Miracle-dz/UniFAR-FTUs huggingface dataset:
Meter-Wave Synthetic Aperture Radar for Concealed Object Detection

T he book being reviewed, “Meter-Wave Synthetic Aperture Radar for Concealed Object Detection” authored by Hans Hellsten, is a must read for anyone working or wanting to work in the area of SAR and foliage, ground, and building penetration. It would be useful for professionals in the radar community, those at universities, and for courses. The book will be a bible for many in the field. Readers benefit from an author who has decades of practical and theoretical experience on the subject. The book is based on the author’s original work in Sweden in this area which started in 1985. It is unique. The author was involved with the following SAR systems: the revolutionary CARABAS 1 (25–90 MHz, which used two parallel 5-m-long dipole wide band inflatable sack antennas deployed from the rear of a small jet aircraft, 1992), CARABAS 2 (25–90 MHz, which used instead two parallel 8m-long dipole wide band rigid antennas on the front of a small jet aircraft, 1997), LORA (CARABAS 2 with addition of 200–600 MHz, designed for examining moving target detection, 2000), and CARABAS 3 (27.5–82.5 MHz and 137.5–357.5 MHz, on small manned helicopter—the Schweitzer 300 C, antenna 1.5 m long, allows change of polarization and also detection of moving targets, 2012). These systems provide ground and foliage penetration. The very successful CARABAS 1 demonstrated its unique penetration capabilities under testing by MIT Lincoln Laboratory using funding from DARPA. The book gives a rigorous detailed coverage of wave propagation through and scattering from foliage and the ground. It starts from basics—Maxwell’s equations. The book deals with resolution on the order of 1 m for near 1-m wavelength SAR systems. These are ultrawideband radar systems. Covered in detail are waveform design, processing, and hardware problems that this resolution and wavelength imposes. To achieve the required accuracy and resolution covered are: methods relying on inertial measurements, satellite navigation data, as well as autofocusing. The book deals with the important problem of operating on a noninterfering basis with communication systems. All claims made by the author are based on proven basics and well-established theory, combined with experimental evidence. The book addresses the detection of stationary and moving surface targets and subsurface targets. Chapter 1 gives a very informative historical coverage of the fine Swedish foliage penetration work. Included are lessons learned. Recommended reading for all radar engineers.
An Optimization Method for Intrusion Detection Classification Model Based on Deep Belief Network

The rapid development and popularization of the network have brought many problems to network security. Intrusion detection technology is often used as an effective security technology to protect the network. The deep belief network (DBN), as a classic model of deep learning, has good classification performance and is often used in the field of intrusion detection. However, the network structure of DBN is generally set through practical experience. For the optimization problem of the DBN-based intrusion detection classification model (DBN-IDS), this paper proposes a new joint optimization algorithm to optimize the DBN’s network structure. First, we design a particle swarm optimization (PSO) based on the adaptive inertia weight and learning factor. Second, we use the fish swarm behavior of cluster, foraging, and other behaviors to optimize the PSO to find the initial optimization solution. Then, based on the initial optimization solution, we use the genetic operators with self-adjusting crossover probability and mutation probability to optimize the PSO to search the global optimization solution. Finally, the global optimization solution constructed by the above-mentioned joint optimization algorithm is used as the network structure of the intrusion detection classification model. The experimental results show that compared with other DBN-IDS optimization algorithms, our algorithm shortens the average detection time by at least 24.69% on the premise of increasing the average training time by 6.9%; compared with the tested classification algorithms, our DBN-IDS improves the average classification accuracy by at least 1.3% and up to 14.80% in the five-category classification, which is proved to be an efficient DBN-IDS optimization method.
Voltage Source Power Line Conditioner for Applications in Local Supply Systems

The paper presents the basic principles of operation of voltage source power line conditioners (VSPLC) as well as their energy and filtration properties. A 4-level cascade based Voltage Source Converter (VSC) is used as a voltage source. The advantages of this topology is relatively simple construction and simple way of balancing DC link voltages. Results of the theoretical and experimental investigations confirm good energetic and filtration properties compared to Active Power Filters (APF).
Pose Determination from Multi-View Image using Deep Learning

A high precision method for position and attitude determination is presented. One of the difficulties in pose determination is the lack of reliable visual features. As a result, we exploit a novel convolutional neural network for stereo matching, so as to solve the problem of image feature extraction. The network is based on a siamese network and incorporates dense block into each branch. There is occlusion problem in the close-range phase of measuring object. In order to solve the occlusion, a multi-view system is established. The measurement results meet the accuracy requirements of pose determination and verify the feasibility of the method.
P-Gram: Positional N-Gram for the Clustering of Machine-Generated Messages

