Miracle-dz/UniFAR-FTUs huggingface dataset:
HealthLog Monitor: Errors, Symptoms and Reactions Consolidated

Advances in reliability research have presented novel techniques for early identification of upcoming failures (both long-term and short-term) as well as sophisticated isolation and mitigation of error occurrences. The existing primitive error monitoring tools usually deliver very basic information of errors and do not allow integration of all system health-related information to be combined under the same context. The absence of common tools becomes more noticeable when new and more aggressive and dynamic schemes are concerned, such as processor undervolting or overclocking. In this paper, we present HealthLog monitor, a flexible Linux system monitoring service that offers a generic abstraction layer to combine health-related information as well as action features. HealthLog is capable of monitoring hardware metrics (performance, sensor, and errors) as well as external health-related data, allowing combined symptom description and reaction features supported by an API. In addition, HealthLog can be programmed to react and initiate mitigation, isolation, and recovery actions. The scope of the monitor is to offer a universal and platform-independent standard for error reporting and system monitoring mechanisms in all system layers. HealthLog is flexibly designed to be a cross-platform service that comes with built-in support of X-Gene processor family and S.M.A.R. T. HDD monitoring, but can be easily extended to support new processors and interfaces. In this paper, we showcase how HealthLog can be used in two real-life scenarios that consider system aging and system undervolting.
A Queuing Delay Utilization Scheme for On-Path Service Aggregation in Services-Oriented Computing Networks

In services-oriented computing networks, packets in the process of routing to a data center must wait for a sufficient amount of data before service aggregation to reduce the network transmission load. However, packets must be uploaded to the data center as soon as possible to reduce delay. With the exponential growth in the number of IoT connected devices, the wait time for packets is longer at routers due to massive amounts of data, which causes a large queuing delay. If this queuing time can be utilized for service aggregation in a service-oriented computing network, the network performance will be substantially improved. Therefore, a queuing delay utilization scheme for on-path service aggregation (SAQD) is proposed in this paper. This scheme has the following innovations: 1) SAQD fully utilizes the queuing delay of packets for service aggregation, which can effectively reduce the transmission volume and communication overhead. Based on the proposed service aggregation algorithm, packets are divided into forwarding packets and aggregating packets, and the service aggregation of aggregating packets is completed by utilizing the transmission time of forwarding packets to ensure that the transmission volume and communication overhead are effectively reduced without additional latency. 2) SAQD can effectively alleviate the traffic pressure of the data center and balance the workload of routers. By the service aggregation and intranet cache of routers, some requests for the data center can be handled by routers, which reduces the traffic pressure of the data center, especially in the peak period. Compared with conventional schemes, the experimental results demonstrate that SAQD reduces the workload of the data center by 55.8%–66.26% and provides users with a better quality of experience by reducing the request response delay by 31.33%~51.41%.
Topical Concerns and Critical Questions Engineering Students Want/Need Answers To: Dependence on Key Groups

This Research to Practice Full Paper examines which topical concerns and critical questions engineering students want/need answers to – based on a survey associated with a list of 201 critical questions (addressing 17 topics) asked by students since 2001. Over this period, the lead author has run an Academic Success and Professional Development (ASAP) mentoring-scholarship program (supported by NSF/industrial funding) aimed at recruiting, training, graduating and placing engineering students. This project-centric program at Arizona State University (ASU) – and the associated community of practice (consisting of learners and faculty-industry-peer mentors), while currently focusing on upper-division transfer students, serves upper-division non-transfers and some graduate students. The list of topics and questions was created to help students ask questions and think about the opportunities/challenges before them. Data for the most recent cohort of 74 scholars is presented and analyzed. It is shown how the list of selected critical topics and questions varies depends on gender, minority status and age. Analysis of the data shows that higher-risk (higher-context) groups (e.g. women, underrepresented minorities) focus more on shorter-term career goals while lower-risk groups focus more on longer-term career and life-planning goals. The information presented should be helpful to program leaders, administrators and educators advising engineering students.
A Superior Image Reconstruction Algorithm Based on Total Variation Regularization for Lensless Imaging Systems

