Miracle-dz/UniFAR-FTUs huggingface dataset:
Reduced Modeling of Unbalanced Radial Distribution Grids in Load Area Framework

A simplified representation of a distribution network offers relevant advantages in limiting the amount of information processed and reducing the computational burden necessary for its treatment, in particular in planning studies, real-time simulations, monitoring and control. The identification of the relevant information for obtaining a correct reduced modeling of a distribution network results from the Load Area (LA) approach. It is based on the concept of groups of nodes/prosumers whose power injection has a similar impact on the operating conditions of distribution grids; the groups are identified through overload and voltage indexes. The paper presents a method for obtaining the reduced equivalent modeling of unbalanced radial grids in the LA framework, once the LAs have been identified. It is based on the backward-forward graph navigation technique for radial grids and a generalization of the modeling of single-phase and two-phase branches. The application and discussion of the method for five different size test cases highlight the computational issues related to the representation and solution of the reduced networks. The results show the feasibility of the proposed method and highlight the computational gains deriving from its adoption.
Efficient Compression Technique for NoC-based Deep Neural Network Accelerators

Deep Neural Networks (DNNs) are very powerful neural networks, widely used in many applications. On the other hand, such networks are computation and memory intensive, which makes their implementation difficult onto hardwareconstrained systems, that could use network-on-chip as interconnect infrastructure. A way to reduce the traffic generated among memory and the processing elements is to compress the information before their exchange inside the network. In particular, our work focuses on reducing the huge number of DNN parameters, i.e., weights. In this paper, we propose a flexible and low-complexity compression technique which preserves the DNN performance, allowing to reduce the memory footprint and the volume of data to be exchanged while necessitating few hardware resources. The technique is evaluated on several DNN models, achieving a compression rate close to 80% without significant loss in accuracy on AlexNet, ResNet, or LeNet-5.
Efficient Temporal Logic Verification by Incremental Abstraction

A recently formulated incremental abstraction method that preserves $\mathrm{CTL}^{*}$-X is further developed and evaluated in this paper. The temporal logic $\mathrm{CTL}^{*}$-X includes both linear temporal logic (LTL) and computational tree logic (CTL), except for the next operator (often denoted by X). For a modular transition system, the incremental abstraction means that any local events, only included in one of the subsystems, are first abstracted. After synchronization more events become local. Repeated abstraction then means that further reduction of local transitions is achieved when more submodels are synchronized. This incremental abstraction is evaluated as a tool for temporal logic verification, and compared with a couple of competitive alternatives. In a preliminary evaluation, the proposed incremental abstraction is often shown to be the most efficient choice for verification of temporal logic properties, especially when some individual submodels are comparatively large.
OASIS: Weakening User Obligations for Security-critical Systems

Security-critical systems typically place some requirements on the behaviour of their users, obliging them to follow certain instructions when using those systems. Security vulnerabilities can arise when users do not fully satisfy their obligations. In this paper, we propose an approach that improves system security by ensuring that attack scenarios are mitigated even when the users deviate from their expected behaviour. The approach uses structured transition systems to present and reason about user obligations. The aim is to identify potential vulnerabilities by weakening the assumptions on how the user will behave. We present an algorithm that combines iterative abstraction and controller synthesis to produce a new software specification that maintains the satisfaction of security requirements while weakening user obligations. We demonstrate the feasibility of our approach through two examples from the e-voting and e-commerce domains.
Downlink Pilots for Hybrid Massive MIMO Under Reciprocity Imperfections

In this letter, we study the performance of a time division duplex (TDD) hybrid massive multiple input multiple output (MIMO) system with imperfect calibration of the transmit and receive radio frequency chains. Using deterministic equivalent (DE) analysis, we establish the loss in the achievable rates due to calibration imperfections. We then discuss limited downlink training to estimate the effective downlink channel, and compare the relative merits of this method against frequency division duplexed (FDD) operation. Finally, via detailed numerical simulations, we evaluate the relative performance of no downlink training, limited downlink training, and FDD mode operation in the presence of reciprocity calibration errors.
The Best of Both Worlds: Combining CUDA Graph with an Image Processing DSL

