Miracle-dz/UniFAR-FTUs huggingface dataset:
Scaling Behaviour of State-to-State Coupling During Hole Trapping at Si/SiO2

The decay length scaling behavior of state-to-state coupling between silicon valence band $(VBMsi)$ and oxygen vacancy defect $(E_{defect})$ in Si/SiO2 interface is thoroughly investigated by density-functional theory. The detailed coupling process is revealed, and the scaling rate with interface-defect distance is obtained. Results show that the coupling constants $(Vc)$ decay with the defect distance is not so fast as the WKB approximation predicts, and the reason lies in the fact that the tunneling picture overestimates the localization of the defect states.
Free ripple input current high boost converter with coupled inductors

In renewable energy power conditioning system (PCS), like photovoltaic and fuel cell sources, the low input cur rent ripple represents a very important issue to improve the source efficiency. This paper proposes a topology of a DC-DC high gain boost converter with a zero input current ripple condition. In particular, this result is obtained by using coupled inductors. A detailed mathematical analysis on the operating principle, the steady-state analysis and the comparison with the same topology without coupled inductors is presented. The proposed concepts are evaluated by simulation results.
Alternating Temperature Stress and Deduction of Effective Stress Levels from Mission Profiles for Semiconductor Reliability

Mission profiles and step-stress life tests are depending on cumulative damage models for reliability analysis. Although cumulative damage models exist, they are rarely verified on empirical data of semiconductor devices. In this work, semiconductor devices from GLOBALFOUNDRIES’ 22FDX® technology are tested for dielectric breakdown. The applied stress is an alternating temperature step-stress and the results are compared with simulated failure behavior of the CE and TFR model for conformity. Also, the inferences from both models about the deduction of effective stress are discussed.
Single electron tunnelling through high‐Q single‐wall carbon nanotube NEMS resonators

By first lithographically fabricating contact electrodes and then as last step growing carbon nanotubes with chemical vapour deposition across the ready‐made chip, many potential contamination mechanisms for nanotube devices can be avoided. Combining this with pre‐defined trenches on the chip, such that the nanotubes are freely suspended above the substrate, enables the formation of highly regular electronic systems. We show that, in addition, such suspended nanotubes provide excellent high‐frequency and low‐dissipation mechanical resonators. The motion detection mechanism of our experiment is discussed, and we measure the effect of Coulomb blockade and the back‐action of single electron tunnelling on the mechanical motion. In addition data on the mechanical higher modes is presented.
Real-Time Augmented Reality with Occlusion Handling Based on RGBD Images

Augmented Reality (AR) is one of the latest developments in human-computer interaction technology. It aims to generate illusions from seamless fusion of virtual objects and real world. Typical AR system requires two basic parts: three-dimensional registration and real-virtual fusion. Occlusion handling is crucial for visual realism. To optimize visual realism, we generated a real-time systematic architecture to operate occlusion handling. The architecture is based on RGBD images, and it consists of three parts: real-time camera tracking system, 3D reconstruction system and AR fusion system. Specifically, we used a two-pass scheme strategy to execute the AR system. The first pass tracks camera poses timely at video rate, which allows the reconstruction results be updated and visualized correspondingly during the scanning. The second pass takes place simultaneously to handle occlusion between virtual objects and real scene according to camera pose. Finally, the render results of virtual objects and the color images are fused to generate AR contents. Our results indicate that this method is stable and precise for occlusion handling, and can effectively improve realism in AR system.
Towards Analysing the Effect of Hybrid Caches on the Temperature of Tiled Chip Multi-Processors

Recent advances in CMOS technology adds more transistors to the chip that are utilised for improving processing capability by adding multiple processing components. These multiple cores raise the data demands leading to larger on-chip caches. Together, these add to the energy consumption as well as heat dissipation. Increase in chip temperature requires efficient cooling mechanisms as high temperatures can damage the onchip circuitry. Thus, the performance enhancement comes at the cost of higher power budget as well as temperature. Large onchip caches occupy significant area of the chip and are major contributors to leakage energy. It is known that as technology scales leakage becomes a prominent component which also affects the chip temperature. This paper aims to control the chip temperature by controlling the leakage energy dissipated by the last level caches (LLCs). Towards this we propose a hybrid LLC that uses a combination of SRAM cache banks and non-volatile memory (NVM) technology based STT-RAM banks. STT-RAM technology has the advantage of high density and low leakage.We demonstrate that low-leakage STT-RAM banks help in reducing the temperature of the tile in which they are located and it also assists in reducing the average chip temperature. Experimental evaluation on an isoarea and iso-capacity architecture that uses a hybrid LLC shows reduction the average chip temperature as well as gives gains in static energy and EDP compared to baseline architecture.
Investigating Vector-Based Detection of Code Clones Using BigCloneBench

