Miracle-dz/UniFAR-FTUs huggingface dataset:
Seque: Lean and Energy-aware Data Management for IoT Gateways

IoT systems with multiple deployed sensor nodes often use gateways to gather, fuse, transform and transmit diverse data acquired from the sensor nodes, e.g., to a cloud server. When being deployed in remote environments, not only the memory and storage, but also energy can be scarce and supply be time-dependent and often unpredictable, e.g. when obtained by energy harvesting. In this realm, this paper proposes a lean and energy-aware methodology called Seque for data management for such gateways. Rather than processing multiple sensor requests at a time and being unconscious of the level of available energy, Seque schedules only one request at a time. Moreover, Seque dynamically decides whether to directly process and transmit data of a request to a cloud server, or alternatively compress and persist data locally on the gateway in expectation of a power failure to postpone the upload to time of recovery from a power shortage. With this scheduling technique, a guarantee can be given that no sensor request admitted will suffer from partial or full loss of data. A reference implementation of Seque is provided with scheduling decisions being calibrated based on energy models of sensor interfaces, CPU system and upload interfaces of a real embedded gateway platform. Presented analysis on whether energy can be sent most by selective compression of data. Finally, the lightweight approach is evaluated in terms of energy consumption, and storage footprint and compared with commercially available database management systems including MongoDB and SQLite. The evaluation shows that Seque provides on average between 51% and 63% lower energy consumption for different data schemas per sensor request and also between 63% and 78% of lower storage requirements, pronouncing its leanness.
Sparse Non-Negative Matrix Factorization for Preclinical Bioluminescent Imaging

Bioluminescent imaging is used in oncology to measure tumoral size and activity via spatio-temporal photon emission counting. Bioluminescent signal analysis often requires delineating regions of interest around each tumor by hand, which complicates quantification in the case of mice bearing multiple tumors. In this work, we propose to use Non-Negative Matrix Factorization with data-adaptive sparsity constraints to enable automated separation of signals emitted from multiple tumors in mice. Results are presented on a set of 18 long-exposure acquisitions.
Energy Efficient Object Detection for Automotive Applications with YOLOv3 and Approximate Hardware

Deep neural networks are the dominant models for perception tasks in the automotive domain, but their high computational complexity makes it difficult to execute them in real time with an acceptable power consumption on general-purpose devices. For this reason, the design of custom ASIC devices for real-time energy-efficient neural network deployment is a hot topic in academia and industry. Low-precision integer arithmetic and approximate computing are two popular optimizations often contemplated for saving hardware resources and power consumption. In this work, we evaluate two approximate computing circuits using different integer precisions in order to study the trade-offs that these techniques offer in terms of energy efficiency and degradation of the neural network outputs. In particular, we apply the approximations to the YOLOv3 object detection network, a popular model for critical applications in the automotive domain. By combining approximate arithmetic circuits with low precision we are able to reduce the power consumption of a MAD unit by over 50% compared to using only quantization.
A Siamese-based Network for the Detection of Osteopenia in Paediatric Digital X-rays of the Wrist

Osteopenia, also known as low bone mass or low bone density, is a disorder characterised by low bone mineral density, which can be viewed as the precursor to osteoporosis. In contrast to the subjectivity of doctors and the necessity for considerable professional training, computer vision does not present similar issues in its applications in the medical domain and has thus been a topic of great interest. In this paper, we propose a Siamese-based Network for detecting paediatric wrist osteopenia. More precisely, the features given by the two branches of our model are concatenated and fed as input to fully connected and Softmax layers in order to make the prediction. In addition, several Convolutional Block Attention Modules (CBAM) were integrated to improve the feature extraction performance. Experimental results using the GRAZPEDWRI-DX dataset indicate that the proposed method has promising potential to detect wrist osteopenia.
A Predictive Control based Scheme for Maximum Power Extraction of PMSG based Wind Turbine Systems

In this proposal, a predictive control scheme is considered for PMSG based wind turbine system in order to maximize power extraction. The main goal is to maintain the turbine tip-speed ratio (and consequently the power coefficient) at its optimal point and regulate the PMSG rotor speed in reaction to the variation of wind speed. The control of generator speed is done by employing a generalized predictive controller. This regulator by taking advantage of the predictive feature can prepare the generator to react to the upcoming wind in advance and consequently increases the efficiency of the turbine. Also, it has been demonstrated that this controller enhances the transient response of the system compared to the conventional methods. The proposed controller has been validated through Matlab/Simulink and an experimental test bench.
Classification and Model Interpretation of Mammography Positioned Quality for Posterior Breast

