Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Assessment of TFET Architectures as 1T-DRAM

An assessment of Tunnel FET (TFET) architectures for capacitorless dynamic memory applications is presented through composite metrics to balance various trade-offs while regulating hole distribution to determine sense margin, retention time and current ratio. The impact of temperature, bias and scalability on device performance is also investigated. Results highlight the potential of TFET to overcome various trade-offs through optimal architecture, and achieve enhanced composite metrics at sub-100 nm gate lengths. (0)
Computer vision based pose detection of agricultural implements without a priori knowledge of their geometry and visual appearance

Many agricultural machines are equipped with an electro-hydraulic control system in order to adjust the implement pose (height and inclination) with respect to the soil. The state of the art pose control involves an estimation of the pose by the drag forces. Our new approach involves a computer vision system to enable the direct measurement of the implement pose. The implement and the soil behind the tractor are observed with a stereo camera mounted on the cab. Image segmentation is based on the optical flow and histograms in combination with our novel bias-free thresholding approach. The pose of the implement is obtained by statistical analysis with multidimensional histograms and a transformation into the appropriate parameter space. The pose detection is performed without a priori knowledge of the implement geometry and of its visual appearance. Our approach is demonstrated in a real-time image processing system applied exemplarily for ploughing with a tractor.
Patient-Provider Interaction System for Efficient Home-Based Cardiac Rehabilitation Exercise

This paper presents an efficient home-based cardiac rehabilitation (CR) exercise system for patient-healthcare provider interaction in cardiac management. The proposed system consists of a wearable device, a smartphone application, and a medical station. A CR specialist prescribes CR exercise for a patient through a medical station. The prescription information is sent to a patient’s smartphone application with Internet’s hypertext transfer protocol. The information is further transferred to a patient’s wearable device via Bluetooth communication. A patient performs home-based CR exercise with the worn wearable device that measures instantaneous heart rates (HRs) accurately even during intensive physical exercise. During the CR exercise, the wearable device recommends appropriate exercise intensity in real time based on the prescribed target HR zone and the measured instantaneous HRs. Furthermore, the wearable device informs the current exercise stage (resting, walking, or running), allowing home-based CR exercise without the help of a medical staff. Upon completion of the CR exercise, exercise records are sent to a smartphone application so that a patient can monitor his or her own exercise records. Exercise records are also sent to the medical station where a CR specialist can monitor these records and provide new exercise prescription. In this paper, we have implemented and demonstrated the full functionality of the overall home-based CR exercise system consisting of a wearable device, a smartphone application, and a medical station. Moreover, we have verified the overall operation of the system and that the proposed system can be an efficient CR exercise tool for remotely sharing CR exercise prescription and exercise records between a patient and a healthcare provider.
A Capacitive Power Transfer System with a CL Network for Improved System Performance

This paper introduces the analysis and optimization of capacitive power transfer system with a CL compensation circuit on the secondary for reduced voltage stress across the electric field coupling interface and less system sensitivity. The system has been mathematically analyzed to understand the effect of the CL circuit. And simulation and experimental studies are also carried out for verifications. It is found by increasing Cp (and reducing Lp accordingly to be tuned to the nominal frequency), the quality factor (Q) can be reduced to make the system less sensitive to circuit parameter and load variations, thus easier to tune. However, the CL network should be carefully designed to reduce the voltage stress and electric field leakage around the interface. A practical circuit is built which has demonstrated 5 W of power transfer across a combined interface capacitance $\mathrm{o}\mathrm{f}\sim 1\mathrm{nF}$ at an operating frequency of 1 MHz, and the system power efficiency is above 65%. The voltage stress and sensitivity reduction have been confirmed practically.
Robust Median Filtering Forensics Using Image Deblocking and Filtered Residual Fusion

Median filtering (MF) is frequently applied to conceal the traces of forgery and therefore can provide indirect forensic evidence of tampering when investigating composite images. The existing MF forensic methods, however, ignore how JPEG compression affects median filtered images, resulting in heavy performance degradation when detecting filtered images stored in the JPEG format. In this paper, we propose a new robust MF forensic method based on a modified convolutional neural network (CNN). First, relying on the analysis of the influence on median filtered images caused by JPEG compression, we effectively suppress the interference using image deblocking. Second, the fingerprints left by MF are highlighted via filtered residual fusion. These two functions are fulfilled with a deblocking layer and a fused filtered residual (FFR) layer. Finally, the output of the FFR layer becomes input when extracting multiple features for further classification using a tailor-made CNN. The extensive experimental results show that the proposed method outperforms the state-of-the-art methods in both JPEG compressed and small-sized MF image detection.
Compressed Data-Gathering Method based on Spatiotemporal Correlation Clustering in Wireless Sensor Networks

