Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-Scene Hyperspectral Feature Selection via Hybrid Whale Optimization Algorithm With Simulated Annealing

Hyperspectral images (HSIs) include hundreds of spectral bands, which lead to Hughes phenomenon in classification task and decrease the classification accuracy. Feature selection can remove redundant and noisy features in the HSIs to overcome this phenomenon. In real applications, we may face a HSI scene with only a few labeled samples. Meanwhile, there are adequate labeled samples in a similar HSI scene. For example, they share the same land-cover classes. The shared information can be used to help the scene with a few labeled samples in feature selection. Traditional single-scene-based feature selection appears powerless in solving such problems. Cross-scene feature selection provides an attractive way to select feature subsets by simultaneously using the information from two HSI scenes. However, spectral distribution may change due to atmospheric conditions, yielding spectral shift. In order to tackle this problem, we propose a cross-domain algorithm based on hybrid whale optimization algorithm with simulated annealing (WOASA). The newly proposed algorithm is dubbed cross-domain WOASA (CDWOASA). CDWOASA simultaneously considers the separability of different land-cover classes and the consistency of selected features between two scenes, leading to discriminative and domain-invariant characters of selected feature subset. Moreover, since the original WOASA is not able to precisely control the dimension of selected features, we propose an improvement using a sorting strategy based on the fitness function value, thus making the output feature dimension precisely controlled. The experimental results on two cross-scene HSI datasets demonstrate the superiority of CDWOASA in cross-scene feature selection.
MobiCharger: Optimal Scheduling for Cooperative EV-to-EV Dynamic Wireless Charging

With ever increasing concerns on environmental issues caused by gasoline fuel based vehicles, electric vehicles (EVs) have attracted more and more attention from governments, industries, and customers [1] . The recent advancements in EVs have great potential to create a more environmentally friendly smart city. However, due to limited battery capacity, most current mainstream EVs still have quite limited driving range (e.g., 100 miles) [2] . How to ensure the continuous running of EVs on a large-scale road network (e.g., metropolitan city, interstate) becomes a major concern.
EnergyDx: Diagnosing Energy Anomaly in Mobile Apps by Identifying the Manifestation Point

Abnormal battery drain (ABD) can negatively impact the user experience of smartphone apps, by consuming an unnecessarily high amount of energy and causing short battery lifetime. Unfortunately, user reports on ABD are usually too vague for app developers to precisely know how and when the ABD manifests. Therefore, it is important to have a diagnostic tool that can help app developers identify the ABD manifestation point for root cause analysis.In this paper, we propose EnergyDx, an automated diagnosis framework that assists developers in pinpointing the functions that either directly lead to or commonly coincide with the manifestation of ABD. EnergyDx features a novel 5-step analysis algorithm to distinguish the real ABD manifestation point from the power transition points caused by normal phone usage. We have prototyped EnergyDx in Android and evaluated it with 40 different real-world apps for diagnosing ABD cases caused by various types of issues. Our results show that EnergyDx reduces, on average, 93% of the amount of code that the developers would need to search for the root causes of ABD.
Stochastic Procurement of Fast Reserve Services in Renewable Integrated Power Systems

Ensuring the security and quality of supply in a power system after a contingency event is one of the most challenging tasks for an electricity system operator. This work is initiated by this challenge and proposes a solution based on the use of provided reserves by fast generators, storage devices, and wind farms. A coordinated model is proposed in a joint energy and reserves market considering their corresponding cost to ensure the adequacy in the simultaneous deployment of reserves for the different sources of uncertainties. The Benders decomposition approach is used in the modeling of the stochastic security-constrained unit commitment, and considering the large-scale and complex nature of the model, acceleration techniques are suggested to reduce the execution time. The proposed model is tested on the 6-bus and the IEEE 118-bus test systems. Numerical results show that the optimal values of reserves successfully address contingencies in both of the critical and normal periods after the contingencies and the optimal solution is calculated in a reasonable computing time.
Towards Hybrid Model for Automatic Text Summarization