CUDA graph is an asynchronous task-graph programming model recently released by Nvidia. It encapsulates application workflows in a graph, with nodes being operations connected by dependencies. The new API brings two benefits: Reduced work launch overhead and whole workflow optimizations. In this paper, we improve the ability of CUDA graph to exploit workflow optimizations, e.g., concurrent kernel executions with complementary resource occupancy. Additionally, we argue that the advantages of DSLs are complementary to CUDA graph, and joining the two techniques can benefit from the best of both worlds. Here, we propose a compiler-based approach that combines CUDA graph with an image processing DSL and a source-to-source compiler called Hipacc. For ten image processing applications benchmarked on two Nvidia GPUs, our approach is able to achieve a geometric mean speedup of 1.30 over Hipacc without CUDA graph, 1.11 over CUDA graph without Hipacc, and 3.96 over another state-of-the-art DSL called Halide.
CL(R)Early: An Early-stage DSE Methodology for Cross-Layer Reliability-aware Heterogeneous Embedded Systems

Cross-layer reliability (CLR) presents a cost-effective alternative to traditional single-layer design in resource-constrained embedded systems. CLR provides the scope for leveraging the inherent fault-masking of multiple layers and exploiting application-specific tolerances to degradation in some Quality of Service (QoS) metrics. However, it can also lead to an explosion in the design complexity. State-of-the art approaches to such joint optimization across multiple degrees of freedom can lead to degradation in the system-level Design Space Exploration (DSE) results. To this end, we propose a DSE methodology for enabling CLR-aware task-mapping in heterogeneous embedded systems. Specifically, we present novel approaches to both task and system-level analysis for performing an early-stage exploration of various design decisions. The proposed methodology results in considerable improvements over other state-of-the-art approaches and shows significant scaling with application size.
mm-Wave Mixer-First Receiver with Passive Elliptic Low-pass Filter

Passive mixer-first receivers have gained interest for their superior linearity, lower power consumption, and smaller footprint. Traditionally, the load of these mixers is a passive or active RC filter. This paper demonstrates a passive mixer-first receiver where the mixer load is a third-order passive elliptic filter realized using on-chip capacitors and spiral inductors so that wide bandwidth and high selectivity are concurrently achieved. The 65nm CMOS prototype covers a tunable frequency between 21 and 29 GHz and achieves 0.5 GHz instantaneous baseband bandwidth, >49 dB stopband to passband rejection, an in-band ICP-1dB of -6 dBm and out-of-band B-1dB >3.4 dBm.
Applicability of Okumura – Hata Model for Wireless Communication Systems in Oman

Current article discusses the usefulness of Okumura-Hata model in urban areas for 2G and 3G communication systems. At some places, applicability of the model is observed and extended to a lower distance from the tower than the specified minimum distance specified in the model. Authors did an extensive drive test and post processed data and proposed theoretical modified model which is presented and compared for different data sets at different locations. 3rd order interpolating polynomial was also used which helps in reasonable prediction for RF engineers.
TReMo: A Model for Ternary ReRAM-Based Memories with Adjustable Write-Verification Capabilities

With the increasing use and advancement of memristors, implementation barriers for ternary systems, such as handling more than two states without any extra hardware, could be broken. In this paper, for the first time to the best of our knowledge, a new memory model on circuit level based on ReRAM is modeled for the ternary applications. This novel ternary memory model benefits from a parallel read method, for accomplishing low-latency read operation, and an often-used write-verification method. In addition, a thorough tool for this ternary memory is developed that performs energy, performance and area estimation which is an extension of the existing nonvolatile memory tool called NVSim.
Non-Orthogonal Multiple Access: A Constellation Domain Multiplexing Approach

In this paper, a novel constellation domain non-orthogonal multiple access (Con-NOMA) system using signal-space diversity technique is proposed for the downlink network. More specifically, users will be multiplexed in the in-phase and quadrature part of the rotated constellation points of M-ary phase-shift keying/quadrature amplitude modulation symbol. The proposed Con-NOMA is capable of bypassing the requirement of successive interference cancellation, joint detection, uneven power assignment, etc., which are required in the power domain NOMA (PD-NOMA). Further investigation shows that the Con-NOMA is superior to PD-NOMA in terms of the achievable data rate for each user as well as the sum-rate of the overall system. The proposed Con-NOMA is then examined under Nakagami-m fading channel by evaluating the closed-form expression of the average symbol error rate (ASER) for each user. After comparison, it is concluded that the error performance of each user in the proposed Con-NOMA system is much better than in the PD-NOMA system.
Routing Topology and Time-Division Multiplexing Co-Optimization for Multi-FPGA Systems