To solve the problem of high energy consumption in traditional data-gathering protocols and to balance the network load, a spatiotemporal correlation-based clustering method for compressive data gathering (SCCM-CDG) is proposed. First, we present a mathematical model to measure the spatiotemporal correlation of neighborhood nodes. Second, a spatiotemporal correlation-based clustering method (SCCM) is proposed and then applied to the data-gathering protocol. Sensor nodes within a cluster send a small number of linear projections to cluster heads using compressive sensing theory, and then cluster heads send sample data along the shortest square distance spanning tree among cluster heads and the sink. Results of the simulation and experiment verify the accuracy of the SCCM algorithm, revealing that the SCCM-CDG algorithm can substantially reduce energy consumption, prolong network lifetime, and promote improvements in data recovery at the sink compared with existing compressive sensing-based data-gathering schemes.
An HRI graduate course for exposing technologists to the importance of considering social aspects of technology

This article illustrates how HRI can be a useful vehicle for exposing technologist students (e.g., in computer science or engineering) to social aspects of technology and to the concrete benefits that a socially aware perspective can bring to technology design, implementation, and use. The article details the strategy and layout taken with the graduate-level Human-Robot Interaction (HRI) course at the University of Manitoba, Canada, between 2011 and 2015, for the purposes of introducing students to social considerations surrounding technology. Further, this paper reflects on the results from three iterations of the course and demonstrates how students have engaged socially aware thinking and analysis in their course projects. This single, introductory HRI course can be a catalyst for encouraging socially aware thinking in technologist students.
Preliminary Case Study on Data Utilization and Collaboration on the Web

Recently, data holders collect various kinds of data owing to the improvement of Internet of Things (IoT) technologies. On the other hand, data analytic can observe even large data owing to the spread of analysis methods/tools and strong computing power. The critical point for accelerating data utilization is the communication of data stakeholders. Data analysts should consider the purpose for which data holders collect data. However, the communication among stakeholders is hard in case some of them are not familiar with data. Innovators Marketplace on Data Jackets (IMDJ) [5] is a workshop method to tackle this problem. In IMDJ workshop, participants state their requirements and create a scenario for solving these requirements based on Data Jackets. Data Jacket (DJ) [4] is a framework to describe structured information about data in natural language, which enable for those who are not familiar with data to discuss based on data. In this paper, we introduce a platform called Web-IMDJ for conducting IMDJ workshop on the web. Web-IMDJ not only reduces the burden of workshops but enables to participate in workshop remotely. By conducting workshop on Web-IMDJ as case study, we found that the number of ideas is as many as previous IMDJ and the capacity of participants is superior in Web-IMDJ.
History of Research and Development of Beam Wireless Power Transfer

This paper aims to describe the history of research and development (R &D) of beam wireless power transfer (WPT) via microwaves. Starting in the 1960s, many interesting beam WPT field experiments have been conducted worldwide. The R&D of the beam WPT walked along a solar power satellite (SPS). The beam efficiency from a transmitting antenna to a receiving antenna is one of the most important parameter of the beam WPT. In this paper, the history is reviewed and future perspectives of the WPT are described
Compute–Forward Multiple Access (CFMA): Practical Implementations

We present a practical strategy that aims to attain rate points on the dominant face of the multiple access channel capacity using a standard low complexity decoder. This technique is built upon recent theoretical developments of Zhu and Gastpar on compute–forward multiple access which achieves the capacity of the multiple access channel using a sequential decoder. We illustrate this strategy with off-the-shelf LDPC codes. In the first stage of decoding, the receiver first recovers a linear combination of the transmitted codewords using the sum-product algorithm (SPA). In the second stage, by using the recovered sum-of-codewords as side information, the receiver recovers one of the two codewords using a modified SPA, ultimately recovering both codewords. The main benefit of recovering the sum-of-codewords instead of the codeword itself is that it allows to attain points on the dominant face of the multiple access channel capacity without the need of rate-splitting or time sharing while maintaining a low complexity in the order of a standard point-to-point decoder. This property is also shown to be crucial for some applications, e.g., interference channels. For all the simulations with single-layer binary codes, our proposed practical strategy is shown to be within 1.7 dB of the theoretical limits, without explicit optimization on the off-the-self LDPC codes.
On the Performance of LMS Communication With Hardware Impairments and Interference

