Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of Serially Coupled Double Microsphere Sensor for Detection of Biomolecules

Microsphere sensors have attracted increasing attention in recent years especially in label-free detection of biomolecules thanks to their very high quality factors which causes to have the high sensitivity and low detection limit. In this paper, two novel structures for microsphere sensors based on serially coupling of two microspheres are proposed. They are modeled using directional coupling theory while considering the loss condition in biosensing applications. The proposed structures are simulated for sensing rodlike bacteria and their key sensing parameters are compared with a single-microsphere sensor. It is demonstrated that by using serially coupled double microsphere sensor, the frequency spacing between adjacent resonances observed at the output spectrum of the sensor is expanded considerably compared to the single-microsphere sensor and therefore it is much easier to track the resonance frequencies variation due to changes in the surrounding medium while the sensor's quality factor remains high.
ABBA Dual-Cross-Polarized Antenna Decoupling for 5G 16-Element Planar MIMO at 28 GHz

Multiple-Input Multiple-Output (MIMO) antenna is mandatory for next generation wireless communications, where correlation between antennas is harmful. This paper proposes antenna clustering with ABBA pattern constructing 16 elements antenna from 4 clusters, where each cluster has dual-cross polarization to reduce the correlation. We conduct a series of computer simulations to design antenna geometry and to verify the coupling effect to the bit-error-rate (BER) performances. Computer simulation results confirm that the proposed design has improvement with efficiency of 91.95%, while the conventional uniform clustering antenna has only 91.18%. This improvement is achievable because the proposed antenna provides lower correlation compared to the conventional uniform clustering MIMO antenna. We also found that BER performances using the proposed antenna is close to the performance of fully independent MIMO antenna indicating that the proposed 16-element MIMO antenna at 28 GHz has been well designed.
A Review on Short-Term Electricity Price Forecasting Techniques for Energy Markets

The electricity price forecasting (EPF) is essential for decision-making mechanisms of market participants to survive in the deregulated and competing commercial environment. Due to special features of the electricity such as seasonality, the constant balance between production and consumption required by the system, and environmental dependencies, electricity prices generally shows extreme volatility and price spikes with the heteroscedasticity. This paper provides a survey of main EPF methodologies and the ultimate goal of this survey is to provide readers insights and guidelines for choosing different EPF techniques for day-ahead electricity markets. For each type of method, we briefly introduce its principle and then describe how it is applied in EPF. Many new EPF techniques developed recently are also discussed, especially the artificial intelligence forecasting methods. The pros and cons of each type of method are provided in a final table so that users can pay attention to when choosing them. In the final section, several promising methods and potential directions for further exploration are presented.
Verification of A MATLAB Program for Shaped Dual Reflector Antenna

A dual reflector antenna is fast becoming a crucial instrument in point-to-point communication system due to their capability to produce high gain. This paper investigates the accuracies of calculation program by using Matrix Laboratory (MATLAB) software based on conventional method of ray tracing concept. The designed antenna is aimed to cover frequency of 28 GHz. This MATLAB program yields the shape of reflecting surfaces, ray tracing, aperture distribution and radiation pattern. According to comparison results, it shows a good agreement between theoretical and simulation results. Accuracy of the developed MATLAB code is ensured.
Model Predictive Current Control for PMSM Drives With Parameter Robustness Improvement

In order to solve the parameter dependence problem in model predictive control, an improved model predictive current control (MPCC) method based on the incremental model for surface-mounted permanent-magnet synchronous motor drives is proposed in this paper. First, the parameter sensitivity of a conventional MPCC method is analyzed, which indicates that the parameter mismatches would cause prediction current error and inaccurate delay compensation. Therefore, an incremental prediction model is introduced in this paper to eliminate the use of permanent magnetic flux linkage in a prediction model. Among the parameter of the incremental prediction model, only inductance mismatch contributes to the prediction error, since the influence of resistance mismatch on the control performance is very small. Therefore, in order to improve the antiparameter-disturbance capability of the MPCC method, an inductance disturbance controller, which includes the inductance disturbance observer and inductance extraction algorithm, is presented to update accurate inductance information for the whole control system in real time. Finally, simulation and experimental results both show that the proposed method can effectively eliminate the influence of the parameter mismatches on the control performance and reduce the parameter sensitivity of the MPCC method.
Combining Convolution Neural Network and Bidirectional Gated Recurrent Unit for Sentence Semantic Classification

