Miracle-dz/UniFAR-FTUs huggingface dataset:
Asymmetric Modulation for Hardware Impaired Systems—Error Probability Analysis and Receiver Design

Error probability study of hardware impaired (HWI) systems highly depends on the adopted model. Considering the distinct improper Gaussian features of HWI systems, captured by recent models, HWI-aware receivers are designed. An optimal maximum likelihood (ML) receiver serves as a performance benchmark, and a sub-optimal linear minimum mean square error introduces a reduced-complexity implementation. Whereas, the conventional HWI-unaware minimum Euclidean distance receiver, based on the proper noise assumption, exhibits substandard performance. Next, the average error probability of the proposed optimal ML-receiver is analyzed, where several tight bounds and approximations are derived for various HWI systems. Motivated by the benefit of improper Gaussian signaling in mitigating HWI, which is proven in recent studies, asymmetric modulation is adopted and optimized for transmission. The numerical results demonstrate a bit error rate (BER) reduction up to 70% of the proposed HWI-aware receivers over HWI-unaware receivers. Moreover, the asymmetric modulation is shown to reduce the BER by 93%. These results signify the importance of incorporating accurate HWI models, designing appropriate receivers and optimizing signal transmission for the BER performance compensation.
CP Antenna Array With Switching-Beam Capability Using Electromagnetic Periodic Structures for 5G Applications

This paper proposes a novel circularly polarized electromagnetic band-gap (EBG) antenna array backed by an artificial magnetic conductor (AMC) that operates in the 26–32-GHz bands. The designed antenna is a <inline-formula> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula> Yagi–Uda antenna array that uses the directors’ semicircles to direct the radiation in the horizontal end-fire direction. The feeding of the array is designed with two parallel T-junction portions forming a parallel feeding network that converts the polarization from linear to circular. An AMC is placed underneath the antenna to suppress the backward radiation and obtain the propagation only in the <inline-formula> <tex-math notation="LaTeX">$+z$ </tex-math></inline-formula> plane. The novel approach is based on the fact that EBGs are placed around the array to give to the antenna, beam-switching capability. This simple technique provides a high beam tilting angle of +90° from the end-fire to the full broadside plane. The final EBG-AMC-antenna array is circularly polarized and presents, at 29 GHz, a gain of 11.9 dBi, and an axial ratio bandwidth of 10% from 28 to 31 GHz. By assessing the high antenna performances presented in this paper, and the high angle beam-switching capability by adding the EBG, the novel array could be seen as a potential candidate for implementation in future 5G applications.
A Sense of Touch for the Shadow Modular Grasper

In this letter, we have designed and built a set of tactile fingertips for integration with a commercial three-fingered robot hand, the Shadow Modular Grasper. The fingertips are an evolution of an established optical biomimetic tactile sensor, the TacTip. In developing the tactile fingertips, we have progressed the technology in areas such as miniaturization, development of custom-shaped finger-pads, and integration of multiple sensors. From these fingertips, we extract a set of high-level features with intuitive relationships to tactile quantities such as contact location and pressure. We present a simple linear-regression method for predicting roll and pitch of the finger-pad relative to a surface normal and show that the method generalizes to unknown depths and shapes. Finally, we apply this prediction to a grasp-control method with the Modular Grasper and show that it can adjust the grasp on three real-world objects from the YCB object set in order to attain a greater area of contact at each fingertip.
Identifying the Reality Gap Between Abstract and Realistic Models Using Evolved Agents and Simulated Kilobots

A common challenge in evolutionary swarm robotics is the transfer of simulated results into real-world applications. This difficulty can arise in a variety of real-world settings and problems such as sensory differences in robots and changes in the environment. We identify this reality gap at a simulation level by comparing the evolved behaviours of simulated Kilobots in two different models with different levels of abstraction. Our aim is to identify the reality gap that occurs at simulation level by increasing the task difficulty and noting differences in outcomes. Insights gained in this process may help rule out any further causes of reality gap when moving to experiments with physical robots.
Design Implementation of 10T Static Random Access Memory Cell Using Stacked Transistors for Power Dissipation Reduction

