Miracle-dz/UniFAR-FTUs huggingface dataset:
Binary Input Layer: Training of CNN models with binary input data

For the efficient execution of deep convolutional neural networks (CNN) on edge devices, various approaches have been presented which reduce the bit width of the network parameters down to 1 bit. Binarization of the first layer was always excluded, as it leads to a significant error increase. Here, we present the novel concept of binary input layer (BIL), which allows the usage of binary input data by learning bit specific binary weights. The concept is evaluated on three datasets (PAMAP2, SVHN, CIFAR-10). Our results show that this approach is in particular beneficial for multimodal datasets (PAMAP2) where it outperforms networks using full precision weights in the first layer by 1:92 percentage points (pp) while consuming only 2 % of the chip area.
A two-wheeled vehicle oriented lane detection algorithm

In this paper, a lane detection and classification algorithm based on a monocular camera for a two-wheeled vehicle in small roll angle conditions is described. The algorithm is designed to work in different illumination conditions, both in night time and daytime. First, all road line markings are identified using a linear lane model exploiting Hough transform and perspective filtering. Identified line markings are classified based on geometric features using a suitable SVM. Then, the boundaries of the actual lane occupied by the vehicle are detected among all the possible lane marking couples. Finally, detected lane is tracked using heuristic rules. The overall strategy has been validated using videos acquired with a motorcycle in different scenarios. Experimental results have proven the robustness of the algorithm with respect to illumination changes and small variations of motorcycle roll angle, with a overall detection accuracy of 94.8% and classification accuracy of 98.1%.
Optimal Path Planning and Tracking for AGV Lane Merging

This paper addresses an innovative algorithm in optimal path planing dedicated for lane change scenarios. The generated trajectory is bounded laterally inside the road boundaries and longitudinally with a security distance from the preceding vehicles. The second part of this paper tackle the design and the implementation of an adaptive computed torque controller which is exclusively applied for automated vehicle control. Finally, a numerical simulation is elaborated to prove the efficacy of the developed approach.
Simplicity and Performance of Direct Current Control DCC Compared with other Identification Algorithms for Shunt Active Power Filter

In recent years, the shunt active power filter SAPF has received much attention with many control techniques and identification algorithms for compensating the current harmonic pollution. The quality of current harmonics compensation depends heavily on the performance of the chosen algorithm of identification. This paper compares several current generation algorithms for a 3-phase hysteresis inverters used as SAPF. The direct current control algorithm DCC, the classical direct power control DPC and the synchronous reference frame algorithm SRF are used to generate the appropriate harmonic currents. The simulation results show an excellent behaviour of the system with a DCC algorithm in the case of balanced network and non-linear loads based on three-phase bridge rectifier with diodes and (R-L) load. The proposed SAPF with DCC algorithm achieved a very low value of total harmonic distortion THDi and the system was in perfect compliance with harmonic limits per IEEE std 519-1992, the THD of current decreases from 28 % without SAPF to 1.47 % with a simple DCC algorithm.
Tissue Intrinsic Fluorescence Spectra-Based Digital Pathology of Liver Fibrosis by Marker-Controlled Segmentation

Tissue intrinsic emission fluorescence provides useful diagnostic information for various diseases. Because of its unique feature of spectral profiles depending on tissue types, spectroscopic imaging is a promising tool for accurate evaluation of endogenous fluorophores. However, due to difficulties in discriminating those sources, quantitative analysis remains challenging. In this study, we quantitatively investigated spectral-spatial features of multi-photon excitation fluorescence in normal and diseased livers. For morphometrics of multi-photon excitation spectra, we examined a marker-controlled segmentation approach and its application to liver fibrosis assessment by employing a mouse model of carbon tetrachloride (CCl4)-induced liver fibrosis. We formulated a procedure of internal marker selection where markers were chosen to reflect typical biochemical species in the liver, followed by image segmentation and local morphological feature extraction. Image segmentation enabled us to apply mathematical morphology analysis, and the local feature was applied to the automated classification test based on a machine learning framework, both demonstrating highly accurate classifications. Through the analyses, we showed that spectral imaging of native fluorescence from liver tissues have the capability of differentiating not only between normal and diseased, but also between progressive disease states. The proposed approach provides the basics of spectroscopy-based digital histopathology of chronic liver diseases, and can be applied to a range of diseases associated with autofluorescence alterations.
A Three-Level Boost Converter with an Extended Gain and Reduced Voltage Stress using WBG Devices

