Miracle-dz/UniFAR-FTUs huggingface dataset:
MARSv2: Multicore and Programmable Reconstruction Architecture SRAM CIM-Based Accelerator with Lightweight Network

Computing-in-memory (CIM) systems reduce the degree of large-scale data movement by performing computation on the memory; this avoids a von Neumann bottleneck. Because of its low-power characteristic, CIM has demonstrated great potential for increasing the energy efficiency of edge devices. This paper presents a multicore and programmable reconstruction architecture using static random-access memory (SRAM) CIM-based accelerator with lightweight network. The proposed architecture uses SRAM CIM macro as the processing element, supporting sparse convolutional neural network computing. This architecture achieves 15.16 TOPS/W system energy efficiency and 747.6 GOPS on the CIFAR10 data set.
Study of Multiscale Fused Extraction of Cropland Plots in Remote Sensing Images Based on Attention Mechanism

Cropland extraction from remote sensing images is an essential part of precise digital agriculture services. This paper proposed an SSGNet network of multiscale fused extraction of cropland based on the attention mechanism to address issues with complex cropland feature types in remote sensing images that resulted in blurred boundaries and low accuracy in plot partitioning. The proposed network contains different modules, such as spatial gradient guidance and dilated semantic fusion. It employs the image gradient attention guidance module to fully extract cropland plot features. This causes the feature to be transferred from the encoding layer to the decoding layer, creating layers full of key features within the cropland and making the extracted cropland information more accurate. In addition, this study also solves the problem caused by a large amount of spatial feature information, which losses easily during the downsampling process of continuous convolution in the coding layer. Aiming to solve this issue, we put forward a model for consensus fusion of multiscale spatial features to fuse each-layer feature of the coding layer through dilated convolution with different dilated ratios. This approach was proposed to make the segmentation results more comprehensive and complete. The lab findings showed that the Precision, Recall, MIoU, and F1 score of the multiscale fusion segmentation SSGNet network based on the attention mechanism had achieved 93.46%, 90.91%, 85.54%, and 92.73%, respectively. Its segmentation effect on cropland was better than other semantic segmentation networks and can effectively promote cropland semantic extraction.
Inverse problems: anniversary and short review of generalized inverse scattering transforms

Three areas are briefly reviewed: how the journal was born, how inverse problems are not ordinary mathematical problems, and how their study either remains connected with the original ‘natural inverse problem’ or extends to mathematical developments which are sometimes not justified by any ‘natural inverse problem’! Examples are given. Related points are also evoked in the second part of the article, where a short topical review of inverse scattering transform extensions is presented.
Detecting synthetic anomalies using median-based residuals in lithium-ion cell groups

Early detection of anomalous operation of battery systems is critical in improving performance and ensuring safety. This paper presents an effective and computationally efficient approach for online anomaly detection using real-time voltage and temperature data from multiple Li-ion cells. Median-based residuals are generated and evaluated using cumulative sum control chart to detect anomalies. Due to the scarcity of anomalous data for evaluating the anomaly detection algorithm, we inject anomalies into nominal experimental data using a physics model-based approach. The proposed anomaly detection approach has low false positive rate and has the capability to detect significant voltage and temperature anomalies. The approach accurately detects anomalies with voltage and temperature deviations greater than 15mV and 1.3°C, respectively, without any missed detection. The approach accurately traces the anomalous cells, distinguishes voltage and temperature anomalies, and identifies the direction of detected anomalies.
Admittance Control based Hemiplegic Exoskeleton Motion Control

