Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Test of Smartphone Finger Photo vs. Touch-based Cross-sensor Fingerprint Recognition

Today’s smart-phone cameras enable touch-less biometric fingerprint capture for practical use. This is inexpensive, comfortable and now fast enough for practical use. This paper deals with the question of how reliably two fingerprints can be compared, when one of them is captured by a smart-phone and the other by a dedicated fingerprint scanner. A multisensor database with 4310 fingerprint images from 108 users was collected. Three touch-based sensors and two touch-less finger photo sensors were used. Latest quality estimations using NFIQ 2.0 and vendor-specific quality metrics complete the study on professional capabilities of finger photo recognition. As side-result of the study, a new touch-less fingerprint processing chain is presented, which reduces the error rates to 1% EER for touch-less- to-touch comparison. This value is comparable to that for touch-to-touch applications.
Analytical Study of Bundled MWCNT and Edged MLGNR Interconnects: Impact on Propagation Delay and Area

This research letter presents an accurate and efficient model for rough-edged multi-layered graphene nanoribbon (MLGNR) and multi-walled carbon nanotube (MWCNT) bundled interconnects. To address the impact of delay and area, an analytical finite-difference time-domain and real-time simulation based approach are considered. In order to obtain the propagation delay, an equivalent single conductor (ESC) model is accurately presented by simplifying a multi-conductor transmission line (MTL) of bundled MWCNT and MLGNR interconnects. The proposed equivalent ESC model is in good agreement to the MTL with an average deviation of 4.6% only. In order to validate the proposed model, the equivalent resistance, inductance, and capacitance values are compared with standard experimental results. It is observed that the model expression possesses a good agreement with experimental data with an average error of below 9.93%. Using the ESC-based driver-interconnect-load setup, an approximate number of MWCNTs in a densely packed bundle is obtained for a rough-edged MLGNR at 22 nm technology to obtain an equivalent delay. In order to acquire the equivalent propagation delay, a fewer number of MWCNTs in a bundle can provide similar performance as of MLGNR for below 200 μm interconnect length. Furthermore, the number of MWCNTs significantly increases for longer interconnects. Therefore, on an average, an edged MLGNR requires 97.98% lesser area compared to the bundled MWCNT for global interconnects. Hence, a rough-edged MLGNR can be treated as a most desired interconnect material as compared to MWCNT bundle for the future interconnect technology.
Overview of Wearable Haptic Force Feedback Devices and A Further Discussion On The Actuation Systems

Haptic force feedback devices are playing more and more important roles in virtual reality applications. During the last two decades, researchers around the world has developed many different types of haptic devices features different technologies. And several commercial haptic device have been introduced to the market. In this paper, several typical haptic devices are discussed, including ground-based and wearable types, and the paper mainly focus on wearable haptic devices, their advantages and drawbacks are listed and compared. Then different kinds of actuators, which are key parts of haptic devices, are demonstrated and discussed, and the conclusion of motor based actuators being more beneficial than other types of actuators are carried out. At last, the current problems and future works of wearable haptic devices are presented
Very Massive Hardware Merge Sorter

The state-of-the-art hardware merge sorter called MMS has the tie-record issue that the records having the same key can cause the problem. MMS solves this issue by inefficient scheme comparing both key and satellite data fields of records to determine whether two records are swapped or not. We propose a high-performance hardware merge sorter (VMS) which adopts an efficient solution to the issue comparing just key fields. We also present the detailed circuit of VMS that adopts some implementation optimizations. We implement and evaluate VMS on a Virtex-7 FPGA. The evaluation results show that our proposed merge sorter requires fewer hardware resources and achieves 1.44x better throughput than MMS when large records are used.
Database Cracking: Towards Auto-tuning Database Kernels

textabstractIndices are heavily used in database systems in order to achieve the ultimate query processing performance. It takes a lot of time to create an index and the system needs to reserve extra storage space to store the auxiliary data structure. When updates arrive, there is also the overhead of maintaining the index. This way, \emph{which} indices to create and \emph{when} to create them has been and still is one of the most important research topics over the last decades. If the workload is known up-front or it can be predicted and if there is enough idle time to spare, then we can a priori create all necessary indices and exploit them when queries arrive. But what happens if we do not have this knowledge or idle time? Similarly, what happens if the workload changes often, suddenly and in an unpredictable way? Even if we can correctly analyze the current workload, it may well be that by the time we finish our analysis and create all necessary indices, the workload pattern has changed. Here we argue that a database system should just be given the data and queries in a declarative way and the system should internally take care of finding not only the proper algorithms and query plans but also the proper physical design to match the workload and application needs. The goal is to remove the role of database administrators, leading to systems that can completely automatically self-tune and adapt even to dynamic environments. Database Cracking implements the first adaptive kernel that automatically adapts to the access patterns by selectively and adaptively optimizing the data set purely for the workload at hand. It continuously reorganizes input data on-the-fly as a side-effect of query processing using queries as an advice of how data should be stored. Everything happens within operator calls during query processing and brings knowledge to the system that future operators in future queries can exploit. Essentially, the necessary indices are built incrementally as the system gains more and more knowledge about the workload needs.
Multichannel Sub-Nyquist Sampling for Ultrasound Imaging Applications

