Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementing Logic Functions Using Independently-Controlled Gate in Double-Gate Tunnel FETs: Investigation and Analysis

Recently, a compact realization of logic gates using double-gate tunnel field effect transistors (DGTFETs) with independently-controlled gate has been proposed. The key elements in the proposed implementation are the suppression of the tunneling at the surface using gate-source overlap and enable tunneling inside the TFET body by choosing appropriate silicon body thickness. Though these implementations are compact, our study reveals that there are a few critical problems: high average subthreshold swing (<inline-formula> <tex-math notation="LaTeX">$SS_{avg}$ </tex-math></inline-formula>), low ON-state current (<inline-formula> <tex-math notation="LaTeX">$I_{ON}$ </tex-math></inline-formula>) and large propagation delay (<inline-formula> <tex-math notation="LaTeX">$t_{pd}$ </tex-math></inline-formula>). In this paper, we examine the root cause of these problems and explore solutions to tackle them. It is demonstrated that the techniques that boost the <inline-formula> <tex-math notation="LaTeX">$I_{ON}$ </tex-math></inline-formula> in a TFET do not necessarily increase the <inline-formula> <tex-math notation="LaTeX">$I_{ON}/I_{OFF}$ </tex-math></inline-formula> ratio in the proposed implementations. The efficacy of these techniques in improving the <inline-formula> <tex-math notation="LaTeX">$I_{ON}/I_{OFF}$ </tex-math></inline-formula> ratio depends on whether the gate-source overlap is able to suppress the tunneling at the surface and restrict the OFF-state current (<inline-formula> <tex-math notation="LaTeX">$I_{OFF}$ </tex-math></inline-formula>). Furthermore, it is demonstrated that, for the AND functionality, compared to a purely silicon-based DGTFET (Si-TFET) employing silicon-germanium heterojunction DGTFET (HJ-TFET) results in an increase in the <inline-formula> <tex-math notation="LaTeX">$I_{ON}$ </tex-math></inline-formula> by <inline-formula> <tex-math notation="LaTeX">$143 \times $ </tex-math></inline-formula>, an increase in the <inline-formula> <tex-math notation="LaTeX">$I_{ON}/I_{OFF}$ </tex-math></inline-formula> ratio by two orders of magnitude and an improvement in the <inline-formula> <tex-math notation="LaTeX">$SS_{avg}$ </tex-math></inline-formula> by 45%, at <inline-formula> <tex-math notation="LaTeX">$V_{DD}=0.6\,\,V$ </tex-math></inline-formula>. Moreover, a compact NAND gate realized using the proposed HJ-TFETs exhibits two orders of magnitude lower <inline-formula> <tex-math notation="LaTeX">$t_{pd}$ </tex-math></inline-formula>, compared to the NAND gate realized using Si-TFET.
Real-Time Load Consumption Prediction and Demand Response Scheme Using Deep Learning in Smart Grids

In smart grids, the Demand Response (DR) strategy constitutes a vital necessity for the electricity management system. DR benefits from the two-way communication between utilities and consumers within the smart grid. However, implementing an efficient DR model basically relies on the real-time adjustments of the load consumption pattern, which requires access to accurate load consumption data. In this paper, a Deep Learning (DL) predictive model is proposed to accurately predict the hourly load consumption. In addition, a DR scheme is illustrated to reduce the peak load demand and avoid the energy deficit. Compared with the state-of-the-art techniques in residential load prediction, the proposed DL predictive model and DR scheme outperforms Linear Regression by 59.82%, Tree Regression by 52%, Support Vector Regression by 57.76%, and Ensembled Boosted Trees by 59.43% in terms of the Root Mean Square Errors.
Experimental validation of a moisture sensor for cellulosic insulation of power transformers

