Miracle-dz/UniFAR-FTUs huggingface dataset:
FPGA Implementation of High-Speed Data Acquisition System for High-Resolution Millimeter Wave Radar

This paper introduces FPGA implementation of high-speed data acquisition system for high-resolution millimeter wave radar. The transfer bandwidth of the Low Voltage Differential Signaling (LVDS) interface of the high-speed data acquisition system is 7200Mbps, which supports the cascade of four millimeter-wave radar sensors. The maximum transfer bandwidth of the DDR3 interface in the system is 12800Mbps, which is able to support high-bandwidth sample data storage of twelve millimeter-wave radar sensors. The maximum transfer bandwidth of Gigabit Ethernet interface in the system is 1000Mbps, which can adjust the transfer rate according to the system requirements, with good scalability. By using the high-speed data acquisition system designed in the article for the cascade of four high resolution 77GHz FMCW millimeter wave radar sensors, the system can achieve imaging with angular resolution close to 1.3 degrees.
Mechanically Programmed Miniature Origami Grippers

This paper presents a robotic gripper design that can perform customizable grasping tasks at the millimeter scale. The design is based on the origami string, a mechanism with a single degree of freedom that can be mechanically programmed to approximate arbitrary paths in space. By using this concept, we create miniature fingers that bend at multiple joints with a single actuator input. The shape and stiffness of these fingers can be varied to fit different grasping tasks by changing the crease pattern of the string. We show that the experimental behavior of these strings follows their analytical models and that they can perform a variety of tasks including pinching, wrapping, and twisting common objects such as pencils, bottle caps, and blueberries.
Implementation and Detection of Modbus Cyberattacks

Supervisory Control and Data Acquisition (SCADA) systems play a significant role in Critical Infrastructures (CIs) since they monitor and control the automation processes of the industrial equipment. However, SCADA relies on vulnerable communication protocols without any cybersecurity mechanism, thereby making it possible to endanger the overall operation of the CI. In this paper, we focus on the Modbus/TCP protocol, which is commonly utilised in many CIs and especially in the electrical grid. In particular, our contribution is twofold. First, we study and enhance the cyberattacks provided by the Smod pen-testing tool. Second, we introduce an anomaly-based Intrusion Detection System (IDS) capable of detecting Denial of Service (DoS) cyberattacks related to Modbus/TCP. The efficacy of the proposed IDS is demonstrated by utilising real data stemming from a hydropower plant. The accuracy and the F1 score of the proposed IDS reach 81% and 77% respectively.
An adaptive page replacement scheme for scientific applications

Memory management is playing an increasingly important role in application performance owing to the rapid increase in the amount of data utilized in modern computing workloads and the slow growth in the capacity of the main memory devices. We also observe that recent scientific applications are processing huge amount of data never been seen before. Scientific applications show a tendency to repeat operations on intermediate data using loops, but such access patterns are hard to be appropriately handled by LRU, which is generally used as a page replacement policy in operating systems. In this paper, we propose an adaptive page replacement (APR) scheme, which deals with looping access patterns in scientific applications. APR detects various looping access patterns online and handles them while considering the resulting performance. It can be implemented using limited events or information (e.g., page faults and access bits) provided by the virtual memory subsystem of operating systems. We evaluate APR through trace-driven simulation with traces extracted from workloads in the SPLASH-2x benchmark. Throughout the comparison with multiple previous schemes, we demonstrate that APR successfully improves the performance over the previous schemes by avoiding their downsides.
Pole-based Localization for Autonomous Vehicles in Urban Scenarios Using Local Grid Map-based Method

Self-localization is a key component of autonomous vehicles in urban scenarios. In this work, we proposed a localization system which is based on pole-like objects such as trees and street lamps. Pole-like objects are extracted from 3D LiDAR point cloud using a cluster-based method. Based on the pole detection results, we propose a new map representation which consists of numerous local grid maps. In order to tackle the data association problem caused by the ambiguity of pole-like landmarks, the detected poles are directly transformed to the local grid map to define a cost function without pole-to-pole matching. The subsequent non-linear optimization method is utilized to minimize the cost function and generate the vehicle pose. We evaluate our localization system on our self-collected dataset. And the proposed system achieves a root mean square error of less than 18 cm for position and less than 0.52 ° for yaw.
Fine-Grained Defect Diagnosis for CMOL FPGA Circuits