An IT system generates messages for other systems or users to consume, through direct interaction or as system logs. Automatically identifying the types of these machine-generated messages has many applications, such as intrusion detection and system behavior discovery. Among various heuristic methods for automatically identifying message types, the clustering methods based on keyword extraction have been quite effective. However, these methods still suffer from keyword misidentification problems, i.e., some keyword occurrences are wrongly identified as payload and some strings in the payload are wrongly identified as keyword occurrences, leading to the misidentification of the message types. In this paper, we propose a new machine language processing (MLP) approach, called <inline-formula> <tex-math notation="LaTeX">${P}$ </tex-math></inline-formula>-gram, specifically designed for identifying keywords in, and subsequently clustering, machine-generated messages. First, we introduce a novel concept and technique, positional <inline-formula> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula>-gram, for message keywords extraction. By associating the position as meta-data with each <inline-formula> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula>-gram, we can more accurately discern which <inline-formula> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula>-grams are keywords of a message and which <inline-formula> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula>-grams are parts of the payload information. Then, the positional keywords are used as features to cluster the messages, and an entropy-based positional weighting method is devised to measure the importance or weight of the positional keywords to each message. Finally, a general centroid clustering method, <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula>-Medoids, is used to leverage the importance of the keywords and cluster messages into groups reflecting their types. We evaluate our method on a range of machine-generated (text and binary) messages from the real-world systems and show that our method achieves higher accuracy than the current state-of-the-art tools.
Extra-dimensional island-style FPGAs (abstract only)

This paper proposes modifications to standard island-style FPGAs that provide interconnect capable of scaling at the same rate as typical netlists, unlike traditionally tiled FPGAs. The proposal uses a logical third and fourth dimensions to create increasing wire density for increasing logic capacity. The additional dimensions are mapped to standard two-dimensional silicon. This innovation will increase the longevity of a given cell architecture, and reduce the cost of hardware, CAD tool and Intellectual Property (IP) redesign. In addition, extra-dimensional FPGA architectures provide a conceptual unification of standard FPGAs and time-multiplexed FPGAs.
Robust Adaptive Triple-Step Control for Lane-Keeping of Autonomous Ground Vehicles*

Active steering control for lane keeping is a significant technique applied for autonomous ground vehicles (AGVs). This paper addresses a problem of high-accuracy control for lane keeping of AGVs subject to time-varying vehicle speed and parameter uncertainties. A robust adaptive triple-step nonlinear control scheme is presented. In this strategy, a nonlinear reference generator is designed to generate a virtual reference signal. Then, a robust triple-step nonlinear controller with adaptive update laws is then proposed for lane keeping of AGVs. It is shown by the Lyapunov stability analysis that the lateral offset of AGV is input-to-state (ISS) in the presence of bounded perturbation of tire stiffness coefficient. The effectiveness of the approach is verified on a high-fidelity full-vehicle model constructed in veDYNA and MATLAB/Simulink.
CFD Analysis of Two Supersonic Nozzles Designed for a High Test Peroxide Thruster

This study presents a numerical simulation method, for predicting the flow of a turbulent compressible fluid through two forms of propelling nozzles (convergent-divergent) operating in stationary regime. The flow gas is products of hydrogen peroxide (H2O2) decomposition. The existence and interaction of several physical phenomena such as shock waves and boundary layer separation are very complex phenomena can significantly affect the reliability and performance of the propulsive nozzles. This numerical studies employed different nozzle, conical shape and parabolic shape. The question of which geometry would work best is the major research question that this study will address. Selected profiles are studied with an external domain to observe the behavior of the flow outside the outlet of the nozzles. The analysis was run on ANSYS fluent 15.0. 7 - 2014. The problem is solved using Navier-Stokes equations, to simulate the different chosen profiles, leading to the definition of the optimal choice for our application.
V-Band End-Fire Radiating Planar Micromachined Helical Antenna Using Through-Glass Silicon Via (TGSV) Technology