As for the ultrasound imaging systems, reducing the high rate sampled data allows more efficient processing, which can translate to both the power consumption reduction and smaller system size. Based on the similar concepts in modulated wideband converter for multiband signals, the scheme of multichannel sampling of streams of pulses can operate at sub-Nyquist rate, which would bring about simplified hardware and robustness to channel failure. So the application of multichannel sampling to the ultrasound imaging is investigated in the paper. The ultrasound signal may be modelled as finite streams of Gaussian pulses. Each channel is comprised of mixing with a properly chosen waveform followed by an integrator. And it has been verified by the experiment that the multichannel sub-Nyquist sampling allows recovery of the delays and amplitudes of ultrasound signals with high accuracy.
Training Translators in the European Higher Education Area

Abstract The creation of the European Higher Education Area (EHEA) has served as a catalyst of pedagogical change in European tertiary education. The adoption of the ‘meaningful learning model’, articulated around sets of learning outcomes and their corresponding curricular objectives, is a good example. This new model requires reliable assessment instruments to evaluate the students’ attainment of the relevant curricular objectives and provide them with meaningful feedback. This paper reports on the adaptation of the Translation and Interpreting degree programme run by Universitat Autònoma de Barcelona to comply with the requirements of the EHEA framework. After examining the impact that the meaningful learning model has on the assessment of students, this article illustrates the difficulties arising from the shift towards the ‘assessment for learning’ framework. A proposed assessment tool – consisting of a task (commented translation), assessment benchmarks and assessment instruments (rubric) – designed for a specific course unit is then explored in detail. Evidence is provided that the assessment tool fulfils quality criteria for validity and transparency and suggestions are made on how to develop this instrument further.
Effect of Thermal Annealing on Absorption and Hole Escape Processes in Type II GaSb/GaAs Quantum Dots: Implications for Solar Cell Design

We present a theoretical study of the rapid thermal annealing process and strain on the electronic, optical, and charge dynamical properties of type II GaSb/GaAs quantum dots (QDs). Our theoretical results show a blueshift and the enhancement of interband absorption spectrum because of inter-diffusion processes and the annealed samples. We have identified that increased annealing temperature makes the hole escape times from such QD shorter while the effect of strain on the hole confinement potential becomes weaker. At the same time, in both structures, unstrained and strained QDs, the intra-band absorption in the valence band (IVBA) is redshifted, and its magnitude is decreased after annealing when compared with the as-grown sample. To explain those effects we discuss how the electron–hole wave function overlap varies with thermal treatment and strain and discussed those effects on radiative and thermal processes. Our results explain and are in agreement with the recent experimental observations on similar structures. We have identified that holes could thermally escape out of the type II GaSb/GaAs QD before they are recombined and will contribute to the enhancement in absorption of QD solar cells. The emission and tunneling times of holes were estimated to be <inline-formula><tex-math notation="LaTeX">$ 10^{-12}$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$10^{-14}$</tex-math></inline-formula> s about one order of magnitude faster than the escape times because of radiative recombination processes, <inline-formula><tex-math notation="LaTeX">$ 10^{-11}$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$10^{-13}$</tex-math></inline-formula> s.
An Optimal Imperfect Maintenance Policy for a Partially Observed System With Obvious Failures and Limited Repairs

In this paper, we investigate an imperfect maintenance optimization problem for a multi-state, Markovian deterioration system with obvious failures under repair restriction based on those non-periodically collected sensor information. Our aim is to adaptively schedule observations and other maintenance actions with taking imperfect maintenance effect into consideration. Different from most existing works, imperfect maintenance here means that repair action can not only restore the system to a less deteriorated level instead of the good-as-new state but also accelerate the deterioration process so that the system can be repaired only a limited number of times before it must be replaced with a new one. Assuming that the system’s deterioration state evolves as a discrete-time Markov chain with a finite state space, and then choosing the information state together with the number of completed repair times as state variable, we formulate the problem as a Markov decision process over an infinite time horizon. In order to increase the computational efficiency, several key structural properties are developed by minimizing the long-run average cost per unit time. Then, special algorithms are proposed to find the optimal maintenance policies. Finally, a numerical example is given to illustrate the effectiveness of the proposed algorithms.
Multi-UAV Rapid-Assessment Task-Assignment Problem in a Post-Earthquake Scenario