In this paper, a new three-level boost (TLB) converter built with Gallium Nitride (GaN) high electron mobility transistors (HEMTs) and Silicon Carbide (SiC) Schottky diodes is presented. The proposed converter has a continuous input current, wide gain range, reduced voltage stress (compared to other TLB converters in literature), and a common ground. The reduction of voltage stress on the power switches enables the utilization of transistors with less rated voltage, hence, the efficiency is enhanced. The operation of this converter at wide range of modulation index values is discussed. The proposed converter is a good candidate for applications that require a universal input such as renewable energy systems, and fuel cell vehicles. The experimental results verify the theoretical analysis and the feasibility of the proposed topology.
A LFM-based adaptive wake-up signal detection approach for underwater acoustic communication system

The paper focuses on wake-up mechanism for underwater acoustic communication (UAC) system. Wake-up mechanisms for UAC terminals play an important role in reducing the power consumption and extending the battery life. Compared with terrestrial wireless counterparts, the wake-up receivers for UAC terminals are challenged by the severe underwater acoustic channels, which are characterized by doubly-selective fading and low signal-to-noise ratio (SNR). Furthermore, the wake-up receiver is with weak processing ability. The paper proposes a wake-up mechanism named as channel-adaptive detection and joint decision (ChAD-JD). ChAD-JD uses linear frequency modulation (LFM) as wake-up signals. In order to increase the detection probability and reduce the probability of false alarm, the novel approach applies channel-adaptive detection and joint decision methods, respectively. Simulation and experimental results show that ChAD-JD is more reliable and effective compared with traditional LFM-based detection methods with a fixed threshold.
Research on Combined Frequency Sensing Technique for Multi-obstacle Environment Positioning

With the network information time development, Internet of Things is widely applied in more fields. The sensing technique becomes indispensable technology to modern applications. The RFID is one of advanced wireless sensor. The paper discusses and analyzes wireless sensor frequency phenomenon and characteristics, focuses on research the integrated frequency method, and puts forward a particular positioning solution for complex obstacles situation by design and realization. This specified design can be extended to most other fields like mobile object tracking, real time environment measure, logistics positioning etc. It is of great research reference significance and application value.
gExtractor: Towards Automated Extraction of Malware Deception Parameters

The lack of agility in cyber defense gives adversaries a significant advantage for discovering cyber targets and planning their attacks in stealthy and undetectable manner. While there has been significant research on detecting or predicting attacks, adversaries can always scan the network, learn about countermeasures, and develop new evasion techniques. Active Cyber Deception (ACD) has emerged as effective means to reverse this asymmetry in cyber warfare by dynamically orchestrating the cyber deception environment to mislead attackers and corrupting their decision-making process. However, developing an efficient active deception environment usually requires human intelligence and analysis to characterize the attackers' behaviors (e.g., malware actions). This manual process significantly limits the capability of cyber deception to actively respond to new attacks (malware) in a timely manner. In this paper, we present a new analytic framework and an implemented prototype, called gExtractor, to analyze the malware behavior and automatically extract the deception parameters using symbolic execution in order to enable the automated creation of cyber deception schemes. The deception parameters are environmental variables on which attackers depend to discover the target system and reach their goals; Yet, they can be reconfigured and/or misrepresented by the defender in the cyber environment. Our gExtractor approach contributes to the scientific and system foundations of reasoning about autonomous cyber deception. Our prototype was developed based on customizing a symbolic execution engine for analyzing Microsoft Windows malware. Our case studies of recent malware instances show that gExtractor can be used to identify various critical parameters effective for cyber deception.
Engagement Estimation in Advertisement Videos with EEG

Engagement is a vital metric in the advertising industry and its automatic estimation has huge commercial implications. This work presents a basic and simple framework for engagement estimation using EEG (electroencephalography) data specifically recorded while watching advertisement videos, and is meant to be a first step in a promising line of research. The system combines recent advances in low cost commercial Brain-Computer Interfaces with modeling user engagement in response to advertisement videos. We achieve an F1 score of nearly 0.7 for a binary classification of high and low values of self-reported engagement from multiple users. This study illustrates the possibility of seamless engagement measurement in the wild when interacting with media using a non invasive and readily available commercial EEG device. Performing engagement measurement via implicit tagging in this manner with a direct feedback from physiological signals, thus requiring no additional human effort, demonstrates a novel and potentially commercially relevant application in the area of advertisement video analysis.
Social Networking and Caching Aided Collaborative Computing for the Internet of Things

The IoT, as the latest information technology, is expected to support the ubiquitous connectivity of anything, anytime and anywhere. Since numerous IoT devices will be connected to cellular networks, the conflicting demands of ultra-low latency, massive connectivity and high reliability impose significant burdens on the operational wireless networks. Exploiting various temporarily unused computational and storage resources by invoking cooperative caching and computing has the potential of supporting these demanding applications. One of the key characteristics of next-generation networks is the boom of applications in social networking. By exploiting the benefits of social networking, the burdens imposed on wireless networks by the IoT applications can be mitigated by the proposed caching assisted collaborative computing IoT framework.
Virtual Oscillator Controller Optimisation Using Low-Bandwidth Communication