In this paper, a method based on admittance control is developed to improve patients’ comfort wearing an exoskeleton robot for rehabilitation training. In our previous work, we designed a modular and convenient hemiplegic exoskeleton robot. The mechanical structure will cause stiffness when it comes to contact with the human body in practical applications. We introduced admittance control to improve the flexibility of the exoskeleton robot and improve the subjects’ comfort. This paper considers coupling of model between the exoskeleton and the human body as the human-computer interaction force of the system at the main human-computer interaction point. Measuring the interaction force between exoskeleton members and limbs, the work uses the virtual admittance model to correct the system's inherent characteristics and studies the control method's stability with Lyapunov's theory. The experiment collected the subject movement information which was converted the movement information of the exoskeleton. Human-computer interaction forces in the thigh and calf were measured while the exoskeleton assisted the subject's movement. The experimental results show that adding the admittance controller reduces the human-computer interaction force from 5g to 2g and 6g to 3g, respectively.
Implement Improved Process Design Using a Lightweight Deep Learning Model to Reduce Hardware Computational Load in Instance Segmentation: Using Apple Dataset

The use of deep learning using image data has already made a lot of progress. The main function is to track or classify related objects with the model of the data set in which the learning has been conducted in the image and it is used in various fields. Therefore, the accuracy has improved a lot, but the proportion of hardware use increases rapidly because the depth of the model is increased and utilized. In this paper, we propose a process to improve results by using the lightweight model and additional procedures in the program in systems using deep learning. In the proposed system, an additional image processing process is performed to increase accuracy in the results after deep learning processing, unlike other systems. It uses residual CPU usage in desktop or embedded environments with GPU, maintains processing speed, and introduces additional image processing processes to mitigate graphic memory usage using lightweight deep learning models. As a result, a system that improves the accuracy of the object area by 6% based on red apples and 16% based on green apples can be designed to reduce hardware costs and increase performance.
Coordinated Path Following for a Class of Underactuated Multi-agent System using Nested Set Stabilization

This paper addresses the multi-agent coordinated path following problem consisting of a class of underactuated vehicles capable of following a class of closed embedded curves in the three-dimensional space and proposes a solution using a nested set stabilization approach. The stabilization of the first set restricts each agent to the assigned path following manifold and ensures that each system stays on the path indefinitely when initialized in the neighborhood of the path. Stabilizing the second set, contained inside the first, solves the position, velocity, acceleration, or jerk-related coordination constraints. The third set, contained in the second set, satisfies the yaw, or yaw-rate-related coordination constraints. The reduction theorem establishes the asymptotic stability of each nested set, and as a result, the multi-agent coordinated path following problem is solved.
Stiffness modeling of redundant robots with large load capacity and workspace

In the robotic machining process, the external force due to the end-effector (EE) and the workpiece can lead to significant deviations of the desired trajectory. In addition, moving platforms have been added to many heavy-duty industrial robots to improve the workspace. The combination of a moving platform and a six-degree-of-freedom industrial robot constitutes the redundant robot system. However, the effect of moving platforms is rarely considered in the redundant robot system for deflection analysis. This paper analyzes the shortcomings of traditional methods for joint stiffness modeling. Considering the advantages and limitations of traditional methods, we propose an effective method for redundant heavy-duty robot stiffness modeling by considering joint and moving platform compliances. Firstly, the relationship equations of the joints and the end-effector (EE) deformation are derived. Secondly, the static equilibrium equations of the moving platform are established in its stiffness matrix expression, and then the whole redundant robot system stiffness model is derived. Finally, simulations are performed to verify the correctness of the stiffness model. This work can be used for motion planning of redundant serial robots and optimization of machining operations.
Sample-data Consensus of Position Synchronization in Static Directed Networks

The consensus problem of position synchronization for general multi-agent systems in static directed network is discussed in this paper. A distributed synchronous periodic sampling control law is proposed, which is applied to the multi-agent system where all agents move with an uniform acceleration. Furthermore, we show that position synchronization consensus may be obtained as long as the sample period of all agents are less than a clearly calculated threshold. Finally, the efficiency of the proposed method is demonstrated through simulation and experiment.
Analysis of the panoramic radiographic density of the mandibular bone in the elderly due to increased cortisol levels