The overflowing of textual data on the web needs an efficient tool that is able to manage and process data. In this context, automatic text summarization has shown a great importance in several application areas. It aims to create a coherent and fluent short version of a document while preserving of the main information. This method allows for a reduction in reading time by condensing relevant information from a large collection of documents. Several automatic text summarization approaches have been proposed in order to entail shorten parts of the document. These methods have good results, but they still need improvements related to the reliability of sentences extraction, redundancy, semantic relationships between sentences, etc. This paper introduces a new hybrid architecture, combining a 2-layer recurrent neural network (RNN) extractive model and a sequence-to-sequence attentional abstractive model. This method uses the advantages of both extractive and abstractive approaches. A given text is first fed into the extractive model to obtain its relevant content, then generalized using the abstractive model, resulting in the text’s summary. First experimental results on real-world data show that the proposed model can achieve competitive results for extractive, abstractive and hybrid models.
Deep Multiple Instance Learning Ensemble for the Acoustic Detection of Tropical Birds

Deep learning algorithms have produced state of the art results for acoustic bird detection and classification. However, thousands of bird vocalizations have to be manually tagged by experts to train most of these algorithms. We use three strategies to reduce this manual work: simpler labels, fewer labels, and less labeled data. The Multiple Instance Learning (MIL) approach provides a framework to simplify and reduce the number of labels, as each recording (bag) is modeled as a collection of smaller audio segments (instances) and is associated with a single label that indicates if at least one bird was present in the recording. In this work, we propose an ensemble of deep neural networks based on the MIL framework to predict the presence or absence of tropical birds in one-minute recordings. As only a relatively small number of training observations (1600) are used to train the algorithm, we compare the performance of the individual networks using log mel-scaled spectrogram and mel-frequency cepstral coefficients. The proposed ensemble of deep MIL networks achieved a 0.98 AUC and 0.92 F1 score performance on the test set, using only a log mel-scaled spectrogram as data representation.
Deep Reinforcement Learning for Computation Offloading and Caching in Fog-Based Vehicular Networks

The role of fog computing in future vehicular networks is becoming significant, enabling a variety of applications that demand high computing resources and low latency, such as augmented reality and autonomous driving. Fog-based computation offloading and service caching are considered two key factors in efficient execution of resource-demanding services in such applications. While some efforts have been made on computation offloading in fog computing, a limited amount of work has considered joint optimization of computation offloading and service caching. As fog platforms are usually equipped with moderate computing and storage resources, we need to judiciously decide which services to be cached when offloading computation tasks to maximize the system performance. The heterogeneity, dynamicity, and stochastic properties of vehicular networks also pose challenges on optimal offloading and resource allocation. In this paper, we propose an intelligent computation offloading architecture with service caching, considering both peer-pool and fog-pool computation offloading. An optimization problem of joint computation offloading and service caching is formulated to minimize the task processing time and long-term energy utilization. Finally, we propose an algorithm based on deep reinforcement learning to solve this complex optimization problem. Extensive simulations are undertaken to verify the feasibility of our proposed scheme. The results show that our proposed scheme exhibits an effective performance improvement in computation latency and energy consumption compared to the chosen baseline.
TCAD-Based Flexible Fin Pitch Design for 3-nm Node 6T-SRAM Using Practical Source/Drain Patterning Scheme

