Reviewerly/exHarmony huggingface dataset:
Deep Learning Inference at the Edge for Mobile and Aerial Robotics

Deep learning inference is an established element for autonomous robots especially in the domain of safety, security, and rescue applications. Autonomous functions based on deep learning inference are considered the spearhead of such robots paving the way for increased demand in onboard computational resources and performance accuracy. Edge computing is steadily improved in terms of computational power but at the same time artificial neural networks are becoming even more deep and complex. To this end, the right selection between the on-board hardware platform and an efficient deep neural network is considered a challenging tradeoff issue in the autonomous system design. In this paper, we investigate the current landscape in deep learning inference at the edge by evaluating the requirements, challenges and available solutions for service-oriented architectures in the safety, security, and rescue domain. Current research directions and best optimization practices are discussed, enriched with computational and accuracy comparisons providing necessary insights for optimal system design.
Resource and Task Clustering based Scheduling Algorithm for Workflow Applications in Cloud Computing Environment

Cloud computing consists of distributed resources and used to provide services to the applications which require huge computation power such as scientific, mathematical, weather forecasting, and biomedical applications. These applications are considered as workflow applications containing many numbers of dependent tasks. The scheduling of these dependent tasks on distributed resources is a critical problem in cloud computing. In this paper, we present a scheduling algorithm that considers clustering of resources and tasks for workflow applications in the cloud computing environment. The given algorithm is an enhancement toHySARC algorithm. Like HySARC, the proposed algorithm first forms clusters of resources and tasks and then applies list scheduling techniques on each of the clusters to schedule tasks. We have estimated and compared the performance of the proposed algorithm with HySARC algorithm on the parameters like clustering time, and makespan, and found that the proposed algorithm performed better than the compared algorithm.
Boosting vector calculus with the graphical notation

Learning vector calculus techniques is one of the major hurdles faced by physics undergraduates. However, beginners report various difficulties dealing with the index notation due to its bulkiness. Meanwhile, there have been graphical notations for tensor algebra that are intuitive and effective in calculations and can serve as a quick mnemonic for algebraic identities. Although they have been introduced and applied in vector algebra in the educational context, to the best of our knowledge, there have been no publications that employ the graphical notation to three-dimensional Euclidean vector calculus, involving differentiation and integration of vector fields. Aiming for physics students and educators, we introduce such “graphical vector calculus,” demonstrate its pedagogical advantages, and provide a variety of exercises containing both purely mathematical identities and practical calculations in physics. The graphical notation can readily be utilized in the educational environment to not only lower the barriers in learning and practicing vector calculus but also make students interested and self-motivated to manipulate the vector calculus syntax and, on their own, heuristically comprehend the language of tensors.
Active learning for road lane landmark inventory with V-ELM in highly uncontrolled image capture conditions

Road landmark inventory is becoming an important data product for the maintenance of transport infrastructures. Several commercial sensors are available which include synchronized optical cameras that allowto build 360° panoramic images of the surroundings of the vehicle used for road inspection. This paper is devoted to the analysis of such panorama images,specifically the area that contains themost relevant information. Road lane landmark detection is posed as a two class classification problem that may be solved bymachine learningapproaches, such as Random Forest (RF) and ensembles of Extreme Learning Machines (V-ELM). Besides model parameter selection, a central problem is the construction of a labeled training and validation datasetto cope with the highly uncontrolled conditions of image capture. Besides, human labor cost makes image data labeling a very expensive process. This paper proposes an open ended Active Learning (AL) approach involving a human oraclein the loop who provides the data labeling and can trigger the AL process when detection quality is degraded by the change in imaging conditions. The paper reports encouraging results over a collection of sample images selected from an industrial road landmark inventory operation. As an additional contribution, this paper assesses the ability of AL to overcomesome of the issues raised by highly class imbalanced datasets.
Voice Pathology Detection Based on Deep Neural Network Approach

Abstract The advancement of technology offers solution to the complex problems faced by the society and brings the wellbeing of the individuals. Smart healthcare is prominent nowadays for diagnosis, treatment and constant monitoring which reduces visitation of hospital, transport cost and waiting time. Voice pathology is a decease which affects the person vocal cord so that one who facing difficult in speech. If the decease not identified in time, it leads to permanent loss of voice for an individual. Traditionally, the decease is identified through oral examination or manual procedures. Due to the advent of smart phone, one can record the voice and send it to the cloud server for processing. Our system classifies the voice data and provides the decision to the user. This greatly reduces the transport cost, and waiting time for oral examination at medical center. The mobile phone recorded the patient voice data and it will be stored into the cloud. The voice data is synthesized to signals and with the help of deep Neural network the voice pathology can be identified. The system has been tested with the data set and the test result shows promising.
Leveraging orientation for weakly supervised object detection with application to firearm localization