This paper investigates the performance of a dual-hop decode-and-forward (DF) relaying-aided land mobile satellite communication over Shadowed-Rician (SR) fading channels. A practical model for the satellite relaying system is first developed, where the impacts of satellite multi-beam antenna, radio propagation loss, and random shadowing are taken into account. Next, by assuming that the multi-beam satellite suffers from hardware impairments (HIs) and is perturbed by interference signals, we derive an equivalent end-to-end signal-to-interference-plus-noise-and-distortion-ratio of the system, and justify that the maximum ratio transmission at the source and the maximum ratio combining at the destination are the optimal transmit-receive beamforming schemes on the proposed HIs model. Then, closed-form expressions for the probability density function (PDF) of the sum of independent and identically distributed (i.i.d) squared SR random variables in the case of integer and rational Nakagami- $m$ fading parameters are derived. Based on the derived PDF, new analytical expressions for the outage probability (OP) and average throughput are obtained in the presence of HIs and interference. Moreover, the asymptotic OP and average throughput at high signal-to-noise ratio are investigated to reveal the achievable diversity order of the system. Finally, Monte Carlo simulation results are provided to corroborate the analytical results.
Seasonal Multi-temporal Pixel Based Crop Types and Land Cover Classification for Satellite Images Vsing Convolutional Neural Networks

Nowadays, Satellite images have become a major source of data for many aspects of development. Land and crops classification using satellite images is a recent important subject. From the other side, Deep Convolutional Neural Networks (DCNNs) is a powerful technique for understanding images. This paper describes a pixel based crops and land cover classification originating from one source satellite imagery represented by Sentinel satellite and based on several dates for the same agricultural season. We propose a DCNN architecture based on multi-temporal data that was fed to a one-dimension (1-D DCNN). The proposed architecture is compared with other methods of satellite image classification algorithms; such as Support Vector Machines (SVMs), Random Forests (RFs) and k-Nearest Neighbors (k-NNs). Experiments are conducted for the mutual experiment of major crops and land cover classification for Al-Fayoum governorate in Egypt. The 1-D DCNN achieves about 89% accuracy using 10 spectral bands from Sentinel-2 satellite imagery database for the area of interest. The proposed architecture although it outperforms other methods, needs further research to optimize the memory usage.
GenERes: A Genealogical Entity Resolution System

Entity resolution is the problem of identifying and linking different manifestations of the same real-world object. This is an important step for many databases to ensure a clean version of the data and to leverage the information from multiple views of the same entity. At Ancestry, we have many manifestations of the same person in our databases. For example, the same person may be found in multiple family trees, or a person may have multiple types of records which refer to him or her, such as birth, marriage, or death records. The ability to resolve entities helps us unlock powerful genealogical discoveries for our users. To resolve these entities, we have developed a robust, scalable machine learning method which works across many different types of genealogical content as well as time and place. We find substantial improvements over a previous rule-based system and demonstrate how a machine-learning-related approach can also allow for interpretability. While we focus our example within the realm of genealogical data and historical records, we provide a model architecture and some learnings which should be applicable to many entity resolution domains.
Analytical Approach of Director Tilting in Nematic Liquid Crystals for Electronically Tunable Devices

This paper presents an analytical expression that models the tilt angle of directors in a nematic liquid crystal (LC), depending on its elastic properties, its dielectric anisotropy, and the quasi-static electric field applied. The analytical solution obtained is fast and easily computable in comparison with numerical estimations and is of special interest in radiofrequency; for instance, for the LC modeling in full-wave electromagnetic simulators in the design process of electronically tunable devices, such as microwave phase shifters or electronically steerable antennas for satellite communications. Subsequently, a comparison is made between numerical approaches (self-implemented shooting method) and the analytical formulas when varying the parameters of the LC, being demonstrated its usefulness. The average LC director is then obtained and used to form the full permittivity tensor that completely characterizes the electrical properties of the material. Finally, an electromagnetic simulation is carried out to show the capabilities of the LC as a tunable phase shifter. It is shown that only 5 cm of a commercial 200- $\mu \text{m}$ LC mixture is necessary to achieve 360° of the maximum variable phase shift at the 30-GHz band.
Multilingual Sentiment Analysis for a Swiss Gig

