Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Location Privacy-Preserving Authentication Scheme for Cooperative Spectrum Sensing

In cognitive radio networks, cooperative spectrum sensing can improve the performance of spectrum utilization and accuracy of spectrum result. However, it suffers from security and privacy threats. Firstly, secondary users’ sensing reports that have to be sent to the fusion center are heavily correlated to their locations, thereby leading to the leakage of locations of secondary users in the process of reports transmission and aggregation. Moreover, malicious secondary users are likely to submit fake sensing reports or alter sensing reports of other secondary users, which finally result in a wrong aggregated result. To address these questions, we propose an efficient location privacy-preserving authentication scheme for cooperative spectrum sensing. To be specific, our scheme incorporates a reputation mechanism and enables reliable secondary users to participate in cooperative spectrum sensing. Selected users with pseudo IDs instead of real identities send encrypted sensing reports to the fusion center. This can eliminate the correlation between sensing reports and secondary users and prevent the fusion center from associating sensing reports with secondary users’ real identities while decrypting and aggregating reports, thereby protecting the location privacy of secondary users. Besides, to ensure a true aggregated result, we utilize elliptic curve cryptography technique to verify the legitimacy of sensing reports when the fusion center receives them. Theoretical analyses show that the proposed scheme protects the location privacy of secondary users. Numerical results demonstrate that the proposed scheme brings less overhead than previous schemes.
Design and properties of logic circuits based on memristor devices

In this paper, a logic circuit based on memristor devices is discussed for future digital applications. Its I-$V$ characteristics are compared to a physical memristor to verify the model similarity with a real memristor device. An interface circuit is introduced between the memristor model and the CMOS components. The interfacing circuit is composed of a memristor (the resistance state) and a transistor. This interfacing circuit is very important to incorporate and integrate memristors into CMOS based digital applications. In this context, we examined the model behavior under the configuration of the 1T1R OxRRAM. We performed a circuit-level comparison of the proposed design with other designs proposed in the literature. We validate a non-volatile NAND gate based on 1T1R cell, then we discuss its possible configuration states “00”, “01”, “10”, and “11”. In addition, we implement this 1T1R cell on a crossbar array application and examine its operation. Thus, these memory-based CMOS circuits are potentially beneficial for future large-scale applications.
Socialbots: Impacts, Threat-Dimensions, and Defense Challenges

Online Social Networks (OSNs) are the modern communication media at the peak of Internet technology that facilitate users' ability to connect with friends and celebrities, and to disseminate breaking news and updates on real-life events. Connections and interactions among OSN users generate a massive amount of data containing a rich collection of knowledge that could be useful to various realworld data modeling and predictive analytics problems like open-source intelligence, business intelligence, event prediction, and product recommendations. On the Web, adversaries explore and target products and services for misuse and vulnerabilities, and OSNs are no exception. Their large user base, easy-to-use functionality, and open nature further attract antisocial elements to these OSNs. In OSNs, cybercrimes and illicit activities are generally committed using various forms of fake profiles, such as clone profiles,1 sybil,2 and bots.
Underactuated Gecko Adhesive Gripper for Simple and Versatile Grasp

Gecko-inspired adhesives have several desirable characteristics in robotic grasping: controllable activation and deactivation of adhesion, ability to grasp and release with minimal disturbance, and grasping without the need of form closure. Previously proposed grippers with this technology either require a complex activation mechanism or multiple activation steps. In this paper, we present an underactuated gecko-inspired adhesive gripper that can grasp a wide range of curved surfaces using a single actuator through a simple tendon-driven mechanism that attaches and adheres in one step. We derive a theoretical model of the adhesive contact area and resulting gripper grasp force, which is verified experimentally. The actual performance of the proposed mechanism is demonstrated by successfully grasping several surfaces with different curvature diameters.
An Analog CMOS Implementation for Multi-layer Perceptron With ReLU Activation

This paper presents an analog circuit comprising a multi-layer perceptron (MLP) applicable to the neural network(NN)-based machine learning. The MLP circuit with rectified linear unit (ReLU) activation consists of 2 input neurons, 3 hidden neurons, and 4 output neurons. Our MLP circuit is implemented in a 0.6 μ m CMOS technology process with a supply voltage of ± 2.5 V. An experimental case is conducted to demonstrate the feasibility and effectiveness of the MLP circuit. The simulation results show that our MLP circuit has a power dissipation of 200mW, a wide range of working frequency from 0 to 1MHz, and a moderate performance in terms of the error ratio.
Metamaterial Absorber based on Minkowski Fractal Patch for Stealth Applications