In a vector-based approach to detecting code clones from source code, all code fragments in the source are mapped to a vector space and then code fragments are detected as code clones if they are neighbors in the vector space. So far, our research group has developed a vector-based approach using TF-IDF and cosine similarity. For the improvement of the vector-based approach, we preliminary investigated what kind of vectorization algorithms and similarity measurements are effective in terms of recall and detection time. In this paper, we present preliminary investigation results using BigCloneBench, a large-scale code clone benchmark.
Light Field Display: An Adaptive Weighted Dual-Layer LCD Display for Multiple Views

The light field display technology can overcome the dizziness caused by accommodation-convergence conflict in traditional display and has more comfortable, natural visual experiences. This paper focuses on the multi-layer spatial light modulation method of the light field display (cascaded display). However, light field display is faced with a series of problems such as large consumption of computing resources, and poor display quality. To overcome the defects, we optimize the existing nonnegative matrix factorization algorithms in the light field display according to the characteristics of the human eyes. By adding the dynamic weighting factors in the procedure of optimization, we achieve a rank-1 dynamic light field display factorization result in just a few iterations under the framework of cascaded display, which is faster and require less computing resources. We build a prototype of the light field display and we believe it a better performance of the dynamic light field if combined with the real-time eye-tracking techniques.
Gate-Stack Engineered NBTI Improvements in Highvoltage Logic-For-Memory High-ĸ/Metal Gate Devices

Potential solutions for the reliability challenges of high-kmetal gate (HKMG) integration into DRAM high-voltage peripheral logic devices are reported. A detailed study of Negative Bias Temperature Instability (NBTI)-degradation, supported by physical analysis, assessing the impact of various tuning components within the stack (interface layer, high-k fluorination and/or cap, metal gate) is presented. The presence of Nitrogen throughout the HKMG stack can originate either from high-k processing or metal-nitride gate electrode. It is shown that preventing nitrogen diffusion towards the Si/SiO2 interface region, together with AIOx(and F) incorporation at the HKMG interface, can tune device threshold voltage and modulate access to donor trap-defect bands. The result of these effects is a vast improvement in NBTI performance.
Automatically Generating API Usage Patterns from Natural Language Queries

Automatically generating code from natural language query is a very promising but much challenging direction. Existing approaches either try to generate the whole code or only predict a small part of critical code elements such as API sequence. Meanwhile, API usage patterns, including APIs and API-related control-flow statements, have the moderate complexity, but can provide enough code framework information and are very helpful for developers to implement various functionalities. Therefore, in this work, we study the problem of generating API usage patterns, represent API usage patterns by one special constrained tree API-MCTree and design one new API-MCTree decoder for automatically transforming natural language queries to API usage patterns, which can leverage both the difference of control-flow statement types and the syntactic knowledge of API usage patterns. We evaluate our model with annotated code snippets in real Java projects collected from GitHub, and the experimental results show that our approach is effective and outperforms the related approaches.
Topology and Shape Preserved Lightweight of Shell Models Utilizing Heat Diffusion

Lightweight is one of the most important research subjects in modern manufacturing. However, the research on lightweight of shell models is rare, and most limited in topological changes. This paper proposes a topology and shape preserved lightweight framework of shell models that consists of model analysis, lightweight modeling and analysis, and 3D printing and practical validation in an optimum iterative procedure. Specifically, firstly, both geometric features and empirical features are introduced to construct a frame structure. Secondly, a heat diffusion is exploited to simulate the stress distribution due to two reasons, one is that they have the similar physical transmissibility, the other is that the heat diffusion is smooth, and it guarantees that the thickness variation is smooth and natural without restriction on the degrees of freedom. Then, a local iteration consists of local heat simulation and stress analysis is utilized to further improve the efficiency. Finally, we use 3D printer to manufacture testing models, and apply them to practical verification and feedback. Our extensive experiments have exhibited many attractive properties, including the flexibility and freedom of the thickness variation, the effectiveness and credibility of the lightweight.
Advanced Circuit Reliability Verification for Robust Design

The growth in safety-critical applications for automotive electronics and mainstream adoption of FinFET technologies have resulted in significant increase in IC design reliability challenges. IC designers must contend with advanced circuit complexity with stringent requirements on performance, low power, design robustness for safety, and reliability such as electro-migration and device-aging. For safety-critical applications such as Autonomous Driving, ADAS, and Connected Car, IC designers are now looking to adopt rigorous and systematic methodologies to meet functional safety standard. In this paper, we will discuss the models used on circuit reliability verification, and application of these models to assess IC reliability for targeted requirements with latest Synopsys' AMS solution for robust design.
Research on Multi indexes Accurate Quantification Method of Generator Primary Frequency Regulation Performance Evaluation

