Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced Truncated Statistic CFAR Based on Median Absolute Deviation and Weighted Log-likelihood Estimation

The actual working environment of radar is complex, including many interferences and non-homogeneous clutter, which seriously degrade the detection performance of radar. This paper proposes an enhanced truncated statistic CFAR (ETS-CFAR) based on median absolute deviation and weighted log-likelihood estimation. The algorithm has good detection performance in multi-target environment, and moreover, ensures good performance in clutter edge environment, which has a good ability to maintain a constant false alarm rate. The median absolute deviation is applied to improve the estimation of truncation depth, which is a robust measure and insensitive to the outlier, and the statistical interference outliers are eliminated. The weighted log-likelihood estimation is adopted to optimize the determination conditions of the weighted coefficients and improve the parameter estimation of the clutter. The proposed ETS-CFAR algorithm is evaluated in different clutter environments through simulation, and the superiority of the algorithm is explained.
Nutrient Profiles of Food Items in Adolescent Diet: A Cluster Analysis of Data Collected from A High School Citizen Science Project

A healthy diet during adolescence is essential to youths' physical and mental development. This study performed a cluster analysis on a dietary record dataset collected through a high school citizen science project. The analysis was performed based on nutrients rather than food groups to characterize the adolescent dietary patterns better. The resulting nine food clusters have distinct nutrient profiles and vary significantly in size.
Contextual fusion of classifiers under variable atmospheric conditions for coastal surveillance

Monitoring maritime areas is a challenging task which generally benefits from sensing systems such as radars or cameras, with different electromagnetic or optical capabilities. Infrared (IR) cameras in particular allow night and day surveillance, but their image is affected by maritime atmospheric conditions, e.g., the aerosol concentration. Further classification process thus inherits this image degradation resulting in possibly poor target classification results. To overcome this issue, we propose in this paper a contextual classifiers fusion system where two neural networks are trained into two environmental contexts and further combined with Bayesian reasoning. Indi-vidual classifier's reliability is considered, enabling to balance between the two classifiers depending on the uncertain context of use. Additionally, we apply an imprecise decision rule for a greater flexibility allowing a compromise between two criteria of accuracy and specificity. Results are obtained on simulated classifiers' outputs as well as on a synthetic dataset of IR images degraded with atmospheric context. It is shown that the proposed approach allows the possibility to increase accuracy while foregoing specificity and adds explainability.
A Machine Learning Rooted Pre-characterization Method for Floating Random Walk Based Capacitance Extraction of Multi-dielectric VLSI Interconnects

Green's Function Table(GFT) and Weight Value Table (WVT) with more than two dielectric layers suffer from a significant growth in storage and memory. A machine learning based method to approximate three-dielectric GFTs using two-dielectric ones during run-time random walk procedure is presented in this article. We explore the numerical relationship between two- and three-dielectric GFTs, and accordingly apply deep neural network (DNN) model to compute three-dielectric GFTs out of the pre-characterized two-dielectric GFTs during run-time hence no pre-characterization of three-dielectric GFTs is required. We utilize automated machine learning (AutoML) tool to find the satisfied DNN configurations. Two test cases under an advanced process node are employed to validate our method, where the self capacitance error is less than 5% in over 93% probability compared with two-dielectric FRW process, and the memory usage is proved to be 390X times lower than the direct storage of three-dielectric GFTs.
A User Study of a Cable Haptic Interface with a Reconfigurable Structure

Cable-based visuo-haptic interfaces usually adopt simple structures, either being wearable devices, like gloves or jackets, or grounded interfaces with fixed anchor points and many cables providing force feedback. Regarding the latter type of interface, the force and visual feedbacks are often not co-located. While planar cable haptic interfaces with variable structures have been designed, the benefits of such structures have not been investigated. Likewise, co-located visual feedback using head-mounted display (HMD) is always discarded when using grounded cable interfaces. This paper presents a desktop-sized cable-driven parallel robot with a three-degree-of-freedom variable structure and six cables. Our objective is to evaluate the potential benefits or drawbacks of this structure and the use of an HMD through user-based experiments. For this purpose, a manipulation task where the users have to push a sphere along a line and a free exploration task were designed. The performances of the participants are evaluated in terms of trajectory accuracy and task execution time, and their experiences are collected with a questionnaire. Results indicate that the variable configuration presents slightly reduces the perceived physical and cognitive load with minimal impact on performance and that the HMD improves immersion and manipulation by providing more stereo-scopic cues.
On the Educational and Professional Implications of Integrating Mind Mapping in Software Testing

