Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring AMR Sensors’ Performance Limits Using an Agile, High-Speed Set–Reset Pulse Generation Circuit

This work explores the performance characteristics of anisotropic magnetoresistance (AMR) sensors when operating with very short set/reset current polarization pulses. AMR sensor’s sensitivity, offset, and power consumption are measured and reported using the sequences of set/reset polarization pulses of different durations. In order to perform these measurements, a circuit topology for generating agile current pulses with variable duration down to 40 ns, i.e., about fifty times shorter than similar circuits reported in the literature, was devised. It is demonstrated that AMR sensors maintain their sensitivity and offset even with polarization pulses two orders of magnitude shorter than those recommended in the literature. This allows for the faster magnetic field sampling and the lower power consumption which can significantly broaden the application space of AMR sensors.
A Simple Approach for Determination of Numerical Values of Ferrite Nonlinear Susceptibilities

This article presents a straightforward approach for determination of numerical values of nonlinear susceptibilities of soft magnetic ferrites. It is shown that numerical values of susceptibilities can be calculated from the measured amplitudes of harmonics in the output voltage of ferrite core transformer. For this purpose, useful expressions for the susceptibilities are derived and as example, numerical values of the largest nonlinear susceptibilities those of the third and fifth orders are calculated. Additionally, errors of the measured susceptibilities also are determined. Based on the expressions obtained, the analysis of phase shifts between components of flux density on different frequencies and the magnetic field strength also is performed. The comparison of the calculated and measured output voltage waveforms allows concluding that in the frequency range where losses are small and can be neglected, the susceptibility of the third order is negative but that of the fifth order is positive.
Q-Learning-Based Task Offloading and Resources Optimization for a Collaborative Computing System

Mobile edge computing (MEC) can effectively overcome the shortcomings of high-latency in mobile cloud computing (MCC) by deploying the cloud resources, e.g., storage and computational capability, to the edge. However, the limited computation capability of the MEC restricts the scalability of offloading. Therefore, the basic requirements of the MEC system are to explore effective offloading decisions and resource allocation methods. To address it, we develop a collaborative computing system composed of local computing (mobile device), MEC (edge cloud) and MCC (central cloud). Based on the proposed collaborative computing system, we design a novel Q-learning based computation offloading (QLCOF) policy to achieve the optimal resource allocation and offloading scheme by prescheduling the computation side for each task from a global perspective. Specifically, we first model the offloading decision process as a Markov decision process (MDP) and design a state loss function (STLF) to measure the quality of experience (QoE). After that, we define the cumulation of STLFs as the system loss function (SYLF) and formulate an SYLF minimization problem. Due to the difficulty to directly solve the formulated problem, we decompose it into multiple subproblems and preferentially optimize the transmission power and computation frequency of the edge cloud by the quasi-convex bisection and polynomial analysis method, respectively. Based on the precalculated offline transmission power and edge cloud computation frequency, we develop a Q-learning based offloading (QLOF) scheme to minimize the SYLF by optimizing offloading decisions. Finally, the numeral results show that the proposed QLOF scheme effectively reduces the SYLF under different parameters.
Multiple Kernel Independent Component Analysis for Anti-jamming of Communication Radio

The anti-jamming of communication radio has attracted intensive attentions from communication community. The blind source separation (BSS) is much effective when the jamming has entered. The multiple kernel independent component analysis (MKICA) is proposed instead of a single one, which can not only combine multiple kernels corresponding to different notions of similarity or information from multiple feature subsets, but also fuse distinctions of multiple kernels. The core of algorithm is the kernel canonical correlation analysis (KCCA), where the efficient use of multiple kernel trick makes it more suitable for different sources and more robust for different distributions. Hence, there will be a remarkable improvement of separation accuracy. Numerical experiments based on the artificially synthesized data and intermediate frequency (IF) signal containing clear voice demonstrate state-of-the-art performances of proposed algorithm. It is ideal for separation of useful communication signals when confronting with various jamming in the anti-jamming field.
NAV-Landmarks: Deployable 3D Infrastructures to Enable CubeSats Navigation Near Asteroids