Power transformers comprise one of the most important assets in the power systems. Damage in a power transformer can lead to a collapse in the electrical transmission network, disturbing a big number of users. The cellulosic insulation plays a key role in the life of power transformer. Several studies have demonstrated that high levels of moisture in the cellulosic insulation increase its rate of ageing limiting the power transformer's life expectancy. In this work, the experimental validation of a sensor aimed at determining the moisture content of cellulosic insulation is presented. The working principle of the sensor is based in the use of the Frequency Dielectric Spectroscopy method (FDS) to relate the main dielectric characteristics of the sensor and those of the transformer solid insulation. The validation presented in this work considers different temperatures and moisture contents under equilibrium and non-equilibrium conditions. Additionally, the experimental dielectric curves of the sensor are compared with simulation data obtained by means of a finite-element model that reproduces the applied experimental conditions, to validate the modeling process.
A Simple and Efficient IQ Data Compression Method Based on Latency, EVM, and Compression Ratio Analysis

Latency, error vector magnitude (EVM) and compression ratio (CR) are performance evaluation factors of IQ data compression. The purposes of IQ data compression are to minimize the latency, EVM and to maximize the CR. However, there is a tradeoff between them. In this paper, based on the evaluation factors, we analyze existing IQ data compression schemes and propose a simple and efficient method from the analyzed results. The proposed method is composed of partial cyclic prefix and cyclic suffix insertion, <inline-formula> <tex-math notation="LaTeX">$K/L$ </tex-math></inline-formula> decimation, low-order bits removal, and partial Huffman code. It presents the techniques to minimize the EVM caused by <inline-formula> <tex-math notation="LaTeX">$K/L$ </tex-math></inline-formula> decimation and to optimize the number of removable low bits and available Huffman encoding bits based on the average signal power and desired EVM. The proposed method can achieve the CR of 4 times or more in about 2% EVM for the LTE downlink and uplink. The simulation results show that the proposed method is applicable to SIMO and MIMO environments.
Information Theoretic Based Comparative Analysis of Different Communication Signals in Plants

This paper compares the information-theoretic aspects of different communication signals in plants. To be specific, we consider two signals responsible for the inter-cellular communication in plants: electro-chemical and mechanosensitive signals. There is a need for a comprehensive comparison of the communication properties of these different signals in plants for different configurations of receiver cells. In this work we address this research problem using molecular communication theory. We compare the mutual information and the information propagation speed for these communication signals. In addition we compare the impact of refractory period on the mutual information and information propagation speed for these signals in plants. The key result of this paper is that, based on the propagation mechanism, better communication performance can be achieved by electro-chemical signals as compared to mechanosensitive signals. We further find that a short refractory period leads to higher mutual information and propagation speeds.
Tri-Band Antenna Array with Defected Ground Structure for mm-Wave 5G Applications

This work presents a tri-band planar antenna array envisioned for assimilation into future 5G mm-wave wireless communication applications. The proposed assembly comprises of four antenna elements excited by a $1 \times 4$ T-junction power divider/Combiner and Defected Ground Structure (DGS). The antenna array presented in this work is modeled on Rogers RT/Duroid 5880 substrate with an overall size of $14 \times 60 \times 0.787$ mm3. The frequency bands covered by the antenna system are centered at 29, 37.37 and 41 GHz with 10 dB bandwidths of 1.25, 1.3 and 2.14 GHz respectively. The peak gain attained by the antenna array is 11.8 dB. The proposed design with compact, simple and low profile assembly along with good radiation characteristics ascertains to be appropriate for mobile terminals and handheld wireless devices.
Crime Intention Detection System Using Deep Learning

Circuit Television Cameras (CCTV’s) are widely used to control occurrence of crimes in the surroundings. Although CCTV’s are deployed at various public and private areas to monitor the surroundings there is no improvement in the control of crimes. This is because CCTV requires human supervision which may lead to human prone errors like missing of some important crime events by human while monitoring so many screens recorded by CCTV’s at same time. To overcome this issue, we came up with Crime Intension Detection System that detects crime in real time videos, images and alerts the human supervisor to take the necessary actions. To alert the supervisors or nearby police station about the occurrence of crime. We added SMS sending module to our system which sends SMS to concern person whenever crimes are detected. The proposed system is implemented using Pre-trained deep learning model VGGNet-19 which detects gun and knife in the hand of person pointing to some other person. We also compared the working of two different pre-trained models like GoogleNet InceptionV3 in training. The results obtained with VGG19 are more accurate in terms of training accuracy. This motivated us to use VGG19 with little fine tuning to detect crime intention in videos and images to overcome the issues with existing approaches with more accuracy. And we made use of Fast RCNN and RCNN these algorithms are well known as Faster RCNN this helps us to draw the bounding box over objects in images like person, gun, knife and some untrained images are marked with N/A. Algorithms help for detection and classifications of objects over images.
A novel approach to improve the Record Linkage process