Radar absorbing structures (RAS) have gained significant importance in aerospace domain with the advent of stealth technology. However, it is still a challenging task to achieve high levels of power absorption over a broad range of frequencies with minimum thickness. Advanced electromagnetic techniques like application of frequency selective surfaces (FSS) are attractive options for obtaining superior absorption characteristics at reduced thickness. However, FSS-based absorbers using patches as unit cell are usually narrowband due to resonance conditions. In this regard, the present paper proposes a novel metamaterial absorber based on Minkowski fractal patch configuration for X-band applications. Several unit cells with dimensions optimized for resonance at closely spaced frequencies have been used in a single absorber. The full wave simulation results clearly illustrate the broadening of bandwidth accomplished using dimensionally different fractals arranged appropriately on the absorber.
Position Estimation for Ultra-Low Speed Gimbal Servo System of SGMSCMG Based on Linear Hall Sensors

In the low speed servo system of single gimbal magnetically suspended control moment gyro (SGMSCMG), the accuracy of rotor position is an important factor which influences the speed accuracy. Linear hall sensors with small volume and high precision are usually adopted to the compact equipment and suitable for SGMSCMG. However, the signals detected by hall sensors contain lots of harmonics and noise which influence the accuracy significantly. For this reason, a novel precise position detection method based on 2-step cascaded Kalman filter with phase compensation scheme is proposed to extract the precise position information from ‘dirty’ signals. The proposed method is advantageous since it is very simple and practical, and the phase compensation scheme can primely compensate the phase delay caused by cascaded Kalman filter. Finally, the experiments are carried out in a 50Nm SGMSCMG, and the results show that the method can effectively estimate the angular position of the ultra-low speed servo motor, and the position accuracy can be improved by 85.7%.
Backward-Emitting Silicon Nitride Optical Phased Array Enabling Efficient Wavelength-Tuned Beam Steering

Silicon nitride (SiN) optical phased arrays (OPAs) have emerged as a potential alternative to their silicon counterparts, due to their potential use in enhanced solid-state light detection and ranging. Grating antennas based on a SiN waveguide suffer from a limited beam steering efficiency, owing to the relatively lower effective refractive index of the waveguide. To mitigate this limitation, we propose and demonstrate a backward-emitting SiN OPA incorporating a reinforced grating vector, which provides efficient wavelength-tuned beam steering along the longitudinal direction. Two backward-emitting OPAs were primarily characterized by their steering efficiency and the spectral emission for the main lobe, and they were compared with a forward-emitting device using a weakened grating vector. The results indicated that strengthening the grating vector for the antenna improved the beam steering efficiency but adversely affected the dependence of its directionality on the wavelength. The directionality was particularly inspected, with respect to major design parameters for the grating antennas, including the pitch and etch depth of a SiN grating, as well as the thickness of a buried oxide and SiN core layer. The proposed backward-emitting OPA scheme is expected to significantly promote its feasibility as an advanced beam scanning device.
Study of Copy-Move Forgery Detection Techniques in Images

With the evolution of tools used for image editing in today’s world, the images can be manipulated with ease. Resizing, cloning, cropping, etc., becomes easy and fast while on the other end, a great challenge in front of the world is determining whether an image has been tempered or not. A very popular manipulation method for tempering images is Copy-move forgery in which a part of an image is duplicated and pasted in some other location of the same image. Major research has been going on in detection of copy-move forgery. A detailed review and analytical discussion has been presented in this paper along with pros and cons of each of the detection techniques used for copy-move forgery detection.
Dynamic Torso Posture Compliance Control for Standing Balance of Position-Controlled Humanoid Robots

Compliance is important for humanoid robots, especially a position-controlled one, to perform tasks in complicated environments with unknown disturbances. This paper presents a dynamic torso compliance method for position-controlled humanoid robots to adapt to external disturbances when standing through the controllers based on a viscoelastic model and a double inverted pendulum. The viscoelastic model is used to realize ankle compliance by control the foot postures according to forces and torques measured by force/torque sensors mounted between the foot and ankle. And the double inverted pendulum is adopted to obtain the dynamic compliant movements of torso and calculate the desired torques of ankle compliance controller. With the proposed method, a position-controlled robot can perform impressive torso posture compliance and maintain balance on both rigid and soft ground (turf), which is validated through experiments on the BHR-5T, a position-controlled humanoid robot with high reduction ratios.
AC/DCC : Accurate Calibration of Dynamic Camera Clusters for Visual SLAM

In order to relate information across cameras in a Dynamic Camera Cluster (DCC), an accurate time-varying set of extrinsic calibration transformations need to be determined. Previous calibration approaches rely solely on collecting measurements from a known fiducial target which limits calibration accuracy as insufficient excitation of the gimbal is achieved. In this paper, we improve DCC calibration accuracy by collecting measurements over the entire configuration space of the gimbal and achieve a 10X improvement in pixel re-projection error. We perform a joint optimization over the calibration parameters between any number of cameras and unknown joint angles using a pose-loop error optimization approach, thereby avoiding the need for overlapping fields-of-view. We test our method in simulation and provide a calibration sensitivity analysis for different levels of camera intrinsic and joint angle noise. In addition, we provide a novel analysis of the degenerate parameters in the calibration when joint angle values are unknown, which avoids situations in which the calibration cannot be uniquely recovered. The calibration code will be made available at https://github.com/TRAILab/AC-DCC
A Phase Corrected Substrate Integrated Waveguide Horn Antenna with Air Holes for Narrow Beamwidth in the H-plane