Many keywords in a sentence that represents the semantic propensity of the sentence. These words can exist anywhere in the sentence, which poses a great challenge to sentence semantic classification. The current sentence semantic classification methods usually tackle this problem by the use of attention mechanism, and most of them utilize softmax function to calculate each word’s weight. According to the observation that a word with higher score carries more valuable information in sentence modeling, this paper presents a novel low-complexity model termed as CNN-BiGRU by integrating both convolution neural network (CNN) and bidirectional gated recurrent unit (BiGRU). Both the contextual representations and the semantic distribution are obtained through BiGRU, and the latter is constrained to a Gaussian distribution. In addition, the proposed model utilizes a shallow word-level CNN to obtain intermediate representations, and the score of each word is denoted as the Euclidean distance between the intermediate representations and the semantic distribution. Then, the final representations are obtained by the combination of the contextual representations and the score of each word, and thus, the model learns a compact code for sentence sentiment classification and can be trained end-to-end with limited hyper-parameters. In conclusion, the proposed model is able to focus both the keywords and the underlying semantics of the words. Comprehensive experiments are conducted on seven benchmarks. Compared with the state-of-the-art models, our model has excellent performance.
Automated Grading of Handwritten Essays

Automatic grading of handwritten essays is vital in evaluating the performance of students in educational settings, particularly in situations where language experts are rare. We build a system capable of taking the input as handwritten essays in image format and outputs the grading on the scale of 0-5; 0 being the worst and 5 being the best. The overall system integrates Optical Handwriting Recognition (OHR) and Automated Essay Scoring (AES)/grading. The handwritten essay is transcribed using a network composed of Multi-Dimensional Long Short Term Memory (MDLSTM) and convolution layers. The loss function is Connectionist Temporal Classification (CTC). The AES model is a 2-layer artificial neural network with a feature set based on pretrained GloVe word vectors. The results of grading of essays are compared for transcriptions of essays received from OHR system and transcriptions of essays done manually. The mutual agreement between the two shows a Quadratic Weighted Kappa score of 0.88. The results indicate that though the current OHR systems have transcription errors but as a whole can perform well for an application like AES.
A Study of the Relationship between a Mechanical Coupling Metric and Gait Characteristics for an Ankle-Actuated Biped Robot

This paper presents a study of the relationship between the magnitude of coupling between the actuated and unactuated degrees of freedom for an ankle-actuated biped robot and the robustness and cost of transport of the gait. It extends prior results that considered only the instantaneous coupling at individual poses to analyze the cumulative effects of coupling over entire trajectories. The coupling metric is general in that it can be computed directly from the Lagrangian of the system. By using a two-link biped model, a family of ankle-actuated candidate gaits defined by fourth-order Bézier polynomials is generated, and the feasibility of the gaits is verified in the sense that the unilateral constraint of ground reaction force is satisfied. The correlation between the coupling metric and the maximum magnitude of disturbance that can be rejected is significant. It indicates that robust gaits tend to have small coupling under zero disturbance so that the “reserve” coupling may be utilized to reject the disturbance. Moreover, gaits with smaller cost of transport under zero disturbances have smaller coupling and therefore should be more robust.
Prior Knowledge Driven Energy for Saliency Detection