Organizations around the world lose trillions of dollars due to poor data quality problems. In the last years, the awareness about the importance of data quality made the stakeholders invest a lot of money in order to improve the quality of the stored data. One of the main processes in data quality field is the Record Linkage process (RL). Record linkage is the process of identifying the tuples that refer to the same real world entity. Without blocking, the RL process can end up by billions of comparisons when dealing with large datasets. Blocking reduces the number of comparisons by dividing the data into blocks in a way that only the records in the same block will be compared to each other. In this paper, we propose a novel approach in the record linkage field based on the K-Modes algorithm as a blocking step, K-Modes propose a major advantage because the algorithm deals directly with the categorical data. The Obtained results showed that our proposition stands as a powerful approach in the record linkage field where it outperforms most of the approaches that exist in the literature.
Temporal Spiking Recurrent Neural Network for Action Recognition

In this paper, we propose a novel temporal spiking recurrent neural network (TSRNN) to perform robust action recognition in videos. The proposed TSRNN employs a novel spiking architecture which utilizes the local discriminative features from high-confidence reliable frames as spiking signals. The conventional CNN-RNNs typically used for this problem treat all the frames equally important such that they are error-prone to noisy frames. The TSRNN solves this problem by employing a temporal pooling architecture which can help RNN select sparse and reliable frames and enhances its capability in modelling long-range temporal information. Besides, a message passing bridge is added between the spiking signals and the recurrent unit. In this way, the spiking signals can guide RNN to correct its long-term memory across multiple frames from contamination caused by noisy frames with distracting factors (e.g., occlusion, rapid scene transition). With these two novel components, TSRNN achieves competitive performance compared with the state-of-the-art CNN-RNN architectures on two large scale public benchmarks, UCF101 and HMDB51.
Donation after circulatory death today: an updated overview of the European landscape

Donation after circulatory death (DCD) has become an accepted practice in many countries and remains a focus of intense interest in the transplant community. The present study is aimed at providing a description of the current situation of DCD in European countries. Specific questionnaires were developed to compile information on DCD practices, activities and post‐transplant outcomes. Thirty‐five countries completed the survey. DCD is practiced in 18 countries: eight have both controlled DCD (cDCD) and uncontrolled DCD (uDCD) programs, 4 only cDCD and 6 only uDCD. All these countries have legally binding and/or nonbinding texts to regulate the practice of DCD. The no‐touch period ranges from 5 to 30 min. There are variations in ante and post mortem interventions used for the practice of cDCD. During 2008–2016, the highest DCD activity was described in the United Kingdom, Spain, Russia, the Netherlands, Belgium and France. Data on post‐transplant outcomes of patients who receive DCD donor kidneys show better results with grafts obtained from cDCD versus uDCD donors. In conclusion, DCD is becoming increasingly accepted and performed in Europe, importantly contributing to the number of organs available and providing acceptable post‐transplantation outcomes.
Investigation of the Scalability of Emerging Nanotube Junctionless FETs Using an Intrinsic Pocket

