Miracle-dz/UniFAR-FTUs huggingface dataset:
Wintering Sandhill Crane exposure to wind energy development in the central and southern Great Plains, USA

ABSTRACT Numerous wind energy projects have been constructed in the central and southern Great Plains, USA, the main wintering area for midcontinental Sandhill Cranes (Grus canadensis). In an initial assessment of the potential risks of wind towers to cranes, we estimated spatial overlap, investigated potential avoidance behavior, and determined the habitat associations of cranes. We used data from cranes marked with platform transmitting terminals (PTTs) with and without global positioning system (GPS) capabilities. We estimated the wintering distributions of PTT-marked cranes prior to the construction of wind towers, which we compared with current tower locations. Based on this analysis, we found 7% spatial overlap between the distributions of cranes and towers. When we looked at individually marked cranes, we found that 52% would have occurred within 10 km of a tower at some point during winter. Using data from cranes marked after tower construction, we found a potential indication of avoidance behavior, whereby GPS-marked cranes generally used areas slightly more distant from existing wind towers than would be expected by chance. Results from a habitat selection model suggested that distances between crane locations and towers may have been driven more by habitat selection than by avoidance, as most wind towers were constructed in locations not often selected by wintering cranes. Our findings of modest regional overlap and that few towers have been placed in preferred crane habitat suggest that the current distribution of wind towers may be of low risk to the continued persistence of wintering midcontinental Sandhill Cranes in the central and southern Great Plains.
A Cost-Effective Embedded Nonvolatile Memory with Scalable LEE Flash®-G2 SONOS for Secure IoT and Computing-in-Memory (CiM) Applications

We introduce a cost-effective, reliable and energy efficient embedded flash memory technology and its applications. A charge trapping type of Silicon-Oxide-Nitride-Oxide-Silicon (SONOS) with twin select-gates structure has been demonstrated on 55-nm bulk CMOS technology. It is potentially scalable on advanced fully depleted (FD)-SOI or 3D Fin-FET devices below 28-nm node. Those feasibilities are shown by TCAD simulations and existing 55-nm planar bulk silicon data. Secure and low-power applications are introduced that are using nonvolatile (NV)-SRAM by combining with SRAM cell and flash cell. Besides, analog computing-inmemory (CiM) based on flash is also introduced for energy efficient artificial intelligence (AI) applications in edge computing.
A Fast Compression Algorithm Based on the Variable Block for 3D Point Cloud Data

In this paper, a fast compression algorithm based on the variable block for 3D point cloud data is proposed, the key of the method is to reduce the temporal redundancy through finding the temporal correlation between the two consecutive frames. The first, the macroblock is generated by voxelizing the point cloud data. Then, the two consecutive frames are aligned, and the corresponding macroblocks inside the two consecutive frames are preliminarily judged whether they are consistent according to the point number of the macroblock and the color variance. Finally, the ICP algorithm is exploited to further match the two macroblocks. When the result of the ICP algorithm is lower than the Threshold, the macroblock in the current frame is replaced by the motion vectors for compressing the point cloud data. The motion vectors are acquired by the ICP algorithm applied for the corresponding macroblock between two consecutive frames. The Predicted-frame (P frame) consists of the motion vectors and no matched macroblock in the current frame while the Intra-frame (I frame) is the point cloud data. In our method, in order to improve the compression ratio, the size of the macroblock is set as a variable parameter. The experimental results demonstrate that the time delay will be decreased, meantime the compression ratio will be increased in our method. The visual result of our method also will be more smooth and the performance of our method is better than the state-of-the-art method.
Reweighted L1 Norm Penalized LMS Fourth Algorithm of Solar Grid Interfaced System for Alleviating Power Quality Problems

This study investigates an applicability of a reweighted l1 norm penalized least mean square fourth (RL1-LMSF) algorithm and an improved multivariable filter based frequency locked loop (IMVF-FLL) through the performance demonstration of a three-phase solar-grid interfaced system in a double stage topology. The effectiveness of the system is ascertained by considering grid voltages unbalance, voltage sag/swell, unbalanced dc offset, unbalance in loads, and variation in solar irradiance. This RL1-LMSF algorithm adaptively extracts fundamental component of nonlinear load currents for estimation of reference grid currents with fast convergence and least steady-state error. An IMVF-FLL is used to elicit positive sequence components of grid voltages at abnormal grid conditions. The adaptability to input frequency deviations, harmonics suppression, dc offset rejection, and fast response of IMVF-FLL, during dynamic conditions, enables accurate estimation of unit templates. These unit templates maintain sinusoidal and balanced reference grid currents at varying conditions. The harmonic distortions in grid currents are strictly following the standard of IEEE-519. The photovoltaic array is kept at maximum power point using a boost converter together with a technique based on incremental conductance. The system is simulated in MATLAB environment, and test results recorded using a hardware setup emphasize its relevance.
Batch-Assisted Verification Scheme for Reducing Message Verification Delay of the Vehicular Ad Hoc Networks

