Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective Method for Wire Bonds Rework Using Conductive Epoxy

The development of wire bonded chips requires us to go through multiple designs to achieve the best packaging performances. Nevertheless, some of the designs may not be tested due to defects preventing the wire bonds to be effectively completed. Unless an effective way to rework such failed assemblies is feasible, the invested resources in the design and manufacture are lost. The MiQro Innovation Collaborative Center, Bromont, QC, Canada, has developed a method to rework such assemblies. In this paper, we present a method where two silver-filled conductive epoxies are used to repair failed gold wire bonds. The mechanical adhesion and electrical properties of such rework assemblies were compared to reference gold wire bonds. The rework method consists of reattaching the sheared end of the wire to the landing pad on which a drop of conductive epoxy is deposited. Each group is at least composed of 40 samples for statistical analysis of the method reproducibility. Experiments were conducted on two identical test chips with gold pads on each substrate. A matrix of 1-mil-diameter gold wire bonds was realized. Then, on each substrate, the first group of wire bonds was used as a reference and the others were sheared at the stitch bond and reworked with two different silver-filled conductive epoxies. The pull tests revealed that all the wires (reference and reworked with epoxy 1 and 2) rupture at the neck of the first ball bond at a similar break load (~12 g). Visual inspection using the cross section of the reworked area showed no visual defect. We could also see that the deposited drop from epoxy 1 is much more likely to make a better electrical connection due to the presence of smaller silver flakes and a better capability to wet both the wire and the pad. The electrical resistance of reference and reworked wire bonds were measured on a two-probe station. On both samples, reworked wire bonds with epoxy 1 have similar resistance (94 $\text{m}\Omega$ ) than the reference (91 $\text{m}\Omega$ ) ones. The ones reworked with epoxy 2 have an electrical resistance (214 $\text{m}\Omega$ ) more than twice than the reference ones. Reliability testing showed increasing electrical resistance throughout 1000 deep thermal cycling cycles for epoxy 2, while epoxy 1 remained stable until 500 cycles. We demonstrated that failed wire bonds could be effectively reattached to the substrate using the appropriate conductive epoxy. This method enables us to reduce development costs by reworking failed considered assemblies to avoid the waste of resources and delays in the run to the market for the new product.
Object Recognition, Localization and Grasp Detection Using a Unified Deep Convolutional Neural Network with Multi-task Loss

Recognize an object and detect a good grasp in unstructured scenes is still a challenge. In this paper, the problem of detecting robotic grasps is expressed by a two-point representation in an unstructured scene with an RGB-D camera. A deep Convolutional Neural Network is designed to predict good grasps in real-time on GTX1080, with using region proposal techniques. A contribution of this work is our proposed network framework can perform classification, location and grasp detection simultaneously so that in a single step, it not only recognizes the category and bounding-box of the object, but also finds a good grasp line. Besides, in training process, we minimize a multi-task loss objective function of object classification, location and grasp detection in order to train the network end-to-end. Our experimental evaluation on a real robotic manipulator demonstrates that the robotic manipulator can fulfill the grasping task effectively.
Light-Induced Waveguide With Directional Transmission

Herein, we propose and demonstrate that the light-induced optical polymeric waveguides can function as the fully online optical connectors with directional transmission characteristics. The light-induced optical waveguides, which can be used for the optical couplings between single-mode fibers, are fabricated by using a photopolymerizable resin system formed by a 405-nm laser. Subsequently, the transmission characteristics of the light-induced waveguides with different parameters are investigated, and the experimental results reveal that the (directional) transmission characteristics can be modified by controlling the structure of the light-induced waveguides by controlling the illumination time (single-side) or the ratio of the illumination intensity (dual-side). Additionally, the preliminary experimental results reveal that the light-induced optical waveguides can function as the optical connectors. The light-induced waveguide build-up process, which is simple and effective, can be applied to the fabrication of polymer-based optical waveguide modules and for optical interconnections. Simultaneously, this present technique could be a feasible method of incorporating devices such as filters, isolators, and polarizers into fibers, which can potentially contribute to further expansion of integrated optoelectronic system.
A Trajectory Modification Method for Tool Operation Based on Human Demonstration Using MITATE Technique