The detrimental lateral band-to-band tunneling (L-BTBT) governing the OFF-state performance of the junctionless (JL) FETs is more pronounced in emerging Nanotube (NT) transistor architectures. This restricts the scaling of NT JLFETs irrespective of their ultimate electrostatic gate control due to additional core gate. Therefore, in this paper, we propose a symmetric intrinsic pocketed P<sub>i</sub>-NT JLFET which has narrow intrinsic pockets on both sides of the channel region leading to a diminished L-BTBT induced lateral parasitic BJT action in the emerging NT JLFETs. Using calibrated 3-D simulations, we demonstrate that the incorporation of an intrinsic pocket decreases the OFF-state current by around 2 orders of magnitude in the P<sub>i</sub>-NT JLFET for a gate length of 20 nm, leading to a significant ON-state to OFF-state current ratio (<inline-formula> <tex-math notation="LaTeX">$I_{\text{ON}}/I_{\text{OFF}}$ </tex-math></inline-formula>) of 10<sup>8</sup>. Furthermore, we also show an improvement in the performance of the emerging NT junctionless accumulation mode (JAM) FETs which exhibits a degraded performance compared to NT JLFETs due to enhanced L-BTBT irrespective of their higher ON-state current. The inclusion of the intrinsic pockets in NT JAMFET (Pi-NT JAMFET) reduces the L-BTBT originated OFF-state by 3 orders of magnitude for a gate length of 20 nm leading to an impressive <inline-formula> <tex-math notation="LaTeX">$I_{\text{ON}}/I_{\text{OFF}}$ </tex-math></inline-formula> ratio of 10<sup>8</sup>. Moreover, the proposed P<sub>i</sub>-NT JLFET and P<sub>i</sub>-NT JAMFET exhibit an impressive <inline-formula> <tex-math notation="LaTeX">$I_{\text{ON}}/I_{\text{OFF}}$ </tex-math></inline-formula> ratio of ~ 10<sup>8</sup> and 10<sup>6</sup>, respectively, with more than 4 orders of remarkable reduction in the leakage current even when the gate length is scaled to 10 nm. Additionally, the proposed architectures exhibit lower sensitivity to the gate length modulation unlike their conventional counterpart. The P<sub>i</sub>-NT transistors exhibit superior immunity against the short channel effects of threshold-voltage roll-off due to the reduced electrostatic source/channel-to-drain coupling. Furthermore, we show that incorporating gate engineering of the dual-material gate (DMG) further enhances the performance of the P<sub>i</sub>-NT transistor. The DMG-P<sub>i</sub>-NT transistors exhibit an enhanced <inline-formula> <tex-math notation="LaTeX">$I_{\text{ON}}/I_{\text{OFF}}$ </tex-math></inline-formula> ratio ~ 10<sup>11</sup> achievable with the proper tuning of the dual metal gate work functions. Thus, our proposed device architecture enhances the scalability of the NT JLFETs and NT JAMFETs for realizing them in the future technology nodes.
Robust Online Learning Method Based on Dynamical Linear Quadratic Regulator

In this paper, a novel algorithm is proposed for inferring online learning tasks efficiently. By a carefully designed scheme, the online learning problem is first formulated as a state feedback control problem for a series of finite-dimensional systems. Then, the online linear quadratic regulator (OLQR) learning algorithm is developed to obtain the optimal parameter updating. Solid mathematical analysis on the convergence and rationality of our method is also provided. Compared with the conventional learning methods, our learning framework represents a completely different approach with optimal control techniques, but does not introduce any assumption on the characteristics of noise or learning rate. The proposed method not only guarantees the fast and robust convergence but also achieves better performance in learning efficiency and accuracy, especially for the data streams with complex noise disturbances. In addition, under the proposed framework, new robust algorithms can be potentially developed for various machine learning tasks by using the powerful optimal control techniques. Numerical results on benchmark datasets and practical applications confirm the advantages of our new method.
Rapid Altitude Tracking for Air-Breathing Hypersonic Vehicle With Limited Angle of Attack

This paper focuses on the altitude tracking issue for air-breathing hypersonic vehicles. It proposes a rapid altitude tracking method with synthetical consideration on the angle of attack constraint, which is crucial to keep the scramjet away from the inlet unstart. For the inner-loop, the novel compensator is considered in the conjunction of the backstepping approach to design a flight path angle tracking controller. The compensator helps the controller to limit the angle of attack with the uncertainties. For the out-loop of the control system, the altitude controller involves the combination of Bang-Bang control and the sliding mode technique to generate the command which accommodates the constraint on the angle of attack. The atmosphere density model is taken into account, which makes the proposed method more adaptable to the altitude variation. The effectiveness of the proposed control method is evaluated in the simulations.
Vehicle Classification with Deep Learning