In terms of preventing traffic accidents, improving traffic efficiency and ensuring personal safety, the research on vehicular ad hoc networks (VANETs) is of great significance. Message authentication is an important security foundation for VANETs. With the rapid growth of the number of access terminals, the existing computing power of the VANETs will not be able to meet the fast message verification service load of large-scale dynamic networks. This article proposes a novel distributed collaborative authentication method. By selecting a reasonable number of assistance verification terminals in the VANETs system and cooperating with the roadside unit (RSU) to jointly undertake the task of network message verification, the purpose is to reduce the verification delay and achieve fast message verification. The simulation results show that in a large-scale connected vehicle with a large number of system terminals, the system message verification delay of our scheme is shortened to one tenth of the centralized verification system message verification delay.
Designing Plasmonic Hot Carrier Devices for Uncooled Infrared Photodetection

Surface plasmon resonance transduces photons into energetically “hot” electrons, which provides new Schottky-based platforms for infrared photodetectors without need for cooling infrastructure. Material design principles towards a multiphysics computation tool are presented where performance is governed by plasmonic activity, density-of-states, and the Schottky barrier.
Grasp Detection Based on Faster Region CNN

The robot grasp technology has recently aroused increasing research interests thanks to its foundation and importance in the field of robotics. Based on the deep learning method, this paper introduces a grasp detection model with the improved model Faster Region Convolutional Neural Network (Faster-RCNN). The orientation of the ground truth box in the grasp detection is random, so the orientation issue is one of the key points in grasp detection and differs from the other object detection researches. To tackle with this problem, this paper applies the five-dimensional parameters to represent the grasp rectangle. The method puts forward the improved Region Proposal Network (RPN) to export the tilted graspable region, including the size, the location, the orientation and the score belongs to the grasp class or non-grasp class. The RPN extracts the candidate proposals via using a more efficient CNN, instead of the inefficient selective search method. In the classification branch, the softmax function works to determine whether the anchor box is foreground or background. The regression of the angle is performed in the regression branch. In addition, the improved Non-Maximum Suppression (NMS) is used to generate the optimal inclined predicted grasp rectangle. To cope with the insufficient data size in the Cornell Grasp Dataset, the data augmentation and transfer learning methods are applied in the training phase. During the test, the results reveal that the detection accuracy of the model proposed in this paper on the dataset is 92.3% in terms of the image-wise splitting and 92.5% with respect to the objective-wise splitting on the Cornel Grasp Dataset, respectively.
Smart Antenna System with Memcapacitors

Intelligent RF circuits and systems can potentially be implemented with memory devices. The characteristic of a memcapacitor and its application to RF band are discussed. Then, a memory phase shifter is proposed using the transmission lines loaded with memcapacitors. The phase shift over 360° is achieved with a low insertion loss when properly selecting the states of memcapacitors. A smart antenna system incorporated with the proposed memory phase shifters is designed. The main beam can be adjusted to the direction of arrival of the incident wave so as to achieve an optimal reception. Intelligent RF applications are expected with various memory devices.
A Proximity Based Community Detection in Temporal Graphs

With very fast global instant messaging services, communication networks of today are changing rapidly. To accurately represent such dynamic networks, a special type of a graph, known as temporal graph is indispensable. Majority of the community detection algorithms are designed for static graphs. So, an efficient conversion of temporal graphs to static graphs, while retaining important temporal information, enables the use of any standard community detection algorithm easily. This paper proposes a novel community detection method by constructing static graphs from temporal networks. The proposed method is validated through experiments. Results show that our technique leads to considerable runtime reduction upon dealing with large graphs comprise multiple community structures.
DLASE: A light-weight framework supporting Deep Learning for Edge Devices

With the rapid growth of the Internet of Things (IoT), there is a massive number of constrained devices connected to the internet, resulting in the generation of large data volume. The deep learning (DL) has become a promised solution to extract more valuable knowledge from collected data but struggle to execute DL algorithms due to limited computing resources of IoT devices. Leveraging the computation power of cloud computing, we could offload the data to cloud servers for processing and response results to the devices. But, this paradigm leads to a significant increase in latency and security risks. Therefore, there have been many efforts to perform DL algorithms on edge devices, which are close to the data sources. Deploying various DL systems to such edge devices a complex process due to the diversity of DL models and running environment configuration. In this paper, we propose a light-weight framework supporting to deploy deep learning as a service running on edge devices, namely DLASE. There are two key advantages of our proposal. The first one is that we enable remote deployment of various deep learning models to edge devices. The other one is that these models are run as a service to serve requests from IoT devices. To demonstrate the light-weight and flexibility of DLASE, we practically evaluate the framework performances (consumed memory, CPU, latency) by running various combinations of DL models and edge devices.
Novel TLS Signature Extraction for Malware Detection