This SRAM cell is composed of combination of normal transistors and high voltage threshold transistors (sleep transistors) and can store one bit of data. Moreover, the designed SRAM architecture is composed of 4x16 decoders, pre-charge circuit, write or read selector circuit, sense amplifier circuit, write driver circuit, bank selector, column selector and SRAM cell arrays. The modified SRAM reduces the power dissipation by using sleep transistor stacking technique in a basic SRAM cell. Moreover, the designed SRAM memory is also partitioned into four banks, which contain 2048 SRAM cells each and implemented in a 128x64-cell array. The overall design has an average power of 7.71mW and the overall results performance were compared to the conventional 6T SRAM architecture. Both the modified and conventional SRAM architectures have the same design blocks except the SRAM cell. The operating voltage and frequency used in this design is 1.2V and 500 MHz, respectively. In addition, the modified SRAM in this paper has 8192 bits of memory capacity and implemented in TSMC 65nm CMOS Technology.
Multi-Individual Mammographic Image Registration Based on Global-Local Integrated Transformations

Mammography has been the most reliable and effective screening tool for the early detection of breast cancer. Registration of mammographic images plays an important role in computer-aided detection (CAD) systems for detecting breast cancer in mammograms. For the purpose of comparison based abnormality detection. , mammographic image registration is usually performed on temporal mammograms or right and left mammograms of one object. We propose a method for multi-individual mammographic image registration in order to analyze mammograms of different women under the same framework. This enables normality or abnormality modelling based on different objects. However. , the traditional free-form model based registration algorithm cannot register mammographic images of different individuals accurately. , and the registration efficiency is low. In this paper. , a non-rigid image registration method based on an integration of global coarse registration and local fine registration is presented. The mean squared gray-level difference is used as the similarity measure. The Limited-memory Broyden Fletcher Goldfarb Shanno (L- BFGS) optimization algorithm is employed to optimize the registration parameters. Ten pairs of mammographic images of different cases selected from the Mammographic Image Analysis Society (MIAS) database are used for experiments. Experimental results show that. , compared with the traditional free-form model based algorithm and the Diffeomorphic Demons registration algorithm. , it is indicated that the proposed method improves the registration accuracy and efficiency of multi-individual mammographic images.
Interactive Topic Model with Enhanced Interpretability

Although existing interactive topic models allow untrained end users to easily encode their feedback and iteratively re-fine the topic models, their unigram representations often result in ambiguous description of topics and poor inter-pretability for users. To address the problems, this paper proposes the first phrase-based interactive topic model which can provide both high interpretability and high interactivity with human in the loop. First, we present an approach to augment unigrams with a list of probable phrases which offers a more intuitively interpretable and accurate topic description, and further efficiently encode users’ feedback with phrase constraints in interactive processes of refining topic models. Second, the proposed approach is demonstrated and examined with real data.
Serum β-endorphin in primary fibromyalgia syndrome: a controlled study

Serum beta-endorphin was assayed without knowledge of study subject category in 44 consecutive patients with primary fibromyalgia syndrome, 3 patients with rheumatoid arthritis (RA), and 30 normal controls, all females. Mean serum beta-endorphin levels were 81 +/- 28 pg/ml in patients with fibromyalgia, whereas those in normal controls and patients with RA were 73 +/- 17 pg/mg and 73 +/- 18 pg/ml, respectively. These differences were not statistically significant. Serum beta-endorphin levels did not correlate with relevant clinical variables in either fibromyalgia or RA groups.
Recovery Guarantee of Sparse Binary Sensing Matrices under Greedy Algorithm

The theory of compressed sensing was established independently by Donoho and by Candès et al. The main result of compressed sensing is that one can recover a high-dimensional sparse signal through a small number (far less than the signal dimension) of linear random measurements by convex optimization. It means that a sparse high dimensional signal can be compressed as a low dimensional signal. Orthogonal matching pursuit (OMP) and l1-minimization are two main reconstruction algorithms. The OMP algorithm is an iterative greedy algorithm and has the advantage of easy implementation. In this paper, we use a class of structured sparse binary matrices to be measurement matrices and study their recovery guarantee under the greedy OMP algorithm. These matrices are parity-check matrices of LDPC codes. It was reported that according to simulation results, parity-check matrices constructed by progressive edge-growth (PEG) algorithm can have better recovery rate than random Gaussian matrices under the OMP algorithm. But there is still no literature about theoretical analysis on this topic. In this paper, we show that the OMP algorithm does not provide the same recovery guarantee as l1-minimization for general parity-check matrices of LDPC codes. A modified OMP algorithm that reaches the same recovery guarantee as l1-minimization is proposed.
Research and Construction of Junior High School Subject Q&A System Model based on Deep Learning