Automatic detection of firearms is important for enhancing the security and safety of people, however, it is a challenging task owing to the wide variations in shape, size and appearance of firearms. Also, most of the generic object detectors process axis-aligned rectangular areas though, a thin and long rifle may actually cover only a small percentage of that area and the rest may contain irrelevant details suppressing the required object signatures. To handle these challenges, we propose a weakly supervised Orientation Aware Object Detection (OAOD) algorithm which learns to detect oriented object bounding boxes (OBB) while using Axis-Aligned Bounding Boxes (AABB) for training. The proposed OAOD is different from the existing oriented object detectors which strictly require OBB during training which may not always be present. The goal of training on AABB and detection of OBB is achieved by employing a multistage scheme, with Stage-1 predicting the AABB and Stage-2 predicting OBB. In-between the two stages, the oriented proposal generation module along with the object aligned RoI pooling is designed to extract features based on the predicted orientation and to make these features orientation invariant. A diverse and challenging dataset consisting of eleven thousand images is also proposed for firearm detection which is manually annotated for firearm classification and localization. The proposed ITU Firearm dataset (ITUF) contains a wide range of guns and rifles. The OAOD algorithm is evaluated on the ITUF dataset and compared with current state-of-the-art object detectors, including fully supervised oriented object detectors. OAOD has outperformed both types of object detectors with a significant margin. The experimental results (mAP: 88.3 on AABB & mAP: 77.5 on OBB) demonstrate effectiveness of the proposed algorithm for firearm detection.
Efficient High-Performance FPGA-Redis Hybrid NoSQL Caching System for Blockchain Scalability

The inspiring blockchain technology has got successes and many adoptions in areas besides cryptocurrencies as its benefits have been explored and successfully tested. Scalability is one of the biggest challenges of blockchain. Due to the huge increasing size of blockchain data, many devices (lightweight nodes) especially IoT depend on full blockchain servers, hence there is a need to reduce the workload on the servers for high performance. This paper presents a high performance and efficient hybrid (multilevel) and distributed NoSQL caching system of FPGA and Redis for improving the scalability (throughput) of blockchain applications. We investigate performance bottlenecks in blockchain and design an efficient Gigabit Ethernet FPGA NoSQL cache architecture that works in synergy with Redis database via Hiredis C client. Curl and Jansson are used to connect with the blockchain. We design a customized SHA-256 core for the efficient caching specific for blockchain. Our results revealed an improvement of 103 times when cache hit occurs on the FPGA without cache miss. When cache miss occurs on the FPGA, up to 4.09 times improvement is obtained with the proposed FPGA–Redis system compared to FPGA only system. Small FPGA area utilization and less power consumption were also achieved.
Using Skeleton based Optimized Residual Neural Network Architecture of Deep Learning for Human Fall Detection

For the last few decades, human posture recognition has gained mammoth popularity specifically in elderly fall detection. This study proposes a modified deep learning neural network architecture using OpenPose library for two category problem of human fall detection. OpenPose library computes and draws 18 points skeleton on frames. The proposed approach achieves an accuracy of 97.46% on publicly available, standard Multi Camera Fall dataset and outperforms existing state-of-the-art deep learning image classification Residual Network architectures ResNet-50 and ResNet-101 which have an accuracy of 94.63% and 70.18% respectively.
A Convolutional Neural Network Approach for The Diagnosis of Breast Cancer

Most common cancer in females is found to be Breast cancer which is a widespread disease. One out of eight females worldwide are affected by this cancer only. We can detect this cancer by detecting malignancy from breast tissues. There are various types of computer-aided techniques and approaches which are used by doctors for detecting cancer. The major objective of this paper is to build a well-defined model for the recognition of breast cancer by expending various parameters. Different types of machine learning and deep learning methodologies are used for the classification of malignant and benign tissues. In this we are using a dataset that obtains 569 samples with 30 features, this dataset is majorly called the Wisconsin dataset. Many techniques are implemented on this dataset we are using deep convolutional neural network (CNN) and Machine learning methodology (KNN) for the diagnosis and training purpose and then compare the results of both the techniques. Deep convolutional NN is implemented on the google platform called the Google Colab on the other side KNN is implemented on the Anaconda Spyder platform. The best accuracy achieved from KNN is 96.49%. To improve the performance and accuracy we implemented CNN on the same dataset and then achieved 99.41% accuracy. Deep learning is extensively useful in getting the best and optimal results in other performance matrics such as precision, recall, F1-score and AVC-ROC - 98.64%,97.61 %, 98.08%, 97.61% respectively.
An Efficient Palette Generation Method for Color Image Quantization