The increasing societal dependence on software, and the negative consequences of undesirable behavior of software, calls for ever more attention to software quality. The purpose of software quality control is activities that support the achievement of desirable levels of software quality, and software testing is one such activity. In order to preserve the intent of software testing, it must be deliberated carefully, and given necessary time and due diligence, before being acted upon. In that regard, this paper adopts a human-centered approach to software testing, embraces mind mapping as a lightweight technique for early software testing-related commitments, provides elements of a theoretical basis for mind mapping, and offers a guided tour of software testing-related mind maps using practical examples relevant in academia as well as industry.
Distributed Swarm Navigation with Factored Filters

This work introduces the PNT Chain, a fully distributed filtering solution to collaborative positioning with ranging radios in GNSS-degraded and denied environments. The PNT (Positioning, Navigation, and Timing) Chain uses an Undirected Acyclic Bayes Graph (UABG) to factor a Kalman filter on the poses of an entire swarm into tractable cliques. The cliques are distributed across the physical nodes, which in combination with the use of equivalent propagation and equivalent measurement messages, keeps network traffic to an absolute minimum. The distributed PNT Chain algorithm is compared with an idealized (and impractical) centralized filter, and a legacy relative navigation algorithm in two scenarios where only two UAVS in a swarm are receiving GNSS signals. In both cases, the distributed PNT Chain achieves a positional accuracy comparable with the completely centralized filter, and an order of magnitude better than the legacy approach, while only requiring 22% the computation cost of the centralized filter.
Multi-instantiated Block Top-layer Routing Technique Based on Steiner Tree Algorithm

As the scale of chips becomes larger and larger, the time and labor cost of the overall design of a large single chip becomes higher and higher. Therefore, it has become a widely adopted solution to divide one large-scale design into several smaller blocks and realize the functions of each block respectively. In this paper, a multi-instantiated block routing technique is proposed based on the improved Steiner tree algorithm. The algorithm divides wire networks into grids and uses FLUTE to construct and decompose the minimum rectangular Steiner tree of all multipin wire networks, which will decompose any multi-pin connection into several two-pin connections. It abstracts the functions of multi-instantiated blocks involved, and adds fixed pins on the edge of them. The routing process will be conducted inside every block respectively. In order to verify the accuracy and validity of the proposed method, a series of verification experiments are carried out, which shows that the proposed multi-instantiated block routing algorithm can effectively reduce the path redundancy caused by multiple pass-throughs in blocks and improve the routing process efficiency.
A Miniature Direct-Drive Hydraulic Actuator for Wearable Haptic Devices based on Ferrofluid Magnetohydrodynamic Levitation

Hydraulic and pneumatic actuators in haptics offer the advantage of soft and compliant interfaces, with the drawback of cumbersome driving devices and limited modulation capabilities. We propose a miniature hydraulic actuator based on a linear electromagnetic motor with an embedded ferrofluid sealing. The solution has two main advantages: it shows no static friction due to the magnetohydrodynamic levitation effect of the ferrofluid, and the output force can be scaled (by varying the radius of the actuator) without introducing noise and friction of mechanical reduction mechanisms. Moreover, soft and compliant interfaces in the form of actuated pouches can be obtained on wearable devices with embedded actuators. As a concept prototype, we present here a compact and soft haptic thimble integrating the proposed actuator: experimental characterization at the bench, and perception experiments with the final prototype, evaluate the low-noise rendering capability of the method.
The East-West Pendulum

Part 1 Introduction to the West-West pendulum: the East-West index Asian religions and philosophies Asian technology and economies. Part 2 The roots of Chinese culture: background to Chinese culture Chinese emigration the Chinese family the future for the Chinese. Part 3 Differing responses to the West: China Japan Thailand Korea Indonesia Malaysia/Signapore the Philippines. Part 4 Risk and reward analysis of Asian countries today: how to measure risk reward analysis economic risk analysis in history inflation in Asia.
Hydrometeor Classification for Dual Polarization Weather Radar Based on SE-ResNeXt Combined with SVM

Aiming at the defect that the traditional fuzzy logic hydrometeor classification algorithm relies too much on expert experience to set parameters, a hydrometeor classification method based on the structure of squeeze-excitation residual neural networks (SE-ResNeXt) combined with support vector machine (SVM) was proposed in this paper. Firstly, the data of four polarization parameters (horizontal reflectivity factor, differential reflectivity, correlation coefficient and differential propagation phase constant) collected by S-band KOHX dual polarization weather radar were stacked into three-dimensional matrix blocks and preprocessed to make the data set required by the model, and we trained the model. Then, according to the classification results of SE-ResNeXt for the target matrix block and combined with SVM classifier, the hydrometeors in the block were classified. The model that can effectively identify four types of hydrometeors and one type of biological echo was built. Finally, the 0.5° elevation scanning data of KOHX radar is tested, and the classification accuracy of the model for target hydrometeor is 95.13%. We also test the model with the scanning data of different elevations and different radar, the result showed good robustness.
Stein Variational Gradient Descent for Non-Bayesian Particle Flow

