Miracle-dz/UniFAR-FTUs huggingface dataset:
Obstacle Detection and Avoidance Algorithm for Autonomous Mobile Robot using 2D LiDAR

The autonomous mobile robot play vital role in military applications and can perform difficult and dangerous tasks like neutralizing explosives, walking through minefields, rescue life of soldiers in risky situations. The advancement of research in field of autonomous robot needs higher degree of automacy and is more challenging in unknown terrain. The implementation of system requires extortionate perception of its surroundings to safely navigate in an unstructured environment. So, accurate Obstacle Detection and Avoidance technique is prime focus in field of mobile robot. This paper proposes efficient obstacle detection and avoidance model based on 2D LiDAR for autonomous mobile robot. The proposed method extracts spatial information from laser point-cloud using segmentation and clustering methods. The Convex hull algorithm is utilized to identify accurate geometrical structure of obstacle. This method also generates forward orientation angle using principal of optimal cost function and visibility graph path planning method is employed to establish optimal route to destination. The proposed obstacle detection and avoidance method utilized simple mathematical model and efficiently achieved good real time performance. The reliability of proposed model is tested on MATLAB simulation platform.
Image Estimation in the Presence of Irregular Sampling, Noise, and Pointing Jitter

We consider an acquisition system where a continuous image is reconstructed from a set of irregularly distributed noisy samples. Moreover, the system is affected by a random pointing jitter which makes the actual sampling positions different from the nominal ones. We develop a model for the system and derive the optimal, minimum variance unbiased (MVU) estimate. Unfortunately, the latter estimate is not practical to compute when the data size is large. Therefore, we develop a simplified, low resolution model and derive the corresponding MVU estimate, which has a drastically lower complexity. Moreover, we analyze the estimators’ performance by using both theoretical analysis and simulations. Finally, we discuss the application to the data of the Photodetector Array Camera and Spectrometer (PACS) instrument, which is an infrared photometer on board the Herschel satellite.
Getting Crevices, Cracks, and Grooves in Line: Anomaly Categorization for AQC Judgment Models

The production of high-end manufactured products requires expensive human aesthetic quality control (AQC) in the form of e.g. visual inspection at multiple stages during production. Current standards for aesthetic quality control focus on the process, i.e. identifying the source of anomalies and lack an observer-oriented classification (describing the perceived appearance). We found a need for a perceptual categorization of anomalies that can help assessors making quality judgments and we present a judgment model for AQC. To this end, we studied visual inspection task flows, and processes especially around limit samples at Bang & Olufsen. Based on perceptual responses in the human visual system, we propose a categorization consisting of open and closed shapes. The latter contains areas, lines, alignment, and constellations. The categorization can help in automating AQC using computer vision.
Selective Face Deidentification with End-to-End Perceptual Loss Learning

Privacy is a highly debatable topic in the modern technological era. With the advent of massive video and image data (which in a lot of cases contains personal information on the recorded subjects), there is an imminent need for efficient privacy protection mechanisms. To this end, we develop in this work a novel Face Deidentification Network (FaDeNet) that is able to alter the input faces in such a way that automated recognition fail to recognize the subjects in the images, while this is still possible for human observers. FaDeNet is based an encoder-decoder architecture that is trained to auto-encode the input image, while (at the same time) minimizing the recognition performance of a secondary network that is used as an socalled identity critic in FaDeNet. We present experiments on the Radbound Faces Dataset and observe encouraging results.
Chinese Handwriting Imitation with Hierarchical Generative Adversarial Network

Automatic character generation, expected to save much time and labor, is an appealing solution for new typeface design. Inspired by the recent advancement in Generative Adversarial Networks (GANs), this paper proposes a Hierarchical Generative Adversarial Network (HGAN) for typeface transformation. The proposed HGAN consists of two sub-networks: (1) a transfer network mapping characters from one typeface to another preserving the corresponding structural information, which includes a content encoder and a hierarchical generator . (2) a hierarchical adversarial discriminator which distinguishes samples generated by the transfer network from real samples. Considering the unique properties of characters, different from original GANs, a hierarchical structure is proposed, which output the transferred characters in different phase of generator and at the same time, making the True/False judgment not only based on the ﬁnal extracting features but also intermediate features in discriminator. Experimenting with Chinese typeface transformation, we show that HGAN is an effective framework for font style transfer, from standard printed typeface to personal handwriting styles.
Left Ventricle Segmentation and Volume Estimation on Cardiac MRI using Deep Learning