In recent years, lensless imaging system has been developed in certain areas. Generally, the objective scene and the measurements of the sensor are in a linear relationship in lensless imaging systems, which requires a computational algorithm to reconstruct an image of the scene. Existing methods include Tikhonov regularization and total variation(TV)-based regularization using partial differential equation(PDE). However, they either can’t reduce noise effectively, or run slowly. We propose a superior TV-based image reconstruction algorithm by employing variable splitting and Bregman iteration. Firstly, we split the objective function into two subproblems via introducing an auxiliary variable, and then solve them by Tikhonov regularization and anisotropic TV regularization separately. Finally, we alternate the two subproblems to achieve the optimal solution. Experiments on synthetic data with different level of degradations show that our algorithm has a better performance than other methods.
Integration of Renewable Energy into Electric Vehicle Charging Infrastructure

Electric vehicles have great potential to help reduce the United States’ oil consumption and greenhouse gas emissions if used on a large scale. However, consumers have shown concern with the dependability of electric vehicles due to their limited charging infrastructure and long charging times. Furthermore, the electrification of transportation allows for the ability to be powered by renewable energy. Renewable energy would reduce gas emissions even further, but the sources of energy are known to be intermittent. This paper proposes a distributed battery storage system to close the gap between renewable energy production and consumers’ demand. In this system, electric vehicle charging stations, instead of charging vehicles directly from the grid and energy sources, would store energy in batteries. The proposed system seamlessly integrates renewable energy as it compensates for its intermittent nature by allowing energy to be charged in proportion to the rate electricity is produced. The objective of the proposed system is to integrate renewable energy without compromising energy reliability at electric vehicle charging stations.
Demand Side Management for Active Power Control of Autonomous Hybrid Power System

The maintenance of microgrid frequency at its nominal value is extremely essential to ensure its stable operation and also to maintain the quality of the system supply. This paper uses/implements direct load control under demand response in addition to automatic generation control to contain the deviations in frequency of the system. The proposed Matlab model of the microgrid is made up of the wind turbine generator (WTG), the diesel engine generator (DG) and some controllable loads such as refrigerator (REF), heat pump (HP) and plugged in hybrid electric vehicle (PHEV), without any storage device. For controlling the controllable loads, an intelligent demand response (DR) controller is used to monitor and adjust the system frequency. The DR controllers used in this paper are the proportional plus integral (PI) controller and the proportional-integral-derivative (PID) controller, the parameters of which are optimized using Genetic Algorithm (GA) and Firefly Algorithm (FF). Simulation studies on three different scenarios of abrupt generation change and load variation confirms that PID controller tuned with Firefly Algorithm gives better frequency regulatien.
Are Respiratory Enzymes the Primary Sources of Intracellular Hydrogen Peroxide?*

Endogenous H2O2 is believed to be a source of chronic damage in aerobic organisms. To quantify H2O2 formation, we have generated strains of Escherichia colithat lack intracellular scavenging enzymes. The H2O2 that is formed within these mutants diffuses out into the medium, where it can be measured. We sought to test the prevailing hypothesis that this H2O2 is primarily generated by the autoxidation of redox enzymes within the respiratory chain. The rate of H2O2 production increased when oxygen levels were raised, confirming that H2O2 is formed by an adventitious chemical process. However, mutants that lacked NADH dehydrogenase II and fumarate reductase, the most oxidizable components of the respiratory chain in vitro, continued to form H2O2 at normal rates. NADH dehydrogenase II did generate substantial H2O2 when it was when overproduced or quinones were absent, forcing electrons to accumulate on the enzyme. Mutants that lacked both NADH dehydrogenases respired very slowly, as expected; however, these mutants showed no diminution of H2O2 excretion, suggesting that H2O2 is primarily formed by a source outside the respiratory chain. That source has not yet been identified. In respiring cells the rate of H2O2 production was ∼0.5% the rate of total oxygen consumption, with only modest changes when cells used different carbon sources.
Factorised Hidden Layer Based Domain Adaptation for Recurrent Neural Network Language Models