Nowadays, there are many traffic surveillance systems which are installed in almost every city to record events and traffic. The surveillance system is used for various objectives, e.g. vehicles searching and real-time traffic monitoring, etc. For the searching purpose, the system can be used by policeman such as outlaw’s vehicle identification in crime. Typically, the officers manually identify the vehicle in recorded video according to its appearances. Although the accuracy of this approach is good, it is time-consuming and inclined to faults due to human fatigue for long duration videos. Moreover, hiring employees is costly. Recently, there are several machine learning methods which can be applied to classify vehicles, e.g. Fuzzy Logic, Decision Tree, Adaboost, Random Forest, Neural Network, etc. Convolutional Neural Network (CNN) is also one of such methods. CNN is a type of Deep Learning which is in the category of the neural network. The method is very well-known in image recognition field at the present because of its performance. In the proposed vehicle classification, there are two vehicle characteristics, i.e. types and colors. Types consist of four classes while colors consist of seven classes. CNN is then used as to classify vehicle images. The experimental results show that CNN can achieve high performance in real-world applications.
Energy Efficient Non-Disjoint Set Based Target Coverage Scheme for Wireless Sensor Networks

A Wireless Sensor Network(WSN) is gaining importance due to its widespread applications but such networks have limited energy. The sensor nodes are randomly deployed to monitor targets inside the deployment region. Maximization of active subsets of sensors covering all the target points is one of the important feature in WSNs to enhance the network lifetime. In order to save energy of the deployed sensors, target must be monitored by atleast one sensor at any time. This paper proposes a non-disjoint set based target coverage scheme for WSN to maximize active subsets of sensors covering all targets. The estimation of non disjoint set is done based on residual energy and the distance between the sensor and the targets. The simulation evaluation shows improved performance in terms of network lifetime.
A Hierarchical Bidirectional GRU Model With Attention for EEG-Based Emotion Classification

In this paper, we propose a hierarchical bidirectional Gated Recurrent Unit (GRU) network with attention for human emotion classification from continues electroencephalogram (EEG) signals. The structure of the model mirrors the hierarchical structure of EEG signals, and the attention mechanism is used at two levels of EEG samples and epochs. By paying different levels of attention to content with different importance, the model can learn more significant feature representation of EEG sequence which highlights the contribution of important samples and epochs to its emotional categories. We conduct the cross-subject emotion classification experiments on DEAP data set to evaluate the model performance. The experimental results show that in valence and arousal dimensions, our model on 1-s segmented EEG sequences outperforms the best deep baseline LSTM model by 4.2% and 4.6%, and outperforms the best shallow baseline model by 11.7% and 12% respectively. Moreover, with increase of the epoch’s length of EEG sequences, our model shows more robust classification performance than baseline models, which demonstrates that the proposed model can effectively reduce the impact of long-term non-stationarity of EEG sequences and improve the accuracy and robustness of EEG-based emotion classification.
Audio Tempo Estimation Method Improved by Rhythm Pattern and Data Augmentation

Tempo is the intuitive attribute of audio music, since people could feel fast or slow expressively and detect salient pulses to form perceived tempo value naturally. Nonetheless, for some audio, the tempo value could be ambiguous due to complex metrical level, different composing habit and creating style. Even though most of audio have the predominant tempo with consensus between the listeners, the others could have two dominant tempi. The challenge and goal of tempo estimation is to discriminate the salient tempi, mostly one or two tempos, related to the metric level by analyzing the audio signal directly. In this study, we propose the rhythm patterns of long-term periodicity curve derived from tempogram to improve the saliency detection. Besides, the data augmentation method is also invented to conquer the deficiency and representative of the three training datasets. The performance is evaluated on three public datasets in which the accuracy of “GiantSteps” dataset even outperforms the state-of-the-art tempo estimator of convolutional neural network implementation.