Miracle-dz/UniFAR-FTUs huggingface dataset:
Designing a Control Architecture for the Knee Prosthesis Using ANFIS and Supervisory PI Controller

this paper presents a control architecture for knee joint actuator in one gait cycle. The control architecture includes Fuzzy subsystem that is designed. Recursive Least Squares (RLS) and Adaptive Network-based Fuzzy Inference System (ANFIS) methods are applied and compared to choose the best method for identification of the fuzzy system. After that PI and Supervisory PI controllers are utilized to achieve the desired force. The coefficients of PI supervisory controller are designed with suitable and compatible membership functions for the best results. External disturbance is exerted to the output of our closed-loop control system to simulate uneven surface for walking. Results indicate that the robustness of PI and PI supervisory controller is acceptable.
A Novel LSTM-Based Speech Preprocessor for Speaker Diarization in Realistic Mismatch Conditions

In this study, we investigate on the effects of deep learning based speech enhancement as a preprocessor to speaker diarization in quite challenging realistic environments involving the background noises, reverberations and overlapping speech. To improve the generalization capability, the advanced long short-term memory (LSTM) architecture with the novel design of hidden layers via densely connected progressive learning and output layer via multiple-target learning is proposed for preprocessing. We build the deep model using synthesized training data pairs generated from WSJO reading-style speech and more than 100 noise types. Surprisingly, this proposed preprocessor demonstrates a strong generalization capability to speaker di-arization with the realistic noisy speech in highly mismatched conditions, in terms of the speaking style, interferences, and the interaction between them. Tested on three challenging tasks, namely AMI, ADOS, and SeedLings, the state-of-the-art diarization system with the novel LSTM-based speech preprocessor can yield consistent and significant reductions of diarization error rate (DER) over the systems using unprocessed noisy speech and traditional enhancement methods.
Fast Object Detection for Quadcopter Drone Using Deep Learning

The paper presents our research progress in the development of object detection using deep learning based on drone camera. The grand purpose of our research is to deliver important medical aids for patients in emergency situations. The case can be simplified into delivery of an item from start to the goal position. We will exploit the drone technology for transporting items efficiently. In sending process, our drone must detect the object target, where the items will be delivered. Therefore, we need object detection module that can detect what is in video stream and where the object is by using GPS as well. To implement the module, we use combination of MobileNet and the Single Shot Detector (SSD) framework for fast and efficient deep learning-based method to object detection. The ability of deep learning to detect and localize specific objects is studied by conducting experiments using drone camera and, as comparison, using stereo camera Minoru.
Bounding Drift in Cooperative Localisation Through the Sharing of Local Loop Closures

Handling loop closures and intervehicle observations in cooperative robotic scenarios remains a challenging problem due to data consistency, bandwidth limitations and increased computation requirements. This paper develops a general cooperative localisation and single vehicle Visual SLAM framework that includes direct intervehicle observations and pose to pose loop closures on each vehicle with states shared as required. This fuses single vehicle SLAM with cooperative localisation and avoids data association of map data across limited communication networks. The base problem is developed as a factor graph with each vehicle solving local subgraphs that are split based on intervehicle observations. We modify the order of variable elimination in subgraphs through manipulation of the square-root of the Information matrix to extract updates that include the historic states involved in the loop closures and do not require transmission of other states not involved in the measurement or retransmission of previously shared states. We demonstrate the effect on localisation accuracy and bandwidth using data captured from a set of five robots observing each other and landmarks compared to both single vehicle SLAM, pure cooperative localisation and a centralised solution.
SNR-Constrained Heuristics for Optimizing the Scaling Parameter of Robust Audio Watermarking

