Miracle-dz/UniFAR-FTUs huggingface dataset:
Speaker Differentiation Using a Convolutional Autoencoder

In this work, a deep learning solution for differentiating speaker voices in audio given two microphone sources is presented as a step towards solving the cocktail party problem. A convolutional autoencoder was trained using a small sample size of data to associate audio snippets with categorical labels. Audio snippets collected as part of this work were used for training and evaluating the model. Audio was converted to mel-frequency cepstrum representation prior to classification. The collective processed data was labeled according to the person or collection of persons speaking. The model was trained and evaluated using data with two, three, four, five, and six categories. The result was a model that recognizes when different people are speaking in a 2-person, 3-person, 4-person, 5-person, and 6-person conversation with an accuracy of 99.29%, 97.62%, 96.43%, 93.43%, and 88.1%, respectively. Experimental comparisons between the five versions of the model are presented.
Continuous Task Transition Approach for Robot Controller Based on Hierarchical Quadratic Programming

Robots with high degrees of freedom (DoFs), such as humanoids and mobile manipulators, are expected to perform multiple tasks simultaneously. Hierarchical quadratic programming (HQP) can effectively compute a solution for strictly prioritized tasks. However, the continuity of the control input is not guaranteed when the priorities of the tasks are modified during operation. In this letter, we propose a continuous task transition method for HQP-based controller to insert, remove, and swap arbitrary tasks without discontinuity. Smooth task transition is assured because our approach uses activation parameters of the new and existing tasks without modifying the control structure. The proposed approach is applied to various task transition scenarios including joint limit, singularity, and obstacle avoidance to guarantee stable execution of the robot. The proposed control scheme has been implemented on a 7-DoF robotic arm, and its performance is demonstrated by the continuity of the control input during various task transition scenarios.
Comparative evaluation of soil properties using CPT and DMT in‐situ tests

The piezocone penetration test (CPT) and flat dilatometer test (DMT) are routinely used in geotechnical practice and are often considered adequate for detailing of soil profile and determination of soil properties. This paper presents comparison of results of ground characterization and evaluation of soil properties based on the combined use of CPT and DMT at four different locations, each with different ground composition. Evaluation of both tests in terms of soil type, undrained shear strength, constrained modulus and friction angle is presented and results are compared. In some cases the results are also compared with laboratory data. The main objective is to show that companion sets of CPT and DMT at a given site could be used to better define the geotechnical ground model and to increase confidence in the evaluation of soil properties.
A Multi-Fault Dynamic Compaction Technique for Test Pattern Count Reduction

With the advance of IC fabrication technology, the manufacturing test data volume keeps growing because more fault models must be considered to ensure the product quality. In this paper, we develop a multi-fault dynamic compaction (MFDC) technique to reduce the number of test patterns by improving the pattern compaction efficiency. Compared to conventional single-fault dynamic compaction, the resulting test set is more compact, which lowers the test application time and cost. Simulation results on benchmark and industry circuits show an average of 18% pattern count reduction without fault coverage degradation.
On the Robustness of Deep Features for Audio Event Classification in Adverse Environments

Deep features, responses to complex input patterns learned within deep neural networks, have recently shown great performance in image recognition tasks, motivating their use for audio analysis tasks as well. These features provide multiple levels of abstraction which permit to select a sufficiently generalized layer to identify classes not seen during training. The generalization capability of such features is very useful due to the lack of complete labeled audio datasets. However, as opposed to classical hand-crafted features such as Mel-frequency cepstral coefficients (MFCCs), the performance impact of having an acoustically adverse environment has not been evaluated in detail. In this paper, we analyze the robustness of deep features under adverse conditions such as noise, reverberation and segmentation errors. The selected features are extracted from SoundNet, a deep convolutional neural network (CNN) for audio classification tasks using raw audio segments as input. The results show that the performance is severely affected by noise and reverberation, with room for improvement in terms of robustness to different kinds of acoustic scenarios.
An Energy Efficient Tour Construction Using Restricted k-Means Clustering Algorithm for Mobile Sink in Wireless Sensor Networks