Saliency detection on images has experienced substantial progress in recent years on the basis of deep neural network (DNN). However, there may exist secondary saliency in the background that distracts DNN learning and mistakes the secondary salient regions as saliency. To address this issue, we propose a dual-term energy to improve the inference of saliency on top of DNN estimation, where dense term smoothens salient regions in pixel scale and sparse term extracts prior knowledge to differentiate saliency and non-saliency superpixels. Our prior knowledge including extra- and intra-region priors, contributes to improving overall saliency detection. The extra-region prior knowledge estimates the saliency probabilities for different pre-partitioned regions to eliminate the secondary saliency. The intra-region prior knowledge helps to group the salient regions that otherwise could be ignored by DNN predictor, and thus to provide more complete saliency definition. We evaluated our model on 8,465 images from four well-recognized saliency detection benchmarking datasets, and compared our model to six state-of-the-art comparative methods. Experimental results demonstrated that our model outperformed the state-of-the-art counterpart with improvements of up to 2.51% in terms of F-measure.
Scattering Cross-Section Estimation Using Passive Imaging in Reverberating Elastic Plates: Case Study of Rigid Isotropic Inclusion

We investigate here how passive imaging based on noise correlation can serve to retrieve the scattering strength in a reverberating elastic plate. In previous works, we demonstrated the possibility to detect and localize passively a single scatterer in a reverberating elastic plate combining a transducer array and beamforming imaging, when a diffuse flexural wave field is approximately generated. In particular, when the noise is non uniformly distributed over the plate surface, we can take benefit from reverberations to accelerate the convergence towards the Green's functions. A continuity is addressed here to passively characterize a single scatterer in a thin plate. Numerical simulations are conducted in the presence and absence of a defect within the plate. We calculate the differential correlation matrix obtained in these cases (i.e., with versus without the scatterer) as a measure of the temporal variations in the Green's function due to the defect. By back-propagating the differential matrix, an image is obtained with a maximum intensity on the defect location. We also develop a formula that relates this focal spot on the defect position to the scattering strength of the scatterer. The formula are numerically validated using simulations in an aluminum plate. We substantiate the reliability of this approach through a classical method for the measurement of the scattering cross-section based exclusively on direct arrivals. Finally, some defect localization images are shown where the amplitude represents the scattering cross-section of the defect at 5 kHz and 25 kHz.
Turbo-Type Message Passing Algorithms for Compressed Robust Principal Component Analysis

Compressed robust principal component analysis (RPCA), in which a low-rank matrix <inline-formula><tex-math notation="LaTeX">$\boldsymbol {L}$</tex-math></inline-formula> and a sparse matrix <inline-formula><tex-math notation="LaTeX">$\boldsymbol {S}$</tex-math></inline-formula> are recovered from an underdetermined amount of noisy linear measurements of their sum <inline-formula><tex-math notation="LaTeX">$\boldsymbol {L}+\boldsymbol {S}$</tex-math></inline-formula>, arises in various applications such as face recognition and video foreground/background separation. This problem can be solved by Bayesian inference based iterative algorithms. However, most existing Bayesian algorithms factorize <inline-formula><tex-math notation="LaTeX">$\boldsymbol {L}$</tex-math></inline-formula> into the product of two rank-<inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> matrices, and estimate the two rank-<inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> matrices (rather than <inline-formula><tex-math notation="LaTeX">$\boldsymbol {L}$</tex-math></inline-formula> itself) in the iterative process, where <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> is the rank of <inline-formula><tex-math notation="LaTeX">$\boldsymbol {L}$</tex-math></inline-formula>. On one hand, this factorization is not essential to the original problem and so may cause a potential performance loss. On the other hand, the existing Bayesian algorithms assume a certain probability model for the low-rank matrix <inline-formula><tex-math notation="LaTeX">$\boldsymbol {L}$</tex-math></inline-formula> and the sparse matrix <inline-formula><tex-math notation="LaTeX">$\boldsymbol {S}$</tex-math></inline-formula>, whereas the probability model of <inline-formula><tex-math notation="LaTeX">$\boldsymbol {L}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\boldsymbol {S}$</tex-math></inline-formula> is usually difficult to acquire in real applications. In this paper, we develop a Bayesian message passing algorithm, termed turbo-type message passing (TMP), for the compressed RPCA problem. We show that the proposed TMP algorithm significantly outperforms the state-of-the-art compressed RPCA algorithms, and requires a much lower computational complexity. We also show that TMP does not assume any prior probability model for <inline-formula><tex-math notation="LaTeX">$\boldsymbol {L}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\boldsymbol {S}$</tex-math></inline-formula>; TMP even does not require the knowledge of the environmental information, such as the rank of <inline-formula><tex-math notation="LaTeX">$\boldsymbol {L}$</tex-math></inline-formula> and the sparsity level of <inline-formula><tex-math notation="LaTeX">$\boldsymbol {S}$</tex-math></inline-formula>. Therefore, TMP gives a promising approach for real applications of the compressed RPCA problem.
Attention With Sparsity Regularization for Neural Machine Translation and Summarization