Bayes’ rule provides an undoubtedly powerful framework for statistical inference; however, the assumptions inherent in Bayesian filtering often cannot be realized in physical systems. Oftentimes, the true Bayesian posterior probability density function (pdf) is infinite-dimensional and lacks tractable implementations, in addition to errors induced by inaccurate realizations of the prior and likelihood pdfs. Though particlebased methods can provide versatile and computationally efficient approximations of Bayes’ rule, they lack the theoretical ability to mitigate estimation errors incurred by erroneous measurement modeling. This work merges Stein Variational Gradient Descent, a nonlinear particle flow update scheme, with generalized variational inference, a method for formulating optimal non-Bayesian posteriors, to produce tractable variational posterior pdfs that remain robust to modeling errors. The new framework is demonstrated to outperform conventional filtering approaches in a simplified relative spacecraft navigation scenario.
Robust Control of the Mobile Robot Based on the General Control Performance Index

Mobile robot is a complex nonlinear system with uncertainty. A novel robust control method is proposed for the robust control of uncertain mobile robot systems based on the new control performance index $\hat{\beta}$. The control performance index $\hat{\beta}$ is a dynamic depiction of the constraint-following error, which can guarantee the control design flexibility. Regardless of the size of uncertainty, the control ensures that the control performance index $\hat{\beta}$ is uniform boundedness and uniform ultimate boundedness. In the control, there is a control design parameter $\kappa$ that can be chosen within a specific range, and the parameter can be optimized to achieve lower control cost and better control performance. This optimization problem is expressed as an intelligent decision control problem in which one control parameter deals with two system performance indicators. In order to reduce the control force of the system and ensure optimal control performance, the parameter $\kappa$ is selected. Finally, simulation analysis of the mobile robot is conducted to validate the robust control design.
Sitpose: A Siamese Convolutional Transformer for Relative Camera Pose Estimation

Relative Camera Pose Estimation (RCPE) aims to calculate the translation and rotation between two frames with overlapped regions, which is crucial to computer vision and robotics. This paper presents a novel siamese convolutional transformer model, SiTPose, to regress relative camera pose directly. SiTPose is distinguished in three aspects: (1) With a cross-attention feature extractor and a compact transformer encoder, extreme rotation errors (> 150°) are significantly reduced: from 9.7‰ with the state-of-the-art 8-Points to 1‱ on the 7Scenes dataset. (2) SiTPose is also robust to narrow-baseline cases (slight rotation angle and large translation between neighboring frames), while existing RCPE methods mainly focus on wide-baseline cases. (3) SiTPose can be flexibly extended to geometry-based vSLAM (namely SiTSLAM) in a multi-threaded way to prevent tracking lost and scale ambiguity problems. Results on multiple datasets show that SiT-SLAM yields a marked improvement in robustness and localization accuracy in complex scenarios, e.g., RMSE error is reduced from 26.36m with the classic ORBSLAM3 method to 6.94m on the KITTI-09.
Learning continuous piecewise non-linear activation functions for deep neural networks

Activation functions provide the non-linearity to deep neural networks, which are crucial for the optimization and performance improvement. In this paper, we propose a learnable continuous piece-wise nonlinear activation function (or CPN in short), which improves the widely used ReLU from three directions, i.e., finer pieces, non-linear terms and learnable parameterization. CPN is a continuous activation function with multiple pieces and incorporates non-linear terms in every interval. We give a general formulation of CPN and provide different implementations according to three key factors: whether the activation space is divided uniformly or not, whether the non-linear terms exist or not, and whether the activation function is continuous or not. We demonstrate the effectiveness of our method on image classification and single image super-resolution tasks by simply changing the activation function. For example, CPN improves 4.78% / 4.52% top-1 accuracy over ReLU on MobileNetV2_0. 25 / MobileNetV2_0. 35 for ImageNet classification and achieves better PSNR on several benchmarks for super-resolution. Our implementation is available at https://github.com/xc-G/CPN.
Measurement of Rhythmical Movements in Street Dance for Quantifying Movement Timing Skills