abstract There has been much research on efficient energy utilisation to prolong the life-span of wireless sensor networks and other tiny devices, with various techniques deployed to address energy consumption issues. The aim of this paper is to build on previous research and further investigate the use of a mobile sink for data collection in wireless sensor networks. We aim to find an optimal path for a mobile sink to collect a single packet from each sensor via a single hop and return back to the starting point such that, subject to the length constraint L, total energy wastage is minimised. We have previously referred to this problem as the minimum energy cost mobile sink restricted tour problem and showed that this is NP-hard. We were inspired by the concept of the k-means clustering algorithm and propose a restricted k-means clustering algorithm. In this approach, we first divide the sensing field into a set of k clusters such that the radius of each cluster is R, where R is the maximum transmission range of the sensor. We iteratively increase the value of k until all the sensors are covered under the length constraint. Simplicity, efficiency, and flexibility are the most important and distinctive features of this algorithm. The technique is implemented to evaluate the algorithm and compare it to our previous algorithm. Our simulation results outperformed the previous technique.
AN UNCONSTRAINED ELLIPSE FITTING TECHNIQUE AND APPLICATION TO OPTIC CUP SEGMENTATION

We present a novel method for fitting an ellipse to scattered data based on least-squares minimization. The new technique has several advantages over the standard ellipse fitting techniques. For one, it is constraint-free and computationally inexpensive thus making it easy to implement. Also, despite the absence of constraints, execution of the model always results in an ellipse fit. Additionally, the model results in a singular solution for a given set of datapoints. The proposed model is compared with standard techniques and shown to have the ability to fit an accurate ellipse even when other methods either fail to be ellipse-specific or take up excessive computation time for execution. An application to the problem of segmentation of the optic cup in retinal fundus images, is also presented. Experimental validation and performance comparisons show that the proposed technique is competitive with the state-of-the-art methods.
Development of Machine Learning-based Predictive Models for Air Quality Monitoring and Characterization

One of the biggest environmental problems right now is air pollution. Air quality is needed to be consistently monitored and assessed to ensure better living conditions. The U.S. Environmental Protection Agency (EPA) uses the air quality index (AQI) to standardize the air quality. However, AQI requires precise and accurate sensor readings and complex calculations, making it not feasible for portable air quality monitoring devices. The aim of this paper is to find an alternative way of monitoring and characterizing air quality through the use of integrated gas sensors and building predictive models using machine learning algorithms that can be used to obtain data-driven solutions to mitigate the risk of air pollution. The proposed methodology is implemented by building a prototype for the integrated sensors using DHT 11 temperature and relative humidity sensor, MQ2, MQ5 and MQ135 gas sensors. Five predictive models are developed in the study, k-nearest neighbors (KNN), support vector machine (SVM), Naïve-Bayesian classifier, random forest and neural network. Results show that the researchers are able to obtain an accuracy of 98.67%, 97.78%, 98.67%, 94.22%, and 99.56% for all the five models respectively, having the neural network to be the best performing model.
Learning Spatiotemporal Features With 3DCNN and ConvGRU for Video Anomaly Detection

Video anomaly detection aims to analyze the abnormal events or behaviors from massive monitoring video data, which is extremely challenging due to the ambiguous definition of abnormal behavior and the complex monitoring scene. Feature representation based on the hand-crafted of video local spatial area is more complicated, and it is difficult to learn the essential feature from the input video. In this paper, a deep autoencoder network combined with 3DCNN and ConvGRU is proposed to learn the spatiotemporal features for video anomaly. Firstly, 3DCNN and bidirectional ConvGRU are used to encode the local-global spatial features and short-long-term temporal features in the spatiotemporal dimension. Secondly, the reconstruction branch is introduced to reconstruct video frames, while the prediction branch is utilized to make the encoder to learn the better spatiotemporal feature at the training phase. In addition, the regularization of adjacent frames in a loss function is carried on to improve the temporal feature. The weights of the C3D model trained by action recognition are transferred to 3DCNN to prevent model overfitting. Experiments on real anomaly datasets shows the effectiveness of our proposed deep model.
Visibility Restoration of Single Haze Images Based On Feature Aggregation