The attention mechanism has become the de facto standard component in neural sequence to sequence tasks, such as machine translation and abstractive summarization. It dynamically determines which parts in the input sentence should be focused on when generating each word in the output sequence. Ideally, only few relevant input words should be attended to at each decoding time step and the attention weight distribution should be sparse and sharp. However, previous methods have no good mechanism to control this attention weight distribution. In this paper, we propose a sparse attention model in which a sparsity regularization term is designed to augment the objective function. We explore two kinds of regularizations: $L_{\infty }$-norm regularization and minimum entropy regularization, both of which aim to sharpen the attention weight distribution. Extensive experiments on both neural machine translation and abstractive summarization demonstrate that our proposed sparse attention model can substantially outperform the strong baselines. And the detailed analyses reveal that the final attention distribution indeed becomes sparse and sharp.
A Compact Resistor-Based CMOS Temperature Sensor With an Inaccuracy of 0.12 °C (3 $\sigma$ ) and a Resolution FoM of 0.43 pJ $\cdot$ K $^{2}$  in 65-nm CMOS

This paper presents a compact resistor-based CMOS temperature sensor intended for dense thermal monitoring. It is based on an <inline-formula> <tex-math notation="LaTeX">$RC$ </tex-math></inline-formula> poly-phase filter (PPF), whose temperature-dependent phase shift is read out by a frequency-locked loop (FLL). The PPF’s phase shift is determined by a zero-crossing (ZC) detector, allowing the rest of the FLL to be realized in an area-efficient manner. Implemented in a 65-nm CMOS technology, the sensor occupies only 7000 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}~^{\mathrm{ 2}}$ </tex-math></inline-formula>. It can operate from supply voltages as low as 0.85 V and consumes 68 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>. A sensor based on a PPF made from silicided p-poly resistors and metal–insulator–metal (MIM) capacitors achieves an inaccuracy of ±0.12 °C (3<inline-formula> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula>) from −40 °Cto 85 °C and a resolution of 2.5 mK (rms) in a 1-ms conversion time. This corresponds to a resolution figure-of-merit (FoM) of 0.43 pJ<inline-formula> <tex-math notation="LaTeX">$\cdot \text{K}~^{\mathrm{ 2}}$ </tex-math></inline-formula>.
Use of Hierarchical Cascading Technique for FEM Analysis of Transverse Mode Behaviors in Surface Acoustic Wave Devices

This paper describes application of the hierarchical cascading technique (HCT)to the 3D FEM analysis of transverse mode behaviors in Surface acoustic wave (SAW)devices. Two methods are implemented; one is impedance calculation, and another is calculation of SAW scattering coefficients at boundaries. It is shown that HCT enables rapid calculation for successive simulation with scanning design parameters. Thus, techniques discussed in the paper are quite effective for optimization of device structures. It is also shown that GPGPU can accelerate the HCT calculation dramatically, especially for 3D cases discussed in this paper.
Upper Estimates of the Deviations in Linear Dynamical Systems Subjected to Uncertainty