Language models, which are used in various tasks including speech recognition and sentence completion, are usually used with texts covering various domains. Therefore, domain adaptation has been a long-ongoing challenge in language model research. Conventional methods mainly work by the addition of a domain dependent bias. In this paper, we propose a novel way to adapt neural network-based language models. Our proposed approach relies on a linear combination of factorised hidden layers, which are learnt by the network. For domain adaptation, we use topic features from latent Dirichlet allocation. These features are input into an auxiliary network, and the output of this network is used to calculate the hidden layer weights. Both the auxiliary network and the main network can be trained jointly by error backpropagation. This makes our proposed approach completely unsupervised. To evaluate our method, we show results for the well-known Penn Treebank and the TED-LIUM dataset.
Development of Electric Scooter Battery Pack Management System

The rise of electric transportation includes not only four-wheeled vehicles but two-wheeled personal mobility devices as well. One of such is the electric scooter which can be used for personal as well as commercial use as in this case. The present status of a scooter design and development is described – the need for an in-house designed battery management system is given. A set of commercial monitoring integrated circuits are used in conjunction with central controller and measurement circuitry to perform cell monitoring, balancing, current measurement, charge control and emergency shutdown. Experimental validation of proposed system is given.
Discriminative sparse representation learning using multiclass hinge loss

Sparse representation methods have been researched widely in recent years. Sparse representation classification methods, such as sparse representation classifier (SRC) and label-consistent K-SVD (singular value decomposition) learn classification parameters, dictionary, and sparse representation simultaneously, so that they find an optimal sparse representation to discriminate categories. However, these classifiers use least square error (LSE) strategy to design the classifiers. LSE of the empirical risk is not optimal for classifiers because even if a training sample correctly classified, it may increase the empirical cost. We, therefore, introduce the hinge loss to design the classifier. The hinge loss is employed in support vector machines, and it shows better performance than LSE based methods. We provide an optimization algorithm to minimize the proposed criterion that is the linear combination of the hinge loss and sparse representation error. Experimental results show that the proposed method exhibited conventional sparse representation classification methods.
Thermal and electrical properties of a novel 3-element mixed insulation oil for power transformers

Mineral oil has been used in power transformers for more than 100 years. Extensive research has been conducted on mineral oil substitutes in many countries due to the requirements of green energy and environmental protection. Natural esters are good substitutes for mineral oil. They present fire safety, environmental, and insulation ageing advantages over mineral oil. However, natural esters possess high viscosity, high dielectric loss, high acidity, and poor oxidation stability, and these properties require changing the structural design of transformers. In this study, a novel three-element (mineral, soybean, and palm) mixed insulation oil was successfully developed. This mixed insulation oil combines the advantages of mineral oil and natural esters, and its key parameters satisfy the requirement of IEC 60296:2012. The thermal and electrical properties of the novel three-element mixed insulation oil, including viscosity, heat transfer performance, permittivity, resistivity and breakdown voltage, were studied comprehensively. Comparison of the thermal and electrical properties of the novel mixed insulation oil with those of common mineral oil showed that the former exhibits better performance than the latter and is suitable for use in power transformers.
Education of Electrical Engineering Students about Ethics and Societal Impacts in Courses and Co-curricular Activities

