Miracle-dz/UniFAR-FTUs huggingface dataset:
Attention-Based Sign Language Recognition Network Utilizing Keyframe Sampling and Skeletal Features

Sign language recognition(SLR) is a multidisciplinary research topic in pattern recognition and computer vision. Due to large amount of data from the continuous frames of sign language videos, selecting representative data to eliminate irrelevant information has always been a challenging problem in data preprocessing of sign language samples. In recent years, skeletal data emerged as a new type of data but received insufficient attention. Meanwhile, due to the increasing diversity of sign language features, making full use of them has also been an important research topic. In this paper, we improve keyframe-centered clips (KCC) sampling to get a new kind of sampling method called optimized keyframe-centered clips (OptimKCC) sampling to select key actions from sign language videos. Besides, we design a new kind of skeletal feature called Multi-Plane Vector Relation (MPVR) to describe the video samples. Finally, combined with the attention mechanism, we also use Attention-Based networks to distribute weights to the temporal features and the spatial features extracted from skeletal data. We implement comparison experiments on our own and the public sign language dataset under the Signer-Independent and the Signer-Dependent circumstances to show the advantages of our methods.
Video Shot Boundary Detection Based on Feature Fusion and Clustering Technique

For the problems of low accuracy and high complexity in detection of gradual shot boundary and long shot, a new video shot boundary detection algorithm based on feature fusion and clustering technique (FFCT) is proposed. In the algorithm, the interval frames of video sequence are selected, converted to gray images and scaled by sampling. With the frames, the speed-up robust features (SURF) and fingerprint features are extracted from non-compressed domain and compressed domain, and then the extracted features are fused. Next, K-means method is used to cluster the fused features, and linear discriminant analysis (LDA) is introduced to map the clusters to realize cohesion within classes and looseness among classes. Finally, the correlation of the feature classes between frames is calculated, and the features in each class are selected through density calculation and matched to realize the coarse detection and fine detection of video shot boundary. In the experiment, compared with the latest representative algorithms, it has the highest accuracy for the proposed algorithm. In particular, the detection of gradual shot boundary and long shot are also more accurate. Meanwhile, the average time consumption is also reduced. The experimental results show that the proposed algorithm has high accuracy and time efficiency, especially for gradual shot boundary and long shot detection.
A Dual-Arm Robot for Collaborative Vision-Based Object Classification

Collaborative robots are an evolution of industrial robotics to enhance the safety of human operators to work side-by-side carrying out complex tasks which could not be done before. Adding vision to such a robot can make it smart in terms of detecting right workpieces while performing its tasks. This paper focuses on ABB YuMi© collaborative robot improvement with an external machine vision system to sort objects using both manipulators independently. Use of two arms to perform two pick and place tasks while using a single overhead camera system is presented in the paper. The work involves ABB RobotStudio for robot programming and National Instrument’s LabVIEW for machine vision. Identifying different objects re-orienting them and organizing for pick and place, chosen as the example to demonstrate external machine vision integration with ABB dual-arm Yumi© robot.
Proposed Techniques to Optimize the DW and ETL Query for Enhancing data warehouse efficiency

To improve time-bound decision capability of any business corporates, they have to perform data-analysis based on historical and current data. For handling this data, data warehouse (DWH) plays a crucial role. Further-more as time passes, the data also grows exponentially, because in data warehouse historical data also needs to be maintained. If we further drill now-a-days Extract Transform and Load (ETL) tools are being used to perform extract, transform and loading of data. These ETL tools are based on any specific language like in case of Informatica ETL tool, which is based on Structured Query Language (SQL) language. So, it’s imperative to enhance the efficiency of these languages to enhance the capacity of ETL tools and this will significantly impact the performance of data warehouse ETL work, and by this data can also be provided in faster manner for analysis and reporting purposes. Additionally, data quality is also an important factor, as data-analysis will be performed on the same data supplied by data warehouse. So, it’s highly concerned to improve the data quality by applying data-quality checks before supplying for data-analysis purpose. So, we have worked in this direction and gone through various research papers which are mainly focused on query optimization and data quality improvements. After going through all research papers, we have not only summarized the performance tuning tips given in these papers but also worked and experimented various things, and then we have proposed some unique techniques for query optimization and improved data quality techniques. After which we have incorporated it in data warehouse to enhance overall performance of data warehouse. These proposed techniques can be applied in any place wherever queries are being used to extract/transform and/or load the data to enhance the performance.
A Multi-mRNA Host-Response Molecular Blood Test for the Diagnosis and Prognosis of Acute Infections and Sepsis: Proceedings from a Clinical Advisory Panel