In this paper, we demonstrate a V-band planar micromachined helical antenna (PHA) with end-fire radiation on the glass substrate. The planar rectangular helical configuration is realized using the novel through-glass silicon via (TGSV) technology. The proposed micromachined antenna is designed and fabricated on a borosilicate glass substrate of thickness <inline-formula> <tex-math notation="LaTeX">$350~\mu \text{m}$ </tex-math></inline-formula>, which has a very low-dielectric loss compared to silicon at millimeter-wave bands. The proposed PHA is fed by a microstrip line, and the planar helical configuration with 3.25 turns with a truncated ground plane is designed for achieving wideband end-fire radiation with seven tungsten-coated silicon vias and six connected gold arm patterns, which are fabricated using the TGSV technology. The electrical length of the proposed antenna is (<inline-formula> <tex-math notation="LaTeX">$3\,\,\lambda _{\text{o}} \times 1.4\,\,\lambda _{\text{o}}$ </tex-math></inline-formula>). The designed antenna operates at the center frequency of 58 GHz. A prototype of the proposed antenna is fabricated by micromachining technology and tested. The simulated and measured results show that the proposed antenna has a wide operational bandwidth of 50.3 to 65 GHz for <inline-formula> <tex-math notation="LaTeX">$\vert \text{S}_{ {11}} \vert < -10$ </tex-math></inline-formula> dB with a fractional bandwidth (FBW) of 25.5%. The measured peak gain is 6.3 dBi, and measured efficiency is 62% at the center frequency of 58 GHz. All measurements are in close agreement with simulated results. The proposed planar helical antenna with end-fire radiation is useful for applications in traveling-wave-tubes operating in millimeter-wave and higher frequencies, millimeter-wave on-board wireless communication, radar imaging, and tracking applications.
UNIVERSITY STUDENTS DESCRIBES: HOW I LEARNED THE BEST?

Learning is a dynamic process. There are some factors that affect learning positively or negatively and in other words, facilitate or complicate learning. Learner, learning methods, types of issues to be learned and the learning environment are some of these factors. The purpose of this research to determine studentsâ€™ opinions studying at the university which course they have seen until this time how they learn best. The working group consists of totally 140 students studying in 2015-2016 academic year at Necmettin Erbakan University. The study conducted in the form of qualitative research method and data were collected by semi-structured interviews. In the form of student interviews were asked to write the lessons they have learned best and explain how they learn best. The data were analyzed by content analysis. According to findings it has found that students have learned mathematics, history, literature and Turkish lessons the best. It has revealed that they learned better via expression, problem solving and through experience.
Robust Audio Sensing with Multi-Sound Classification

Audio data is a highly rich form of information, often containing patterns with unique acoustic signatures. In pervasive sensing environments, because of the empowered smart devices, we have witnessed an increasing research interest in sound sensing to detect ambient environment, recognise users’ daily activities, and infer their health conditions. However, the main challenge is that the real-world environment often contains multiple sound sources, which can significantly compromise the robustness of the above environment, event, and activity detection applications. In this paper, we explore different approaches in multi-sound classification, and propose a stacked classifier based on the recent advance in deep learning. We evaluate our proposed approach in a comprehensive set of experiments on both sound effect and real-world datasets. The results have demonstrated that our approach can robustly identify each sound category among mixed acoustic signals, without the need of any a priori knowledge about the number and signature of sounds in the mixed signals.
Composable Component Interfaces for Time-Triggered Systems

When building real-time systems out of networked components, the use of temporal-firewall interfaces between the components supports a composable system design. This paper shows that, depending on the requirements of an application, the temporal-firewall interfaces of a time-triggered communication system can be accessed in two ways, either as data-sharing interfaces with asynchronous access or as time-aware interfaces with time-synchronized access. The paper describes each of these two ways of interfacing with a time-triggered communication system and characterizes their differences.
A Neural-Network-Based Method for RUL Prediction and SOH Monitoring of Lithium-Ion Battery

The prognostic and health management (PHM) of lithium-ion batteries has received increasing attention in recent years. The remaining useful life (RUL) prediction and state of health (SOH) monitoring are two important parts in PHM of the lithium-ion battery. Nowadays, the development of signal processing technology and neural network technology introduces new data-driven methods to RUL prediction and SOH monitoring of the lithium-ion battery. This paper presents a neural-network-based method that combines long short-term memory (LSTM) network with particle swarm optimization and attention mechanism for RUL prediction and SOH monitoring of the lithium-ion battery. Before predicting RUL of the lithium-ion battery, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is utilized for the raw data denoising, which can improve the accuracy of prediction. A real-life cycle dataset of lithium-ion batteries from NASA is used to evaluate the proposed method, and the experiment results show that when compared with traditional methods, the proposed method has higher accuracy.
Visual Search on Aerial Imagery as Support for Finding Lost Persons

The mountain rescue services in Croatia and BiH have been using UAVs on search and rescue missions for years. They prove to be very effective on the hard-to-reach Mediterranean karsts landscape, characterized by low but not too dense vegetation. After mapping some terrain segment and collecting a large number of precise and detailed footage from above, images are visually scanned and examined by search rescue member/s. In this paper we will examine the effectiveness of visual search of human observers for the purpose of finding missing/lost persons on aerial imagery.
Robust $H_{\infty}$ Controller Design for Active Vibration Suppression with Input Constraints

This study deals with robust $H_{\infty}$ controller design for active vibration suppresion system where the input constraints are taken into consideration in addition to model uncertainties that are present in the system model. Two different approaches are utilized in order to implement the input constraints into the controller design process. The robust $H_{\infty}$ controller design algorithms involving input constraints are carried out via linear matrix inequalities and the performance of obtained controllers is illustrated in simulation studies where a two-story structure supplied with an Active-Mass-Damper(AMD) is used.
An Anomaly-Based IDS for Detecting Attacks in RPL-Based Internet of Things