In spread spectrum (SS) based robust audio watermarking, the scaling parameter is an important factor for balancing between robustness and imperceptibility. There have been intense studies of the embedded parameter optimization in light of the signal-to-noise ratio (SNR), but little attention has been given to the constrained SNR. Moreover, traditional population-based stochastic search algorithms for optimizing the embedded parameter significantly increase the computation pressure of the corresponding audio watermarking schemes. This paper comprehensively investigates the effect of the constrained SNR on the optimization of the scaling parameter, from both model and algorithmic perspectives. Specifically, the empirical relationship between the scaling parameter, robustness, and imperceptibility is first analyzed in detail. Next, an SNR-constrained optimization model is presented. Then, to solve the proposed model and find the current optimal scaling parameter for watermark embedding, a binary search algorithm and a heuristic search (HS) algorithm are, respectively, developed. Finally, we embed the proposed model and heuristics in the SS-based audio watermarking scheme and compare the integrated technique (called SS-SNR-HS) with the existing similar schemes. The experimental results demonstrate that SS-SNR-HS not only is computationally simple, but also achieves better balance between imperceptibility and robustness and, thus, seems promising in copyright protection of online digital audio.
FaNeuRobot: A Framework for Robot and Prosthetics Control Using the NeuCube Spiking Neural Network Architecture and Finite Automata Theory

Limb amputation is a global problem. Prosthetic limbs can enhance the quality of life of amputees. To this end, anthropomorphic design and intuitive manipulation are two essential requirements. This paper presents a motor control framework for prosthetic control through Brain-Machine Interface (BMI) using Finite Automata Theory, and NeuCube Evolving Spiking Neural Network (SNN) architecture. Voluntary control of prosthetics requires decoding motor commands from the Central Nervous System of the amputee. Selection of the most suitable biomedical signal depends on many parameters such as level of amputation and muscle atrophy. Non-invasive BMI's have the possibility of supporting a wider range of amputees as it extracts the motor commands from the brain. In this paper, we present a proof of concept study on whether a cognitive computational model that is inspired by the motor control of the human body through muscle synergies combined with an anthropomorphic mechanical design, can result in accurate and robust prosthetic control through a noninvasive BMI. In future, learning of a complex Finite Automata that reflects complex upper limb motor behaviours will be investigated.
Exploring the Non-Local Similarity Present in Variational Mode Functions for Effective ECG Denoising

ECG is corrupted by various noises and denoising of ECG is central to proper diagnosis of cardiac diseases. The main objective of this work is to exploit the efficacy of non-local means (NLM) for ECG denoising. In the presented work, noisy ECG signal is decomposed into variational mode functions (VMFs) using variational mode decomposition (VMD) technique. The decomposed VMFs represents the different frequency band of the noisy ECG signal. The non-local similarity present in each VMFs were exploited using NLM estimation for effective ECG denoising. The two-stage VMD decomposition and NLM estimation process is performed on different set of VMFs at both stages. The proposed method is tested upon MIT-BIH Arrhythmia database. The denoising performance of existing techniques is compared to proposed method. The computed results shows that the proposed method gives superior denoising performance.
Trajectory Replanning for Quadrotors Using Kinodynamic Search and Elastic Optimization

We focus on a replanning scenario for quadrotors where considering time efficiency, non-static initial state and dynamical feasibility is of great significance. We propose a real-time B-spline based kinodynamic (RBK) search algorithm, which transforms a position-only shortest path search (such as A * and Dijkstra) into an efficient kinodynamic search, by exploring the properties of B-spline parameterization. The RBK search is greedy and produces a dynamically feasible time-parameterized trajectory efficiently, which facilitates non-static initial state of the quadrotor. To cope with the limitation of the greedy search and the discretization induced by a grid structure, we adopt an elastic optimization (EO) approach as a post-optimization process, to refine the control point placement provided by the RBK search. The EO approach finds the optimal control point placement inside an expanded elastic tube which represents the free space, by solving a Quadratically Constrained Quadratic Programming (QCQP) problem. We design a receding horizon replanner based on the local control property of B-spline. A systematic comparison of our method against two state-of-the-art methods is provided. We integrate our replanning system with a monocular vision-based quadrotor and validate our performance onboard.
A Novel Semantic Attribute-Based Feature for Image Caption Generation

