Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual-Phase Shift Control of a Multilevel Dual Active Bridge Converter

The dual active bridge (DAB) converter is considered one of the best DC-DC converters because of its several attractive features, making it the most prominent candidate for many applications, such as solid-state transformers (SSTs). However, the conventional topology of the DAB presents several drawbacks for its utilization in high-voltage direct current (HVDC), since the very steep dv/dt given by the two-level operation of the converter causes insulation and electromagnetic interference (EMI) issues and significant power losses. This paper proposes a mixed-DAB topology consisting of a modular multilevel converter (MMC) on the primary side and a conventional full-bridge on the secondary side. Moreover, the dual-phase shift (DPS) method is used for controlling the MMC-DAB converter in order to provide multiple voltage levels at the primary side and three levels at the secondary side, which greatly alleviates the dv/dt stress imposed on the transformer and on the power switches at both sides, reduces losses, and minimizes the reactive power. The proposed converter topology is evaluated in MATLAB/Simulink software, and various simulation results are presented.
Introducing Social Gamification to a Biomedical Signal Processing Course: A Case Study

There is a trend towards larger and more diverse classes in higher education. In order to account for this diversity and consider different student profiles, we introduced social gamification elements to the structure of a B.Sc. course from the field of Science, technology, engineering, and mathematics (STEM). In this work, we report results of this case study for winter semester 2022. The basic idea was to offer students different types of tasks from that they can choose from according to their preferences: Taking end-of-lecture quizzes, submitting solutions to Jupyter Notebook exercise sheets, presenting a solution orally in front of the class, or answering questions to a scientific paper as a group task. With each task, a certain amount of points was associated that the students could “earn”. In order to gain insight into the acceptance of the gamification elements, we analyse the points reached by each student over time and results of a voluntary lecture evaluation at the end of the course. 29 students from multiple study programs took part in the course and reached an average of 639 out of 1,405 points with a standard deviation of 336. We observed different types of student behaviour being either competitive vs. cost-effective behaviour, or social vs. self-dependent behaviour. The course was evaluated positively and the overall rating based on the questionnaire was improved compared to previous years.
Parametric Dictionary Learning for Topological Signal Representation

The aim of this paper is to introduce a novel dictionary learning algorithm for sparse representation of signals defined over regular cell complexes. Leveraging tools from Hodge theory, we inject the underlying topology in the dictionary structure by parametrizing it as a concatenation of sub-dictionaries that are polynomial of Hodge Laplacians. The learning problem is cast as the joint optimization of the topological dictionary coefficients and the sparse signal representation, which is efficiently solved via an iterative alternating algorithm. Numerical results on synthetic data show the effectiveness of the proposed procedure in learning sparse representations of topological signals.
A Human-Scale Permanent Magnetic Actuation System for Wireless Millirobots*

Small-scale robots which are wirelessly actuated have a huge potential to perform biomedical tasks, such as targeted drug delivery. Traditional electromagnetic actuation systems, however, often have limited accessible volume, which restricts the controllable working space of the robots for in vivo medical applications at human scale. In this work, we report a new magnetic actuation set-up comprised of a pair of static magnets and a rotating magnet, which generates a rotational magnetic field vector on a virtual conical surface, in a large accessible volume of $50 \times 15 \times 20 \text{cm}^3$. The magnetic field is numerically simulated and verified by experiments. A millimeter-sized, gear-shaped magnetic robot (GearBot) with sharp spikes is developed. We demonstrate its ability to crawl on biological tissue phantoms with full controllability to follow designed complicated trajectories. The permanent magnetic actuation set-up which requires neither an expensive power amplifier nor a coil cooling system has a large enough accessible volume that can in future accommodate a human patient.
Malicious Firmware Detection on Embedded Systems Using Deep Learning

IoT devices are extensively employed in various domains like smart homes, transportation, and health systems. However, with the growing usage of these devices, the probability of the occurrence of attacks is also increasing. One of the most prevalent attacks on these systems involves attempts of malicious firmware installation. This study aims to detect such malicious installations or firmware updates through the use of multilayer convolutional neural networks (CNN) on an embedded system. A CNN structure is created and trained by feeding firmware binary data which are converted to images. This trained model is then deployed on an embedded board for real-time, low-cost performance, and portability. The method determines whether the firmware is malicious or not as specified by the user and allows for installation accordingly. The proposed CNN-based model is compared to Autoencoder (AE) model in terms of accuracy. The proposed model is also analyzed under changing parameters including pooling functions, optimization functions, filter size, and layer dense. Derived from the outcomes of the experiments, suggested model is superior to AE models and has the highest accuracy achieved by multilayer CNN models with 91.19% under the parameters which are max pooling function, Adam optimizer, $3\times 3$ filter size, and 32,64,128 dense. The results indicate that the suggested model is portable, low-cost, and easily adaptable to different problems.
Inventory Control with Lateral Transshipment Using Proximal Policy Optimization