Objectives: Mandibular bone is generally formed by intramembranous ossification. Bone density mandible can be assessed from the trabecular pattern. In the elderly, the hormone cortisol affects bone formation through bone resorption by blocking calcium. The purpose of this study was to determine the correlation of cortisol levels to mandibular bone density in elderly women and men. Materials and Methods: This study was an analytic survey, where the population was determined from secondary patient data in the form of 31 panoramic radiographs and 31 laboratory results of cortisol examination, the research sample was divided into two groups. The bone density of the mandible was assessed from the trabeculae of the neck condyles region of the right and left. Density assessment using the ImageJ software program on panoramic radiographs was an examination that is often done in dentistry. Results: This study showed that the lowest density was in the group of women over 60 years. The results of the t-test with the independent sample obtained a p-value of > 0.05, which indicated there is no significant difference. The negative correlation value indicated that the relationship that occurs is opposite, p-value <0.05 which indicated the lower bone density, the higher cortisol level. Conclusion: Based on the results of the study, there was a correlation that with the increasing age of both women and men there was an increase in cortisol levels and a decrease in mandibular density.
The Log-Normal Distributions of Coronal Mass Ejection-Related Solar Flares and the Flare/CME Model of Gamma-Ray Bursts

It is known that gamma-ray bursts (GRBs) are highly time-variable and the statistical distribution of physical variables show log-normal. The solar flares are also highly time-variable phenomena, but the physical quantities of them show power-law distribution. GRBs are believed to be the emission from the relativistic outflow from the central engine. On the other hand, the outflow from the Sun is known as coronal mass ejections (CMEs). We analyze the distributions of some quantities of CMEs and the solar flares related to CMEs (CME-related solar flares) to compare with the distributions of GRBs. We found the distributions of X-ray peak fluxes of CME-related solar flares and the speed of CMEs are very similar to log-normal distribution. Hence the distributions of GRBs are similar to those of CME-related solar flares and CMEs, and the ejection mechanisms of outflow from the central engine of GRBs and the Sun might be similar, that is, magnetic reconnection. We propose the new model of the central engine of GRBs by the analogy of solar flares and CMEs.
Divide and recombine: Autoregressive models and STL+

Han, Xiang Ph.D., Purdue University, December 2014. Divide and Recombine: Autoregressive models and STL+ . Major Professor: William S Cleveland and Bowei Xi. In this thesis multiple methods are proposed and applied to the Akamai CIDR time series data. The Akamai network is one of the world’s largest distributed-computing platforms, with more than 250,000 servers in more than 80 countries. It is responsible for 15-20 percent of all web traffic. We obtained 110 GB raw CIDR data over a 18 month period, collected on the Akamai network from November 2011 to April 2013. The Seasonal-Trend Decomposition procedure based on loess (STL+) is used to model the CIDR series. Motivated by the CIDR series analysis, we propose a general prediction based model selection procedure, where extensive visual diagnostics are part of the procedure for selecting the best performing model. Factorial experimental designs are used to explore the parameter space. We evaluate the performance of different models for the CIDR series using our proposed prediction based model selection procedure. Furthermore the analysis and modeling of the CIDR series is performed under the Divide and Recombine for large data framework. And we conduct a theoretical Divide and Recombine time series estimation study. We also study the performance of Divide and Recombine estimates for Gaussian auto-regressive time series, Gaussian long range dependent series, and auto-regressive series with tails heavier than Gaussian.
QUANTUM MARGINAL PROBLEM AND REPRESENTATIONS OF THE SYMMETRIC GROUP

We discuss existence of mixed stateAB of two (or multy-) com- ponent system HAB = HA HB with reduced density matricesA, �B and given spectraAB,�A,�B. We give a complete solution of the problem in terms of linear inequalities on the spectra, accompanied with extensive tables of marginal inequalities, including arrays up to 4 qubits. In the second part of the paper we pursue another approach based on reduction of the problem to representation theory of the symmetric group.
Lidar-based Adaptive Pedestrian Pose Segmentation Method