Image captioning is challenging because it connects computer vision and natural language processing. It requires not only sensing objects but also the interrelations and context in an image to generate natural language descriptions. In this paper, we propose to extract a novel visual feature weighted by salient semantic attributes, which is fed to the encoder of Long Short Term Memory (LSTM). Semantic attributes are important to exploit more semantic-related information in images and describe the salient scenes to enhance the accuracy of generating image captions. Based on the Multiple Instance Learning (MIL) architecture on VGG-16 network, we design transferring rules that map high probability attributes to the feature vector in fc7 layer. It results in more semantic-related visual features. Our model can recognize richer details of images effectively and achieve the state-of-the-art performance on MSCOCO 2014 dataset under standard metrics.
End-to-End Neural Network Based Automated Speech Scoring

In recent years, machine learning models for automated speech scoring systems were mainly built using data-driven approaches with handcrafted features as one of the main components. However, the remarkable successes of deep learning (DL) technology in a variety of machine learning tasks has demonstrated its effectiveness in extracting features. Although there have been some efforts in utilizing DL technology for the automated speech scoring task, a thorough investigation of learning useful features is still missing. In this paper, we propose an end-to-end solution that consists of using deep neural network models to encode both lexical and acoustical cues to learn predictive features automatically. Experiments also confirm the effectiveness of our proposed solution compared to conventional methods based on handcrafted features.
Point Cloud Subjective Evaluation Methodology based on 2D Rendering

Point clouds are one of the most promising technologies for 3D content representation. In this paper, we describe a study on quality assessment of point clouds, degraded by octree-based compression on different levels. The test contents were displayed using Screened Poisson surface reconstruction, without including any textural information, and they were rated by subjects in a passive way, using a 2D image sequence. Subjective evaluations were performed in five independent laboratories in different countries, with the inter-laboratory correlation analysis showing no statistical differences, despite the different equipment employed. Benchmarking results reveal that the state-of-the-art point cloud objective metrics are not able to accurately predict the expected visual quality of such test contents. Moreover, the subjective scores collected from this experiment were found to be poorly correlated with subjective scores obtained from another test involving visualization of raw point clouds. These results suggest the need for further investigations on adequate point cloud representations and objective Quality assessment tools.
Correntropy-Based Adaptive Filtering of Noncircular Complex Data

Real world complex-valued signals typically exhibit rotation-dependent distributions (noncircularity), and significant performance gains in learning algorithms can be obtained by accounting for information beyond the standard second-order noncircularity (impropriety). To this end, we introduce a new closed form definition of complex correntropy which is general enough to cater for both circular and noncircular distributions in complex data, and serves as a basis for a novel cost function for widely linear adaptive filtering, termed the maximum improper complex corren-tropy criterion (MICCC). A stochastic gradient adaptive filtering algorithm is developed based on the MICCC, and its standard and complementary convergence and stability analyses are conducted with respect to both the circularity of the estimation error and the kernel size in the underlying Parzen estimator. Performance advantages over the strictly linear correntropy algorithm (MCCC) and the mean square error based complex least mean square (CLMS) and augmented CLMS (ACLMS) are demonstrated through analysis and simulations.
Coherence Bounds for Sensing Matrices in Spherical Harmonics Expansion

The mutual coherence provides a basis for deriving recovery guarantees in compressed sensing. In this paper, the mutual coherence of spherical harmonics sensing matrices is examined for a class of sensing patterns common in practice and is used as a figure of merit for designing sensing matrices. We will show that for each sampling pattern, the coherence is lower bounded by the inner product of two Legendre polynomials with different degrees. In some practical situation, it is desirable to have sampling points on a sphere follow a regular pattern, hence, facilitating the measurement process. It will be shown that for a class of sampling patterns, the mutual coherence would be at its maximum, yielding the worst performance. Finally, the sampling strategy is proposed to achieve the derived lower bound.
A Nonlinear 3D Geometric Tongue Model