We propose a novel method for a daily assistive robot to learn a new task from its user who does not have technical knowledge. “MITATE;’ which is a human capacity to communicate a motion of a tool using a different tool and environment, provided inspiration to this system. The proposed system automatically estimates the mapping from the task space of the user's to the robot's. This enables the online teaching of a task by showing the target motion with a different tool and a different environment from the robot. We evaluated the proposed method with daily tasks experimentally, and the results proved that the proposed method is effective.
Impact of Behavioral and Textual Features on Opinion Spam Detection

Opinion spam or fake review detection is extensively a widespread and established problem. Due to this, it has received a substantial amount of research attention in the past decade. The prime actors responsible to orchestrate a spamming situation are commonly known as opinion spammers. Such individuals aim at manipulating online review platforms by either promoting or demoting the reputation of a target product or business, thereby misleading the consumers. By considering opinion spam as a classification problem, a variety of spamming features viz., behavioral, textual, network-based etc., have been engineered and utilized in the literature. However, examining the effectiveness of these features has not been studied so far. This paper aims at investigating the impact of behavioral and textual features in connection to both review-centric (spam detection) and reviewer-centric (spammer detection) settings. For the same, support vector machine (SVM) models have been trained on YelpZip dataset. Our results show that behavioral features comprehensively outperform textual features in detecting both spam and spammers. Additionally, the posterior analysis performed on both feature sets also confirms the same.
Autonomous Aerial Manipulation Using a Hexacopter Equipped with a Robotic Arm

This paper presents the implementation of an autonomous aerial manipulation using a hexacopter equipped with a two DOF robotic arm. The kinematic and dynamic models are developed by considering the dynamic characteristics of the combined manipulation platform. A novel adaptive sliding mode controller is proposed for both position and velocity control. By building SSD (Single Shot Detection) deep neural network based on deep learning, an object detection solution is developed. The three dimensional coordinates of the target object relative to the multirotor are obtained via combining the vertical plane position obtained from object detection law with the depth value from stereo camera. Finally, with the proposed controller and object detection law, an autonomous flight experiment is accomplished including approaching, grabbing and delivering the target object. The proposed approaches are demonstrated with effectiveness and could be utilized in various manipulation applications.
Model for Torque Estimation of Pump System with Horizontal Pipe Network

This paper investigates the possibility of torque estimation of Centrifugal Pump System (CPS) with horizontal pipe network topology. It illustrates the concept of a model-based scheme for hydraulic system simulation. The simulation model developed for CPS takes into consideration the hydraulic properties of the pipe network based on the geometrical characteristics of a pipeline such as a length, diameter, and friction coefficient. The model allows not only to study the torque behavior for a given hydraulic system but also to implement it later for hardware-in-the-loop (HIL) simulation. Additionally, the paper provides an example of working point (WP) calculation method based on the detection of a cross section of performance and system curves of a particular CPS. All variables of the system in the current model can be changed gradually until reaching the desired values. The model helps to estimate the torque in the broad range based on the hydraulic parameters of a network and a rotational speed of a pump.
On the Secrecy Capacity of 2-user Gaussian Interference Channel with Independent Secret Keys

This paper considers the problem of secure communication over a 2-user Gaussian interference channel (GIC) with shared key of finite rate between the transmitter-receiver pair with strong secrecy constraint at the receiver. The main contributions of the paper lies in obtaining a novel achievable scheme which uses a combination of one-time pad, stochastic encoding and superposition based coding scheme, and outer bound on the secrecy capacity region of the 2-user GIC. The main novelty of the derivation of the outer bound lies in the selection of the side-information to be provided to the receiver and using the secrecy constraints at the receiver. The results highlight the role of secret key in the encoding of messages to enhance the system performance in interference limited scenarios.
Graphene Enhanced Leaky Mode Resonance in Tilted Fiber Bragg Grating: A New Opportunity for Highly Sensitive Fiber Optic Sensor