Answering questions occupies a very important position in course education.But the traditional face-to-face approach to answering questions, both for educators and learners, has gradually failed to meet the needs. However, there are many deficiencies in the automatic question answering system of education. In recent years, deep learning has made great progress in the field of Natural Language Processing, which makes it possible to apply it in the field of junior high school education. In this paper, on the basis of the improved cyclic neural network, the mixed deep neural network is firstly constructed to better learn the deep characteristics of the sentence by deep learning and word2vec. Finally, the experiment is carried out on the data set of junior high school biology, and the validity of the model is proved by the experimental results.
IoT Based Automated Greenhouse Monitoring System

Greenhouses are climate controlled structures with walls and roof specially designed for offseason growing of plants. Most greenhouse systems use manual systems for monitoring the temperature and humidity which can cause discomfort to the worker as they are bound to visit the greenhouse every day and manually control them. Also, a lot of problems can occur as it affects the production rate because the temperature and humidity must be constantly monitored to ensure the good yield of the plants. Internet of Things is one of the latest advances in Information and Communication Technologies, providing global connectivity and management of sensors, devices, users with information. So the combination of IoT and embedded technology has helped in bringing solutions to many of the existing practical problems over the years. The sensors used here are YL69 moisture sensor and DHT11 (Temperature & Humidity sensor). From the data's received, Raspberry PI3 automatically controls Moisture, Temperature, Humidity efficiently inside the greenhouse by actuating an irrigating pipe, cooling fan, and sliding windows respectively according to the required conditions of the crops to achieve maximum growth and yield. The recorded temperature and humidity are stored in a cloud database (ThingSpeak), and the results are displayed in a webpage, from where the user can view them directly.
On Internal Modeling of the Upright Postural Control in Elderly

The second most common cause of injury in the elderly population is falling. In an effort to understand the mechanism behind the reduced ability to maintain balance in any posture or activity, we study the performance of the central nervous system as a controller of the body, while maintaining the balance in some postures or activities. Towards this direction, forty-five subjects aged over 70 were tested in different trials of quiet stance: a) hard stable surface with open eyes, b) stable surface with closed eyes, c) soft unstable surface with open eyes, and d) unstable surface, while eyes were closed. In the sequel, the body kinematics were described by legs and trunk segment angles in the sagittal plane, while the muscle activations were described by a weighted sum of rectified EMG signals from tibialis anterior and gastrocnemius muscles of left and right legs. Using the neuro-science hypothesis and adaptive control theory, a completely novel model was identified for the CNS based on the feedback internal model. The proposed model is able to predict the output commands, based on a recurrent neural network, while the efficiency of the proposed scheme has been proven based on multiple experimental results, showing that the model can sufficiently predict the muscle activity based on the optimum sensory inputs.
A Transfer Learning Method Based on Residual Block

In order to obtain high image representations in limited amount of datasets, a transfer learning method based on residual block is proposed. In this method, we follow a transfer learning approach by increasing the number of layers of the network to extract the higher order statistical features of the image. The main idea is to conduct feature transfer by means of ResNet (Deep Residual Network) model with setting ImageNet dataset as source domain. Firstly, all image data are preprocessed with data enhancement. Then, on the basis of modifying the source model's fully-connected classification layer, the adjustment module–residual block is added to the end of the network. Finally, after training the adjustment module, the deep model is achieved. Through transfer learning and deep feature extraction, the capability of feature recognition that impacted by content differences between source domain and target domain will be improved. Experiments show that our method achieves 97.98% accuracy on MNIST dataset and 90.45% accuracy on CIFAR-l 0 dataset, respectively. The experimental results demonstrate that the performance of our proposed method is significantly better than the existing transfer learning methods.
Detecting Changes in User Behavior to Understand Interaction Provenance during Visual Data Analysis

Analysts can make better informed decisions with “Explainable AI”. Visualizations are often used to understand, diagnose, and refine AI models. Yet, it is unclear what type of interactions are appropriate for a given model and how the visualizations are perceived. Furthering research into sensemaking for visual analytics may be useful in understanding how user’s are interacting with visualizations for AI and in developing a naturalistic model of explanation. Conventional approaches consist of human experts applying theoretical sensemaking models to identify changes in information processing or utilizing recorded rationale provided by the users. However, these approaches can be inefficient and inaccurate since they heavily rely on subjective human reports. In this research, we aim to understand how data-driven techniques can automatically identify changes in user behavior (inflection points) based on user interaction logs collected from eye tracking and mouse interactions. We relay the results of a supervised classification system using Hidden Markov Models to predict changes in a visual data analysis of a cyber security scenario. Preliminary results indicate a 70% accuracy in identifying inflection points. These preliminary results suggest the feasibility of data-driven approaches furthering our understanding of sensemaking processes and interaction provenance.
NOV-CFI: A Novel Algorithm for Closed Frequent Itemsets Mining in Transactional Databases