Power systems around the world continue to change with the increasing adoption of distributed generation in the form of renewable energy technologies. The increasing penetrations of intermittent renewable energy technologies necessitates improvements in power sharing control, with microgrids being a popular arrangement for large numbers of distributed energy sources. In recent years a new method for autonomously sharing power has been researched known as virtual oscillator control (VOC). Autonomous/Wireless power-sharing methods are however susceptible to steady state power sharing error due to heterogeneous network impedances. This paper presents a novel low-bandwidth secondary controller that adjusts the set-points of the virtual oscillator controller to mitigate power-sharing error caused by heterogeneous network impedances. The virtual oscillator controller is shown to exceed the performance of the droop controller during transient events, however a small inherent power coupling is demonstrated in the virtual oscillator system with heterogeneous network resistances.
A Review of Low-End, Middle-End, and High-End Iot Devices

Internet of Things (IoT) devices play a crucial role in the overall development of IoT in providing countless applications in various areas. Due to the increasing interest and rapid technological growth of sensor technology, which have certainly revolutionized the way we live today, a need to provide a detailed analysis of the embedded platforms and boards is consequential. This paper presents a comprehensive survey of the recent and most-widely used commercial and research embedded systems and boards in different classification emphasizing their key attributes including processing and memory capabilities, security features, connectivity and communication interfaces, size, cost and appearance, operating system support, power specifications, and battery life and listing some interesting projects for each device. Through this exploration and discussion, readers can have an overall understanding on this area and foster more subsequent studies.
Decoding of Binary Mental Arithmetic Based Near-Infrared Spectroscopy Signals

There has been an increase interest for functional near-infrared spectroscopy (NIRS) in recent years since it is a non-invasive technique as well as few restrictions to the subjects and not affected by electrical noise. In this study, we analyzed mental arithmetic based NIRS signals that it can be helpful for patients like dyscalculia where difficulty learning or lack of attention problem exists. So, it is important that the mental arithmetic is effectively separated from NIRS signal. For this purpose, first, we determined change in oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) concentrations by applying the modified Beer-Lambert law to NIRS data set. After Hilbert transform (HT)+ sum derivative (SD) based features were extracted from pre-processed HbR and HbO, these features were classified by k-nearest neighbors. The average classification accuracy (CA) rates of 82.87% and 84.94% were calculated from the HT+SD based features that best determine the mental arithmetic of the HbR and HbO signals, respectively. It can be said that the proposed method is effective for this dataset, in view of the fact that these values are 2.17% and 1.34% higher than CAs calculated in the literature for HbR and HbO, respectively.
A Data-Driven Residential Transformer Overloading Risk Assessment Method

Residential transformer population is a critical type of asset that many electric utility companies have been attempting to manage proactively and effectively to reduce unexpected transformer failures and life loss that are often caused by overloading. Within the typical power asset portfolio, the residential transformer asset is often large in population, has the lowest reliability design, lacks transformer loading data, and is susceptible to customer loading behaviors, such as adoption of distributed energy resources and electric vehicles. On the bright side, the availability of more residential service operation data along with the advancement of data analytics techniques has provided a new path to further our understanding of residential transformer overloading risk statistically. This paper develops a new data-driven method that combines transformer temperature rise and insulation life loss simulation model with clustering analysis technique. It quantitatively and statistically assesses the overloading risk of residential transformer population in one area and suggests proper risk management measures according to the assessment results. Multiple application examples for a Canadian utility company have been presented and discussed in detail to demonstrate the applicability and usefulness of the proposed method.
Multi-objective optimal dispatch of AC-DC Hybrid Microgrid

This paper proposes a multi-objective optimal dispatch strategy for dispatchable distributed generation units in an AC-DC hybrid microgrid. The proposed method determines optimal droop values of dispatchable distribution generation units to minimize total operation cost and emission in an AC-DC hybrid microgrid. Two objectives (minimization of operation cost and emission) are represented in the fuzzy domain by fuzzy membership functions. The membership function having the minimum value is considered as the membership value of the aggregated objective. The membership function value of the aggregated objective is maximized to obtain the optimal solution. Optimization is carried out using particle swarm optimization technique. A 6-bus AC-DC hybrid microgrid test system is used for the simulation study. Simulation results demonstrate the potency of the suggested method.
Prevalence, Risk Factors, and Antibiogram of Bacteria Isolated from Milk of Goats with Subclinical Mastitis in Thika East Subcounty, Kenya