In the United States, heart disease is the leading cause of death for both men and women, accounting for 610,000 deaths each year [1]. Physicians use Magnetic Resonance Imaging (MRI) scans to take images of the heart in order to non-invasively estimate its structural and functional parameters for cardiovascular diagnosis and disease management. The end-systolic volume (ESV) and end-diastolic volume (EDV) of the left ventricle (LV), and the ejection fraction (EF) are indicators of heart disease. These measures can be derived from the segmented contours of the LV; thus, consistent and accurate segmentation of the LV from MRI images are critical to the accuracy of the ESV, EDV, and EF, and to non-invasive cardiac disease detection. In this work, various image preprocessing techniques, model configurations using the U-Net deep learning architecture, postprocessing methods, and approaches for volume estimation are investigated. An end-to-end analytics pipeline with multiple stages is provided for automated LV segmentation and volume estimation. First, image data are reformatted and processed from DICOM and NIfTI formats to raw images in array format. Secondly, raw images are processed with multiple image preprocessing methods and cropped to include only the Region of Interest (ROI). Thirdly, preprocessed images are segmented using U-Net models. Lastly, post processing of segmented images to remove extra contours along with intelligent slice and frame selection are applied, followed by calculation of the ESV, EDV, and EF. This analytics pipeline is implemented and runs on a distributed computing environment with a GPU cluster at the San Diego Supercomputer Center at UCSD.
Multi-Task Autoencoder for Noise-Robust Speech Recognition

For speech recognition in noisy environments, we propose a multi-task autoencoder which estimates not only clean speech features but also noise features from noisy speech. We introduce the deSpeeching autoencoder, which excludes speech signals from noisy speech, and combine it with the conventional denoising autoencoder to form a unified multi-task au-toencoder (MTAE). We evaluate it using the Aurora 2 dataset and CHIME 3 dataset. It reduced WER by 15.7% from the conventional denoising autoencoder in the Aurora 2 test set A.
Application of kriging models for a drug combination experiment on lung cancer

Combinatorial drugs have been widely applied in disease treatment, especially chemotherapy for cancer, due to its improved efficacy and reduced toxicity compared with individual drugs. The study of combinatorial drugs requires efficient experimental designs and proper follow‐up statistical modeling techniques. Linear and nonlinear models are often used in the response surface modeling for such experiments. We propose the use of kriging models to better depict the response surfaces of combinatorial drugs. We illustrate our method via a drug combination experiment on lung cancer and further show how proper experimental designs can reduce the necessary run size. We demonstrate that only 27 runs are needed to predict all 512 runs in the original experiment and achieve better precision than existing analyses.
Bindctnet: A Simple Binary Dct Network for Image Classification

Convolution neural networks play an important role in the image classification tasks. However, it is time consuming to train the network and the cost of memory resources is usually high. In this paper, a simple and effective network named BinDCTNet is presented by using the binary discrete cosine transform(BinDCT) to extract the feature-maps and a hyper-parameter to reduce dimension of the extracted feature. The proposed network has extremely low computing complexity and there is almost no parameters needed to be stored. Experiments are carried out on the hand written digit dataset MNIST and the vehicle logo VLOGO dataset. The results show that the proposed network achieves the state-of-the-art accuracy with fast speed and low memory cost, which makes it applicable on mobile and embedded devices.
NanoStreams: A Microserver Architecture for Real-Time Analytics on Fast Data Streams

Ever increasing power consumption has created great interest in energy-efficient microserver architectures but they lack the computational, networking, and storage power necessary to cope with real-time data analytics. We propose NanoStreams, an integrated architecture comprising an ARM-based microserver, coupled via a novel, low latency network interface, Nanowire, to an Analytics-on-Chip architecture implemented on Field Programmable Gate Array (FPGA) technology; the architecture comprises ARM cores for performing low latency transactional processing, integrated with programmable, energy efficient Nanocore processors for high-throughput streaming analytics. The paper outlines the complete system architecture, hardware level detail, compiler, network protocol, and programming environment. We present experiments from the financial services sector, comparing a state-of-the-art server based on Intel Sandy Bridge processors, an ARM based Calxeda ECS-1000 microserver and ODROID XU3 node, with the NanoStreams microserver architecture using an industrial workload. For end-to-end workload, the NanoStreams microserver achieves energy savings up to 10.7×, 5.87× and 5× compared to the Intel server, Calxeda microserver and ODROID node, respectively.
Live Structural Modeling Using RGB-D SLAM

This paper presents a method for localizing primitive shapes in a dense point cloud computed by the RGB-D SLAM system. To stably generate a shape map containing only primitive shapes, the primitive shape is incrementally modeled by fusing the shapes estimated at previous frames in the SLAM, so that an accurate shape can be finally generated. Specifically, the history of the fusing process is used to avoid the influence of error accumulation in the SLAM. The point cloud of the shape is then updated by fusing the points in all the previous frames into a single point cloud. In the experimental results, we show that metric primitive modeling in texture-less and unprepared environments can be achieved online.
An Architecture Utilizing Human Emotions and Activities Recognition for Remote Monitoring