This article describes an efficient method to generate a color palette for color image quantization. The method consists of two stages. In the first stage, the initial palette is generated. Initially, the color palette is an empty set. First, the N colors are generated according to the data distribution of the input image in the RGB (Red, Green, Blue) color space. Then, one color is selected from the N colors and this color is added to the initial palette, and the step is repeated until the color number of the initial palette is equal to K. In the second stage, the quantized image is generated using the fast K-means algorithm. There are many sampling rates used in this study. For each sampled pixel, a fast searching method is employed to efficiently determine the closest color in the palette. Experimental results show that the high-quality quantized images can be generated by the proposed method. When the sampling rate equals 0.125, the computation time of the proposed method is less than 0.3 s for all cases.
A contour property based approach to segment nuclei in cervical cytology images

Abstract Background Segmentation of nuclei in cervical cytology pap smear images is a crucial stage in automated cervical cancer screening. The task itself is challenging due to the presence of cervical cells with spurious edges, overlapping cells, neutrophils, and artifacts. Methods After the initial preprocessing steps of adaptive thresholding, in our approach, the image passes through a convolution filter to filter out some noise. Then, contours from the resultant image are filtered by their distinctive contour properties followed by a nucleus size recovery procedure based on contour average intensity value. Results We evaluate our method on a public (benchmark) dataset collected from ISBI and also a private real dataset. The results show that our algorithm outperforms other state-of-the-art methods in nucleus segmentation on the ISBI dataset with a precision of 0.978 and recall of 0.933. A promising precision of 0.770 and a formidable recall of 0.886 on the private real dataset indicate that our algorithm can effectively detect and segment nuclei on real cervical cytology images. Tuning various parameters, the precision could be increased to as high as 0.949 with an acceptable decrease of recall to 0.759. Our method also managed an Aggregated Jaccard Index of 0.681 outperforming other state-of-the-art methods on the real dataset. Conclusion We have proposed a contour property-based approach for segmentation of nuclei. Our algorithm has several tunable parameters and is flexible enough to adapt to real practical scenarios and requirements.
An Iterative Extended kalman filter algorithm applying Doppler and bearing measurements for underwater passive target tracking

Based on bearing and Doppler frequency measurements, this paper presents a new method of state estimation for passive underwater target applying Extended Kaman filter (EKF). By linearizing the measurement equation through Jacobian, the state of passive underwater target is estimated with iterative algorithm. The Monte-Carlo simulation results show that the method can efficiently improve the accuracy of state estimation for passive underwater target. The performance of proposed algorithm for state estimation is better than that with bearing only observation applying EKF algorithm. Presented approach and obtained results may be useful in underwater passive target tracking and command decision-making application.
Intrinsic dimension estimation based on local adjacency information

The intrinsic dimension (ID) of a data set is crucial for data processing, especially for high-dimensional data sets. In order to obtain an accurate ID estimate, two neighborhoods of sample points with a radius ratio of k are considered. The ratio of the number of sample points contained in the two neighborhoods is denoted as q∊. When the data set is located on a d-dimensional submanifold in RD, the expected value of q∊ is kd. Based on this consideration, we redefine the adjacency matrix by using the local adjacency information of sample points and propose a new ID estimation method known as ID(k). The ID(k) algorithm contains only one parameter, the scaling ratio k, and we outline the criterion through which the user can select an appropriate k value. We demonstrate the convergence of the new method both theoretically and experimentally. Experimental results from artificial and real data sets show that the estimates obtained by this new ID(k) method are closer to the true intrinsic dimension than those derived using similar methods.
Prediction of Treatment Medicines With Dual Adaptive Sequential Networks

Predicting treatment medicines is a key task in many intelligent healthcare systems. Prediction of treatment medicines can assist doctors in making informed prescription decisions for patients according to their Electronic Health Records (EHRs). However, predicting treatment medicines is a challenging task due to the following reasons: (1) heterogeneous nature of EHR data that typically includes laboratory results, treatment records, disease conditions, and demographic information; (2) complex correlations among EHR sequences, including inter-correlations between sequences and temporal intra-correlations within each sequence; (3) temporal dynamics of these correlations changing with disease progression. In this paper, we predict treatment medicines for patients with dual adaptive sequential networks (DASNet). Specifically, DASNet is designed with three components. First, a decomposed adaptive long short-term memory network (DA-LSTM) is designed to capture the intra- and inter-correlations in multiple heterogeneous temporal sequences. Then, we develop an attentive meta learning network (AT-MetaNet) to learn dynamic weight parameters for DA-LSTM, thus enabling it to model various correlation structures. Finally, we employ an attentive fusion network (AT-FuNet) to incorporate historical information and collectively fuse representation embeddings of heterogeneous data to predict treatment medicines. Our results on the public MIMIC-III dataset covering 11 medical conditions demonstrate that the proposed end-to-end model can achieve the state-of-the-art prediction performance while providing clinically useful insights.
Synthesis and Analysis-By-Synthesis of Modulated Diplophonic Glottal Area Waveforms