Current diagnostics are insufficient for diagnosis and prognosis of acute infections and sepsis. Clinical decisions including prescription and timing of antibiotics, ordering of additional diagnostics and level-of-care decisions rely on understanding etiology and implications of a clinical presentation. Host mRNA signatures can differentiate infectious from noninfectious etiologies, bacterial from viral infections, and predict 30-day mortality. The 29-host-mRNA blood-based InSepTM test (Inflammatix, Burlingame, CA, formerly known as HostDxTM Sepsis) combines machine learning algorithms with a rapid point-of-care platform with less than 30 min turnaround time to enable rapid diagnosis of acute infections and sepsis, as well as prediction of disease severity. A scientific advisory panel including emergency medicine, infectious disease, intensive care and clinical pathology physicians discussed technical and clinical requirements in preparation of successful introduction of InSep into the market. Topics included intended use; patient populations of greatest need; patient journey and sample flow in the emergency department (ED) and beyond; clinical and biomarker-based decision algorithms; performance characteristics for clinical utility; assay and instrument requirements; and result readouts. The panel identified clear demand for a solution like InSep, requirements regarding test performance and interpretability, and a need for focused medical education due to the innovative but complex nature of the result readout. Innovative diagnostic solutions such as the InSep test could improve management of patients with suspected acute infections and sepsis in the ED, thereby lessening the overall burden of these conditions on patients and the healthcare system.
Overmodulation Methods for Modulated Model Predictive Control and Space Vector Modulation

A two-level voltage source inverter (2L-VSI) when working under overmodulation regulation potentially has low performance, especially during large reference or load changes. To address this problem, a new calculation method is proposed in this article by determining a new reference vector from the boundary of linear-modulation zone as a best vector. The reference vector is synthesized by one or two vectors on the boundary line. Therefore, this technique simplifies the calculations to obtain the duty cycles and uses these duty cycles to identify the overmodulation zones. The proposed method is applied to the modulated model predictive control (MMPC) and the space vector modulation, respectively, in a 2L-VSI. A comprehensive assessment mathematically proves that both methods are in consistence with each other. Simulations and experiments are carried out to show that the proposed methods can reduce the computational burden and implementation complexity and improve the performance of the 2L-VSI in terms of fast dynamic response and smooth transition between the linear-modulation zone and overmodulation zone.
Low-Power Area-Efficient 8-bit Coarse-Fine Resistor-String DAC

This paper presents low-power area efficient 8-bit 500 kS/s Coarse-Fine Resistor-String Digital to Analog Converter (CFRS-DAC). Coarse-Fine structure in the proposed DAC reduces unit resistors as compare with the conventional R-string DAC. Proposed DAC reduces unit resistors to 28 % and 70 % of inverted-ladder DAC and Conversional R-string DAC, respectively. Class-B Opamp buffer is used to reduce the offset errors and power consumption. The proposed structure shows an Effective Number of Bits (ENOB) of 7.917 bits, Signal to Noise Ratio (SNR) of 49.42 dB, and Spurious Free Dynamic Range (SFDR) of 56.79 dB at post-layout simulation level. Static performance results shows that Max. Differential Nonlinearity (DNL) is 0.004 LSB and Max. Integral Nonlinearity (INL) is 0.024 LSB. It consumes $65.23 \mu mathrm{W}$ with supply voltage of 3.3 V and it has an active area of 0.0297 mm2.
Supervised machine learning-based prediction for dry mouth oral adverse drug reactions

Adverse drug reactions (ADRs) are defined as an unintended and harmful response that occurs with the ingestion of a certain drug. ADRs result in an appreciably harmful or unpleasant reaction that determines the success or failure of a drug. In this way, the generation of effective models for the prediction of ADR during the drug development process is of high relevance for human health. In this work, we present a complete proposal based on supervised machine learning to study dry mouth oral ADRs for the first time. Our approach integrates different drug properties, such as, chemical (fingerprint), biological (target protein, transporters and enzymes) and phenotypic (therapeutic indications and other known adverse reactions), all of them obtained from public databases that are combined on two and three levels. We employ different tree- based classification algorithms (AdaBoost and Random Forest), with the aim of obtaining the best predictors of the oral RAM studied. 14 models were generated, which gave an average AUC of 0.82 and an accuracy of 78%, where the best model with AdaBoost gave and accuracy and AUC of 87% and 0.89, respectively for the prediction of dry mouth oral ADR
DVB-T based Forward Scatter Radar for Small Target Surveillance