The performance evaluation of primary frequency regulation (PFR) of generator is helpful to improve the frequency stability of power grids. Existing research cannot comprehensively and accurately evaluate the PFR performance of generators, and the research methods have the disadvantage of poor adaptability. Based on the simulation model of PFR of generator, this method takes vertiginous rate, regulation limit, dead zone and response delay time of the PFR as model key parameters. The improved genetic algorithm is used to find the model parameters which have the smallest error comparing with the generator actual output. Those parameters are used as the evaluation index of the PFR performance of the generator. Thus the method can more comprehensively and accurately evaluate the performance indexes of the generator primary frequency regulation. Finally, based on the measured data of the primary frequency regulation of the generator in Northwest Power Grid, the response output of the primary frequency regulation model and identified parameters are compared with the actual output, the validity and accuracy of the evaluation method are verified.
Multi-Discussing across Issues in GitHub: A Preliminary Study

Social coding sites like GitHub has enabled developers to easily contribute their comments on multiple issues and switch their discussion between issues, i.e., multi-discussing. Discussing multiple issues simultaneously may enhance the work efficiency of developers. However, multi-discussing also relies on developers' rationally allocating their time and focus, which may bring different influence to the resolution of issues. Therefore, investigating how multi-discussing affects the issue resolution is a meaningful research question which can help developers understand the benefits and limitations when they switch their discussion between issues. In this paper, we present a preliminary study of the impact of multi-discussing on issue resolution in GitHub projects, by using quantitative methods. First, we collect and analyzed data from 631 GitHub projects to explore how multi-discussing affects the average resolution latency of project issues. Further, we develop method for measuring the rate and breadth of a developers' discussionswitching behavior, and we use regression modeling to study how discussion-switching affects the single issue resolution latency. We find that multi-discussing is a common behavior of developers in GitHub projects. Also, multi-discussing is associated with shorter average issue resolution latency of project. However, during a single issue resolution, more participants' discussion-switching tend to bring longer issue resolution latency. Our study motivates the need for further research on the multi-discussing.
Voltage Transfer Ratio Comparison of Wireless Power Transfer System with Magnetic Path

Recently, wireless power transfer (WPT) technology is applying to various applications from low power sensor network to high power electric propulsion system. Adopting a magnetic path is one of the candidates for improving power transfer efficiency. In this paper, characteristics comparison of WPT system with various types of magnetic path is described and experimented in the view point of voltage transfer ratio.
Twitter Classification of Public Service Complaints

This Public service become one of the factors of public satisfaction level to government performance. Complaints figure out publics expectation and needs. Understanding the complaints lead to a possibility to improve public services quality. Twitter is a popular micro-blogging in society. Society can express through their activity, experiences, complaints through the internet easily and real time. In this research the classification society complaints through tweeters into water, electricity, and roads. The twitter classification is built using the K-Nearest Neighbor (KNN) algorithm. The feature selection in this research are using term frequency (TF), document frequency (DF), information gain, and chi square. In this research, combination of features that have been produced from the previous feature selection. The experiments result shows that K-NN is able to classify complaints The accuracy of complaints detection and complaints classification are achieved at 83.75% and 77.08% respectively. For the classification of types of water complaints, the average values generated for each parameter are 87.5% for precision, 87.8% for recall, and 87.49% for F-Mesure testing.
Predicting Which Pull Requests Will Get Reopened in GitHub

In GitHub, integrators inspect submitted code changes, make evaluation decision, and close pull requests. However, some pull requests may get reopened for further modification and code review. It is important to predict reopened pull requests immediately after pull requests' first close, and help integrators reopen pull requests in time. If pull requests are reopened a long time after their close, they may cause conflicts with newly submitted pull requests, add software maintenance cost, and increase burden for already busy developers. To the best of our knowledge, we present the first look at predicting reopened pull requests in GitHub. We propose an approach DTPre which is an automatic predictor of reopened pull requests based on Decision Tree classifier. DTPre mainly analyzes code features of modified changes, review features during evaluation, and developer feature of contributors. We evaluate the effectiveness of DTPre on 7 Open Source projects containing 100,622 pull requests. Experimental results show that DTPre has high performances by achieving a precision of 95.53%, recall of 99.01% and F1-measure of 97.23% on average. In comparison with predictors based on neural network, naïve Bayes, logistic regression and SVM, DTPre based on decision tree improves F-1 measures by 41.76%, 59.45%, 42.25% and 9.98% on average across 7 projects.
A Structural Retention Migration Affinity Propagation Algorithm