Nanotechnology is an important technological alternative to overcome the limitations of complementary metal-oxide-semiconductor (CMOS) technology. Various circuit implementation methods based on nanotechnology have been proposed, and their most important characteristics are a high defect ratio and defect tolerance through circuit reconfiguration. CMOS-nanowire-MOLecular (CMOL) field-programmable gate array (FPGA) circuits are advanced logic circuit structures that combine the advantages of CMOS and nanotechnology. However, researches on defect diagnosis methods for the reconfiguration of CMOL FPGA circuits are barely conducted. In this paper, efficient circuit configuration methods for defect diagnosis of the CMOL FPGA circuits are proposed to address the problem. Also, diagnosis algorithms for both stuck-at open and stuck-at close defects are introduced. Experimental results show that with the proposed methods, diagnosis is possible for CMOL fabrics with up to 20% stuck-at open defects and 0.02% or more stuck-at close defects.
Capacity of Unmanned Aerial Vehicle Assisted Data Collection in Wireless Sensor Networks

Due to the flexibility and mobility, unmanned aerial vehicle (UAV) can work as a movable sink to receive the data collected by sensors in wireless sensor networks (WSNs). This paper analyzes the capacity of UAV assisted data collection in WSNs, which provides a guideline for the parameters optimization of data collection in the presence of UAVs. In this paper, the service area of UAVs covers the area where sensors are distributed. The charging points for UAVs are placed around the service area, which provides energy supply for UAVs. The charging point is the starting and ending point of a UAV’s trajectory. The service area is partitioned into multiple service cells. UAVs traverse these service cells to receive the data collected by the sensors in the service cells. The per-node capacity and average execution time of UAVs are used as two metrics to measure the performance of data collection in WSN. The upper and lower bounds of per-node capacity are derived respectively. It is discovered that the number of UAVs, the number of service cells and the trajectories of UAVs affect the per-node capacity of WSN. The per-node capacity can be optimized by adjusting the numbers of UAVs and service cells. Two path planning algorithms of UAVs are designed. With path planning, the per-node capacity is optimized to be closer to the upper bound, which achieves highly efficient data collection. The simulation results verify the correctness of the derived results.
IoT Waiter Bot: A Low Cost IoT based Multi Functioned Robot for Restaurants

The modern era is Robotic and IoT era. Robots are reducing human works. Robots are broadly using in Restaurants, Farming, Hospitals and various fields now a days. This work results a low cost IoT Robot that can work like a waiter in a restaurant. The developed robot can follow line and avoid obstacles. The robot can take and serve orders to the customers. It reads RFID from tables to recognize particular orders from customers. The collected data will be saved in a cloud database. Taken orders by robots will automatically go to the chefs. Each order bill will also be generated on the robot. The robot will be able to take the bills also. The full system is made in very low cost than previously made systems. It will not increase unemployment problem too.
A Linearized Model for an Ornithopter in Gliding Flight: Experiments and Simulations

This work studies the accuracy of a simple but effective analytical model for a flapping-wings UAV in longitudinal gliding flight configuration comparing it with experimental results of a real ornithopter. The aerodynamic forces are modeled following the linearized potential theory for a flat plate in gliding configuration, extended to flappingwing episodes modeled also by the (now unsteady) linear potential theory, which are studied numerically. In the gliding configuration, the model reaches a steady-state descent at given terminal velocity and pitching and gliding angles, governed by the wings and tail position. In the flapping-wing configuration, it is noticed that the vehicle can increase its flight velocity and perform climbing episodes. A realistic simulation tool based on Unreal Engine 4 was developed to visualize the effect of the tail position and flapping frequencies and amplitudes on the ornithopter flight in real time. The paper also includes the experimental validation of the gliding flight and the data has been released for the community.
Natural Scene Facial Expression Recognition with Dimension Reduction Network

As an external manifestation of human emotions, expression recognition plays an important role in human-computer interaction. Although existing expression recognition methods performs perfectly on constrained frontal faces, there are still many challenges in expression recognition in natural scenes due to different unrestricted conditions. Expression classification belongs to a pattern recognition problem where intra-class distance is greater than the inter-class distance, which leads to severe over-fitting when using neural networks for expression recognition. This paper proposes a novel net-work structure called Dimension Reduction Network which can effectively reduce generalization error. By adding a data dimension reduction module before the general classification network, a lot of redundant information is filtered, and only useful information is left. This can reduce the interference by irrelevant information when performing classification tasks and reduce generalization error. The proposed method does not require any modification to the classification network, only a small dimension reduction module needs to be added in front of the classification network. However, it can effectively reduce generalization error. We designed big and tiny versions of Dimension Reduction Network, both exceeds our baseline on AffectNet data set. The big version of our proposed method surpassed the state-of-the-art methods by more than 1.2% on AffectNet data set. Our code will open source3 when the paper is accepted.
Molecular insights into the phylogeny of the leafy liverwort family Lophoziaceae Cavers