An adaptive pedestrian target poses segmentation algorithm based on point cloud alignment is proposed for pedestrian target pose segmentation, using which adaptive calibration of threshold parameters can be achieved. In this paper, we introduce a bilateral filtering method to remove outliers and reduce the influence of noise on the target point cloud. Using the Random Sample Consensus method to segment all planar point clouds, using an adaptive threshold calibration method based on the Fast Global Registration and point cloud precision alignment to improve the cluster segmentation module, that will achieve adaptive segmentation of pedestrian postures. Based on the LIDAR dataset with four pedestrian poses for experimental validation, the algorithm in this paper can precisely segment road pedestrian targets by poses. Compared with the traditional Euclidean clustering method and DBSCAN clustering method, the results respectively improve the accuracy of the algorithm in different poses by 1.61% and 2.75%, which proves the effectiveness and progressiveness of the adaptive clustering algorithm.
Building and Testing Fine-Grained Dataset of COVID-19 Tweets for Worry Prediction

The COVID-19 outbreak has resulted in the loss of human life worldwide and has increased worry concerning life, public health, the economy, and the future. With lockdown and social distancing measures in place, people turned to social media such as Twitter to share their feelings and concerns about the pandemic. Several studies have focused on analyzing Twitter users’ sentiments and emotions. However, little work has focused on worry detection at a fine-grained level due to the lack of adequate datasets. Worry emotion is associated with notions such as anxiety, fear, and nervousness. In this study, we built a dataset for worry emotion classification called “WorryCov”. It is a relatively large dataset derived from Twitter concerning worry about COVID-19. The data were annotated into three levels (“no-worry”, “worry”, and “high-worry”). Using the annotated dataset, we investigated the performance of different machine learning algorithms (ML), including multinomial Naïve Bayes (MNB), support vector machine (SVM), logistic regression (LR), and random forests (RF). The results show that LR was the optimal approach, with an accuracy of 75%. Furthermore, the results indicate that the proposed model could be used by psychologists and researchers to predict Twitter users’ worry levels during COVID-19 or similar crises. © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.
A Survey on Software/Hardware Fault Injection Tools and Techniques

Computational systems need to be dependable, but they are vulnerable to defects, errors, failure and faults, and they affect their behaviour. Fault injection is one of the useful techniques that have been used to evaluate the behaviour of the system by intentionally introduced fault, so as to determine its effect on the system behaviour. There are many tools and techniques used to inject faults, categorized under software and hardware techniques. To keep pace with progress and development over time, developers need to be aware off tools and techniques according to their needs, when using either software or hardware. In this paper we make a survey for several tools and techniques which are mostly used these days. As we compare between them based on their basic functions, source need, how to inject faults and feedback about each one.
A classical approach for the analysis of generalized linear mixed models.