Tilted fiber Bragg grating (TFBG) presents many unique spectral characteristics for sensing. The widespread approaches to date are based on the cut-off mode resonance and surface plasmon resonance (SPR), whereas the leaky mode resonance is ignored in the literature. Herein, we theoretically demonstrate that the s-polarized (or TE/HE) leaky mode resonance (and the guided mode resonance) can be efficiently enhanced (and suppressed) by integrating graphene on the TFBG for a highly sensitive sensor. In contrast, the p-polarized (or TM/EH) mode (both leaky mode and guided counterpart) presents slight variation. The enhancement principle is discussed based on the variation in the mode characteristics induced by the graphene. The results show that the graphene enhanced leaky mode resonance presents ultra-sensitive intensity response but insensitive wavelength response to the extremely small analyte perturbation. The sensitivities are achieved up to 14108dB/RIU and 5232.6dB/RIU for the first two leaky modes in gaseous media respectively, which are 121 and 13 times higher than that of bare TFBG. This novel sensing platform provides a promising technique that can compete with the widespread SPR approach in fiber optic sensing fields, which opens up new opportunities for industrial, environmental, and biochemical sensing applications.
Image Super-Resolution Using Memory Mechanism

Though there are some deep learning approaches that have acquired satisfied results on image super-resolution (SR), many lacks intelligence in the real sense. This paper explores and finds a specific correlation between semantic category information and SR procedure, to construct a complete SR reconstruction system which can realize online working and learning. The proposed system flow takes the memory mechanism of human as a reference and is divided into three parts, sensory memory, short-term memory (STM) and longterm memory (LTM). The test evaluation of the experiments shows that this framework can be adpoted to obtain a better performance than other traditional and deep learning methods. From these results, it can be concluded that the conventional wisdom of divide and conquer is also applicable to deep learning based methods.
Frequency Response System Identification and Flight Controller Tuning for Quadcopter UAV

This paper presents methodology to determine dynamic models and the suitable controllers for FY450 Firefly quadcopter. Frequency response system identification technique via software package Comprehensive Identification from Frequency Response (CIFER) was applied in this research. Pixhawk hardware with PX4 Flight stack firmware was implemented as a flight controller. The measurement of inputs and outputs during hovering flight maneuver with frequency sweep input was utilized to determine the unknown parameters of SISO mathematical model. Then after mathematical models were identified, the dynamic models of roll, pitch and yaw response respectively were employed to tuning the gains of PX4 controller structure with globally offline optimization technique. With this presented method, satisfied flight dynamics meets the desired performances without the requirement of numerous in-flight tuning.
Development of a Thermal Equivalent Circuit to Quantify the Effect of Thermal Paste on Heat Flow Through a Permanent Magnet Alternator

This paper describes the thermal modeling of a permanent magnet alternator (PMA). The principal focus is on, to investigate the effect of adding thermal paste into the machine end winding region. A thermal lumped parameter network is proposed to quantify the change in heat flow paths for a flange mounted alternator. The thermal model is implemented in Simulink, which allows many different heat paths to be easily combined. Since the addition of a thermal paste introduces new axial heat flow by conduction paths between the stator windings and frame, the developed thermal network considers the detailed heat flow paths in the PMA. The thermal network is extended to the machine frame and part of the mounting plate. It is shown that the axial heat flow has been improved by 5.6% for the PMA with the addition of the thermal paste. This in turn reduces winding temperature by around 10.5% at standstill dc tests. The model accuracy has been validated by performing finite-element analysis thermal simulations and experimental results.
Beam Tracking Under Highly Nonlinear Mobile Millimeter-Wave Channel