Emotions and activities are an integral part of human lifestyles and daily living. They occur all the time in everyone as humans are always in an emotional state and also performing an activity. This paper proposes an architecture that combines Human Emotion Recognition (HER) and Human Activity Recognition (HAR) to monitor people. It utilizes emotion physiological signals and accelerometer data harvested from individuals to recognize human emotions and activities respectively in addition to location information in order to remotely monitor such individuals and trigger alerts if necessary especially in a distress state situation that requires emergency services. The basic concepts of accelerometer data based HAR and physiological signals based HER and how they can be combined to monitor individuals in an emergency response system are succinctly presented.
Contexts-States-Aware Access Control for Internet of Things

The more and more connected devices and rapidly developing Internet of Things (IoT) applications are the foundations of the future smart cities which provide ubiquitous services. The extension and proliferation of the technology brings huge security challenges, especially for the infrastructural IoT applications in the open environments. The traditional access model, such as Role-Based Access Control (RBAC) cannot provide the flexible fine-grained access control which is required due to dynamic changing users and environments. On the other hand, some other features of the IoT applications like constrained-resources devices and large-scale deployments make it very difficult to apply Attribute-Based Access Control (ABAC). Furthermore, the ABAC mechanism cannot control the way that the requester uses the services once the requester obtains the access permission. To address these issues, in this paper, we propose an access control model based on ABAC with Contexts-States-Awareness. The proposed model is implemented by using Semantic Web technologies with a sample ontology for the model and some access control policies in SWRL (Semantic Web Rule Language). We also give a logical architecture which is the extension from the reference architecture of XACML eXtensible Access Control Markup Language specification.
Fewer is More: Image Segmentation Based Weakly Supervised Object Detection with Partial Aggregation

We consider addressing the major failures in weakly supervised object detectors. As most weakly supervised object detection methods are based on pre-generated proposals, they often show two false detections: (i) group multiple object instances with one bounding box, and (ii) focus on only parts rather than the whole objects. We propose an image segmentation framework to help correctly detect individual instances. The input images are ﬁrst segmented into several sub-images based on the proposal overlaps to uncouple the grouping objects. Then the batch of sub-images are fed into the convolutional network to train an object detector. Within each sub-image, a partial aggregation strategy is adopted to dynamically select a portion of the proposal-level scores to produce the sub-image-level output. This regularizes the model to learn context knowledge about the object content. Finally, the outputs of the sub-images are pooled together as the model prediction. The ideas are implemented with VGG-D backbone to be comparable with recent state-of-the-art weakly supervised methods. Extensive experiments on PASCAL VOC datasets show the superiority of our design. The proposed model outperforms other alternatives on detection, localization, and classiﬁcation tasks.
Measuring Uncertainty in Deep Regression Models: The Case of Age Estimation from Speech

Age estimation from speech recently received a lot of attention. Approaches such as i-vectors and deep learning have been successfully applied to this task achieving great performance. However, one drawback of those methods is that they produce a hard age estimation without any kind of confidence measure about the quality of the prediction. Designing systems with the ability to provide a confidence measure about their output is extremely valuable for several applications where the cost of making bad decisions is worse than making no decision, e.g., forensics. In this paper, we propose a novel framework to jointly predict the age and its estimation uncertainty in a context of neural regression model. This model is trained using probabilistic fashion instead of using the classical minimum mean square error objective used for regression tasks. The probabilistic output corresponds to a Gaussian posterior. The proposed neural network will estimate both the posterior mean which corresponds to the predicted age and the variance which quantifies the uncertainty of the prediction. We evaluated our approach on two different datasets NIST SRE 2008 − 2010 and Switchboard.
Geometric Information Based Monaural Speech Separation Using Deep Neural Network

The performance of deep neural network (DNN) based monaural speech separation methods is limited in reverberant and noisy room environments. In this paper, we propose a new DNN training target which incorporates geometric information describing the target speaker and microphone to improve the performance in reverberant and noisy room environments. The experiments are based on the IEEE corpus and the NOISEX database and real impulse responses (RIRs). The objective evaluations, short-time objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ) confirm the efficiency of the proposed direct path ratio mask (DRM).
Robust Haze Removal Via Joint Deep Transmission and Scene Propagation