In this article, we analyzed the stability, power, performance, and area of 6T-SRAMs using a promising scaling booster, i.e., source/drain patterning (SDP) scheme for the 3-nm technology node based on 3-D TCAD simulation. SDP scheme allows to decrease the spacing between transistors by downsizing the source/drain epitaxy. Proposed 5-nm and 3-nm SDP-SRAM (SDP-SRAM5 and SDP-SRAM3) are compared with conventional 5-nm node SRAM (Conv-SRAM5), quantitatively. Unlike a Conv-SRAM5, SDP-SRAMs have more design margins according to the two fin position parameters: fin location adjustment (FLA) and separation pitch adjustment (SPA). The optimized layout has the maximum FLA by significantly decreasing the back-end-of-line (BEOL) bitline and internal node capacitance. Read and write static noise margins were comparable (< 10 mV) between the SDP-SRAM and Conv-SRAM. The SDP-SRAM5 improved the read access time 18% compared to the Conv-SRAM5 in low-power (LP) applications, but the SDP-SRAM3 degraded it by 11% in high-performance (HP) applications because of the narrow M2 pitch. Both the SDP-SRAM5 and SDP-SRAM3 improved write access time about 10% compared to a Conv-SRAM5 in LP applications. The cell area of the SDP-SRAM3 decreased about 0.64 times compared to a Conv-SRAM5 without severe degradation of performance. The SDP scheme provides flexible layout designs enabling a high-density SRAM, promising for 3-nm node logic applications.
Cloudlet Computing: Recent Advances, Taxonomy, and Challenges

A cloudlet is an emerging computing paradigm that is designed to meet the requirements and expectations of the Internet of things (IoT) and tackle the conventional limitations of a cloud (e.g., high latency). The idea is to bring computing resources (i.e., storage and processing) to the edge of a network. This article presents a taxonomy of cloudlet applications, outlines cloudlet utilities, and describes recent advances, challenges, and future research directions. Based on the literature, a unique taxonomy of cloudlet applications is designed. Moreover, a cloudlet computation offloading application for augmenting resource-constrained IoT devices, handling compute-intensive tasks, and minimizing the energy consumption of related devices is explored. This study also highlights the viability of cloudlets to support smart systems and applications, such as augmented reality, virtual reality, and applications that require high-quality service. Finally, the role of cloudlets in emergency situations, hostile conditions, and in the technological integration of future applications and services is elaborated in detail.
On The Application of Domain Adversarial Neural Network to Fault Detection and Isolation in Power Plants

In this paper, we introduce a new Fault Detection and Isolation (FDI) approach based on Transfer Learning (TL) for improving health monitoring systems of gas turbines under varying working conditions. Nowadays, researchers have found intelligent algorithms a reliable tool for condition monitoring of mechanical systems and processes. In this regard, modern automation systems in many industries, including power plants, are heavily utilizing machine learning algorithms. However, the performance of data-driven methods depends on the consistency of data distribution. Unfortunately, this assumption might not be satisfied with real-world problems. In this research, we contribute to finding a solution to this problem, which is a crucial barrier to many intelligent condition monitoring systems. We used domain adversarial training of neural networks to find models that can adapt to new working conditions of gas-turbines. Accordingly, a well-known gas-turbine simulator is employed to simulate the process behavior under various working conditions, and it is illustrated that even small variations in working conditions cause a dramatic decline in the performance of models. We demonstrate that the proposed TL-based FDI approach can be successfully employed to cope with the inconsistency of data distribution in process systems.
Unsupervised ageing detection of mechanical systems on a causality graph

Multivariate time series (MTS) have specific features that complicate their analysis: interactions in space and time between the MTS components, variable length, absence of trivial alignment between samples and high dimensionality. Hence, finding a representation of MTS from which we can extract meaningful information is a challenging task. In general, specific assumptions are needed to obtain a valuable representation. In this paper, we assume that a dataset of MTS samples has an underlying causal structure that we can exploit to represent samples. Our contribution is a new representation framework that consists of first finding the overall causality graph in a studied dataset and then mapping each sample onto G to obtain a causality-based representation. Since causality isG an important feature underlying MTS data, we claim and show that representating samples on G is meaningful. We name this method Sequence-to-Graph (Seq2Graph). We apply Seq2Graph on health monitoring tasks, using two MTS datasets coming from ageing mechanical systems.
Feasibility Study of Power Line Communications for Backhauling Outdoor Small Cells