A cross-sectional study was carried out to determine the prevalence and risk factors of subclinical mastitis in dairy goats in Thika East Subcounty, Kenya. Further the bacterial pathogens and their antibiogram were investigated. Farm level data on risk factors were obtained from 41 farmers using questionnaires. Milk was obtained from 110 lactating dairy goats and tested for submastitis using California Mastitis Test (CMT). The prevalence of subclinical mastitis at goat level was estimated to be at 50.9% using CMT, out of which 86.5% yielded bacteria on culture. The significant risk factors associated with the occurrence of subclinical mastitis were cleaning schedule (p=0.022, OD=1.047) and parity of the goat (p=0048, OD=1.37). Higher prevalence of subclinical mastitis was observed for goats residing in houses cleaned at least once a fortnight. Does in the first parity were least affected. 169 bacterial isolates were obtained from culture, of which 52 isolates from major classes of isolated bacteria were tested for antibiotic sensitivity to six antibiotics. Fourteen different bacteria were isolated and identified from the milk samples. Coagulase-negative Staphylococci (20.7%), Serratia spp. (19.5%), Citrobacter spp. (16%), Klebsiella spp. (11%), Staphylococcus aureus (10.7%), Enterobacter spp. (6.5%), Escherichia coli (5.9%), Proteus spp. (3%), Corynebacterium spp. (1.8%), Morganella spp. (1.8%), Streptococcus spp. (1.2%), Providencia spp. (0.6%), Micrococcus spp. (0.6%), and Staphylococcus intermedius (0.6%) were isolated and identified from the samples. All the isolates were resistant to Penicillin G, while 98% of the isolates were sensitive to Streptomycin. In conclusion, the study showed that a large proportion of goats were affected by subclinical mastitis, with the main bacteria being Staphylococci spp. and coliforms, and that most of the tested antibiotics can be used in the treatment of mastitis. Farmers need to be trained on improved control of mastitis through adoption of good dairy husbandry and milking practices.
Monocular 3D Vehicle Trajectory Reconstruction Using Terrain Shape Constraints

This work proposes a novel approach to reconstruct three-dimensional vehicle trajectories in monocular video sequences. We leverage state-of-the-art instance-aware semantic segmentation and optical flow methods to compute object video tracks on pixel level. This approach uses Structure from Motion to determine camera poses relative to vehicle instances and environment structures. We parameterize vehicle trajectories with a single variable by combining object and background reconstructions. The naive combination of vehicle and environment reconstruction results in inconsistent motion trajectories due to the scale ambiguity of SfM. We determine consistent object trajectories by projecting dense vehicle reconstructions on the terrain surface. Our scale ratio estimation approach shows no degenerated camera-vehicle-motions. We demonstrate the usefulness of our approach using publicly available video data of driving scenarios. We extend this evaluation showing trajectory reconstruction results using drone footage. We use synthetic data of vehicles in urban environments to evaluate the proposed algorithm. We achieve an average reconstruction-to-ground-truth distance of 0.17 meter.
Channel Characteristics in Rural Railway Environment at 28 GHz

Nowadays, rail traffic is expected to evolve into a new era of "smart rail mobility", where trains, infrastructure, travelers and goods will be increasingly interconnected. Railway communications are required to support various high-data-rate applications, the communication system should be carefully designed, which makes railway scenario becomes an important communication scenario in the 5G era. Millimeter-wave (mmWave) bands and novel technologies like resource allocation, multiple access and multiple-output beam-forming are proposed in the realization of this goal. In this paper, the mmWave channel characteristics of rural railway scenario are studied via a calibrated ray-tracing simulator. The large-scale parameters of the channel characteristics, including the path loss, root-mean-square (RMS) delay spread, Rician K-factor, angular spreads, and cross-polarization ration (XPR) are explored. The statistical properties, decorrelation distance and cross-correlations are analyzed. The studied channel characteristics can be practically used to support the link level and system level design of the communication system in the similar environments.
Optimal Economic Dispatch with Valve-Point Effect and Multiple Fuel Option via L-Shade Algorithm

The present research is designed to present an Improved Success-History based Adaptive Differential Evolution (L-SHADE) to solve a practical Economic Dispatch Problem (ED P). Simultaneously involving both of the multiple fuel option and valve-point effects make the EDP formulation more and more complex non-smooth optimization issue. Noting that the L-SHADE serves to introduce an adaptive mechanism whereby improves the control parameters selection and through the application of a linear population size reduction technique, reduces linearly the population size throughout the optimization process. Thus, enabling to yield better offspring for the next generation. For an effective evaluation of the proposed design's relevant performance, the L-SHDE optimizer is subjected to the IEEE 10 unit test system involving multiple fuel option and valve point effect. The experiment results confirm the ability of the L-SHADE algorithm to obtain best optimum results with lowest computational time in respect of the state of-the-art optimization approaches.