According to the criteria revealed by the PGMI evaluation system, fat visualized posterior to glandular tissue is a significant criterion for mammography positioned quality evaluation. This paper introduces the quality evaluation method in craniocaudal-view mammograms. The proposed method implements a classifier with a convolutional neural network (CNN) and then analyzes the inner and outer regions of the posterior breast. To find the proper CNN model, we experiment with the classification with five CNNs, including, Squeezenet, Densenet-121, Alexnet, Vgg-19, and Resnet-101. Furthermore, analyzing the gradient-weighted class activation mapping extracted from CNN models interprets what features the model observes. The experiment results demonstrate that Densenet-121 is the best choice for positioned quality evaluation of posterior breasts.
Downlink Multiple Access in Optical Mobile Communications: NOMA and OMA Comparison

Optical mobile communication (OMC) introduces optical intelligent reflecting surface (IRS) into optical wireless communication systems, which makes the channel configurable and provides possibility for supporting multiple mobile users. However, the introduction of IRS also makes the calculation of achievable rate quite difficult, and different access methods will bring variation in performance. In this paper, we analyze the rate region of IRS-aided OMC system with different access methods. It is found that orthogonal multiple access entitles larger rate region than non-orthogonal multiple access in some situations, which is quite different from conventional optical wireless communication systems without IRSs.
Oral Cancer Risk Prediction by Using Gray Wolf Optimizer-based Multifactor Dimensionality Reduction

Geneticists struggle to identify susceptibility genes for complex diseases. Multifactor Dimensionality Reduction is a crucial machine learning method for gene-gene interaction studies. However, Multifactor Dimensionality Reduction’s exhaustive search for optimal gene combinations can be computationally demanding with large datasets. To overcome this, we propose an enhanced approach using the Gray Wolf Optimizer to reduce computation time in Multifactor Dimensionality Reduction. This method improves the detection of gene-gene interactions efficiently.
Binaural Audio Generation with Data Augmentation from 360° Videos

We present a novel binaural audio generation method with data augmentation from 360° videos. Visually informed binaural audio generation requires ground truth pairs of video and binaural audio. However, collecting diverse ground truth requires a lot of effort, and low data diversity reduces the generalization performance of the model. Our method introduces the data generation from 360° videos to solve the low diversity of ground truth. Experimental results show that our method improves the generalization performance of the binaural audio generation model and that 360° video is effective in generating video and pseudo-binaural audio pairs.
DD-WGAN: Generative Adversarial Networks with Wasserstein Distance and Dual-Domain Discriminators for Low-Dose CT

X-ray computed tomography (CT) is a mainstream medical imaging modality. The widespread use of CT has made image denoising of low-dose CT (LDCT) images a key issue in medical imaging. Deep learning (DL) methods have been successful in this area over the past few years, but most DL-based dual-domain methods directly filter the sinogram domain data, which is prone to induce new artifacts in the reconstructed image. This paper proposes a new method called DD-WGAN, which has an image domain generator network (IDG-Net) and two discriminator networks, namely the image domain discriminator network (ID-Net) and the sinogram domain discriminator network (SD-Net). We use dual-domain discriminators to balance the data weights of sinogram and image. Experimental results show that the proposed method achieves significantly improved LDCT denoising performance.
Scalable Interconnection between Distributed Metaverse and Physical World

The distributed management of metaverse is necessary since the services of different companies lack standardization and can be subject to unilateral decisions regarding their terms of use and termination. On the other hand, the connection between the virtual and the real world becomes increasingly important with the proliferation of the metaverse. In this paper, we propose a system for expanding and standardizing metaverse functionality under a distributed configuration, where IPFS is used for distributed content management, enabling the incorporation of digital content and IoT devices into the metaverse. We demonstrate that the distributed file system realizes scalable sharing of the world data.
A Novel Non-Voltatile Magnetic Majority Gate with Voltage-Gated Spin-Orbit Torque Magnetic Tunnel Junction Cascaded in Series for Low Power and High Reliability

This paper proposes a novel non-volatile magnetic majority gate (NVMMG), which employs the advanced emerging voltage-gated spin-orbit torque magnetic tunnel junction (VGSOT-MTJ) to store its input logic operands. To achieve high reliability, one differential memory cell is proposed, which is mainly composed of five access transistors and two VGSOT-MTJs in a complementary state to store a 1-bit input logic operand, called 5T-2MTJ cell. To achieve low power, the proposed NVMMG is designed by cascading multiple 5T-2MTJ cells in series. In this way, the output of the proposed NVMMG can be characterized by the resistance sum of these cascaded VGSOT-MTJs with no access transistor, thereby further im-proving its reliability. By using a physic-based VGSOT-MTJ compact model and a commercial CMOS 40 nm design kit, we have demonstrated the functionality of the proposed NVMMG and evaluated its performance. Simulation results show that for its logic operation with three inputs, it can achieve a fast speed of $\sim 1.11$ ns and low energy dissipation of $\sim 4.07\text{fJ}$. For its write operation, it can achieve a fast write speed of $\sim 1$ ns and low energy dissipation of $\sim 10.66\text{fJ}$. Monte-Carlo simulation results show that the proposed NVMMG can achieve no error rate when the TMR exceeds 240%.
Measurement Methodology