Autonomous operations in the proximity of Near Earth Objects (NEO) are perhaps the most challenging and demanding type of mission operation currently being considered. The exceptional variability of geometric and illumination conditions, the scarcity of large scale surface features and the strong perturbations in their proximity require incredibly robust systems to be handled. Robustness is usually introduced by either increasing the number and/or the complexity of on-board sensors, or by employing algorithms capable of handling uncertainties, often computationally heavy. While for a large satellite this would be predominantly an economic issue, for small satellites these constraints might push the ability to accomplish challenging missions beyond the realm of technical possibility. The scope of this paper is to present an active approach that allows small satellites deployed by a mothership to perform robust navigation using only a monocular visible camera. In particular, the introduction of Non-cooperative Artificial Visual landmarks (NAV-Landmarks) on the surface of the target object is proposed to augment the capabilities of small satellites. These external elements can be effectively regarded as an infrastructure forming an extension of the landing system. The quantitative efficiency estimation of this approach will be performed by comparing the outputs of a visual odometry algorithm, which operates on sequences of images representing ballistic descents around a small non-rotating asteroid. These sequences of virtual images will be obtained through the integration of two simulated models, both based on the Apollo asteroid 101955 Bennu. The first is a dynamical model, describing the landing trajectory, realized by integrating over time the gravitational potential around a three-axis ellipsoid. The second model is visual, generated by introducing in Unreal Engine 4 a CAD model of the asteroid (with a resolution of 75 cm) and scattering on its surface a number N of cubes with side length L. The effect of both N and L on the navigation accuracy will be reported. While defining an optimal shape for the NAV-Landmarks is out of the scope of this paper, prescriptions about the beacons geometry will be provided. In particular, in this work the objects will be represented as high-visibility cubes. This shape satisfies, albeit in a non-optimal way, most of the design goals.
Si-Substrate Removal for AlGaN/GaN Devices on PCB Carriers

This work presents a Si-substrate removal technique for AlGaN/GaN devices on PCB carriers. The Si-removal method is explained and experimentally applied to PCB-embedded AlGaN/GaN-on-Si devices. The PCB-embedding package is reopened at the thermal pad by laser-removal and the Si-substrate is removed by wet chemical etching. The paper presents a direct comparison of the electrical characteristics for devices before and after Si-removal. Different substrate related effects are observed and investigated. The pulsed drain currents degrade after Si-removal, due to the absence of the thermal substrate capacitance. Furthermore, the dynamic on-state resistance is increased by surface trapping on the exposed backside. Measurement results are discussed and physically interpreted. The on-state resistance is unaffected in the range of $175 \mathrm{m}\Omega$ before and after Si-removal. The measurements show an increase of the off-state voltage from 600 V to 1400 V for devices after Si-removal.
One-to-Sixteen Wavelength Multicast of Optical Single-Sideband OFDM Signals Using a Single Pulsed Pump

We demonstrate an effective wavelength multicast scheme to generate 16 new channels with low penalty and high spectral efficiency using a single optical pump. The scheme capitalizes on cross-phase modulation of an input signal by a rate-multiplied pump pulse train to generate multiple wavelength channels. The pulse rate is multiplied in an energy-efficient way via the temporal Talbot effect. In the experiment, we have successfully achieved more than 20 multicast copies of a 40-Gb/s QPSK OFDM single-sideband signal. The copied channels are spaced by 50 GHz which is determined by five times multiplication on the repetition rate of a 10-GHz input pump pulse train. The BER performances of the central 16 channels are evaluated, showing less than 1.3-dB OSNR difference among the channels. Compared to the original single-sideband signal, the central channel experiences only a 0.1-dB OSNR penalty in our approach. The result verifies the effectiveness of our scheme in realizing spectrally efficient and high-channel-count wavelength multicast by using a single pump generated with relatively low-speed electronic resources.
Arduino and Remote based Gesture Keyboard

Different keyboards are available in the market, with variations in size, layouts, programs, and functionalities. Recently, virtual keyboards are also in use but for every single keyboard, the user should remember the arrangement of keys in order to enter different characters. }{Remote-based gesture keyboard is a system that serves on the Internet of Things [IoT] architecture and embedded with Arduino as well as the accelerometer and machine learning process that enforces the system to perform in the desired direction. The remote-based gesture keyboard is the model, where the user can enter text and number in a text editor by moving a hand to the specific motion of characters in the air using Arduino. Arduino operates as a remote where gestures relocate into the air, translating into letters, and exposing them on a computer screen.
Internet of Things and Social Media: A review of Literature and Validation from Twitter Analytics

In today's Era, we are associated with the Internet of Things (IoTs) by some means every time. One broad use of IoTs devices like a smartphone which is utilized for each segment of people. People are using IoT devices either by knowing that they are using or either unconsciously. At the same time, the research related to IoT is also growing at an accelerated rate which leads to asking questions like how much people are aware of IoTs that they are using every day. How much are academic research on the IoT, and Social media matured? This paper presents a comparative analysis of findings from the research on IoT & Social media and from the public talks on IoT on Social Media. We have used the Social media analytics framework for analyzing the data collected from Twitter. Our findings suggest that academic research is more focused on technical aspects of IoT artifacts, while public talks are more towards the use of IoT in different sectors like healthcare, artificial intelligence, etc.
BLHNN: A Novel Charge Prediction Model Based on Bi-Attention LSTM-CNN Hybrid Neural Network