This study aimed to measure the rhythms of three types of movements in street dance and to quantify the differences depending on the level of proficiency. We developed an experimental paradigm to quantify the rhythms of street dancers’ movements. 20 street dancers, 10 trained and 10 novices participated in the experiment. The results of the experiment suggested that there was a difference in the accuracy of maintaining the same movement timing in time and slow when comparing trained and novice dancers. In addition, as in the HKB model of auditory-motor coordination, the novice showed a phase transition phenomenon between fast and slow, while the trained maintained the coordination pattern. This suggests that street dance experts have mastered not only the skill to match the rhythm of music accurately but also the skill to maintain the rhythm of unstable movements that are out of sync with the timing of the sound. This experimental system and the quantified results can be used to evaluate the proficiency of dancers quantitatively and to understand the learning status of body movements.
3D Compact Thermal Model and its Application on Fast Chip Level Thermal Simulation

This paper presents a three dimensional fast thermal simulation tool named 3DDCTM for chip level integrated circuits, which is developed based on Compact Thermal Model (CTM) and Model Order Reduction (MOR) algorithm. The tool provides various types of solution including steady conduction, unsteady conduction, and a Smart Compact Thermal Model for Dynamic Thermal Management (DTM). A nonuniform orthogonal grid generation module is integrated into the tool and could automate generate body fit grids based on presetting grid constraints. 3DDCTM requires much less computational time with a similar accuracy compared to traditional thermal simulators. Due to the ability of reading JEDEC the exchange of electronic thermal system level simulation models (ECXML), the present tool supports import model established by commercial simulators, which greatly enhances the extent of the tool. The results could be loaded and shown in the open source visualization application Paraview. The thermal behavior of a chip with high-end packages is investigated for verification. The simulation results are discussed and it is found that the present thermal tool shows an excellent performance not only on good accuracy but also less computational time compared with typical thermal commercial software.
Rooted Android Devices Risk Assessment using Analytic Hierarchy Process

Attackers are targeting rooted Android mobile devices to gain access to confidential data such as credit cards and banking transactions. Despite the removal of rooting applications from Google Play Store, attackers still provide easy rooting methods through third-party application stores. Previous studies have focused on rooting detection systems, but they have ignored Android rooting risk assessment, impacting device security. This research introduces a risk assessment framework for Android devices named ARAS, which uses three risk criteria: system, privacy, and financial criteria. ARAS extracts Android static analysis features and adopts Analytic Hierarchy Process (AHP) pairwise comparison methodology to decide the rooting risk level. The proposed scoring model is applied to a rooted device dataset to demonstrate the risk level assessment. ARAS introduces four levels of risk: low, medium, high, and critical risk levels, providing a decision support system for allowing or denying rooted devices access to applications and confidential information.
Learning Deep Photometric Stereo Network with Reflectance Priors

Photometric stereo recovers the surface normals of an object from images with varying shading cues. Conventional photometric stereo methods attempt to use handcrafted reflectance models to approximate surface normals, while deep learning-based networks have shown a much more powerful ability to handle non-Lambertian objects. However, none of the existing deep learning methods explores how prior reflectance information can be used to optimize surface-normal prediction. In this paper, we first present the introduction of reflectance prior to deep photometric stereo models. Our explorations include how the reflectance prior can simplify the optimization of deep networks by reparametrizing the weights, and (2) eliminate the impacts of surfaces with spatially varying reflectance for all-pixel input photometric stereo methods. To achieve these goals, we propose a residual fusion module (RFM) in our method, which explicitly extracts features useful for surface-normal recovery and removes those features influenced by reflectance. Additionally, we design a shading extractor with multi-scale and global-local feature fusion operations, which can fuse features with different receptive fields and better utilize the non-maximum features missing in the max-pooling operation. Experiments and ablation studies verify the accuracy and effectiveness of the proposed reflectance prior network on a widely used benchmark.
Weed Recognition in Sesame Field Using Computer Vision Algorithms

Deep learning is one aspect of machine learning that is growing rapidly in today's research, and one of its major applications is the use of computer vision in object detection especially in agricultural sector. In agricultural production, weeds have become a crucial factor affecting it because of its menace to crops and the environmental pollution herbicides used in weeding causes, but with the advancement in agricultural practices, identifying and distinguishing weeds from crop is paramount. To achieve the precise targeting of weeds in a sesame farm which will go a long way in helping farmers in their decision making, comes the application of effective object detector and image classifier using deep learning. In this paper, machine learning model built with Tensor flow/ keras, and a bounding box model was used as a convolutional neural network in detecting the object in images from the sesame farmland. The images served as an input into the layers of the neural network where the image size was rescaled, max pooled, and important features extracted and then exiting in the two outputs: classification for object class and localization for the bounding box. The train history with the losses that occurred in the train, validation class and bounding box was analyzed, and the classification result of the proposed model showed a satisfactory performance.