With an increase in the population of older adults in developed nations globally, research on radar-based human activity recognition for reliable and accurate fall event detection has accelerated exponentially. Radars are safe, contactless, and privacy-preserving sensors that facilitate ‘aging in place. ’ A plethora of research papers have been published in this field in the last five years. The primary goal of all research works is to recognize the human activities from the backscattered radar returns. Despite being a well-researched field, technology-transfer from lab to market is implausible due to several underlying issues that are yet to be addressed. These issues will serve as potential barriers when implementing the developed technologies in real-life. This article aims to reveal some of these issues that are important for successful technology-transfer of radar-based human activity recognition systems, and potential solutions to mitigate these issues are proposed.
Single-Molecule Detection on Nanoparticles-on-Nanoposts

Advancements in nanotechnology and biology have led to the development of planar surfaces for biosensing and diagnostics, with gold nanoparticles being the adsorption support material of choice. However, controlling the number of biomolecules on the surface of gold nanoparticles and the fast assembly of biomolecules on the surface is challenging. To address this, a kelp root system-inspired 7-ARM PEG-holdfast was developed for the precise mounting of biomolecules onto the gold nanoparticles. These smartly designed holdfasts have a single post for the covalent attachment of biomolecules and are tunable providing control of a defined number of biomolecules per nanoparticle and efficient fluorescence detection on the nanoparticle surface. This study demonstrated fluorescence readout enhancement on the surface of the gold nanoparticle at a mounting resolution of one holdfast per nanoparticle. As a proof of concept, a single avidin-bound holdfast mounted on the gold nanoparticle was recognized by fluorescently labelled biotin molecules. This approach has potential applications in biosensors, tissue engineering scaffolds, and microfluidic devices operated at single molecule scales.
Evaluating the Joint Use of Alternate Internet Delivery Models in Deterministic Rural Networks

Rural residents are often faced with the problem of expensive yet unreliable Internet connectivity. This coupled with the sparse population/business density, poses a detriment to the general development of such communities. In mitigating this disparity, some rural areas have adopted alternate Internet models to improve access to Internet services within their community. Such alternate models include communal access points, delay tolerant networks, and resource sharing networks. Thus, this work investigates the joint use of the aforementioned alternate Internet models for improving connectivity. Further, this work explores methods of routing time-dependent data in quasi-deterministic scenarios within these challenged rural environments. We formulate the routing problem as a maximum unsplittable multicommodity flow problem and propose an approximation algorithm. Using data representative of a rural Appalachian region, we evaluate our approximation algorithm and find that it performs close to optimal. We also propose a routing protocol based on our approximation algorithm. Using the same data, the results show that the proposed routing protocol performs comparably well with state-of-the-art routing protocols in delivering messages under time constraints. Additionally, the proposed routing protocol offers an overhead cost that is at least 3 times lower, on average, than other well-known routing protocols.
One-class Classification Using Autoencoder Feature Residuals for Improved IoT Network Intrusion Detection

Networks are rapidly evolving to include more Internet of Things devices as we grow to rely on them for smart home services, health infrastructure, and industrial development. Along with this proliferation, these networks are often the target of cyberattacks that seek to take advantage of their low processing power and the complexity that is involved in protecting heterogeneous networks. We seek to improve our network intrusion detection capabilities for Internet of Things networks by developing methods of detection that utilize the power of deep learning and have the ability to detect zero-day attacks. In this work, we outline a novel feature generation process using autoencoder feature residuals that can be combined with one-class classifiers to effectively detect network attacks on Internet of Things networks using no attack data during the training process. Moreover, we show that our novel feature sets are able to outperform using an original feature set leading to a reduction of typical model hyperparameter tuning activities.
Using the Spectral Similarity Ratio and Morphological Operators for the Detection of Building Locations in Very High Spatial Resolution Images