In order to defect drawbacks of Dark Channel Prior(DCP)-based dehazing methods, which suffer from undesirable artifacts and color distortion, we introduce multidimensional theory to achieve effective prediction of haze free images. Local manifold similarity is first employed to reduce the initial estimation error. And then we employ signal filtering method based on contrast-based Gaussian curvature to refine the rough transmission map. The experimental results have shown that the images restored by our algorithm are more clear and natural than start-of-art approaches.
MODELING SIGNALS OVER DIRECTED GRAPHS THROUGH FILTERING

In this paper, we discuss the problem of modeling a graph signal on a directed graph when observing only partially the graph signal. The graph signal is recovered using a learned graph filter. The novelty is to use the random walk operator associated to an ergodic random walk on the graph, so as to define and learn a graph filter, expressed as a polynomial of this operator. Through the study of different cases, we show the efficiency of the signal modeling using the random walk operator compared to existing methods using the adjacency matrix or ignoring the directions in the graph.
A Fast Reconstruction Method for IR Small Target Image

Real-time processing is extremely important for IR(Infrared) imaging system. In order to increase the speed of Compressive Sensing(CS) reconstruction, A new fixed-subspace pursuit method assisted with target attribute is presented in this paper. IR small target image has many characteristics, such as sparsity, energy clustering, the prior of target size, etc. Based on Subspace Pursuit (SP), the target characteristics are exploited to divide the subspace, which is helpful to select more optimal dictionary subspace. At the same time, the method makes it possible recover numbers of non-zero coefficients for an image by only one iteration. Experimental results show that TA-FSP could reconstruct the IR small target image more effectively than other existing algorithms, especially under the unknown sparsity of signal assumption.
Comparison of ring-down response estimation between RDT and NExT under ambient condition

This paper describes two data driven methods to extract the ring-down signatures of the random responses for the study of low frequency electromechanical oscillations. The two methods are: random decrement technique (RDT) and the natural excitation technique (NExT). The objective is to compare and contrast the performance of the methods. The correlation coefficients between the extracted ring-down signal and the impulse signal, the accuracy of damping ratio and frequency estimated by RDT-ERA method and the NExT-ERA method are the measures of the performance of the algorithms. Their performances are evaluated using an ideal second-order system, and PMU measurements from Sichuan power grid of China.
Attentive Convolution: Equipping CNNs with RNN-style Attention Mechanisms

In NLP, convolutional neural networks (CNNs) have benefited less than recurrent neural networks (RNNs) from attention mechanisms. We hypothesize that this is because the attention in CNNs has been mainly implemented as attentive pooling (i.e., it is applied to pooling) rather than as attentive convolution (i.e., it is integrated into convolution). Convolution is the differentiator of CNNs in that it can powerfully model the higher-level representation of a word by taking into account its local fixed-size context in the input text tx. In this work, we propose an attentive convolution network, ATTCONV. It extends the context scope of the convolution operation, deriving higher-level features for a word not only from local context, but also from information extracted from nonlocal context by the attention mechanism commonly used in RNNs. This nonlocal context can come (i) from parts of the input text tx that are distant or (ii) from extra (i.e., external) contexts ty. Experiments on sentence modeling with zero-context (sentiment analysis), single-context (textual entailment) and multiple-context (claim verification) demonstrate the effectiveness of ATTCONV in sentence representation learning with the incorporation of context. In particular, attentive convolution outperforms attentive pooling and is a strong competitor to popular attentive RNNs.1
FACE AGING AS IMAGE-TO-IMAGE TRANSLATION USING SHARED-LATENT SPACE GENERATIVE ADVERSARIAL NETWORKS

