection and a Case Study Using Multi-temporal Satellite Imagery

Satellite imagery based change detection plays an important role in analyzing the after effects of natural disasters, detecting the changes in city limits due to rapid urbanization, updating the map database, monitoring the factors impacting agriculture, etc., The remote sensors mounted on satellites or aircrafts absorb the light reflected by the earth’s surface. The output of these sensors will be a digital image which represents the scene being perceived. In order to extract the useful information from these images, various image processing techniques need to be employed. In this paper, a detailed outline of the steps and various techniques used for detecting the changes in multi temporal remote sensing images is discussed and a case study is done by taking multi-temporal Landsat-8 images covering Hyderabad city. Image differencing method is applied in order to find the changes in the Hyderabad city limits over 2013December and 2017 December time periods.
Performance of Implementing Smart DFT in Micro Phasor Measurement Unit

The advance of the signal processing, this paper implement a more accurate digital measurement algorithm, Smart DFT(SDFT) in Micro Phasor Measurement Unit(μPMU), which is based on Discrete Fourier Transforms(DFT) to estimate frequency information from Taiwan power system. μPMU, the sensor we plan to acquire the information from the power system by using the signal of outlet voltage-level 110V in Taiwan, such as frequency, voltage and angle. The performance of SDFT implemented in μPMU represents a more precise frequency information when frequency fluctuation occurred just as the frequency of power system. We offer the results of simulations, stable frequency generated from waveform generator and real frequency from main electricity to compare SDFT with DFT method which implemented in μPMU.
Surface Subsidence Monitoring in Eastern Liaoning Province using Sentinel-1B SAR Interferometry from 2017 to 2019

Some cities in eastern Liaoning province have suffered from surface subsidence due to excessive pumping of groundwater for the past few years. The Time series differential interferometry (TSInSAR) provides an efficient surface subsidence survey and monitoring method for land subsidence in large areas. In this paper, 25 Sentinel-1B TOPS mode images from 2017 to 2019 were adopted to map ground subsidence in major cities such as Shenyang, Anshan and Liaoyang in eastern Liaoning province by TSInSAR technology. Meanwhile we retrieve the deformation information in the surrounding iron mining areas of Anshan. Results show that most regions of Shenyang, Anshan and Liaoyang are stable and the deformation rate is less than 1cm/year. The main subsiding areas are located in Shoushan area and Taian country (Anshan), Dongling district (Shenyang). The maximum deformation rate in Taian country (Anshan) and iron mining areas is up to 14.49cm/year. Finally, we analyzed the influencing factors of surface subsidence in more detail, such as groundwater exploitation, urban construction and land use, and iron mining. InSAR results can provide a scientific basis for urban planning and land subsidence monitoring in eastern Liaoning province.
A Brief Review on Robotic Grippers Classifications

Manipulators are used for various applications to make tasks easier or decrease the risk of tasks deemed impossible, dangerous or difficult for humans. A robotic manipulator can be equipped with different types of end effectors to perform diverse tasks. Grippers are one of the most commonly-used robot end of arm tools. Depending on the application in hand for the robotic system, various types of grippers are needed. Therefore, selecting the proper one is a significantly important aspect in the design process. To the best knowledge of the authors, there are not enough scholarly research papers focusing on classification for robot grippers. In this paper, a brief review on different categories of grippers are presented. The goal of this paper is to provide a brief informational summary on different classifications, since proper selection of gripper plays a vital role in robot manipulator’s efficiency and performance.
Electronically assisted automatic waste segregation

Increasing urbanization has led to a major waste management crisis with the proliferation of improperly planned structures having no proper facility to collect, segregate and process waste. Domestic waste has increasing chemical and plastic content. These chemicals do not perish unless treated properly. The treatment also necessitates timely collection, segregation and if possible decomposition, reuse or recycling. Human intervention has been the most popular way to segregate waste, but when it comes to working with a mixture of wastes, it puts their health and hygiene at stake. It is always better to treat waste through the help of robots which can handle waste in any hazardous environment. An automated waste collection and segregation system based on a robotic assembly and machine learning based classification is developed. A robotic arm with a distance sensor will pick up the waste and place it on a binary classifier platform which has a camera attached to capture the image and an algorithm to classify the waste as biodegradable or non-biodegradable into their respective bins.