The Internet of Things (IoT) is a concept that allows the networking of various objects of everyday life and communications on the Internet without human interaction. The IoT consists of Low-Power and Lossy Networks (LLN) which for routing use a special protocol called Routing over Low-Power and Lossy Networks (RPL). Due to the resource-constrained nature of RPL networks, they may be exposed to a variety of internal attacks. Neighbor attack and DIS attack are the specific internal attacks at this protocol. This paper presents an anomaly-based lightweight Intrusion Detection System (IDS) based on threshold values for detecting attacks on the RPL protocol. The results of the simulation using Cooja show that the proposed model has a very high True Positive Rate (TPR) and in some cases, it can be 100%, while the False Positive Rate (FPR) is very low. The results show that the proposed model is fully effective in detecting attacks and applicable to large-scale networks.
Mixed H2/H∞ Balanced Truncations for Discrete Time Linear Systems*

A mixed H2/H∞ balanced realization is proposed for discrete time systems, where an H2 Lyapunov and an H∞ Riccati equations have the same positive definite diagonal matrix. Based on this realization, a balanced truncation is possible, which gives a reduced-order model. It is shown that the H2 norm of any reduced-order model based on this realization is not greater than that of the original model. The H∞ norm of the reduced-order model is also bounded by the value which appears in the Riccati equation and gives an upper bound of the H∞ norm of the original model. A key idea for proving these properties is that the Riccati equation is rewritten by a matrix equation which does not contain inverse matrix via the bounded real lemma. Upper bounds of the H2 and the H∞ norms of the error system between the original model and the reduced-order model are also established. These properties are illustrated through a numerical example.
Power Quality Disturbance Recognition Based on Multiresolution S-Transform and Decision Tree

It is important to find an effective method for power quality (PQ) disturbance recognition under the challenges of increasing power system pollution. This paper proposes a PQ disturbance signal recognition method based on Multiresolution S transform (MST) and decision tree (DT). For improving recognition accuracy, adjustment factors are introduced to obtain a controllable time-frequency resolution. On this basis, five feature statistics are obtained to quantitatively reflect the characteristics of the analyzed power quality disturbance signals, which is less than the traditional S-transform-based method. As the proposed methodology can effectively identify the PQ disturbances, the efficiency of the DT classifier could be guaranteed. In addition, the noise impacts are also taken into consideration, and 16 types of noisy PQ signals with a signal-to-noise ratio (SNR) scoping from 30 to 50 dB are used as the analyzed dataset. Finally, a comparison between the proposed method and other popular recognition algorithms is conducted. The experimental results demonstrate that the proposed method is effective in terms of detection accuracy, especially for combined PQ disturbances.
Improving Opinion Formation Models on Social Media Through Emotions

Opinion formation models describe the opinion dynamics of interacting people. Social media are drastically increasing and have become one of the most critical media for people interactions. According to psychological researches, one’s emotion diffuses across interacting people. Furthermore, emotion affects people’s opinion. The emotion contagion also happens through social media via the users’ posts and affects the readers. Therefore, emotion is an essential element in opinion formation models in a social network which has attracted little attention. In this paper, we show how considering emotion in opinion formation model for online social networks improves the model. We have used a dataset containing some debates from the CreateDebate.com website. Two classifiers, with and without considering emotions, have been implemented based on the social impact model of opinion formation to predict the stances of the users’ next post in the dataset and the results have been compared with the dataset. The experiment results lead us to conclude that considering emotions improves the accuracy and precision of the social impact model of opinion formation in social media.
FANNCortexM: An Open Source Toolkit for Deployment of Multi-layer Neural Networks on ARM Cortex-M Family Microcontrollers : Performance Analysis with Stress Detection

We present FANNCortexM, an open-source toolkit built upon the Fast Artificial Neural Network (FANN) library to run lightweight neural networks on ARM Cortex-M series microcontrollers. The toolkit takes a neural network trained with FANN and generates code targeted at execution on low-power microcontrollers either with a floating-point unit (i.e., ARM Cortex-M4F and M7F) or without a floating-point unit (i.e., ARM Cortex M0-M3). The toolkit is optimized in terms of memory and computational resources. We demonstrate its functionality on the basis of a sample application scenario performing stress detection on a wearable multi-sensor bracelet. Experimental results show a high classification accuracy of 96% for the target application scenario, and low latency of only a few microseconds while keeping the memory requirements (11kB flash storage, 36kB RAM) far below the limitations of the device. Power measurements show a power consumption of only 1.6mW, thus allowing continuous stress detection for 29 days.