Here, a novel approach is proposed to generate age progression (i.e., future looks) and regression (i.e., previous looks) of persons based on their face images. The proposed method addresses face aging as an unsupervised image-to-image translation problem where the goal is to translate a face image belonging to an age class to an image of a different age class. To address this problem, we resort to adversarial training and extend the UNsupervised Image-to-image Translation (UNIT) framework to multi-domain image-to-image translation, since several age classes are considered. Due to the shared-latent space constraint of UNIT, the faces belonging to each age class/domain are forced to be mapped to a shared-latent representation. Low-level features are used to perform the transitions between the domains and to generate age progressed/regressed images. In addition, the most personal and abstract features of faces are preserved. The proposed Aging-UNIT framework is compared to state-of-the-art techniques and the ground truth. Promising results are demonstrated, which are attributed to the ability of the proposed method to capture the subtle aging transitions.
Integration of Communication Networks and Control Systems Using a Slotted Transmission Classification Model

In this paper, we present a communication abstraction for Networked Control Systems that is characterized by a slotted transmission classification model. We discuss, how such a model can be implemented over local area networks by using IEEE Time Sensitive Networking methods. Furthermore, it is shown how asymptotic stability can be analyzed for linear systems that communicate over such a network. Based on the stability result, a controller design procedure is derived that takes the information captured in the network model into account. Further topics and related open problems that are implicated by the proposed model are briefly discussed as an outlook.
Study of Annealing Temperature Effect on the Photovoltaic Performance of BiOI-Based Materials

Bismuth oxyiodide (BiOI) is expected to be promising material for photovoltaic devices since it has good activity under the visible range. Here, we studied the annealing treatment on BiOI and its effect on the photovoltaic application. Firstly, the synthesized BiOI from Bi(NO3)3 and KI was annealed at varied temperatures (100–550 °C). The structural investigation by X-ray diffraction and Raman spectroscopy analysis was supported with morphology and optical analysis by scanning electron microscope (SEM) and UV-Visible spectroscopy. Due to the heating treatment, it could result in iodine-deficient bismuth-based materials, namely Bi7O9I3, Bi5O7I, and β-Bi2O3. Secondly, the photovoltaic test measurement was performed by solar simulator air mass (AM) 1.5 illumination which presented the current-voltage curve from each material. The enhancement of photovoltaic performance was given by the increase of temperature up to 300 °C. At that temperature, the performance of the device which consisted of Bi7O9I3 achieved three times higher efficiency than the annealed parent BiOI at 100 °C. Hence, the structural changing owing to the oxygen addition to BiOI structure had an impact on the photoelectrochemical cell. Based on this work, it is possible to attempt BiOI derivation with suitable holes and electron transport layers for better photovoltaic performance.
An Aesthetic Preference Prediction System for Assessing Natural Images Based on Photo Complexity and Composition Evaluation

Photo aesthetics is a field in computer vision, which aims at analyzing the beautifulness of a photo. With the advancement of imaging technology, people capture and produce photos easily but they find it difficult to sort out good ones. Therefore, in this paper, we propose a method for efficient photo aesthetics analysis for quality classification. We developed some features based on human psychology and criterions on photography, including complexity, texture, color, composition. Particularly, the Sobel edge detector and Harris corner points are extracted to assess the image complexity, and some color and composition features proposed in previous literature are improved for better analysis accuracy.The supervised machine learning methods are applied for photo aesthetics analysis, and the SVM is used to measure the effectiveness of the proposed features. In our experiments, both image databases CUHKPQ and CUHK are used for evaluation. In the experiment, the CUHKPQ dataset is divided into seven thematic categories, whereas the CUHK dataset contains all categories. The experimental results show that the proposed method is able to classify the photo aesthetic quality efficiently with lower dimension compared to other related work. In the CHUK dataset, the accuracy above 88.72% and in the CUHKPQ dataset, the accuracy is up to 96.4%.
Understanding the Bandwidth Limitations in Monolithic 1.3 μm InAs/GaAs Quantum Dot Lasers on Silicon