This paper investigates the target detection capability of a Passive Forward Scatter Radar (PFSR) exploiting a DVB-T transmitter as illuminator of opportunity. Specifically, it is shown that conventional processing schemes adopted in PFSR might suffer from significant performance degradation when dealing with OFDM waveforms of opportunity compared to the case of FM radio broadcast. In fact, a non-negligible increase of the disturbance background is observed in the final spectrogram, which yields an undesired masking effect on weak target echoes. Therefore, we propose a signal-based processing technique to mitigate the observed effect and improve the target detection capability. The proposed approach exploits the possibility to recover a good copy of the transmitted signal based on its digital nature; this is then used to cancel the fluctuating component of the output arising from the adopted modulation of the waveform of opportunity. The effectiveness of the proposed approach is proved against simulated data.
Chemical shift changes on micellization of linear alkyl benzenesulfonate and oleate

The proton NMR of linear alkyl benzenesulfonate (LABS) has been studied as a function of concentration. On micellization, aromatic protons meta to the sulfonate group show a large upfield shift (0.25 ppm) whereas the aromatic protons in ortho position remain unshifted. When water is solubilized in polar core of the LABS reverse micelles in CDCl 3 , it is aromatic protons ortho to the sulfonate group that show a large upfield shift whereas the aromatic protons in meta position remain unshifted
Effects of Thermal Relaxation on Temperature Elevation in Ex Vivo Tissues During High Intensity Focused Ultrasound

The introduction of thermal relaxation makes non-Fourier heat transfer more effective than Pennes equation to reflect rapid bio-heat transferring during HIFU, however, the contribution of thermal relaxation in specific tissues needs to be further clarified. In this study, we first measured the thermal relaxation times of porcine muscle and porcine fat. Combining with experimental measurements, the effects of thermal relaxation on temperature elevation in the tissues were investigated by using Pennes equation, thermal wave model of bio-heat transfer (TWMBT) and dual phase-lag (DPL) bio-heat transfer. Results showed that: a) The thermal relaxation times of porcine muscle and porcine fat are experimentally determined as 5.71± 0.11 s and 5.02± 0.06 s, respectively. b) In the absence of cavitation, DPL bio-heat transfer is more accurate to predict the temperature elevation at the focus and 2 mm from the focus than Pennes equation and TWMBT. Particularly, comparing with the reported of bologna (16 s) used in most of the theoretical analysis, the utilization of the measured thermal relaxation time for a specific tissue in DPL bio-heat transfer is more effective in predicting the temperature elevation during HIFU. c) Acoustic cavitation and nonlinear propagation is easier to happen in fat, under which all bio-heat transfer models are failed to forecast the temperature elevation induced by HIFU. The results demonstrate that different tissues have different thermal relaxation, DPL bio-heat transfer with the measured thermal relaxation times of specific tissues can accurately predict the temperature elevation during HIFU without cavitation.
Toffoli gate in twisted anisotropic and multihelical optical fibers

We demonstrate the ability to use multihelical and twisted anisotropic fibers to implement the all-fiber stable logical element controlled-controlled-NOT gate (Toffoli gate). In twisted anisotropic fibers, we imply the radial number and the topological charge of input optical vortex as the control bits and the circular polarization carries the target bit. In multihelical fiber, the circular polarization and the radial number carry the control bits, while the topological charge carries the target one.
High- Resolution, Contiguous SAR Imaging Using Co-Located MIMO Arrays: Experimental Proof of Concept

The paper introduces a novel Synthetic Aperture Radar (SAR) imaging concept with linear Multiple-Input, Multiple-Output (MIMO) radar arrays, which are becoming prolific for short-range sensing in various emerging application spaces. Specifically, the concept makes use of Digital Beam-Forming (DBF) techniques that are enabled in those systems to provide contiguous azimuth imaging, as in stripmap SAR, but with a fine spatial resolution matching that of a spotlight SAR. The main principles of the concept are analytically derived and experimentally verified in laboratory conditions with calibrated targets, as a springboard for a more in-depth system investigation.
Evaluation of Neural Network Architectures for Classification of Sonar Echoes in Air

In this contribution, we will classify sonar targets in air. The gained semantic environment information may be beneficial for autonomous mobile systems, such as robots and transport systems, for navigation in unknown as well as challenging settings with little or ambiguous optical and electromagnetic features. Examples for aforementioned environments may include food processing plants as well as medical buildings, in which obstacles are often comprised of transparent plastic or glass and may also have large shiny/reflecting surfaces. Targets are classified into three generic categories (flat, convex, concave), based on echoes from three subsequent recording positions. Multiple artificial neural networks with different architectures are designed, trained and evaluated as classifiers. The networks show promising prediction accuracy and an embedded implementation appears to be feasible. Especially, combinations of capsule networks and long short-term memory networks appear to be promising candidates for high classification performance.
A SysML profile for mechanical assembly