Diplophonia is a type of disordered voice in which two simultaneous pitches are perceived. Most commonly in diplophonic voices, the vocal folds are divided into two parts that vibrate at different frequencies. The glottal area is the projected area of the space between the vocal folds. The glottal area in time is referred to as the glottal area waveform (GAW). The GAW is modeled for diplophonic voice by superimposing two partial GAWs (pGAWs) that are trains of single-peak pulses with different pulse frequencies, i.e., fundamental frequencies ($f_o$s). In current kinematic models of diplophonic vocal fold vibration, the pGAWs are assumed to be quasiperiodic. This assumption is mitigated here by modulating pulse-to-pulse cycle length and amplitude. Both random and deterministic modulations are considered. Deterministic modulations depend on the difference of the pGAWs' instantaneous phases. Model GAWs are fitted to input GAWs using an analysis-by-synthesis approach which we refer to as `modulated pulse trains decomposition' (MPD). MPD is shown to be applicable to diplophonic as well as to nondiplophonic types of dysphonia, which include multi-pulse patterns, random timing behaviours, and chaos. It is mostly robust against modulations but degraded by large random modulations. MPD is compared to a deep autoencoder neural network, and the WaveGlow neural network. In terms of time-domain fitting errors, MPD outperforms the other two approaches unless random modulations are large. MPD outperforms the best of the other two approaches by up to approximately 5 dB. For large random modulations, the deep autoencoder network achieves the smallest fitting errors. In terms of magnitude spectrum fitting errors, WaveGlow is superior except for natural input GAWs containing only nondiplophonic types of dysphonia. Also pulse timing errors are shown to be advantageous for MPD.
Dehazing buried tissues in retinal fundus images using a multiple radiance pre-processing with deep learning based multiple feature-fusion

Fundus is the inner part of the eye that is noticed while viewing through the pupil during an eye check-up. It’s made up of the retina, the macula and the optic disk. Fundus images are used to inspect and track the progression of disease-related abnormalities. Medical background images taken using high-resolution cameras lack transparency, sharpness, and an optimum level of brightness and contrast. These are critical factors that define the extent at which human experts or automated devices based on artificial intelligence can effectively distinguish between healthy and abnormal tissues. Current work describes a two-stage pre-processing image with deep learning based residual features and dynamic gradient features for enhancing retinal fundus images. In the first stage, a sequence of enhanced images with varying radiance is generated using an image brightening, contrast enhancement, and color restoration algorithm. This stage provides a sufficient improvement in contrast, sharpness and color restoration compared to the original image. But it’s also correlated with a high level of noise. From the visual results, this amount of noise can be detected. It will also decrease the Peak Signal To Noise Ratio (PSNR). Thus the second stage, which serves as a filter which combines the power of the proposed Multiple Feature-Fusion-based Deep Learning Network and the exposures extracted from the first stage image series. The technique is demonstrated in the following sections and tested in depth using a standard data set. Visual and quantitative experiments also indicate that the new approach is also capable of achieving superior performance over existing image dehazing techniques.
Simultaneous phase-shifting interferometer with a monitored spatial light modulator flexible reference mirror

A simultaneous phase-shifting interferometer with a monitored spatial light modulator (SLM) flexible reference mirror is proposed to balance the flexibility and accuracy of aspheric-surface in-process measurements. In this method, polarization simultaneous phase-shifting camera systems are applied to reduce the influence of environmental vibrations on the in-process measurements. An SLM reference mirror is employed to improve the flexibility of in-process measurements. A device is integrated to monitor the SLM surface in order to improve measurement accuracy caused by the spatial phase nonuniformity and modulation instability of the SLM. Thus, the SLM surface is monitored and the aspheric surface is measured simultaneously in only one interferometer, which presents the advantages of a compact structure and simple calibration. A flat acrylic mirror with an unknown surface figure error is measured by the proposed interferometer. Cross tests demonstrate the feasibility of this interferometer.