In this paper, we present measurements and simulations of the small-signal modulation response of monolithic continuous-wave 1.3 μm InAs/GaAs quantum dot (QD) narrow ridge-waveguide lasers on a silicon substrate. The 2.5 mm-long lasers investigated demonstrate 3 dB modulation bandwidths of 1.6 GHz, D-factors of 0.3 GHz/mA1/2, modulation current efficiencies of 0.4 GHz/mA1/2, and K-factors of 2.4 ns and 3.7 ns. Since the devices under test are not designed for high-speed operation due to their long length and hence long photon lifetime, the modulation response curves are used as a fitting template for numerical simulations with spatiotemporal resolution to gain insight into the underlying laser physics. The obtained parameter set is used to unveil the true potential of the laser material in an optimized device geometry by modeling the small-signal response at different cavity lengths, mirror reflectivities, and for different numbers of QD layers. The simulations predict a maximum 3 dB modulation bandwidth of 5 GHz to 7 GHz for a 0.75 mm-long cavity with 99% and 60% high-reflection coatings and ten QD layers. Modeling the impact of dislocations on the dynamic performance qualitatively reveals that enhanced non-radiative recombination in the wetting layer leaves the modulation bandwidth of QD lasers on silicon almost unaffected, while dislocation-induced optical loss does not pose a problem, as long as sufficient gain is provided by the QD active region.
Speaker localization using the direct-path dominance test for arbitrary arrays

Speaker localization in reverberant environments is an important and timely challenge in audio signal processing, with new methods recently developed to meet this challenge. One of these methods is the direct-path dominance (DPD) test showing robustness to both reverberation and noise. However, this method has been developed for processing in the spherical harmonics domain and is restricted to microphone arrays with a spherical configuration. In this paper, we propose a new method for extending the DPD test approach to arrays with arbitrary configuration, by removing the frequency dependence of the steering matrices using local focusing transformations to support frequency-smoothing and robustness to reverberation. A simulation study for evaluating the performance of the method for a binaural array is presented, validating the theoretical developments.
Power Allocation with a Wireless Multi-cast Aware Routing for Virtual Network Embedding

As wireless sensor networks evolve towards comprising more powerful devices, they offer opportunities for distributing applications into the network and processing data in-network. Examples for such applications often come from the signal processing or data analysis domain. We describe such applications by overlay graphs, consisting of functional blocks with predefined interconnections that may even have feedback loops. We treat these functional blocks as virtual functions that can be easily moved (reprogrammed) among the network nodes. This property allows us to use the concept of virtual network embedding (VNE) for placement and routing. VNE has only partially been considered for wireless environments; specifically, the interaction between embedding and the wireless multicast advantage has not been fully explored. We cast our problem as mixed integer linear programming (MILP) formulation for uniform transmit power and mixed integer quadratic constraint programming (MIQCP) for power allocation and compare between both methods.
Dynamic MRI Reconstruction Using Tensor-SVD

In this paper we propose to reconstruct dynamic magnetic resonance images from highly sparse sampling k-t space data by enhancing the low rankness and sparsity simultaneously. We introduce Tensor Singular Value Decomposition (t-SVD) instead of matrix SVD to maintain the structure of dynamic MR images. The reconstruction is casted into an optimization framework where the tensor nuclear norm (TNN) minimization is used to enhance the low rankness and the l1 norm minimization of tensor gradient along each mode is applied to enhance the sparsity. In addition, we utilize alternating direction method of multipliers (ADMM) algorithm to efficiently solve the proposed optimization problem. Experimental results demonstrate the superior performance of the proposed method.