This paper presents the proximal policy optimization algorithm to find the optimal policy for the inventory control problem with lateral transshipment. The inventory control problem with lateral transshipment in a three-echelon supply chain is formulated as a Markov decision process. The supply chain consists of one supplier, two warehouses, and two retailers, where lateral transshipment is permitted between the warehouses. The demands at the retailers are independent and stochastic. Proximal policy optimization is used to find the optimal ordering policies and the optimal lateral transshipment policies for two warehouses to maximize the profit of the inventory system. To evaluate the performance of the proximal policy optimization algorithm, value iteration is also implemented to solve the inventory control problem. The simulation method is then used to evaluate the performance of both algorithms. The experimental results show that the policy obtained by the proximal policy optimization algorithm performs close to the optimal policy obtained by value iteration without knowing the transition probability of the system.
Simultaneous Clock Wire Sizing and Shield Insertion for Minimizing Routing Blockage

Clock shields are convenient for clock signal integrity, but excessive coupling capacitance that they incur makes it difficult to achieve usual clock design goals such as clock latency and clock skew. Selective shield insertion combined with clock wire sizing is addressed with a goal of minimizing routing blockage, under constraints of maximum clock latency and clock skew. Our approach consists of two components: (1) Message passing neural network (MPNN) model is developed to predict clock arrival time at each clock tree component, e.g. clock buffer, ICG, and flop. (2) Actual clock wire sizing and shield insertion is performed through mixed integer quadratically constrained programming (MIQCP) formulated at each clock tree stage, one by one; this is enabled by the predicted clock arrival time from MPNN. The experiment demonstrates that the predicted clock arrival time from MPNN is close to reference one with R-squared value of 0.954. Clock tree designed with the proposed method indicates the reduction of routing blockage by 38% on average of a few test circuits, compared to state-of-the-art.
Assessing NFV Network Recovery Strategies after Random Failures and Targeted Attacks

This paper investigates the critical role of network survivability under random failures or targeted attacks in Network Function Virtualization (NFV) environments and assesses the survivability of NFV networks under different recovery strategies. Through a comprehensive application of the ClusPR framework, we simulated various failure events, which include both random and targeted failures. Our survivability assessment focuses on the temporal dimension and compares different repair sequences in network recovery and explores potential mitigation strategies. These strategies involve re-optimization of network function placement upon structural network changes and prioritization of node repairs according to different centrality measures. Our findings revealed that the resilience of the network was greatly dependent on its specific topology and that the efficacy of the recovery strategies varied accordingly. In some cases, the use of sub-optimal strategies appeared to decrease the network survivability, demonstrating the importance of the recovery strategy. The results provide valuable insights into improving the survivability of NFV networks in the face of failures and can have implications for network management and policy.
Flooded Cropland Identification Using SAR Data and Transfer Deep Learning

Synthetic aperture radar (SAR) technology has significant advantages in flood detection. However, deep learning models trained on one type of SAR satellite data often perform poorly on other SAR data sources. To address the challenge of transferring deep learning models between different SAR datasets, we propose a flood detection method that combines self-supervision with ResUNet. Experiments conducted in the vicinity of Wuchang City demonstrate that our proposed method effectively transfers the Sentinel-I data training model to LT -1 data and improves the accuracy of flood detection.
A Novel Approach for Solving MPI for Multi-Target ToF Imaging Using Subdivision-Based Nested Compressed Sensing

Time-of-Flight (ToF) cameras have become a popular imaging modality for macroscopic scene detection. In particular, amplitude-modulated continuous wave (AMCW) ToF cameras use the phase difference between sent and received signals for object depth reconstruction. However, various sources of multipath reflections exist in practice, causing each ToF pixel to erroneously receive a superposition of multiple reflections instead of a single bounce. This leads to distortions in the phase difference and consequently, errors in the depth maps. Compressed Sensing methods have emerged as an effective approach to solve this multipath interference (MPI) problem. However, it has two major disadvantages—large sensing matrix size leading to high computational load, and a high mutual coherence causing reconstruction failure. This paper introduces a subdivision-based nested compressed sensing algorithm that aims to alleviate these known disadvantages. Measurements at multiple modulation frequencies are used to isolate the $k$ interfering signals in the time domain. Simulation results are presented with a noise performance analysis and results based on real multitarget measurement data are also discussed.
Deep Embedded Clustering Regularization for Supervised Imbalanced Cerebral Emboli Classification Using Transcranial Doppler Ultrasound