The rapid assessment of earthquake-stricken regions immediately after a seismic event is crucial for earthquake relief operations. Since unmanned aerial vehicles (UAVs) can quickly reach the affected areas and obtain images, they are widely used in the post-earthquake rapid assessment. However, sensor noise and other unavoidable errors can affect the quality of images acquired by sensors attached to the UAVs, which can, in turn, reduce the quality of the assessment. We defined a new problem in the application of multiple UAVs in the rapid assessment of earthquake-stricken regions. The rapid-assessment task-assignment problem (RATAP) was used to construct the assignment plan for multiple UAVs in a rapid-assessment task while considering the weights of potential targets, the endurance of the UAVs, and the sensor errors. The RATAP was formulated as a variant of the team orienteering problem (TOP) called the revisit-allowed TOP with reward probability (RTOP-RP). We then developed an efficient hybrid particle swarm optimization with simulated annealing (HPSO-SA) algorithm, which produced a high-quality solution for the RATAP, and confirmed the effectiveness and rapidity of our algorithm through numerical experiments. Finally, we conducted a case study based on real-world data from the 2008 Wenchuan earthquake in China to demonstrate our approach.
Faces are Protected as Privacy: An Automatic Tagging Framework Against Unpermitted Photo Sharing in Social Media

On social platforms like Facebook, it is popular and pleasurable to share photos among friends, but it also puts other participants in the same picture in jeopardy when the photos are released online without the permission from them. To solve this problem, recently, the researchers have designed some fine-grained access control mechanisms for photos shared on the social platform. The uploader will tag each participant in the photo, then they will receive internal messages and configure their own privacy control strategies. These methods protect their privacy in photos by blurring out the faces of participants. However, there is still some defect in these strategies due to the unpredictable tagging behaviors of the uploader. Malicious users can easily manipulate unauthorized tagging processes and then publish the photos, which the participants want them to be confidential in social media. To address this critical problem, we propose a participant-free tagging system for photos on social platforms. This system excludes potential adversaries through automatic tagging processes over two cascading stages: 1) an initialization stage will be applied to every new user to collect his/her own portrait samples for future internal searching and tagging, and; 2) the remaining unidentified participants will be tagged in cooperative tagging stage by the users who have been identified in the first stage. For the system evaluation of efficiency and effectiveness, we conducted a series of experiments. The results demonstrated the tagging efficiency (96% tagging rate and 0.77s/photo tagging speed on average), photo masking and unmasking efficiency (0.13s/face on average), and the privacy preserving performance (over 90% identities in both group and individual photo are preserved).
On the Economic Significance of Stock Market Prediction and the No Free Lunch Theorem

Forecasting of stock market returns is a challenging research activity that is now expanding with the availability of new data sources, markets, financial instruments, and algorithms. At its core, the predictability of prices still raises important questions. Here, we discuss the economic significance of the prediction accuracy. To develop this question, we collect the daily series prices of almost half of the publicly traded companies around the world over a period of ten years and formulate some trading strategies based on their prediction. Proper visualization of these data together with the use of the No Free Lunch theoretical framework gives some unexpected results that show how the a priori less accurate algorithms and inefficient strategies can offer better results than the a priori best alternatives in some particular subsets of data that have a clear interpretation in terms of economic sectors and regions.
Multi-stream I3D Network for Fine-grained Action Recognition

Recent research in the area of action recognition has focused on coarse-grained action recognition, and there have been few studies on fine-grained action recognition. In response to this phenomenon, we propose a method for fine-grained action recognition using a deep convolutional network. This method uses the I3D network, which has achieved great success in the area of coarse-grained action recognition, as the basic network architecture. At the same time, the human pose and hand are extracted for obtaining local features of the fine-grained action. The I3D network is then used to extract RGB video frames, optical flow, human pose, and hands features, respectively. Finally, these features are combined. Since there are multiple different input streams input to the I3D network, our method is called a Multi-stream I3D Network. We validated this method on the MPII Cooking 2 dataset and reported the results in detail.
Retention Characteristics of Hf0.5Zr0.5O2-Based Ferroelectric Tunnel Junctions