This study describes a nonlinear geometric tongue model based on MRI and Cone-beam CT (CBCT) data. Comparing with the conventional geometric tongue model, the proposed tongue model is controlled by several prototype vertices, and the relationship between tongue mesh vertices and prototype vertices are modeled with quadratic functions. The results indicate that: i) quadratic models do improve the reconstruction performance of tongue mesh, especially in the tongue root region; ii) the quadratic model which use the cross-prototype-vertex information achieves the best performance of tongue mesh reconstruction; iii) the reconstruction performance can be further improved if an extra prototype vertex TP in the tongue root region is taken into account, even if TP is estimated from the measured prototype vertices.
Improved Audio-Visual Laughter Detection Via Multi-Scale Multi-Resolution Image Texture Features and Classifier Fusion

Efforts are afoot to design better context-aware human-computer interaction techniques that have knowledge of both their surrounding and the affective state of the user. One of the most important nonverbal behavioural cues for affective human-machine interaction is laughter. Automatic detection of laughter is an interesting, yet challenging problem, which in recent years has gained increased attention from both the academic and industrial communities. The majority of existing laughter detection systems rely on either audio or video modalities. Humans, however, typically rely on audio-visual cues during conversation and/or interaction, thus it is expected that improved results can be achieved if both modalities are used. In this work, we propose a multimodal framework that analyzes audio and video channels separately, then fuses their decisions. Conventional speech spectral and prosodic features are used, whereas new multi -scale multiresolution binarized statistical image features are proposed due to their improved expressive power. Experiments with the publicly available MAHNOB Laughter database show that decision level fusion based on support vector machine classifiers leads to improved performance over single modality approaches, as well as over previously-proposed methods, all whilst requiring just a fraction of the computational power.
C16‑ceramide and sphingosine 1‑phosphate/S1PR2 have opposite effects on cell growth through mTOR signaling pathway regulation.

Recently, sphingolipid derivatives, such as ceramide and sphingosine‑1‑phosphate (S1P), have emerged as key modulators in apoptotic cell death and cell proliferation. This study aimed to clarify the underlying signaling pathways of ceramide and S1P involved in breast cancer cell proliferation. Ceramide acyl chain length is determined by six mammalian ceramide synthases (CerS). We overexpressed CerS1 to 6 in MCF‑7 cells to examine whether ceramide signaling propagation varies as a function of acyl chain length. Among the six CerS, only CerS6 overexpression reduced phosphorylation of Akt, S6 kinase (S6K), and extracellular signal‑regulated kinases (ERK) as shown by western blotting. In addition, CerS6 overexpression reduced MCF‑7 cell proliferation. This effect was partially reversed by co‑treatment with MHY1485, an activator of mammalian target of rapamycin (mTOR), demonstrating an important role for the mTOR pathway in the CerS6‑mediated decrease in MCF‑7 cell proliferation. ERK inhibition, but not Akt inhibition, along with mTOR inhibition synergistically reduced MCF‑7 cell proliferation as measured by MTT assay. Notably, the expression of CerS6 and S1P receptor 2 (S1PR2), or CerS6 and sphingosine kinase 1 (SphK1), were negatively correlated according to the invasive breast carcinoma patient cohort in The Cancer Genome Atlas database. In addition, both SphK1 overexpression and S1P addition increased mTOR phosphorylation as shown by ELISA, while S1PR2 inhibition had the inverse effect. These data suggest that CerS6 and SphK1 regulate mTOR signaling in breast cancer cell proliferation. Moreover, mTOR activity can be regulated by the balance between S1P and C16‑ceramide, which is generated by CerS6.
Securing Internet of Things with Software Defined Networking