We propose approaches to the problem of multi-paths beam tracking in millimeter-wave (mmWave) mobile communications. Precise angle tracking is necessary to take advantage of beamforming technology in this mmWave band. The mobile channel is a highly nonlinear function with respect to the state that comprises the beam angle and the channel path gain. Therefore, the beam angle tracking is challenging and crucial. We propose a couple of particle filtering frameworks for beam tracking and show outperforming result of an auxiliary particle filtering approach over recently proposed tracking approaches.
A Music Generation Model for Robotic Composers

This paper presents a new model of automatic music generation which is based on simulated annealing and genetic algorithm. Our model uses simulated annealing algorithm to create melody units on different chords, and regards them as the genes in genetic algorithm, which can pick out a fine combination of these genes. This model is efficient for producing massive fair-sounding pieces of double-track music, when provided with a customized metric. Hence, it can be especially useful for massively producing background musics for video games, or providing inspirations for music composers. In this paper, we detailedly state how to transform the prior knowledge about music theory into a reasonable metric, and propose a sample metric for reference. Using this sample metric, we generate a set of music pieces as test cases. The evaluation results show that these pieces fully satisfy the need of quality and creativity.
Prediction of the JC (B) Behavior of Bi-2212 Wires at High Field

Bi-2212 wires have been produced for much of the last decade with varying architectures, powder sources, and great variability of final <inline-formula><tex-math notation="LaTeX">$J_{C}(B)$</tex-math></inline-formula> properties. As Bi-2212 transitions from an R&D materials development to a magnet technology, means to predictably forecast <inline-formula><tex-math notation="LaTeX">$J_{C}(B)$</tex-math></inline-formula> becomes increasingly valuable. Here we report on characterizations of short samples having <inline-formula><tex-math notation="LaTeX">$J_{C}(15\; \text{T},\,4.2\; \text{K})$</tex-math></inline-formula> varying from 1210 to 6560 A/mm<sup>2</sup>, measured in fields up to 31 T, and drawn from 10 billets. Using both 4 K transport and variable temperature vibrating sample magnetometer measurements, capable of probing the 20 K irreversibility field, <inline-formula><tex-math notation="LaTeX">$B_{{\rm{irr}}}\; = \; \mu _{0}H_{k}$</tex-math></inline-formula>, we found that the vortex pinning properties are very stable across these wires, which all used the same standard powder composition (Bi<sub>2.17</sub>Sr<sub>1.94</sub>Ca<sub>0.89</sub>Cu<sub>2.00</sub>O<sub>8+</sub><italic><sub>x</sub></italic>), even though it has been made by three different manufacturers over nine years. We conclude that a power-law fit <inline-formula><tex-math notation="LaTeX">${J_C} \propto {B^{ - \alpha }}$</tex-math></inline-formula>, where <italic>α </italic> = 0.280 (<italic>σ</italic> = 0.015), works well at 4.2 K over the field range of at least 3–30 T and that <inline-formula><tex-math notation="LaTeX">$\mu _{0}H_{k}(20\; \text{K})$</tex-math></inline-formula> remains stable at 8.6 ± 0.4 T. We conclude that the dominant current limiting mechanism predicting <inline-formula><tex-math notation="LaTeX">$J_{C}(B)$</tex-math></inline-formula> in Bi-2212 is the effective filament connectivity. <inline-formula><tex-math notation="LaTeX">$J_{C}(B)$</tex-math></inline-formula> at high fields may thus be predicted by simple <inline-formula><tex-math notation="LaTeX">$I_{C}$</tex-math></inline-formula> measurements at easily accessible fields in the 3–15 T range.
Steering Control for Autonomously Balancing Bicycle at Low Speed