This paper presents a new Structural Retention Migration Affinity Propagation(SRMAP) algorithm to perform clustering in face of insufficient data scene. It introduces the Transfer learning mechanism into Affinity Propagation(AP) algorithm, and gets the migration AP clustering algorithm with knowledge migration capability. In the process of knowledge migration, the statistic and geometrical structure of data is considered to ensure the quality of migration. The SRMAP algorithm proposed in this paper can be regarded as a generalization of AP algorithm, which can effectively share and migrate information among similar domains, while maintaining the classical form of AP. The performance of our method on KDD 1999 datasets is also demonstrated. The results show that SRMAP algorithm is generally superior to the non-migration clustering algorithm in the process of data scarcity. Meanwhile, the SRMAP algorithm is superior to the previous migration clustering algorithm for considering the distribution and geometric features.
A Multiple Layers Re-ranking Method for Person Re-identification

Pedestrian re-identification (re-ID) is a video surveillance technology for specific pedestrians in non-overlapping multi-camera scenes. However, due to the influence of dramatic changes in perspectives and pedestrian occasions, it is still a huge challenge to find a stable, reliable algorithm in high accuracy rate. In this paper, a multiple layers re-ranking approach is proposed to jointly account for that challenge. The re-ID is viewed as a multiple metrics ranking and optimizing problem by using a Multiple Layers Re-ranking framework. In this paper, multiple metrics are proposed by employing the correlation of different features to exploit comprehensive complementary information. Based on them, a multiple layers re-ranking framework is constructed to optimize and re-rank the initial results, which is more stable and effective than a single metric. Besides, a high similarity set is proposed to reduce the interference of appearance visual ambiguous samples. Through it, more effective candidate samples are selected into the re-ranking framework, improving the robustness. Experimental results on four person re-ID benchmarks demonstrate the effectiveness of the proposed framework. Especially, the matching rate of rank-1, our method outperforms the state-of-the-art methods on these datasets. Our code is available https://github.com/gengshuze/MLRL_re-id.git
Design of Power Intelligent Safety Supervision System Based on Deep Learning

In this paper, a real-time object recognition system based on deep learning is proposed to identify whether the operator has worn the specific safety equipment. Normally this is done by labor force to examine the real-time video feedback from camera, however it is extremely inefficiency and costly. Traditional object recognition methods are heavily relying on human designed features. As a result, their performance will downgrade dramatically under complex environment and cannot be used in real-time application due to low processing speed. In this paper, the original VGG16 neural network of Single Shot Multi-Box Detector (SSD) has been modified by introducing Inception module to improve its sensitivity to small objects, Combined with multi-object tracking technology to bind face recognition results with pedestrian identity, the experiments have proved that proposed system can recognize specific safety equipment of specific operator accurately and in real time at operation site.
A Robust Real-Time Hand Detection and Tracking

We present a robust method for detecting and tracking the human hand in real-time with a single depth camera. Before tracking the hand, we always need to find the appropriate region of hand in depth map obtained by the camera. Our system utilizes the segmentation depth information to look for the candidate hand regions, and gets a more accurate hand region with auxiliary RGB information. Even when it is not the nearest part than other body regions, we can also detect the hand region accurately. Therefore we make fewer assumptions about hand detection. Based on this, we track the human hand by fitting a virtual 3D hand model to preprocessed depth maps. We treat this as an optimization problem which needs to seek the parameters to make sure that the energy function can get minimum values. Our energy function combines 3D/2D information with shape prior and kinematic priors, and keeps the frame smooth by adding temporal smoothness constraint. The objective function is solved effectively using Gauss-Newton method. The relevant experiments demonstrate the accurate hand detection and robust hand tracking in our system.
Design and Analysis of Approximate Multipliers for Error-Tolerant Applications

In applications such as image and video processing, the final output is interpreted by human eyes, which are insensible to small errors in the output. In these cases, approximate circuits play a vital role in achieving low power and high speed designs with small errors in the output. In this paper, three approximate full-adder designs are proposed and they are reused to design approximate Dadda multipliers. For generating partial products in the multiplier, a newly designed AND gate approach is proposed. All the proposed designs are simulated using 90nm UMC technology. The Simulation results indicate that the number of transistors and power consumption of approximate multipliers are reduced by 28% and 32% respectively as compared to conventional Dadda multiplier. This paper also analyses the errors at the multiplier output using different error metrics.