We present a comprehensive survey on how SDN technology can provide security for the IoT environment. We analyze several recently proposed SDN architectures and assess their benefits and drawbacks, and we present a classification to help network security analysts and researchers choose the most appropriate security mechanism according to their security requirements. Additionally, we propose a role based security controller architecture (called Rol-Sec) for the SDN-IoT environment. Finally, we discuss future research challenges that should be addressed when deploying SDN-based solutions for improving IoT security.
Document Quality Estimation Using Spatial Frequency Response

The current Document Image Quality Assessment (DIQA) algorithms directly relate the Optical Character Recognition (OCR) accuracies with the quality of the document to build supervised learning frameworks. This direct correlation has two major limitations: (a) OCR may be affected by factors independent of the quality of the capture and (b) it cannot account for blur variations within an image. An alternate possibility is to quantify the quality of capture using human judgement, however, it is subjective and prone to error. In this work, we build upon the idea of Spatial Frequency Response (SFR) to reliably quantify the quality of a document image. We present through quantitative and qualitative experiments that the proposed metric leads to significant improvement in document quality prediction in contrast to using OCR as ground truth.
In vivo dynamic thermal imaging of skin radiofrequency treatment

In recent years, popularity of radiofrequency (RF) has increased significantly. They are characterized by a low risk of complications and relatively high effectiveness. RF use high‐frequency currents causing oscillating motion of ions resulting in temperature rise stimulating skin regeneration processes. The aim of this work was the thermographic evaluation of the skin exposed to RF of different intensity. The dynamic thermal imaging was used to study the temperature of the skin exposed to RF. The research was carried out in two locations with different adipose tissue content: abdomen (ROI1) and forearm (ROI2). In the ROI1 area, RF was applied at nominal power range from 250 to 1750 W, while in ROI2 area: from 250 to 1000 W. The obtained thermographic data were fitted to exponential functions. A clear dependence of obtained thermokinetic parameters with the anatomical location of exposure to RF was demonstrated. Thicker layer of adipose tissue directly under the skin resulted in obtaining higher maximum temperatures of the skin surface during the procedure (maximum obtained temperature equaled 40.8°C). The temperature of the skin under the head of the device does not translate to subjective patient experiences. In anatomic locations filled with less adipose tissue mass, tolerance to RF is much lower. The dynamics of skin temperature changes, after the RF treatment, can be described by means of a single exponential function where the key parameter is the time constant t1 defining the dynamics of skin temperature changes. The depth of the RF influence is slightly correlated with the RF power.
A Multi-Seed 3D Local Graph Matching Model for Tracking of Densely Packed Cells

Automated tracking of cells in time-lapse live-imaging datasets of developing multicellular tissues is required for high throughput spatio-temporal quantitative measurements of a range of cell behaviors. The tracking of shoot apical meristems (SAM) cells in large-scale microscopy image sequences is challenging, because plant cells are densely packed within a specific honeycomb structure and share very similar physical features. In this paper, we propose a 3D local graph matching model to track the plant SAM cells, by exploiting the cells' tight spatial and temporal contextual information. The proposed 3D local graph matching model is further combined with a multi-seed based majority voting scheme to rectify possible matching errors in the cell correspondence growing process. Compared with the existing 2D local graph matching model, the experimental results show that the proposed method can greatly improve the tracking accuracy for plant cells.
Reactive Magnetic-Field-Inspired Navigation for Non-Holonomic Mobile Robots in Unknown Environments

In this paper, we present a reactive robot navigation method for a non-holonomic mobile robot taking inspiration from the phenomena observed in magnetic fields. The algorithm is shown to be able to guide mobile robots in arbitrary-shaped convex environment without being trapped in local minima by exploiting the local sensory information without priori knowledge about the environment. A preliminary validation study involving simulation of and experiments with a TurtleBot mobile robot platform show the advantage of the proposed method over existing ones.