Since the era of data explosion, data mining in transactional databases has become more and more important. There are many data mining techniques like association rule mining, the most important and well-researched one. Furthermore, closed frequent itemset mining is one of the fundamental but time-consuming steps in association rule mining. Most of the algorithms used in literature find closed frequent itemsets on search space items having at least a minsup and are not reused for mining next time. To deal with this problem, NOV-CFI algorithms are proposed as a new approach in order to quickly detect closed frequent itemsets from transactional databases using an array of co-occurrences and occurrences of kernel item in at least one transaction. Advantages of NOV-CFI algorithms are reuse and easily expanded in distributed systems. Finally, experimental results show that the proposed algorithms are better than other existing algorithms on both real and synthetic datasets.
Detection and Localization of Submodule Open-Circuit Failures for Modular Multilevel Converters With Single Ring Theorem

Submodule (SM) failure detection and localization is crucial to reliability improvement of modular multilevel converters (MMCs), which consist of numerous identical SMs. Upon SM failures, the capacitor voltages of the faulty SMs deviate from those of the healthy SMs. Hence, SM voltage consistency disruption can be utilized as an indicator of SM failures. To enable effective consistency evaluation of numerous SM voltages, the single ring theorem, which is an ideal analyzing tool for large size matrices, is applied to SM failure detection in this paper. The proposed SM failure detection method eliminates the need for ideal-state estimators and thus ensures sufficient robustness in terms of parameter uncertainties. Additional voltage/current sensors are not required, which is beneficial for cost efficiency. A statistical-analysis-based SM failure localization method enabling fast identification of the faulty SMs upon failure detection is also proposed. Both single and multiple SM failure detection and localization can be handled with the proposed method even if the faulty SMs are in different arms. The effectiveness of the proposed SM failure diagnosis method is verified by both simulation in MATLAB/Simulink and experimentation on a 13-level MMC prototype.
Explainable Activity Recognition in Videos

In this paper, we consider the following activity recognition task: given a video, infer the set of activities being performed in the video along with an assignment of activities to each frame in the video. Although this task can be solved accurately using existing deep learning systems, their use is problematic in interactive settings. In particular, deep learning models are black boxes: it is difficult to understand how and why the system assigned a particular activity to a frame. This reduces the users’ trust in the system, especially in the case of end-users who need to use the system on a regular basis. We address this problem by feeding the output of deep learning to a tractable interpretable probabilistic graphical model and then performing joint learning over the two. The key benefit of our proposed approach is that deep learning helps achieve high accuracy while the interpretable probabilistic model makes the system explainable. We demonstrate the power of our approach using a visual interface to provide explanations of model outputs for queries about videos.
A Modified DC Chopper for Limiting the Fault Current and Controlling the DC-Link Voltage to Enhance Fault Ride-Through Capability of Doubly-Fed Induction-Generator-Based Wind Turbine

A simple conventional dc chopper is employed to protect the doubly-fed induction generator (DFIG) from overvoltage; however, it is not capable to keep the transient overcurrent in an acceptable level in stator and rotor sides. Therefore, an effective current-limiting strategy should be incorporated with the dc chopper to improve the fault ride-through capability of the DFIG. In this paper, a modified DC chopper is proposed not only to keep the dc-link voltage in an acceptable range, but also to limit the high-current level in the stator and the rotor sides in a permissible level without incorporating any extra fault-current-limiting strategy. Unlike the conventional dc chopper, in the proposed dc chopper, it is not required to stop rotor-side converter (RSC) switching and employ high-rated-current antiparallel diodes. The proposed modified dc chopper is placed between the dc-link capacitor and the RSC. In the proposed switching strategy of the modified dc chopper, three extra semiconductor switches are included, which are triggered to insert dc chopper resistance either in parallel or series connections with the dc link regarding the dc-link voltage level and the dc-link current level, respectively. Calculation of the dc chopper resistance is discussed. To prove the effectiveness and robustness of the proposed modified dc chopper in terms of both limiting the fault current and controlling the dc-link voltage of the DFIG, symmetrical and asymmetrical grid faults are applied in a power system including the DFIG-based wind turbine modeled in PSCAD/EMTDC so.