In this paper, a novel method of using air holes as a phase correct structure of an H-plane substrate-integrated horn antenna is proposed. The operating mechanism of the antenna is studied. By combining the proposed air hole structure with the loaded dipole array structure, a H-plane substrate integrated waveguide (SIW) horn with an impedance bandwidth of 42.4%(26-40GHz), a gain up to 11 dBi and a narrower beamwidth of the H-plane radiation pattern is obtained. A corrected phase distribution of the electric field over the horn aperture is achieved. The proposed method that can narrow the beamwidth in the H-plane is valuable to the horn antenna array with the reduced sidelobe.
Aircraft Segmentation from Remote Sensing Images using Modified Deeply Supervised Salient Object Detection with Short Connections

Image segmentation is a process of clustering the pixels into some groups. It is essential and useful in image processing and computer vision task. The segmentation result may affect the outcome of many types of work, for example, image recognition and image detection. One of the most exciting works of image segmentation is aircraft segmentation from remote sensing images. This work is very challenging due to many factors, such as the complexity of the background, the shadow, and the various type of aircraft. To handle these problems, in this paper we implement the deeply supervised salient object detection with short connections (DSS) with our dataset which is divided into two categories: simple case and dificult case, and we propose some idea of post-process which can increase some accuracy by using pre-trained and adding L2-normalization in some layer of DSS architecture to create the mask that can reduce the noise from background effectively. From the experimental result, using L2-normalization with our post-processing can increase the performance by 0.61% and 2.14% in the simple case and difficult case respectively, compared with using only the DSS method. So our method performs well when the background is complicated, which always occurs in reality.
Enhancing QoS in a University Network by using Containerized Generic Cache

Ubiquitous access and enhanced Internet speeds have paved ways for online educational reforms at a large scale. There has been a widespread adoption of modern educational applications, ranging from interactive applets, video lessons and online quizzes to remotely conducting laboratory experiments. Consequently, there is a demand to provision more bandwidth to satisfy the users expectations. In this paper, we propose an approach to enhance the Quality of Service (QoS) in a University campus network and efficiently utilize the available bandwidth. Typically within a University, some requests are similar e.g., operating system updates, Linux package installs, Python pip packages and others. These are huge data transfer requests ranging from Megabytes to Gigabytes, and consume a large amount of bandwidth on external access links to the Internet. Redundant requests of this nature from a large user base lead to enormous wastage of bandwidth. The proposed approach overcomes this concern by setting up a containerized forward proxy with a generic cache for popular traffic in the University. Our experiments on a live network at National Institute of Technology Karnataka, Surathkal show that a large number of redundant requests can be successfully served from this Virtualized Network Function (VNF), thereby enhancing the QoS and efficiently utilizing the available bandwidth. The proposed system is able to reduce the latency by over 60% and saves 34GB of data per day on an average. Although the proposed approach is tested in a University environment in this work, it is applicable for other caching requirements with minor modifications. Moreover, since this cache is implemented as a VNF, it is portable and easy to deploy.
A Segmentation Technique of Retinal Blood Vessels using Multi-Threshold and Morphological Operations

Retinal blood vessels are one of the most significant features in the fundus image of the eye, which plays a crucial role in the early screening of different ocular diseases like glaucoma, diabetic retinopathy, cataract and hypertensive retinopathy. Also in the area of biometric systems, blood vessel structure plays a vital role as retina scan is one of the finest and reliable methods. This paper proposed a segmentation technique which accurately extracts retinal blood vessels. The proposed algorithm conducted in three phases (i) pre -processing of image using AHE, CLAHE and average filtering, (ii) Multi-threshold based novel segmentation technique is used and (iii) post processing is done to remove the imperfections and for this morphological operation is employed. This method efficiently segments the vessels and improves the performance parameters. The accuracy of 95.3% is achieved. Implementation part was done in MATLAB 2015a using a DRIVE database openly available online.
In₀.₅₃Ga₀.₄₇As/InAs Composite Channel MOS-HEMT Exhibiting 511 GHz fτ and 256 GHz fmax