Generalized linear mixed models (GLMMs) accommodate the study of overdispersion and correlation inherent in hierarchically structured data. These models are an extension of generalized linear models (GLMs) and linear mixed models (LMMs). The linear predictor of a GLM is extended to include an unobserved, albeit realized, vector of Gaussian distributed random effects. Conditional on these random effects, responses are assumed to be independent. The objective function for parameter estimation is an integrated quasi-likelihood (IQL) function which is often intractable since it may consist of high-dimensional integrals. Therefore, an exact maximum likelihood analysis is not feasible. The penalized quasi-likelihood (PQL) function, derived from a first-order Laplace expansion to the IQL about the optimum value of the random effects and under the assumption of slowly varying weights, is an approximate technique for statistical inference in GLMMs. Replacing the conditional weighted quasi-deviance function in the Laplace-approximated IQL by the generalized chi-squared statistic leads to a corrected profile quasilikelihood function for the restricted maximum likelihood (REML) estimation of dispersion components by Fisher scoring. Evaluation of mean parameters, for fixed dispersion components, by iterative weighted least squares (IWLS) yields joint estimates of fixed effects and random effects. Thus, the PQL criterion involves repeated fitting of a Gaussian LMM with a linked response vector and a conditional iterated weight matrix. In some instances, PQL estimates fail to converge to a neighbourhood of their true values. Bias-corrected PQL estimators (CPQL) have hence been proposed, using asymptotic analysis and simulation. The pseudo-likelihood algorithm is an alternative estimation procedure for GLMMs. Global score statistics for hypothesis testing of overdispersion, correlation and heterogeneity in GLMMs has been developed as well as individual score statistics for testing null dispersion components separately. A conditional mean squared error of prediction (CMSEP) has also been considered as a general measure of predictive uncertainty. Local influence measures for testing the robustness of parameter estimates, by inducing minor perturbations into GLMMs, are recent advances in the study of these models. Commercial statistical software is available for the analysis of GLMMs.
Morphometric Analysis of the Proximal Femur With Its Clinical Correlation in Eastern Uttar Pradesh Region

The anthropometry of the proximal femur holds great clinical significance in designing implants and prostheses for proximal femoral fractures and hip joint arthroplasties. Surgical fixation with a properly matched prosthesis plays a crucial role in improving long-term treatment outcomes and preventing post-operative complications such as osteolysis with aseptic loosening and increased load. The femur is also one of the most commonest used bones for stature estimation. Often during forensic investigations, only fragmented remains of femur are found available from which femoral length is estimated by application of linear regression equations. The estimated femoral length thus obtained is used for stature estimation of the unidentified individual. This study has measured nine bony parameters from the proximal femur in a total of 96 dry femora. These measurements include the vertical head diameter, neck diameter, neck thickness, neck length, neck shaft angle, the transverse diameter of the fovea, longitudinal diameter of the fovea, foveal depth, and the intertrochanteric line length. In addition, the total length of the femur was also measured. The results were tabulated and statistically analyzed using SPSS software, version 25. The mean femoral head diameter was observed to be 41.59±3.25 mm, mean foveal depth was found to be 2.95±0.75 mm, mean foveal transverse and longitudinal diameters were observed to be 11.38±2.35 mm and 15.94±3.37 mm, respectively. The mean neck diameter was 29.45±3.33 mm. Mean neck length and neck thickness were observed to be 36.06±4.94 mm and 27.61±2.71 mm, respectively. Neck shaft angle was noted to range from 109° to 128°, with a mean of 119.08°±5.18°. The mean length of the inter-trochanteric line was measured to be 41.92±3.9 mm. The mean femoral length was observed to be 42.11±2.91 cm. Significant positive correlations were found between the various measured proximal morphometric parameters of the femur. The length of the femur showed a maximum positive correlation with the vertical head diameter, followed by the neck diameter, thickness, and foveal depth. The findings of this study can throw further light on the existing data. They can serve as a guideline for designing better-matched prostheses and implants for hip surgeries in the eastern Uttar Pradesh population.
A Bio-Inspired Territorial Predator Scent Marking Algorithm in Software-Defined Data Center

Data centre networks are intended to meet the data transmission needs of the data centre’s highly networked hosts. The network architecture and routing technique can have a major impact on performance parameters such as latency. The fat-tree network is now one of the most popular data centre network architectures. In recent times, data centre network traffic has been steadily expanding. Because of the high volume of traffic, it is nearly impossible for a single server to satisfy all the client’s requests. In this paper, a bio-inspired Territorial Predator Scent Marking Algorithm (TPSMA) is proposed for load balancing in a fat-tree software-defined data centre. The bio-inspired algorithm was developed to increase network Quality of Service (QoS). The proposed technique is validated using the network emulator Mininet, with the Open Network Operating System ONOS controller. The performance study conducted showed that the throughput and network utilization were improved, hence improving the QoS.