This research explored faculty perspectives on the ethics and societal impacts (ESI) education of electrical engineering (EE) students, in comparison to other engineering disciplines. An Input-Environment-Output model underpins the work, focusing on environmental factors (courses and co-curricular settings) that could influence students’ ESI knowledge. EE participation in the survey of engineering educators was lower than other disciplines (civil, mechanical, chemical), raising concerns about the culture around ESI education in EE. Instructors believed that the most common settings for ESI education of undergraduate EE students were senior capstone design and first-year introductory courses. Compared to other disciplines, fewer faculty believed that EE undergraduates were taught about ESI in sophomore/junior engineering/engineering science courses. The most common ESI topics taught to EE students were: professional practice issues, safety, and the societal impacts of technology. Fewer EE faculty taught sustainability and environmental protection issues compared to other disciplines. Within EE courses where faculty integrated ESI, the most common ESI teaching methods were engineering design, case studies, and examples of professional scenarios. Co-Curricular activities such as IEEE, honor societies and research may also contribute to the ESI education of EE students. Faculty are encouraged to integrate ESI issues into all of their courses and activities.
NFκB Negatively Regulates Interferon-induced Gene Expression and Anti-influenza Activity*

Interferons (IFNs) are antiviral cytokines that selectively regulate gene expression through several signaling pathways including nuclear factor κB(NFκB). To investigate the specific role of NFκB in IFN signaling, we performed gene expression profiling after IFN treatment of embryonic fibroblasts derived from normal mice or mice with targeted deletion of NFκB p50 and p65 genes. Interestingly, several antiviral and immunomodulatory genes were induced higher by IFN in NFκB knock-out cells. Chromatin immunoprecipitation experiments demonstrated that NFκB was basally bound to the promoters of these genes, while IFN treatment resulted in the recruitment of STAT1 and STAT2 to these promoters. However, in NFκB knock-out cells IFN induced STAT binding as well as the binding of the IFN regulatory factor-1 (IRF1) to the IFN-stimulated gene (ISG) promoters. IRF1 binding closely correlated with enhanced gene induction. Moreover, NFκB suppressed both antiviral and immunomodulatory actions of IFN against influenza virus. Our results identify a novel negative regulatory role of NFκB in IFN-induced gene expression and biological activities and suggest that modulating NFκB activity may provide a new avenue for enhancing the therapeutic effectiveness of IFN.
24.5 A Twin-8T SRAM Computation-In-Memory Macro for Multiple-Bit CNN-Based Machine Learning

Computation-in-memory (CIM) is a promising avenue to improve the energy efficiency of multiply-and-accumulate (MAC) operations in AI chips. Multi-bit CNNs are required for high-inference accuracy in many applications [1–5]. There are challenges and tradeoffs for SRAM-based CIM: (1) tradeoffs between signal margin, cell stability and area overhead; (2) the high-weighted bit process variation dominates the end-result error rate; (3) trade-off between input bandwidth, speed and area. Previous SRAM CIM macros were limited to binary MAC operations for fully connected networks [1], or they used CIM for multiplication [2] or weight-combination operations [3] with additional large-area near-memory computing (NMC) logic for summation or MAC operations.
Independent Low-Rank Matrix Analysis Based on Time-Variant Sub-Gaussian Source Model

Independent low-rank matrix analysis (ILRMA) is a fast and stable method for blind audio source separation. Conventional ILRMAs assume time-variant (super-)Gaussian source models, which can only represent signals that follow a super-Gaussian distribution. In this paper, we focus on ILRMA based on a generalized Gaussian distribution (GGD-ILRMA) and propose a new type of GGD-ILRMA that adopts a time-variant sub-Gaussian distribution for the source model. By using a new update scheme called generalized iterative projection for homogeneous source models, we obtain a convergence-guaranteed update rule for demixing spatial parameters. In the experimental evaluation, we show the versatility of the proposed method, i.e., the proposed time-variant sub-Gaussian source model can be applied to various types of source signal.
Performance Monitor Counters: Interplay Between Safety and Security in Complex Cyber-Physical Systems