High intensity transient signals (HITS) can be detected using transcranial Doppler ultrasound monitoring to help stroke prevention. The various types of HITS are by nature imbalanced: solid emboli are rare events, compared to artifacts or gaseous emboli. Therefore, when training deep models on these data, one have to take into account the class imbalance. In this work, we propose a deep embedded clustering (DEC) based regularization technique, DEC-R to handle imbalanced datasets. Our proposed method decompose a classification model into an encoder and a classifier, with DEC-R applied to the former during supervised training. We validate our method on four synthetic datasets and one HITS datasets. The results show that our approach is robust against imbalanced datasets, allowing to increase the classification performances of different models on both, imbalanced (maximum imbalance ratio of 20) and balanced datasets. We achieve an increase of 4.86% and 1.27% in terms of Matthews correlation coefficient on two imbalanced datasets, a synthetic dataset (of 3D points) and a HITS clinical dataset, respectively. Our code can be found in https://github.com/yamilvindas/imbalanced_dec_regularization.
Decentralised Power Sharing Using Non Ideal Boost Converter with Internal Fuzzy logic control

This paper presents a study on power sharing in microgrids using non-ideal unidirectional boost converters. The proposed system employs non-fossil fuel energy source such as wind, solar, and battery energy reservoir, each connected to a separate unidirectional boost converter. Fuzzy logic control is utilized as the primary controller for these DC-DC (boost) converters to address their nonlinearity. The unidirectional boost converters are interconnected through a shared DC link, enabling the implementation of droop control to facilitate load sharing. The droop coefficient is adaptable through decentralized control to achieve the desired power sharing. The simulation and hardware results are utilized to showcase the efficacy of the proposed system in effectively distributing power among multiple distributed energy resources.
Performance Analysis of a Multistatic Joint Sensing and Communication System

In this work, we consider a multistatic joint sensing and communication network composed of a transmitter and multiple receivers capable of estimating the position of a target in the monitored area. In particular, the system consists of a transmitter equipped with multiple antennas (typically a base station) and several receivers that can act as sensors with a single antenna. The transmitter adopts multiple beams to accommodate the communication towards the user equipment and the sensing functionality, with the ability to split the power between the two beams to adjust the sensing/communication trade-off. Processing of target echoes by the sensors produces a bistatic distance estimate or soft maps, which are combined by the fusion center. We then propose two sensor data fusion strategies, least square and soft maps fusion; the former has a negligible impact on the network overhead, while the latter always provides better performance in terms of root mean squared error of target position estimation when considering a small fraction of power devoted to sensing. Finally, we highlight the benefits of cooperation offered by the multistatic configuration, compared to the bistatic one, in terms of power saving for sensing.
Deep Learning Distribution Model Using Osmotic Computing

The utilization of edge computing has proven beneficial to Smart City applications due to its low latency processing on servers located in close proximity or even on the edge device. On the other hand, cloud computing leverages centralized computing power, resulting in significant advantages. Applications employing deep learning models commonly use both cloud computing and edge computing. While edge computing excels in providing rapid response times, devices typically possess limited computational resources. Conversely, cloud computing offers substantial computational power but suffers from longer access latencies. The challenge arises when deep learning applications must address one of these constraints, leading to poor scalability when focusing solely on edge or cloud computing or selecting a fixed distribution model. In this context, this paper aims to demonstrate the adaptive distribution of load leveraging osmotic computing. This approach directly impacts the scalability of deep learning solutions, mitigating the limitations imposed by either edge or cloud computing, improving throughput by up to 29% with low latency variation.
Cross-borehole electromagnetic probing to locate high-contrast anomalies

Electromagnetic (EM) probing between boreholes is useful for locating high‐contrast geophysical anomalies such as a tunnel. Theoretical and experimental studies of EM field interaction with a tunnel show that minima in the received signal can be used for locating the tunnel. The theoretical studies show that as a transmitter and receiver are lowered in separate boreholes, the minima can be interpreted easily to yield both the lateral and vertical positions of the tunnel. The main mechanism of EM field interaction with the tunnel appears to be diffraction, and the spatial variation of the field strength is affected by the tunnel shape. Frequencies from 10 to 70 MHz were studied to assess the usable frequencies. The field in the receiver borehole was an effective diagnostic when a half‐wavelength in the surrounding medium was less than or equal to the diameter of the tunnel. EM probing at two test sites gave the locations of tunnels within 1 ft of the surveyed locations.
Unaliasing of Recorded Signals Based on Blind Source Separation