An In<sub>0.53</sub>Ga<sub>0.47</sub>As/InAs composite channel MOS-HEMT exhibiting peak <inline-formula> <tex-math notation="LaTeX">$f_{\tau } = 511$ </tex-math></inline-formula> GHz and peak <inline-formula> <tex-math notation="LaTeX">${f} _{\max }= 285$ </tex-math></inline-formula> GHz is demonstrated. Additionally, another device exhibiting peak <inline-formula> <tex-math notation="LaTeX">$f_{\tau } = 286$ </tex-math></inline-formula> GHz and peak <inline-formula> <tex-math notation="LaTeX">${f} _{\max }= 460$ </tex-math></inline-formula> GHz is reported. The devices have a 1 nm / 3 nm Al<sub>x</sub>O<sub>y</sub>N<sub>z</sub> interfacial layer and ZrO<sub>2</sub> gate dielectric on a 2 nm / 4 nm In<sub>0.53</sub>Ga<sub>0.47</sub>As / InAs composite channel with a modulation doped In<sub>0.52</sub>Al<sub>0.48</sub>As back barrier. To reduce parasitic gate-source and gate-drain capacitances, a modulation doped In<sub>0.52</sub>Al<sub>0.48</sub>As / In<sub>0.53</sub>Ga<sub>0.47</sub>As / InAs composite quantum well is included between the gate edges and the N+ source and drain. Compared to the work of Wu <italic>et al.</italic>, addition of an In<sub>0.52</sub>Al<sub>0.48</sub>As back-barrier, scaling of S/D metal spacing, and scaling of channel thickness has enabled improved transconductance and increased <inline-formula> <tex-math notation="LaTeX">${f} _{\tau }$ </tex-math></inline-formula>. Short gate length devices <inline-formula> <tex-math notation="LaTeX">${f} _{\max }$ </tex-math></inline-formula> are limit by high <inline-formula> <tex-math notation="LaTeX">$R_{G}$ </tex-math></inline-formula> due to poor metal filling of the T-Gate stem and large <inline-formula> <tex-math notation="LaTeX">$C_{DS}$ </tex-math></inline-formula> due to a conductive etch stop layer. Long gate length devices exhibit better metal filling, reduced <inline-formula> <tex-math notation="LaTeX">$R_{G}$ </tex-math></inline-formula>, and balanced <inline-formula> <tex-math notation="LaTeX">${f} _{\tau }$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${f} _{\max }$ </tex-math></inline-formula>. Devices exhibit 10–15% DC-1 GHz <inline-formula> <tex-math notation="LaTeX">$g_{m,e}$ </tex-math></inline-formula> suggesting that the high-k / semiconductor interface has low <inline-formula> <tex-math notation="LaTeX">$D_{it}$ </tex-math></inline-formula>.
An Automatic Detection Method of Bird’s Nest on Transmission Line Tower Based on Faster_RCNN

The bird’s nest on the transmission line tower has a bad impact on the transmission equipment, and even threaten the safe and stable operation of the power grid. In recent years, the number of bird pest in transmission line is increasing year by year, resulting in increasing economic losses. The traditional bird’s nest identification method of transmission line is time-consuming and labor-intensive, and its security level is low. Therefore, this paper proposes an automatic detection method of bird’s nest on transmission line tower based on Faster_RCNN convolution neural network. This method can automatically identify the location of the bird’s nest on the transmission line tower by using the image collected by unmanned aerial vehicle (UAV). The problem of insufficient training samples and overfitting of neural network classifier is solved by enlarging the bird’s nest image. The experimental results show that this method can effectively detect bird’s nest targets in complex environment, and the highest recall rate can reach 95.38%, the highest F1 score can reach 96.87%, and the detection time of each image can reach 0.154s. Compared with the traditional nest detection method, this method has stronger applicability and generalization ability. It provides technical support for analyzing bird activities and taking effective preventive measures.
A Generic Intelligent Bearing Fault Diagnosis System Using Convolutional Neural Networks With Transfer Learning

It is very important and necessary to diagnose bearing faults timely, quickly, and accurately in practical applications, because the operation status of the bearings is directly related to the performance and reliability of the rotating machinery. Therefore, a generic intelligent bearing fault diagnosis system based on AlexNet with transfer learning was proposed to automatically identify and classify different bearing faults. Transfer learning was used to avoid overfitting problem of deep network. Five bearing faults at two different motor loads and speeds were selected from the Case Western Reserve University (CWRU) bearing dataset to validate the performance of the proposed method. Results showed that compared to previous methods, the proposed method achieved excellent performance, with overall classification accuracy over 99.7%, and fast training and testing times. Feature visualization displayed the common and high-level features of spectrograms of vibration signals learned by the trained classification model. And strongest activations demonstrated the classification model had learned the correct features of each bearing fault. Importantly, there were two testing datasets employed in this study, where the training dataset and testing dataset (2) were completely independent and the number of sub-spectrograms used for testing was 3–5 times greater than those used for training. Thus, all the results suggest that the proposed method is stable, reliable, suitable for diagnosing different bearing faults and has a great potential in practical applications.