The design of a mechanical assembly is a complex task that requires system engineering approach. Today, there is a lack of continuity between the work of system engineer who has a global view on the system and the specialist in CAD development who implements the CAD model of the assembly. This work proposes a SysML profile for the description and analysis of mechanical assemblies. Representing all the details of the assembly, it will be used in the MBSE environment in order to describe the designer’s work. This will enhance the continuity between the data generated by the two actors by checking and validating the design of the proposed solution. A driving wheel of a forklift mechanism is used to illustrate the proposed approach. The developed profile allows a better communication between the system engineer and the CAD user.
Some New Attempts to Process Biological Data

The purpose of this paper is to realize system analysis and algorithm design for biological data. In this paper, primary bladder cancer is taken as a typical example, the structure of the system is extracted by hierarchical clustering method, and the function of the system is mined by convolutional neural network technology. Based on these methods, a complex system structure analysis model and an algorithm are constructed to study the big data system. Furthermore, the feasibility study of relevant theories and methods are carried out while the application and expand of technology are mentioned, combined with the actual data. The effectiveness and practicability of the algorithm and system are also verified by simulation.
Megawatt Electric Motors With High Temperature Superconductors

Superconducting materials opens up new possibilities for electrical machines. They can carry significantly higher current densities compared to traditional copper conductors. Consequently, much higher air-gap magnetic flux densities and current loads can potentially be created, but at the expense of a more complex cryogenic cooling system. This paper presents a study of the possibility of creating a 1 MW electric motor using superconductors for the stator winding. Designs with radial flux and axial flux has been considered. For the design with radial flux, an external rotor motor has been chosen as having the potential to achieve higher power densities. Further optimization of electric motors with an external rotor concerned changes with the geometry of the stator tooth zone and the use of other magnetic rotor systems. During optimization, a reduction in the mass of active materials and a reduction in torque ripple has been obtained in comparison with a standard design of an electric motor with radial magnetic flux. Calculations were also carried out using a copper winding in cryogen, which showed comparable results with the design of an electric motor with high-temperature superconductors.
Automated Excavator Based on Reinforcement Learning and Multibody System Dynamics

Fully autonomous earth-moving heavy equipment able to operate without human intervention can be seen as the primary goal of automated earth construction. To achieve this objective requires that the machines have the ability to adapt autonomously to complex and changing environments. Recent developments in automation have focused on the application of different machine learning approaches, of which the use of reinforcement learning algorithms is considered the most promising. The key advantage of reinforcement learning is the ability of the system to learn, adapt and work independently in a dynamic environment. This article investigates an application of reinforcement learning algorithm for heavy mining machinery automation. To this end, the training associated with reinforcement learning is done using the multibody approach. The procedure used combines a multibody approach and proximal policy optimization with a covariance matrix adaptation learning algorithm to simulate an autonomous excavator. The multibody model includes a representation of the hydraulic system, multiple sensors observing the state of the excavator and deformable ground. The task of loading a hopper with soil taken from a chosen point on the ground is simulated. The excavator is trained to load the hopper effectively within a given time while avoiding collisions with the ground and the hopper. The proposed system demonstrates the desired behavior after short training times.
Optical Synchronization of Multiple Time-of-Flight Cameras Implementing TDMA

Time-of-Flight (ToF) cameras are a promising alternative to current 3-D imaging systems. A setup of multiple ToF cameras is used to overcome their comparatively lower resolution, increase the field of view, and to reduce occlusion. However, the simultaneous operation introduces the possibility of interference resulting in erroneous depth measurements. One option to mitigate interference is the consecutive arrangement of multiple cameras in time slots, such that each camera illuminates the scene exclusively, a procedure known as time division multiple access (TDMA). Instead of requiring a master synchronizing device, an approach is presented requiring no additional hardware or infrastructure to utilize TDMA by adding a synchronization software procedure to the camera’s firmware. It effectively enables a camera to sense the acquisition process of other cameras and rapidly synchronize its acquisition times to operate without interference. During the experimental verification with three ToF cameras no interference occurred. The cameras each acquired 29 depth images per second while synchronizing themselves every 10 s. The proposed synchronization procedure implements an interference-free TDMA operation and overcomes traditional hard-wired synchronization setups, offering an easy setup and a methodology to upgrade existing systems without hardware modifications.
Ultra-low energy pest detection for smart agriculture

Apple is one of the most produced fruits in the world because it is easy to grow, store, and transport. The most significant threat of this crop is the attack of the codling moth, a small insect capable of damaging whole orchards in a few days. To prevent this parasite and to plan effective countermeasures, we present an ultra low power smart camera capable of detecting and recognizing the pest in the field; therefore, a wireless alarm can be transmitted over a long distance. The system implements a machine learning approach based on neural networks on the camera board. The sensor is also provided with long-range radio capability and an energy harvester; it permits to operate indefinitely because of its positive energy balance when deployed in the field. Experimental tests on the proposed energy-neutral smart camera demonstrate a validation accuracy of 93% and only 3.5mJ required for image analysis and classification.