Encryption is the key to network security and privacy. The rapid growth and use of encrypted traffic in the network has inadvertently facilitated malware to adopt encryption and traverse undetected through the network infecting the victim. This paper presents a solution to identify the presence of malware in a network flow from the initial unencrypted Client Hello packet of TLS handshake.We perform feature engineering on the subfields of TLS metadata of Client Hello packet to extract interpretable signatures in the form of numerical features. These features can be effectively used to model a binary classifier and assign a risk score to indicate the presence of malware. This classifier can be deployed at the EDGE for passive monitoring and malicious flows can be redirected to the cloud for deeper inspection.
Hybrid Topological and 3D Dense Mapping through Autonomous Exploration for Large Indoor Environments

Robots require a detailed understanding of the 3D structure of the environment for autonomous navigation and path planning. A popular approach is to represent the environment using metric, dense 3D maps such as 3D occupancy grids. However, in large environments the computational power required for most state-of-the-art 3D dense mapping systems is compromising precision and real-time capability. In this work, we propose a novel mapping method that is able to build and maintain 3D dense representations for large indoor environments using standard CPUs. Topological global representations and 3D dense submaps are maintained as hybrid global map. Submaps are generated for every new visited place. A place (room) is identified as an isolated part of the environment connected to other parts through transit areas (doors). This semantic partitioning of the environment allows for a more efficient mapping and path-planning. We also propose a method for autonomous exploration that directly builds the hybrid representation in real time.We validate the real-time performance of our hybrid system on simulated and real environments regarding mapping and path-planning. The improvement in execution time and memory requirements upholds the contribution of the proposed work.
Eye-in-Hand 3D Visual Servoing of Helical Swimmers Using Parallel Mobile Coils

Magnetic helical microswimmers can be propelled by rotating magnetic field and are adept at passing through narrow space. To date, various magnetic actuation systems and control methods have been developed to drive these microswimmers. However, steering their spacial movement in a large workspace is still challenging, which could be significant for potential medical applications. In this regard, this paper designs an eye-in-hand stereo-vision module and corresponding refraction-rectified location algorithm. Combined with the motor module and the coil module, the mobile-coil system is capable of generating dynamic magnetic fields in a large 3D workspace. Based on the system, a robust triple-loop stereo visual servoing strategy is proposed that operates simultaneous tracking, locating, and steering, through which the helical swimmer is able to follow a long-distance 3D path. A scaled-up magnetic helical swimmer is employed in the path following experiment. Our prototype system reaches a cylindrical workspace with a diameter more than 200 mm, and the mean error of path tracking is less than 2 mm.
SDN-based Machine Learning Powered Alarm Manager for Mitigating the Traffic Spikes at the IoT Gateways

As the adoption of the Internet of Things (IoT) is gaining momentum so are the possibilities of misusing it to hamper the service provided by the IoT applications. IoT devices can be hacked to launch denial of service (DoS) or distributed DoS (DDoS) attack to overwhelm the IoT network right from the IoT gateway (IoT-G) at the edge to the IoT application server from serving the legitimate flows. Monitoring at the IoT-G is required to identify the unusual spikes in the traffic and in response, unified actions are required to provide faster and effective resilience against such attacks. To address these challenges, in this paper, we propose software-defined networking (SDN) based alarm manager design for mitigating the immediate traffic burst at the IoT-G. Our machine learning-powered alarm manager identifies the attack in the network using the historical interpretation of the traffic and generates an alarm to block the attack from overwhelming the IoT network. Based on the results, our solution is capable of detecting the security attack with around 98% precision and then mitigating it.
Collective Adaptive Systems as Coordination Media: The Case of Tuples in Space-Time

Coordination is a fundamental problem in the engineering of collective adaptive systems (CAS). Prominent approaches in this context promote adaptivity and collective behaviour by founding coordination on local, decentralised interaction. This is usually enabled through abstractions such as collective interfaces, neighbour-based interaction, and attribute-based communication. Application designers, then, use such coordination mechanisms to enact collective adaptive behaviour in order to solve specific problems or provide specific services while coping with dynamic environments. In this paper, we consider the other way round: we argue that a CAS model can be used to provide support for high-level coordination models, simplifying their implementation and transferring to them the self-* properties it emergently fosters. As a motivating example, we consider the idea of supporting tuple-based coordination by Linda primitives such that tuples and operations have a position and extension in space and time. Then, we adopt an aggregate perspective, by which space-time is logically represented by a mobile ad-hoc network of devices, and show that coordination primitives can be implemented as true collective adaptive processes. We describe this model and a prototype implementation in the ScaFi aggregate programming framework, which is rooted in the so-called computational field paradigm.
Picking Thin Objects by Tilt-and-Pivot Manipulation and Its Application to Bin Picking