We report on the retention properties of double-layer hafnium zirconium oxide (Hf0.5Zr0.5O2; HZO) based ferroelectric tunnel junctions (FTJ). Utilizing HZO as the ferroelectric layer and aluminum oxide (Al2O3) as the tunneling barrier a scalable FTJ memory operation with good endurance and an on/off ratio of about 10 was achieved. Due to inherent depolarization fields from the double layer structure, the device suffers from strong retention loss over time. An extrapolation to 10 years at room temperature shows vanishing differences between the on and off state currents. We propose a way to avert this retention loss by using a constant bias that can be built-in by a work function difference from the metal electrode. This leads to more stable on-current retention and only small off-current increase, giving rise to an improved retention behavior of the FTJ.
Reconstruction of the Far Field Radiated by an Offset Mounted Volumetric AUT from Nonredundant Spherical Spiral Near-Field Measurements

This work provides an experimental assessment of the near to far-field transformation (NTFFT) with spherical spiral scan, which properly accounts for the mounting of a volumetric AUT in offset configuration. Such a NTFFT, based on the nonredundant sampling representations of electromagnetic fields using a spherical modelling of the antenna under test (AUT), requires the same number of near-field (NF) spiral data when the AUT is mounted both in onset and offset configuration, because this number depends only on the area of the modelling sphere. The reported experimental results prove the effectiveness of the developed NTFFT and of the related two-dimensional optimal sampling interpolation scheme used to reconstruct, from the spiral NF samples, the NF data at the raster grid points required by the classical spherical NTFFT.
A Real-Time Object Detection Accelerator with Compressed SSDLite on FPGA

Convolutional neural network (CNN)-based object detection has been widely employed in various applications such as autonomous driving and intelligent video surveillance. However, the computational complexity of conventional convolution hinders its application in embedded systems. Recently, a mobile-friendly CNN model SSDLite-MobileNetV2 (SSDLiteM2) has been proposed for object detection. This model consists of a novel layer called bottleneck residual block (BRB). Although SSDLiteM2 contains far fewer parameters and computations than conventional CNN models, its performance on embedded devices still cannot meet the requirements of real-time processing. This paper proposes a novel FPGA-based architecture for SSDLiteM2 in combination with hardware optimizations including fused BRB, processing element (PE) sharing and load-balanced channel pruning. Moreover, a novel quantization scheme called partial quantization has been developed, which partially quantizes SSDLiteM2 to 8 bits with only 1.8% accuracy loss. Experiments show that the proposed design on a Xilinx ZC706 device can achieve up to 65 frames per second with 20.3 mean average precision on the COCO dataset.
DC-AC Conversion With Three Phase Sepic Inverter Based On The Boost Converter

DC-AC conversion with three phase single ended primary inductance inverter and boost converter is a advanced design when comparing with conventional topologies . Here only used four-switches and three-phase SEPIC inverter . This pro- posed inverter is designed to reduce the fare, difficulty and switching losses . Converter offer a design for the inverter with the combination of single-ended primary-inductance with DC- DC converter which can be upgrade the phase voltage than traditional FSTP single ended primary inductance inverter . For boost up the output voltage inserting a boost converter (DC-DC) between input side and the four switch three phase single-ended primary-inductance inverter . Constant current control strategy is used with the proposed topology . Working principle , circuit diagram and simulation diagrams also introducing in this paper .
Jamming for Unauthorized UAV Operations-Communications Link

This paper describes the development and evaluation of a set of radio jamming techniques whose purpose is to help to avoid unauthorized unmanned aerial vehicles operations. The implementation is based on low cost software defined radio (SDR) platforms, which allow the development of flexible radio communication systems. The study relies on the use of well-known jamming techniques and provides a thorough analysis of which factors could influence the interference of the communications link. In this sense, the BladeRF X40 board from Nuand was selected as the hardware platform together with the GNURadio software development toolkit. The results obtained in the experimental interference tests showed that it is possible to disrupt the UAV communication link which, when combined with other additional techniques like GPS jamming and spoofing, can allow the implementation of an effective anti-UAV system.
Joint Antenna and Channel Modelling for Communication in Metallic Kitchen Environments

This paper presents a study on the joint antenna and channel modelling for communication in metallic kitchen environments. It allows characterizing and optimizing the wireless communication between the cooking pot and the kitchen hood in a smart kitchen where data acquired by sensors in the pot is used to control the kitchen hob and hood. The delay and path loss characteristics of the wireless channel are obtained from channel sounding measurements. The influence of the metallic pot on the antenna is taken into account by performing measurements with and without pot. The channel sounding measurements show a small delay spread caused by reflections on the hob and hood, and path loss values that are close to the free space path loss for a Line-of-Sight setup but up to 30 dB higher for different setups. The measurements with pot show lower losses, due to a more directive radiation pattern of the antenna when placed next to metal. Path loss and small scale fading margins are used for link budget calculations.