Legal judgment and prediction is a classic traditional task combining artificial intelligence with law. It aims to train a machine judge model to predict the final charges automatically according to the fact descriptions in criminal cases. In recent years, with the successful usage of deep learning techniques on NLP tasks. The researchers explored the application of LSTM model, neuron model and attention mechanism to predict the charges and achieved good results. However, For those crimes whose facts are similar to each other, the existing algorithms can’t get well performance. To address these issues, we propose a novel charge prediction model based on Bi-attention LSTMCNN Hybrid Neural Network (BLHNN) to establish several discriminative properties of charges as internal mapping between fact descriptions and charges. More specifically, first, word2vec algorithm is used to transform the document feature words into fixed dimension word vectors, then TF-IDF value of each feature word in the document is calculated, and TF-IDF value of each feature word is used as the weight to encode the word vectors of all feature words. Then, in order to solve the problem that convolution neural network (CNN) can not get the global features of the text and Bi-LSTM can not focus on the local features of the text in text classification, we adopt a hybrid model including CNN and Bi-LSTM. At the same time, the attention mechanism is introduced to solve the problem of not paying attention to the features of key words in text classification. Finally, the experimental results show that the proposed model can get more accurate text feature information and improve the accuracy of text classification.
KBot: A Knowledge Graph Based ChatBot for Natural Language Understanding Over Linked Data

With the rapid progress of the semantic web, a huge amount of structured data has become available on the web in the form of knowledge bases (KBs). Making these data accessible and useful for end-users is one of the main objectives of chatbots over linked data. Building a chatbot over linked data raises different challenges, including user queries understanding, multiple knowledge base support, and multilingual aspect. To address these challenges, we first design and develop an architecture to provide an interactive user interface. Secondly, we propose a machine learning approach based on intent classification and natural language understanding to understand user intents and generate SPARQL queries. We especially process a new social network dataset (i.e., myPersonality) and add it to the existing knowledge bases to extend the chatbot capabilities by understanding analytical queries. The system can be extended with a new domain on-demand, flexible, multiple knowledge base, multilingual, and allows intuitive creation and execution of different tasks for an extensive range of topics. Furthermore, evaluation and application cases in the chatbot are provided to show how it facilitates interactive semantic data towards different real application scenarios and showcase the proposed approach for a knowledge graph and data-driven chatbot.
Low Latency and Area Efficient VLSI Architecture of 2D Bilinear Interpolation using Brent Kung Adder Based Fast Multiplier

Real-time image processing has extended its applicability that includes significant domains like satellite imaging and medical imaging. Despite such massive usage, the prospect of real-time imaging throttles due to the scarcity of any dedicated hardware. The dearth of any available VLSI architectures for real-time imaging can be attributed to the computational loads of the imaging methods. The 2D bilinear interpolation is an important operation extensively utilized in image scaling, image inpainting, and also in the post-processing step of image registration. But there hardly exists any suitable hardware for real-time image interpolation. In this paper, an innovative first-of-its-kind VLSI architecture for 2D bilinear interpolation has been presented. Our proposed pipelined VLSI architecture is based on our proposed shift and adds based multiplier, which is mostly combinational. In our proposed multiplier, we have used brent kung adder, which is popular for its fast computation. Our proposed architecture 2D bilinear interpolation consists of only some adders, hard shifters, and negligible numbers of logic gates. The presented design is also time-efficient, in the sense, the critical path delay is only restricted by the propagation delay of an adder, thereby ensuring the satisfactory value of maximum operating frequency. As the design is made pipelined, naturally, the throughput rate is high. The power efficiency of our proposed design is quite natural as the design contains only a few basic combinational modules along with some logic gates. The proposed design is realized using verilog before being simulated using the Xilinx Vivado 1S.2 tool. The simulated design is implemented in the FPGA of the Zynq UltraScale+ MPSoC evaluation board for verifying its quality in case of real-time applications. The simulated results and the outcomes of the real-time onboard testing confirm our claim of hardware, time, and power efficiency.
Modeling and Test Generation for Combinational Hardware Trojans

Because of globalization in the semiconductor industry and manufacturing processes, integrated circuits are more exposed to harmful attacks called hardware Trojan. This can be considered as a serious threat to the Integrated circuits. Due to the inclusion of hardware Trojan into the existing circuit, it causes the possible effects like changing the functionality of the circuit and discharges some-secret information to the attacker. In this paper, we designed a hardware Trojan, which consists of two parts namely Trigger used to activate Trojan and payload, which changes the functionality of the chip normally the payload is an XOR gate. 2K×(K-1) Trojans are generated for a one-line trigger merged with one payload line for a circuit with K signal lines. The Trojan is detected by generating test patterns by using standard ATPG Tools which detects conditional stuck-at faults-and allows us to find the Trojan coverage and in addition to that this model is effective in finding out the real Trojans.
A Novel Printed Monopole Antenna With Folded Stepped Impedance Resonator Loading