In this paper, we propose a method of restoring the original signal from aliased signals. Generally, it is challenging to resolve aliasing in post-processing for already aliased signals. However, the proposed method solves aliasing only with a post-processing approach by increasing the number of channels. First, we model the aliased signal as a mixture of the original and aliased components. Next, these components are separated by applying blind source separation to the mixture. Finally, the separated components are converted in the frequency domain to recover the original signal accurately. We confirm the effectiveness of our method through experimental evaluations.
A Collaborative On-Device CNN Execution Considering Model Parallelism for Latency-Critical Applications

The use of deep learning (DL) has become more common in applications that require high accuracy, but the high computational demand of DL makes it challenging to execute on a single resource-constrained end device (ED) due to low computing capability and limited energy. One way to execute DL is to use the mobile cloud computing concept, but it results in intolerable latency, network congestion and raises privacy concerns. In the absence of powerful servers, another solution could be to form a collaboration among multiple EDs to execute DL computation. This paper proposes a distributed collaborative on-device convolutional neural network (CNN) execution scheme using model parallelism for latency-critical applications. In the proposed scheme, the ED that owns the input data uses its communication capabilities to get additional computing capabilities from other EDs. A convex optimization problem is formulated to minimize the energy consumption of all EDs by jointly optimizing the communication and computation parameters along with the number of filters assignment in each convolutional layer in the CNN model to multiple EDs. The problem is then decomposed into two sub-problems to obtain analytical expressions for the optimizing parameters and make the optimization distributed among multiple EDs. The simulation results show the importance of optimizing the communication-computation trade-off and the advantages of collaborative computing.
Temperature Control of Irradiated Biological Samples with Pulse Repetition Frequency Modulation of a Gyrotron

The biological effects of terahertz waves have been attracting attention, and high-frequency gyrotrons are being used as a wave source in recent years. In discussing the effects of terahertz waves, it is particularly important to separate them from thermal effects. Therefore, temperature control of the irradiated sample is essential. In this study, a Proportional-Integral-Differential (PID) controller that can adjust the temperature of the irradiated liquid sample by modulating the pulse repetition frequency of the gyrotron has been developed. By optimizing the PID gains, it was found that the temperature could be maintained at the target value of approximately 35 deg. C with an error of less than 0.1 deg. C.
Using landsat time-series and lidar to inform aboveground carbon baseline estimation in Minnesota

Landsat data has long been used to support forest monitoring and management decisions despite the limited success of passive optical remote sensing for accurate estimation of structural attributes such as aboveground biomass. The archive of publicly available Landsat images dating back to the 1970s can be used to predict historic forest biomass dynamics. In addition, increasing regional scale availability and high sensitivity of LiDAR for biomass mapping also needs exploration of its utility in back-projection modeling. This study has combined recent national forest inventory (NFI) data (2007-2011) with the Landsat data from 1986-2011 and a regional LiDAR dataset acquired by the Minnesota Department of Natural Resources (DNR) to assess the potential of the remote sensing data in predicting aboveground forest biomass back to the 1990 baseline used in the United Nations Framework Convention on Climate Change reporting in the US. Since obtaining cloud-free Landsat images at required seasons for a regional or national study is unlikely, pixel level polynomial models were fitted to a suite of time-series predictors obtained from cloud-free Landsat data of a single scene in Minnesota such that each predictor represented only one growing season between 1986 and 2011. Similarly, selected LiDAR variables were back-projected using Landsat metrics as explanatory variables. The rational for this effort was to obtain a wall-to-wall inventory for any target year that does not have remote sensing data by combining a set of projected predictors and current NFI data. Several candidate models were developed to produce biomass maps for the year 2000 to compare the outputs with the extant map of National Biomass and Carbon Dataset (NBCD) circa 2000 and annual NFI plot measurements. We found that the model including back-projected LiDAR metrics did not significantly improve the prediction accuracy as compared to the model based only on projected Landsat metrics. As the polynomial-projected Landsat-based model provided accuracy similar to the NBCD model, the former may be used for reference mapping back to 1990.
Polymeric Nano formulations in the Management of HIV Associated Neurocognitive Disorder

Background: HIV-Associated Neurocognitive Disorder (HAND) is one of the most prevalent comorbidities in the era of ART. The reported HAND prevalence ranges from 21% to 86%. The use of encapsulated nanosized antiretrovirals in various polymers has shown potential for enhanced permeation into the CNS and other latent viral reservoirs thus providing hope for prevention and treatment of neurocognitive disorders in HIV positive patients. Main text: Methodology HIV-Associated Neurocognitive Disorder (HAND) has been used to describe the spectrum of neurocognitive dysfunction associated with HIV infection. Neurocognitive disorders are a result of a deficit in neurological activity. There is no specific treatment for HAND;however, ART has been used to reverse the disease process and improve cognitive function. Polymeric nanoformulations are solid colloidal systems consisting of a polymer matrix loaded with active therapeutic compounds within or adsorbed