Abstract: Recently there is a growing interest toward elaboration of automatic or semi-automatic building detection in VHSR (very high spatial resolution) images. The complex spectral appearance of buildings in VHSR images has led to studies that exploit mostly the spatial information in these images. Spectral information in addition to spatial and contextual information improves the accuracy of the detection. In this paper, they present a method for the detection of building locations in VHSR multispectral images. Buildings are detected using spectral, spatial and contextual information. First, potential building locations are enhanced on the basis of the spectral similarity between their roofs using the spectral similarity ratio. Next, the HMT (hit-or-miss transform) from mathematical morphology is used to assign pixels to buildings and to define potential building locations. The differential morphological profile is used to identify shadowed areas. Then, building locations are verified based on the proximity of shadows. The method is tested on large WorldView-2 and pan-sharpened Ikonos image subsets. Both subsets included heterogeneous urban types: residential, commercial and industrial areas. The accuracy achieved was high for both subsets, despite the heterogeneous urban types and the different sensors used.
3D Orthogonal SD-OCT Volumes Registration for the Enhancement of Pores in Lamina Cribrosa

The lamina cribrosa (LC), a 3D porous structure of the eye in which optic fibers pass to reach the brain, is critically involved in the diagnosis and pathogenesis of glaucoma, a leading cause of blindness. However, segmenting the LC pores in 3D images is a task that has rarely been investigated, mainly because of the very low signal to noise ratio. To address this problem, we propose a fully automatic method to register and fuse two 3D SD-OCT volumes acquired in orthogonal directions, in order to improve the OCT image quality and thus facilitate the pore detection. Our method relies on a priori knowledge about en-face images to enhance useful pore information and initialize the process. Moreover, the optimization of the cross-correlation function is carried out in several stages in order to ensure the robustness of the process and the accuracy of the result. The quantitative evaluation shows that the proposed method is efficient to register the two 3D orthogonal OCT volumes and the annotated pores, with a distance between aligned pores around 3 pixels (below the pore size).
Computer Aided Design of Tunnel Based on Lonberg Algorithm and Intelligent optimization

The establishment of the spatial alignment model of the left and right tunnel axes is of great importance for the construction and operation of the tunnel. This paper presents a tunnel computer-aided design model based on Lonberg algorithm and intelligent optimization. First, the projection of the central axis of a single tunnel in the plane is assumed to be a straight line, and the central axes of the tunnel and the tunnel are in a plane perpendicular to the plane, so that the problem can be solved in the two-dimensional plane. Second, the projection of the two tunnels’ central axis on the plane, the elevation data on this projection plane are interpolated by three splines, and the interpolation function for each small distance is constructed, and the three-turn angle equation used to solve the coefficients is constructed using the function characteristics, and the three-turn angle equation is solved by Thomas’ algorithm to obtain the interpolation function of the central axis for each small distance. After the specific interpolation function is obtained, the curve integration of the segmentation function is performed using the Lonberg algorithm to obtain the total tunnel length after projection, and then the original tunnel length is calculated by projection inversion. Finally, using the fixed-step search algorithm, the 100-meter calibration is completed.
Parameter Estimation of Second-Life Lithium-Ion Batteries Through Subspace Identification Methods

The batteries of electric vehicles (EVs) are designed to operate until the moment that their capacity reaches 80% of the rated value defined by the manufacturer. After this point, according to standards, the battery should be removed from the EV. Nevertheless, these retired batteries still have a significant amount of capacity, and then, depending on their internal conditions, they can be used in applications with lower power requirements, this concept is called second-life. However, due to the high variability of the internal conditions of lithium-ion cells at second-life conditions, identifying their internal parameters is a challenge, and conventional approaches do not provide satisfactory results. Therefore, this work proposes a methodology to identify the dynamic model of lithium-ion cells in second-life conditions through subspace identification (SSI). Then, parameters related to the state of health (SoH) are derived from the identified models. The obtained results show that: (a) a good fit between the actual system and the identified model was achieved, where a root mean square error (RMSE) of 4.80 mV and a variance accounting for (VAF) of 99.1% were obtained; (b) it was possible to identify the system parameters with a relatively small error, and the estimated parameters can be used to predict the SoH of the given lithium-ion cell; and (c) due to the characteristics of the EVs drive cycles, the parameters of the lithium-ion cell can be identified during the operation of the EV, discarding the need for additional tests after the retirement of the LiB.
Learning Graph Similarity With Large Spectral Gap

Learning a good graph similarity matrix in data clustering is very crucial. The goal of clustering is to construct a good graph similarity matrix such that the similarity of points between the same classes is largest, and the similarity of points between different classes is smallest. In this paper, a more efficient subspace segmentation approach to learn a similarity matrix with large spectral gap is proposed. In our model, a robust self-representation coefficient matrix is learned by utilizing the Schatten- ${p}$ norm instead of the conventional rank function. Besides, the fast block-diagonal structure of the coefficient representation matrix is enhanced by learning and optimizing the co-association matrix with the soft label of clustering results simultaneously in a unified framework. The affinity graphs constructed in this paper can clearly reveal the intrinsic structures of the data sets. Extensive experiments on the real data sets demonstrate that our proposed method can perform better than the state-of-the-art methods.