This paper introduces the technique of tilt-andpivot manipulation, a new method for picking thin, rigid objects lying on a flat surface through robotic dexterous in-hand manipulation. During the manipulation process, the gripper is controlled to reorient about the contact with the object such that its finger can get in the space between the object and the supporting surface, which is formed by tilting up the object, with no relative sliding motion at the contact. As a result, a pinch grasp can be obtained on the faces of the thin object with ease. We discuss issues regarding the kinematics and planning of tilt-and-pivot, effector shape design, and the overall practicality of the manipulation technique, which is general enough to be applicable to any rigid convex polygonal objects. We also present a set of experiments in a range of bin picking scenarios.
An Improved Single Shot Multibox for Video-Rate Head Pose Prediction

Head pose estimation plays a crucial role in attention detection, behavior analysis, human-computer interaction, and eye tracking, etc. The existing landmark-to-pose methods require two steps, not only to detect the key points of the human face, but also to solve the 2D to 3D mapping problem usually by the average head model. We propose an efficient and robust method to monitor the driver’s attention. Firstly, the prevailing object detection algorithm SSD which has inherent capabilities of simultaneous classify and regress, is used to create a lightweight network, which avoids the shortcomings of high coupling and time-consuming of the existing methods. Then, single-scale anchors, which have a less computational cost than multi-scale anchors, are adopted for vehicle environments where the ambient light changes dramatically. Finally, by binning continuous angles into specific classes, the 3D angle regression problem is converted into angle classification and face box regression, and our model directly outputs Euler angles (Yaw, Pitch, and Roll) without detecting face landmarks. Experiments on YawDD result that our approach can efficiently perform detection tasks and estimation tasks under the actual driving environment of various luminosity. The mean average errors of prediction in AFLW2000 and 300W-LP are 6.01° and 2.38°, which demonstrates the accuracy of the proposed algorithm.
Robot-Assisted and Wearable Sensor-Mediated Autonomous Gait Analysis§

In this paper, we propose an autonomous gait analysis system consisting of a mobile robot and custom-engineered instrumented insoles. The robot is equipped with an on-board RGB-D sensor, the insoles feature inertial sensors and force sensitive resistors. This system is motivated by the need for a robot companion to engage older adults in walking exercises. Support vector regression (SVR) models were developed to extract accurate estimates of fundamental kinematic gait parameters (i.e., stride length, velocity, foot clearance, and step length), from data collected with the robot’s on-board RGB-D sensor and with the instrumented insoles during straight walking and turning tasks. The accuracy of each model was validated against ground-truth data measured by an optical motion capture system with N=10 subjects. Results suggest that the combined use of wearable and robot’s sensors yields more accurate gait estimates than either sub-system used independently. Additionally, SVR models are robust to inter-subject variability and type of walking task (i.e., straight walking vs. turning), thereby making it unnecessary to collect subject-specific or task-specific training data for the models. These findings indicate the potential of the synergistic use of autonomous mobile robots and wearable sensors for accurate out-of-the-lab gait analysis.
Standalone Hybrid Renewable Energy DC Grid System with Optimal Energy Management For AC Load Operation

In this paper a hybrid DC micro grid standalone grid system is adopted with four sources (PVA, BESS, Super capacitor and fuel cell) for operating an AC load even during environmental disturbances. An optimal energy management scheme is implied on the grid system for better power sharing capability. The energy management scheme controls the buck & boost converters power electronic devices connected to sources for DC voltage stability. The modelling of the proposed system is designed in MATLAB Simulink software with graphical representations with time domain analysis for different operating conditions.
Single Stage PFC Flyback AC-DC Converter Design

This paper discusses a 100 W single stage Power Factor Correction (PFC) flyback converter operating in boundary mode constant ON time methodology using a synchronous MOS-FET rectifier on the secondary side to achieve higher efficiency. Unlike conventional designs which use two stage approach such as PFC plus a LLC resonant stage or a two stage PFC plus flyback, the proposed design integrates the PFC and constant voltage regulation in a single stage without compromising the efficiency of the converter. The proposed design is advantageous as it has a lower component count. A design of 100 W flyback operating from universal input AC line voltage is demonstrated in this paper. The experimental results show that the power factor (PF) is greater than 0.92 and total harmonic distortion (iTHD) is less than 20% for a load varying from 25 % to 100 %. The experimental results show the advantages of a single stage design.