Delimitation and classification of the large, cosmopolitan liverwort family Lophoziaceae is controversial. Many recent workers have included it in Jungermanniaceae, and even in its strictest sense, internal classification has varied widely among different treatments. We analyse variation in DNA sequences of the chloroplast rps4 gene and the trnG intron to provide resolution of phylogenetic relationships in the leafy liverworts with emphasis on the various elements usually placed in Lophoziaceae. The following conclusions are drawn. Lophoziaceae is not closely related to Jungermanniaceae. Lophoziaceae, and perhaps also Cephaloziellaceae, should be included in Scapaniaceae unless many small families are recognised. Delavayella and Blepharidophyllum are excluded from Scapaniaceae. Jamesonielloideae is a family of its own (Jamesoniellaceae) sister to Adelanthaceae (or a subfamily of Adelanthaceae). The genus Anastrophyllum should be split into Anastrophyllum and Sphenolobus. Lophozia is polyphyletic and the genera Isopaches and Schistochilopsis, and perhaps Obtusifolium, should be recognised while L. sudetica could be transferred to Barbilophozia. Barbilophozia s.str. is monophyletic while Orthocaulis is polyphyletic with the four sampled species appearing in 3 different clades; their relationships are poorly resolved. Lophozia silvicola Buch is clearly separated from L. ventricosa and Jamesoniella oenops from J. colorata at species level.
GENETIC VARIABILITY OF THE REPLICASE (NIB) GENE OF PAPAYA RINGSPOT VIRUS IN NORTHERN INDIA INDICATES COMMON ANCESTRY WITH ISOLATES FROM CHINA AND TAIWAN

The purpose of the present study was to investigate the genetic variability and phylogeny of papaya ringspot virus (PRSV) on the basis of NIb (replicase) gene. The NIb and coat protein (CP) regions of about 2.5 kb of seven PRSV isolates from North India were cloned and sequenced. The North Indian isolates R2R and R5R have higher and lower sequence similarity with other PRSV isolates, respectively. Sequence comparison of NIb gene of 42 PRSV isolates, revealed that the average evolutionary divergence of all sequence pairs were 10.6% and 11.2% at amino acid and nucleotide level, respectively. Transitions were more frequent than transversions in the analyzed sequences. Maximum likelihood phylogenetic tree of combined NIb and CP regions indicated the common ancestry of North Indian isolates with Chinese and Taiwanese isolates. One potential recombination breakpoint in isolate R5R, together with its higher sequence identity and common ancestry with Chinese PRSV isolates, suggest the possible import of the former from the Chinese subcontinent.
Stereo Visual Inertial Odometry with Online Baseline Calibration

Stereo-vision devices have rigorous requirements for extrinsic parameter calibration. In Stereo Visual Inertial Odometry (VIO), inaccuracy in or changes to camera extrinsic parameters may lead to serious degradation in estimation performance. In this manuscript, we propose an online calibration method for stereo VIO extrinsic parameters correction. In particular, we focus on Multi-State Constraint Kalman Filter (MSCKF [1]) framework to implement our method. The key component is to formulate stereo extrinsic parameters as part of the state variables and model the Jacobian of feature reprojection error with respect to stereo extrinsic parameters as sub-block of update Jacobian. Therefore we can estimate stereo extrinsic parameters simultaneously with inertial measurement unit (IMU) states and camera poses. Experiments on EuRoC dataset and real-world outdoor dataset demonstrate that the proposed algorithm produce higher positioning accuracy than the original S-MSCKF [2], and the noise of camera extrinsic parameters are self-corrected within the system.
Constrained Filtering-based Fusion of Images, Events, and Inertial Measurements for Pose Estimation

In this paper, we propose a novel filtering-based method that fuses events from a dynamic vision sensor (DVS), images, and inertial measurements to estimate camera poses. A DVS is a bio-inspired sensor that generates events triggered by brightness changes. It can cover the drawbacks of a conventional camera by virtual of its independent pixels and high dynamic range. Specifically, we focus on optical flow obtained from both a stream of events and intensity images in which the former is much like a differential quantity, whereas the latter is a pixel difference in a much longer time interval than events. This nature characteristic motivates us to model optical flow estimated from events directly, but feature tracks for images in the filter design. An inequality constraint is considered in our method since the inverse scene-depth is larger than zero by its definition. Furthermore, we evaluate our proposed method in the benchmark DVS dataset and a dataset collected by the authors. The results reveal that the presented algorithm has reduced the position error by 49.9% on average and comparable accuracy only using events when compared to the state-of-the-art filtering-based estimator.
Ant Colony Particle Filter Based State of Energy Estimation Method for Lithium-ion Batteries