Recent years have witnessed the growth of the adoption of cyber-physical systems (CPSs) in many sectors, such as automotive, aerospace, civil infrastructures, and healthcare. Several CPS applications include critical scenarios, where a failure of the system can lead to catastrophic consequences. Therefore, anomalies due to failures or malicious attacks must be detected timely. This paper focuses on two relevant aspects of the design of a CPS: 1) safety and 2) security. It analyzes in a specific scenario how the performance monitor counters (PMCs) available in several commercial microprocessors can be from the one hand a valuable tool to enhance the safety of a system and, on the other hand, a security backdoor. Starting from the example of a PMC-based safety mechanism, this paper shows the implementation of a possible attack and eventually proposes a strategy to mitigate the effectiveness of the attack while preserving the safety of the system.
An Adaptive-Resolution Quasi-Level-Crossing-Sampling ADC Based on Residue Quantization in 28-nm CMOS

We present a digitally intensive adaptive-resolution (AR) quasi-level-crossing-sampling (quasi-LCS) analog-to-digital converter (ADC) for Internet-of-Things wireless networks, where the power consumed in data transmission, processing, and storage can be significantly reduced by minimizing the ADC’s gross output bit-rate. The AR quasi-LCS ADC is implemented as a delta-modulator and adopts a 4-bit asynchronous SAR ADC to quantize the residue voltage signal, thus allowing a straightforward implementation of LCS and AR algorithms in the digital domain, as well as yielding a digital-friendly architecture. Fabricated in 28-nm CMOS, this ADC achieves an SNDR of 53 dB over 1.42 MHz signal bandwidth while consuming 205 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> and an active area of 0.0126 mm<sup>2</sup>.
Fast Solution of Electromagnetic Scattering for 3-D Inhomogeneous Anisotropic Objects Embedded in Layered Uniaxial Media by the BCGS-FFT Method

In this paper, the stabilized biconjugate gradient fast Fourier transform (BCGS-FFT) method is used to solve the electromagnetic scattering problem for 3-D inhomogeneous uniaxial anisotropic objects embedded in a layered uniaxial medium. The optical axis of the background medium is perpendicular to the layer boundaries. However, the optical axis of the scatterer can be rotated through any angles. The electric field integral equation (EFIE) is formulated in the layered uniaxial anisotropic background medium. The volumetric roof-top function is used for the basis function as well as the testing function. The dyadic Green’s functions (DGFs) for both the electric field and the magnetic vector potential are used to solve the EFIE, and the results as well as the performance are compared. Several numerical simulations are carried out to validate the accuracy and efficiency of the proposed method. The major new contribution of this paper is to extend the BCGS-FFT method to accommodate the uniaxial anisotropy of the layered background medium. Therefore, the DGFs for the layered uniaxial medium are evaluated before solving the EFIE. In addition, the DGF for the electric field is also used to solve the 3-D scattering problem through BCGS-FFT iterations, which is different from the previous work.
Robust Decentralized Controller Synthesis in Flexure-Linked H-Gantry by Iterative Linear Programming

The dual-drive H-gantry is widely used for high-speed, high-precision Cartesian motion. Compared with the conventional rigid-linked design, the flexure-linked counterpart is able to prevent the damage of joints for its smaller interaxial coupling force. However, there are still barriers to further push up its precision, such as parametric uncertainties due to the inaccurate dynamical model, the possible induced vibration during high-speed motion, and the decentralized control structure required by industries. To maintain the tracking precision of carriages and minimize the vibration of the end effector, we aim to optimize parameters in decentralized controllers with choices of flexure pieces. We find that such decentralized feedback structure yields some uncontrollable but stabilizable states in the closed-loop system, and no direct solution from solving the algebraic Riccati equation is available in this case. Such structural constraint, together with constraints due to stability requirement and model uncertainties facilitates us to formulate an $\mathcal {H}_2$ guaranteed cost control problem within a projected convex domain. From here, efficient numerical procedures are developed to obtain the global optimum by iterative linear programming. The real-time experiment validates the optimality and the robustness of the proposed method.