This paper studies how to use steering control to keep the bicycle balanced at low speed $(< 2\mathrm{m}/\mathrm{s})$. By changing the trail length of the bicycle and designing a corresponding low-speed balance control algorithm, the bicycle can retain the favorable characteristics of simple structure and high efficiency. Firstly, we construct the bicycle dynamic model, and analyze the influence of different trail lengths on the dynamic characteristics of the bicycle. Then, we design the steering control algorithm. The results of both the simulation and experiments on prototypes show that the proposed algorithm can meet the requirements of balance control when the bicycle is running at low speed. After changing the trail length, the minimum speed of the bicycle prototype is reduced from 1.3m/s to 0.58m/s with good stability.
Implementation and Analysis of SVR Based Machine Learning Approach for Real-Time Modelling of Tissue Deformation

Brain tumors are fatal diseases that are spread worldwide and affect all types of age groups. Due to its direct impact on Central Nervous System if tumor cells prevail at certain locations in the brain, the overall functionality of the body is disturbed and chances of a person approaching death accelerate. Tumors can be cancerous or non-cancerous but in many cases, the chances of complete recovery are less and as a result death rate has increased all over the world despite recent advancements in technology, equipment and awareness. So the main concern is to detect brain related diseases at early stages so that it does not spread into vital parts of brain and disrupt body functions. Also, more precise and accurate technologies are required to serve as aid in diagnosis, treatment and surgery of brain. Therefore, its high alarming time to monitor mortality statistics and develop faster and accurate methods to curb the situation by simulating tissue deformation and locating cancerous nodes which is a good area of research. A brain tumor is used to design the deformation model. Early stage detection of tumors is difficult from images. Moreover, the accuracy involved is less. Keeping all this into consideration, a machine learning approach has been developed to detect and model tissue deformation with classification of soft and hard tissues so that the tissues having risk of future problem can also be recognized. The machine learning is the approach which learns from the existing patterns and derives new patterns based on their input parameters. This approach can be used in real time for modeling of tissue deformation in image guided neurosurgery. The patient's deformation model can be designed and brain tumor patterns are given as input on the basis of which tumor in the brain is marked.
Semi-autonomous Collision Avoidance Flight Using Two Dimensional Laser Range Finder with Mirrors for Bridge Inspection

This paper addresses semi-autonomous collision avoidance flight using two dimensional laser range finder with mirrors. One of practical applications of unmanned aerial vehicles (UAV s) can be found in inspection of deteriorated infrastructures such as bridges and tunnels. In such applications, proper environmental recognition and collision avoidance system are of importance because of GPS-denied environment. This paper develops environmental recognition and collision avoidance systems with less computational consumption using two dimensional laser range finder with mirrors. The sensor used in this study is able to scan over horizontal direction and measure vertical direction by changing the laser direction with mirrors. Consequently, the sensor can obtain three dimensional environmental information. In this paper, we describe altitude estimation method under rough ceiling of bridge and obstacle detection system in the flight direction from the point data. The fundamental flight experiments are carried out to demonstrate the validity of the developed system.
A Comparative Analysis of Quality Assurance Automated Testing Tools for Windows Mobile Applications

Rapid propagation in mobile applications has increased the need of delivering high quality mobile applications into the market. Quality assurance of mobile application gains a lot of attention for its importance to distinguish the good apps from the bad ones. To ensure the quality of mobile applications, there are automated testing tools that help in defining and testing the quality of mobile applications. These tools offer, for the mobile software tester and developer, a guideline to improve and maintain the quality of the applications. In this paper, we study and experiment with different automated testing tools for mobile applications on windows platform. We also determine the possibility of using the automated tool to measure the app usability, correctness, robustness, testability, performance, assurance of extensibility, maintainability, scalability, and platform compatibility. The results show that the automated testing tools can be highly useful to in testing usability, correctness and robustness. Moreover, the tools can be also be used for testability and performance. Finally, for assurance of extensibility, maintainability, scalability, and platform compatibility, only a few automated testing tools are available.