Small cells are low-power and low-range radio access nodes that can be used to increase network capacity in reduced areas with high traffic demand and to provide coverage to small isolated zones. As their number is expected to be very high, connecting them to the aggregation point in a cost-efficient manner is a key challenge, and there is consensus that none of the current technologies is valid for all scenarios. Since lamp-posts are one of the most convenient locations for outdoor small cells, this work analyzes the use of power line communications (PLC) over outdoor public lighting networks (OPLN) as a backhaul technology for outdoor small cell deployments. To this end, the characteristics of the PLC channels established in OPLN are firstly assessed. The analysis combines noise measurements performed in existing OPLN and channel responses obtained from a multiconductor transmission line (MTL) model. The attenuation, delay spread and spatial correlation of the multiple-input multiple-output (MIMO) channels are investigated and their influence in the physical layer parameters of PLC systems is discussed. Afterward, the performance achieved by state-of-the-art PLC systems is assessed. To this end, estimations obtained by means of simulations and measurements taken in actual networks are included. Data throughput achieved by PLC systems based on the ITU-T G.hn standard, as well as the expected improvements obtained by $3\times 3$ MIMO systems, are given. Results indicate that PLC can be an interesting technology for coverage-driven small cell deployments with backhaul length shorter than 150 m, offering throughput values similar to existing Sub-6 GHz wireless solutions in non-line-of-sight (NLOS) conditions and wired ones like G.fast.
Two-Step Adaptive Chirp Mode Decomposition for Time-Varying Bearing Fault Diagnosis

Fault diagnosis of rolling bearing has always been the focus of research in the engineering field. Due to the nonstationary conditions, this work has suffered huge challenges. The adaptive chirp mode decomposition (ACMD) is a signal decomposition method developed recently. By using a recursive framework embedded with an iterative algorithm, it can decompose signals one by one. Although this scheme has strong adaptability, the accuracy of reconstructed signals is very rough. Moreover, the estimated components will even deviate from the true results as the iteration progresses when two components are very close. In order to solve the above problems, a novel two-step ACMD is proposed in this article. Specifically, the recursive scheme without an iterative algorithm is only used to decompose signals first, which can determine the number of components. Then, the joint-estimation framework using an iterative algorithm is used to achieve high-resolution component reconstruction. The main idea of this two-step method is to let different schemes do what they do best. Subsequently, we proposed a new fault diagnosis method for rolling bearing under variable speeds based on the proposed two-step ACMD. Finally, both simulation and actual bearing diagnosis cases verify the potential of the proposed method and successfully diagnosed different kinds of rolling bearing faults.
A Gesture Classification SoC for Rehabilitation With ADC-Less Mixed-Signal Feature Extraction and Training Capable Neural Network Classifier

This article presents a fully integrated gesture and gait classification system-on-chip (SoC) for rehabilitation application. In order to reduce the power consumption and area cost on the analog front end, special analog-to-digital converter (ADC)-less mixed-signal feature extraction (MSFE) circuits were designed to directly generate eight commonly used time-domain features to eliminate the area cost of ADC. A fully connected neural network classifier was implemented supporting: 1) on-chip learning to deliver user-specific training for better classification accuracy; 2) dedicated neural network layer to support gait classification; and 3) multi-chip data communication, which transfers only low-dimensional features from the neural network to minimize the communication bottleneck in a sensor fusion environment. A 12-channel test chip was fabricated in a 65-nm low-power process to demonstrate the proposed techniques. The measurements show an average power of <inline-formula> <tex-math notation="LaTeX">$1~\mu \text{W}$ </tex-math></inline-formula> per channel and a 3-ms computational latency as required by the stringent rehabilitation requirement. In addition, the MSFE circuits achieve <inline-formula> <tex-math notation="LaTeX">$3\times $ </tex-math></inline-formula> saving of area compared with the conventional approach, while the communication bandwidth was reduced by <inline-formula> <tex-math notation="LaTeX">$100\times $ </tex-math></inline-formula> due to the transferring of only low-dimensional feature data from the neural network among multiple chips.
Minimum-Degree Distributed Graph Filter Design