In orderto estimatethe state of energy (SOE), an improved algorithm based on particle filter (PF) is proposed in this paper. The ant colony adaptive particle filter (AAPF) combines the ant colony algorithm and adaptive algorithm. It employs an ant colony algorithm to optimize the resampling process of particle filer and adaptive algorithm to reduce the influence of noise from the system. Firstly, an equivalent circuit model is built, and thenits parameters are calculated by the least-square algorithm. Thirdly, the ant colony particle filter (APF) and AAPF are proposed to optimize the resampling of the PF and estimate SOE. Finally, the accuracies of the PF, the extended Kalman filter (EKF), the APF and the AAPF are testedby estimating SOE on driving cycle tests. Experimental results indicate that compared with the EKF and the PF, the errors of the APF decrease by 5% and 1.8% respectively. Combining the APF with the adaptive algorithm, the average error of the AAPF declines byabout 1 % on driving cycle tests.
Software-Defined Networking-Assisted Content Delivery at Edge of Mobile Social Networks

With the explosive growth of mobile devices at the edge of mobile social networks (MSNs), the amount of the content that needs to be transmitted is exploded. Traditional content delivery mechanisms leverage only local information to make routing decisions, which results in both high latency and low delivery rate. Software-defined networking (SDN) is a novel network paradigm, the design philosophy of which could be applied to MSN for improving the content delivery performance. In this article, the centralized control thought of SDN is introduced into MSN to efficiently process social information. The classical routing algorithm of BubbleRap is improved from the perspective of network density, which is the basis of designing the sparse and dense routing mechanisms for MSNs. In addition, flexibly switching between these two routing mechanisms is implemented by a discriminating scheme, achieving efficient yet adaptive routing. The experimental results show that the delivery ratio of sparse routing is up to 83%, and the dense routing could reach up to 93%.
Sylvian-Silva (SFORCE): An Ensembled Boost Approach Towards Machine Learning

Gathering a minimalistic set of convincing evidence for a logical conclusion formed the premises of Occam’s Razor, put forth by Libert Froidmont. Occam’s Razor, although sound and appealing for theoretical proofs, is speculative and vague when it comes to practical scientific experiments with multivariate big data sets. The advent of the "Principle of multiple explanations" forms the basis of all ensemble architectures for Machine Learning. Random forests are an ensemble of decision trees that provide excellent results among its competitors. The main disadvantages of a random forest are the computational cost and the curse of overfitting. This paper presents Sylvian Silva (SFORCE), an Ensembled Boost approach towards Machine Learning that combines the strength of Random Forest ensemble and the boosting approaches. Stagewise Additive Modeling and real-valued confidence related predictions for Multiclass classification and regression form the basis of these boosting algorithms. Boosting ensures that the algorithm gathers the inherent characteristics and has the capability to predict future targets. The SFORCE algorithm’s performance is compelling across six different data sets. SFORCE reports high accuracy levels consistently without overfitting when compared with 17 different machine learning algorithms for classification and 10 different algorithms for regression.
DIGITAL LEADERSHIP - Character and Competency Differentiates Digitally Mature Organizations

This article describes digital leadership–specifically character and competency–that differentiate digitally mature organizations from digitally developing organizations. We assess the differentiated actions of leaders of digitally mature organizations and discuss their results. The study is based on Patterns of Digitization survey with insights from 559 decision makers across five geographic regions–America, Europe, Asia, Africa, and Oceania designed to assess how companies are implementing digital transformation, the various strategies they employ, the investments they make, and the actions they take to achieve large-scale institutionalized digital transformations. The insights gleaned from the study should help lagging companies understand what is involved in implementing a digital transformation and what they need to do to catch up.
Rule Caching in SDN-Enabled Base Stations Supporting Massive IoT Devices With Bursty Traffic

Software-defined networking (SDN) is recognized as a promising solution for the efficient management of numerous devices in the Internet of Things (IoT). To appropriately forward the incoming packets, SDN-enabled devices request the controller for traffic rules resulting in a significant delay. To avoid frequent communication with the controller and reducing the delay, these devices cache the rules as match action pairs in flow tables for a certain amount of time. This is referred to as rule caching. Flow tables are made of ternary content-addressable memories (TCAMs) capable of high-speed parallel lookup. Nonetheless, due to the high expenses and power consumption of TCAMs, flow tables have limited capacity and cannot store rules of all the users. Therefore, the assignment of limited flow tables is a challenge in IoT networks with a large number of users. In this article, we consider an SDN-enabled base station serving a set of users in a cell and is equipped with a finite-capacity flow table. We assume users’ traffic obeys a bursty ON–OFF model and formulate the efficient allocation of flow table entries to users over time. It results in a mixed-integer nonlinear program. We introduce a change of variables that turns the problem into an integer linear program. This is still intractable and cannot support problems with large dimensions such as massive IoT networks. To handle its complexity, we design a time-efficient procedure with close-to-optimal performance. Finally, numerical results demonstrate the significant improvement achieved by the proposed scheme.