We are developing a multilingual sentiment analysis solution for a Swiss human resource company working in the gig sector. To examine the feasibility of using machine learning in this context, we carried out three sentiment assignment experiments. As test data we use 963 hand annotated comments made by workers and their employers. Our baseline, machine learning (ML) on Twitter, had an accuracy of 0.77 with the Matthews correlation coefficient (MCC) of 0.32. A hybrid solution, Semantria from Lexalytics, had an accuracy of 0.8 with MCC of 0.42, while a tenfold cross-validation on the gig data yielded the accuracy of 0.87, F1 score 0.91, and MCC 0.65. Our solution did not require language assignment or stemming and used standard ML software. This shows that with more training data and some feature engineering, an industrial strength solution to this problem should be possible.
User Feedback and Uncertainty in User Guided Binarization

In a child's development, the child's inherent ability to construct knowledge from new information is as important as explicit instructional guidance. Similarly, mechanisms to produce suitable learning representations, which can be transferred and allow integration of new information are important for artificial learning systems. However, equally important are modes of instructional guidance, which allow the system to learn efficiently. Thus, the challenge for efficient learning is to identify suitable guidance strategies together with suitable learning mechanisms. In this paper, we propose guided machine learning as source for suitable guidance strategies, we distinguish between sample selection based and privileged information based strategies and evaluate three sample selection based strategies on a simple transfer learning task. The evaluated strategies are random sample selection, i.e., supervised learning, user based sample selection based on readability, and user based sample selection based on readability and uncertainty. We show that sampling based on readability and uncertainty tends to produce better learning results than the other two strategies. Furthermore, we evaluate the use of the learner's uncertainty for self directed learning and find that effects similar to the Dunning-Kruger effect prevent this use case. The learning task in this study is document image binarization, i.e., the separation of text foreground from page background and the source domain of the transfer are texts written on paper in Latin characters, while the target domain are texts written on palm leaves in Balinese script.
A GaN Single-Chip Front End With Improved Efficiency and Power by Using Class F Approach

This contribution presents a single-chip front end (SCFE) in a 0.25-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> commercial gallium nitride process conceived for <inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula>-band active electronically scanned array applications. The realized SCFE integrates the switching, high-power amplification (HPA) and low-noise amplification (LNA) functionalities, which are required by a half-duplex Tx/Rx module, on the same monolithic microwave integrated circuit, resulting in 7 <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 7 mm<sup>2</sup> chip area. In order to maximize the efficiency and power performances, the HPA was conceived to work in class F, whereas the LNA was optimized for lower drain bias voltage. As compared to the first realization of this chip, such expedients lead to an output power and efficiency improvement of about 0.8 dB and four percentage points, respectively, and a reduction of the power consumption in Rx-mode of about 35%, without compromising noise figure (NF) and gain. Moreover, over a 13% fractional bandwidth in the lower part of the <inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula>-band, the SCFE achieves 1.8 and 30 dB of NF and gain, respectively, in Rx-mode, and more than 46.5 dBm, 48%, and 36 dB, of output power, efficiency, and gain, respectively, in Tx-mode.
InfoLeak: Scheduling-Based Information Leakage

Covert-and side-channel attacks, typically enabled by the usage of shared resources, pose a serious threat to complex systems such as the Cloud. While their exploitation in the real world depends on properties of the execution environment (e.g., scheduling), the explicit consideration of these factors is often neglected. This paper introduces InfoLeak, an information leakage model that establishes the crucial role of the scheduler for exploiting core-private caches as covert channels. We show, formally and empirically, how the availability of these channels and the corresponding attack feasibility are affected by scheduling. Moreover, our model allows security experts to assess the related threat, posed by core-private cache covert channels for a particular system by considering solely the scheduling information. To validate the utility of InfoLeak, we deploy a covert-channel attack and correlate its success ratio to the scheduling of the attacker processes in the target system. We demonstrate the applicability of the InfoLeak model for analyzing the scheduling information for possible information leakage and also provide an example on its usage.
Unknown Crowd Event Detection from Phase-Based Statistics

A new approach for unknown event detection in videos with dense motion, such as crowds or dynamic textures, is developed, without requiring the estimation of optical flow, with no prior knowledge about normal or abnormal events, and with no training. The proposed method directly extracts motion statistics from the phase of the video’s Fourier transform and detects changes in them, and in the video, by applying sequential statistical change detection theory. Focus is placed on the motion component, as videos of densely moving entities, such as temporal textures and crowds, often have a very similar appearance, but different dynamic features. Experiments with synthetically generated datasets demonstrate the method’s operation under various conditions, while experiments on a recently introduced crowd dataset show that it succeeds at detecting new events in videos of crowds, with no training, and no prior knowledge of the location of new events in space and time.