Here we consider the problem of designing finite-impulse-response (FIR) graph filter (GF) in a fully distributed way. For a directed graph with $\mathsf {N}$ nodes, each node designs filter coefficients in a distributed manner, when the knowledge of the graph structure, recognized as global information, is unavailable to each node. By modeling graph signal shifting with observations at a node as a linear dynamical system, we establish fundamental connections between local response of shifting at a node, concerned in the graph signal processing (GSP) field, and the observability of the system, investigated in control theory. The observability, as a measure of how well internal states of a dynamical system can be learned from node's observations, is reflected by the minimal polynomial of a matrix pair related to the system. Specifically, by introducing a notion of observable graph frequencies to a node, we show that the output signals (observations) at a node only contain the spectral components of its so-called observable graph frequencies. Furthermore, we unveil that the observability of a node to the spectral components of a GS is related to the rank of its observability matrix from the perspective of control theory. Our work reveals that partial signal outputs at a node are sufficient to design the FIR GF locally in terms of node-variant (NV) GFs. These findings further enable us to characterize the minimum-degree NV GF, where a minimum number of its shifted GSs are involved in the filter's output, and devise a distributed GF design algorithm for it.
Massage Therapy’s Effectiveness on the Decoding EEG Rhythms of Left/Right Motor Imagery and Motion Execution in Patients With Skeletal Muscle Pain

Objective: Most of effectiveness assessments of the widely-used Massage therapy were based on subjective routine clinical assessment tools, such as Visual Analogue Scale (VAS) score. However, few studies demonstrated the impact of massage on the Electroencephalograph (EEG) rhythm decoding of Motor imagery (MI) and motion execution (ME) with trunk left/right bending in patients with skeletal muscle pain. Method: We used the sample entropy (SampEn), permutation entropy (PermuEn), common spatial pattern (CSP) features, support vector machine (SVM) and logic regression (LR) classifiers. We also used the convolutional neural network (CNN) and attention-based bi-directional long short-term memory (BiLSTM) for classification. Results: The averaged SampEn and PermuEn values of alpha rhythm decreased in almost fourteen channels for five statuses (quiet, MI with left/right bending, ME with left/right bending). It indicated that massage alleviates the pain for the patients of skeletal pain. Furthermore, compared with the SVM and LR classifiers, the BiLSTM method achieved a better area under curve (AUC) of 0.89 for the classification of MI with trunk left/right bending before massage. The AUC became smaller after massage than that before massage for the classification of MI with trunk left/right bending using CNN and BiLSTM methods. The Permutation direct indicator (PDI) score showed the significant difference for patients in different statuses (before vs after massage, and MI vs ME). Conclusions: Massage not only affects the quiet status, but also affects the MI and ME. Clinical Impact: Massage therapy may affect a bit on the accuracy of MI with trunk left/right bending and it change the topography of MI and ME with trunk left/right bending for the patients with skeletal muscle pain.
Online Trip Planning for Public Bike Systems

Public Bike System (PBS) not only provides convenient travel service but also alleviates the last-mile problem. With the increasing awareness of environmental protection and green commuting, people prefer to use the public bike as transportation for short-distance travel. However, the explosion of users in PBS leads to new congestion problems. To relieve the pressure of PBS, there are many types of research on system prediction, operation, and trip planning. However, there is few work focusing on the online trip planning problem. To study the case, we propose an Online Matching Trip Planning algorithm (OMTP), and we prove the theoretical lower bound of OMTP is 1 - 1/e. And then, we consider the short-term conflicts among users and design an Online Group Trip Planning algorithm (OGTP). We design two kinds of experiments- Generated Data Based and Real Data Based. In the generated data based experiment, we reveal the impact of different parameters with the generated trip data. In the real data based experiment, we validate our proposed algorithms with the real trip data set in New York City. The results show that OMTP and OGTP save time per trip on average.