In this talk, the linear dynamical system subjected to uncertainty in the system matrix is considered. Using the linear matrix inequality technique we obtain the upper bounds for the deviations in linear systems. An LMI-based stabilizing feedback procedure is proposed which guarantees “as small as possible” deviations. The results of numerical simulations demonstrate the low conservatism of the obtained bounds.
SAP: A Novel Stationary Peers Assisted Indoor Positioning System

Recent years have witnessed significant advances in indoor positioning. Existing approaches either require cumbersome site surveys or customized hardware, thus hindering their popularity. In this paper, we propose Stationary peers-Assisted indoor Positioning (SAP), a practical indoor positioning solution that is highly scalable and easy-to-deploy. SAP greatly alleviates the pain of fingerprint collection by smartly leveraging the relatively stationary people, namely stationary peers that are largely available in common indoor environments to assist positioning. Once SPs’ locations and the relative distance between SPs and a target are obtained, SAP can apply trilateration to locate the target. SAP incorporates three key modules: a novel accelerometer-based filter that can accurately identify SP, an enhanced fingerprint-based positioning method that can accurately pinpoint SPs’ locations, and a robust acoustic ranging method. We implement a prototype of SAP on the Android platform and evaluate its performance in representative real-world environments. SAP achieves an 80% positioning error of 2.2 m, which is comparable to the most existing smartphone-assisted approaches.
HirePool: Optimizing Resource Reuse Based on a Hybrid Resource Pool in the Cloud

In a cloud environment, the primary way to optimize physical resources is to reuse a physical machine (PM) by consolidating complementary multiple virtual machines (VMs) on it. When considering VMs’ dynamically changing resource demands, one hot research topic revolves around reusing VM migration resources more efficiently. The challenge here is finding the best tradeoff between the VM migration optimization performance and complexity. On one hand, to improve the migration efficiency, VM migration planning is adopted to achieve efficient resource reuse while minimizing the number of VM migrations. On the other hand, the huge number of PMs and VMs in a cloud datacenter often adds considerable complexity to migration planning, which hampers the decision-making process in VM migration. To address these issues, this paper proposes a hybrid resource pool model to reduce the complexity of VM migration planning by limiting the scope of VM migration decisions. Then, based on this model, we use our novel resource-reuse optimization mechanism (called HirePool) to improve efficiency by maximizing resource usage with only a few VM migrations. Finally, we perform simulation tests and actual experiments running on a real cloud platform to evaluate HirePool. Results show that HirePool improves average resource usage by 13%, saves the number of PMs used by 12%, and reduces the average number of migrations (compared with contrast mechanisms) by 31%.
Image-Based Visibility Estimation Algorithm for Intelligent Transportation Systems

Posted road speed limits contribute to the safety of driving, yet when certain driving conditions occur, such as fog or severe darkness, they become less meaningful to the drivers. To overcome this limitation, there is a need for adaptive speed limits system to improve road safety under varying driving conditions. In that vein, a visibility range estimation algorithm for real-time adaptive speed limits control in intelligent transportation systems is proposed in this paper. The information required to specify the speed limit is captured via a road side unit that collects environmental data and captures road images, which are then analyzed locally or on the cloud. The proposed analysis is performed using two image processing algorithms, namely, the improved dark channel prior (DCP) and weighted image entropy (WIE), and the support vector machine (SVM) classifier is used to produce a visibility indicator in real-time. Results obtained from the analysis of various parts of highways in Canada, provided by the Ministry of Transportation of Ontario, show that the proposed technique can generate credible visibility indicators to motorists. The analytical results corroborated by extensive field measurements confirmed the advantage of the proposed system when compared to other visibility estimation methods such as the conventional DCP and WIE, where the proposed system results exhibit about 25% accuracy enhancement over the other considered techniques. Moreover, the proposed DCP is about 26% faster than the conventional DCP. The obtained promising results potentiate the integration of the proposed technique in real-life scenarios.