In this article, a novel printed monopole antenna with folded stepped impedance resonator (SIR) loading is proposed, designed and fabricated with a standard printed circuit board process. The antenna comprises of a printed monopole antenna and a folded SIR with internal coupling etched on the back of the antenna. By loading this folded SIR as a near-field resonant parasitic (NFRP) element, the resonant frequency of the antenna can be reduced by the strong coupling between the folded SIR and the radiation patch of the printed monopole. After reviewing the theoretical analysis of the proposed printed monopole antenna, a prototype antenna has been fabricated and measured. The simulated results agree well with the measured results, and the prediction performance of the antenna is verified. Therefore, the proposed method in this article is a promising candidate for printed monopole antenna design.
Stacked Back Reflector Architecture for Advanced Optical Management in State-of-the-Art Single-Junction μc-Si:H Solar Cells

The present work deals with an advanced technology for efficient light management through back reflection in thin silicon based solar cells, in particular, single-junction microcrystalline silicon (μc-Si:H) solar cells. Semiconductor nanoparticles (Ag2S NPs) have been chosen to design the back reflector layer (BRL) by embedding them within two thin layers of indium tin oxide (ITO). Owing to its nominal parasitic light absorption over a broad wavelength region (300–1100 nm), the Ag2S NPs can reflect back a notable amount of light that were supposed to get transmitted through the relatively less thick (∼2 μm) active microcrystalline Si layer, and hence, can function like nanomirrors. Encapsulation of the Ag2S NPs between the ITO layers provides chemical and physical stability to the nanomirrors. By placing such BRL at the back of this p-i-n based superstrate structure, a significant amount of light (>90%), mainly in the red and near infrared (NIR) region, was found to reflect back to the cell and this resulted in a state-of-the-art photoconversion efficiency of 9.36% for single-junction μc-Si:H solar cells. The reported value is one of the best in the class. As the main light absorption zone for μc-Si:H falls in the red and NIR region (bandgap energy of μc-Si:H is 1.1–1.2 eV), such back reflections become worthy. The experimentally observed facts have also been validated through theoretical simulations.
A Novel Dynamic Locomotion Control Method for Quadruped Robots Running on Rough Terrains

Quadruped robots have excellent application prospects whereas the locomotion control of them on rough terrains is still a challenging problem, especially for those of large scales. The existing methods are either too complicated or lack of accuracies due to assumptions used. This paper presents a novel control algorithm for quadruped robots running on rough terrains inspired by the virtual model control and the model predictive control. State recursions are carried out based on the dynamic model of the trunk during the standing phase. The modeling of the body is implemented in the self-defined motion reference frame that avoids global state estimations and accumulative errors. The force distribution of the standing legs is realized by quadratic optimization involving state predictions. Forces of the swing legs are calculated by the virtual spring-damping model that follow the desired trajectory which is robust to external disturbances. These two sub-controllers are combined by the time-force based state machine. Simulation results show that the quadruped robot obtains the adaptability to rough terrains and robustness to lateral pushes with the proposed method.
ADMorph: A 3D Digital Microfossil Morphology Dataset for Deep Learning

Microfossils, tiny fossils whose study requires the use of a microscope, have been widely applied in many fields of earth, life, and environmental sciences. The abundance and high diversity of microfossils, as well as the need for rapid identification, call for automated methods to classify microfossils. In this study, we constructed an open dataset of three-dimensional (3D) microfossils and proposed a deep learning-based approach for microfossil classification. The dataset, named ‘Archives of Digital Morphology’ (ADMorph), currently contains more than ten thousand 3D models from five classes of 410 million-year-old fishes. The deep learning-based method includes data preprocessing, feature extraction, and 3D microfossil model classification. To assess the method performance and dataset representability, we performed extensive experiments. Compared with multiview convolutional neural networks (MVCNN) (91.54%), PointNet (64.13%), and VoxNet (78.15%), the method proposed herein had higher accuracy (97.60%) on the experimental dataset. We also verified data preprocessing (92.36%) and feature extraction (97.10%). We combined them to obtain the macroaveraging accuracy of 97.60%, the highest accuracy of 100%, and the lowest accuracy of 88.78%. We suggest that the proposed method can be applied to other 3D fossils and biomorphological research fields. The fast-accumulating 3D fossil models might become a source of information-rich datasets for deep learning.