Haze is one of the most important factors which reduce the outdoor image quality. Existing approaches often aim to design their models based on principles of hazes. However, even with exactly modeled haze distribution, it is still a challenging task due to factors in real scenario, such as noises, halos and artifacts. To address limitations of existing approaches for real-world hazy removal problem, this paper proposes a novel framework to incorporate deep residual architectures into a propagation scheme to jointly estimate transmission and clean scene. We evaluate the proposed framework on both widely used benchmarks and real-world low-quality hazy images. Extensive experimental results demonstrate that our method performs favorably against approaches designed only based on haze cues and achieves the state-of-the-art results, compared with both conventional shallow models and deep dehzaing networks.
Learning Lexical Coherence Representation Using LSTM Forget Gate for Children with Autism Spectrum Disorder During Story-Telling

Inability to carry out cohesive narratives has been identified in children with autism spectrum disorder (ASD). However, deriving cohesion measures is often done using manual labeling or relying on expert-crafted features. In this work, we develop a novel LSTM framework to learn the embedded narrative cohesion representation from data directly. Our lexical coherence representation achieves a promising recognition accuracy of 92% in classifying between typically-developing (TD) and ASD children, as compared to 73% by using conventional coherence measures computed from syntactic, word usage, and latent semantic analysis. We perform additional validity analyses on our proposed representation. By experimentally introducing incoherence in the TD's story-telling narratives through word and sentence-level shuffling, the derived lexical coherence representation from these incoherent TD data samples result in a representation closer to those of ASD data samples.
Robust Calibration of Radio Interferometers in Multi-Frequency Scenario

This paper investigates calibration of sensor arrays in the radio astronomy context. Current and future radio telescopes require computationally efficient algorithms to overcome the new technical challenges as large collecting area, wide field of view and huge data volume. Specifically, we study the calibration of radio interferometry stations with significant direction dependent distortions. We propose an iterative robust calibration algorithm based on a relaxed maximum likelihood estimator for a specific context: i) observations are affected by the presence of outliers and ii) parameters of interest have a specific structure depending on frequency. Variation of parameters across frequency is addressed through a distributed procedure, which is consistent with the new radio synthesis arrays where the full observing bandwidth is divided into multiple frequency channels. Numerical simulations reveal that the proposed robust distributed calibration estimator outperforms the conventional non-robust algorithm and/or the mono-frequency case.
Non-iterative recomputation of dense layers for performance improvement of DCNN

An iterative method of learning has become a paradigm for training deep convolutional neural networks (DCNN). However, utilizing a non-iterative learning strategy can accelerate the training process of the DCNN and surprisingly such approach has been rarely explored by the deep learning (DL) community. It motivates this paper to introduce a non-iterative learning strategy that eliminates the backpropagation (BP) at the top dense or fully connected (FC) layers of DCNN, resulting in, lower training time and higher performance. The proposed method exploits the Moore-Penrose Inverse to pull back the current residual error to each FC layer, generating well-generalized features. Then using the recomputed features, i.e., the new generalized features the weights of each FC layer is computed according to the Moore-Penrose Inverse. We evaluate the proposed approach on six widely accepted object recognition benchmark datasets: Scene-15, CIFAR-10, CIFAR-100, SUN-397, Places365, and ImageNet. The experimental results show that the proposed method obtains significant improvements over 30 state-of-the-art methods. Interestingly, it also indicates that any DCNN with the proposed method can provide better performance than the same network with its original training based on BP.
AHA-3D: A Labelled Dataset for Senior Fitness Exercise Recognition and Segmentation from 3D Skeletal Data

Automated assessment of ﬁtness exercises has important applications in computer and robot-based exercise coaches to deploy at home, gymnasiums or care centers. In this work, we introduce AHA-3D, a labeled dataset of sequences of 3D skeletal data depicting standard ﬁtness tests on young and elderly subjects, for the purpose of automatic ﬁtness exercises assessment. To the best of our knowledge, AHA-3D is the ﬁrst publicly available dataset featuring multi-generational, male and female subjects, with frame-level labels, allowing for action segmentation as well as the estimation of metrics like risk of fall, and autonomy to perform daily tasks. We present two baseline methods for recognition and one for segmentation. For recognition, we trained models on the positions of the joints achieving 88 . 2% ± 0 . 077 accuracy, and on joint positions and velocities, achieving 91% ± 0 . 082 accuracy. Using the Kolmogorov-Smirnov test we determined the model trained on velocities was superior. The segmentation baseline achieved an accuracy of 88 . 29% in detecting actions at frame level. Our results show promising recognition and detection performance suggesting AHA-3D’s potential use in practical applications like exercise performance and correction, elderly ﬁtness level estimation and risk of falling for elders.