Time-division multiplexing (TDM) is widely used to overcome bandwidth limitations and thus enhances routability in multi-FPGA systems due to the shortage of I/O pins in an FPGA. However, multiplexed signals induce significant delays. To evaluate timing degradation, nets with similar criticalities are often grouped to form NetGroups. In this paper, we propose a framework concerning routing topology and time-division multiplexing co-optimization for multi-FPGA systems. The proposed framework first generates high-quality topologies considering Net-Group criticalities. Then, inspired by column generation, TDM ratio assignment is solved optimally by Lagrangian relaxation. Experimental results show that our approach outperforms the top three entries of ICCAD 2019 CAD Contest. Moreover, our TDM ratio assignment algorithm can further improve the results of the top three winners to almost as good as ours.
Pyramid LSTM auto-encoder for tool wear monitoring

Tool wear monitoring is necessary to guarantee the product quality in high-speed machining. In this paper, a pyramid long short-term memory network (LSTM) based on spectral features, is proposed to extract tool wear features from multi-source monitoring signals. In order to improve the robustness of the model under varied working conditions, the unlabeled signal is trained by an auto-encoder structure to reduce the information loss. With the LSTM architecture, the model can better reflect the multi-period characteristics of long-term signals and is more suitable for the actual industrial production. The experimental validation of high-speed milling verifies the effectiveness of the proposed method.
Analysis of Harmonics Detection and their Filtration by using Active Band Pass Filter for Domestic Appliances

In modern era, one of the rising power quality (PQ) concern is harmonics distortion (HD). The HD is usually produced by increase in the utilization of nonlinear user loads at domestic level. It not only spoils the PQ but also have harmful effects on electrical appliances. So, this paper presents a new approach to mitigate the HD in home appliances rapidly by utilizing the active band pass filter (ABPF). The fast Fourier transform (FFT) technique is employed for the detection of harmonics in appliances. The band pass filter dramatically affects the response time of active filter. The suggested control methodology is capable to eliminate harmonics from distorted input voltage waveform and provides a pure sinusoidal waveform. The prototype of this model is also implemented. Experimental results along with simulation show that the proposed model is quite effective in eliminating HD despite load variations and also useful to compensate the reactive power in electronic home appliances. Hence, this technique is cost effective for domestic users with high quality performance in all aspects.
Preliminary study on specific microsatellites markers related to growth trait in Fenneropenaeus chinensis

Bulked Segregation Analysis was used to study genetic variation of cultured stock of the sixth generation of Fenneropenaeus chinensis whose growth trait had segregation.Two classes with mixed DNA template of 20 samples were used for microsatellites analysis in seven microsatellites loci.All seven microsatellite loci were polymorphic.A total of 7 unique alleles for each class were found over all loci,with allele size ranging from 0.1 to 1.0 kb.In addition,four successively selected stocks on good growth trait were analyzed by microsatellites technique in above seven microsatellites loci.The results indicated slight differentiation for frequence of most alleles among four successively selected stocks.But some loci showed quite different genetic frequency,which could have resulted from artificial selection.The results showed that the frequency of the alleles of the selected stocks had changed in some loci,the further study on whether the special alleles could be a genetic marker of the selected stocks with good growth characteristic or not is needed.
Islands within islands: The effects of habitat fragmentation, novel community interactions, and climate on Hawaiian Drosophila populations.

iv Introduction. 1 Synergistic effects of anthropogenic disturbance. 1 Hawai'i: An invaluable scientific resource. 3 Hawaiian Drosophila: indicators of change. 4 A system under siege. 6 Study merit and hypotheses. 6 Materials and Methods. 9 Study site description. 9 Sampling methodology. 10 Statistical analyses. 13 Results. 16 Total Drosophilidae. 16 Native Drosophilidae. 17 Exotic "yellow flies". 19 D. tanythrix. 20 Picture-wings. 22 Other Drosophilidae. 27 Drosophilid Species and morphotype richness. 29 iButton temperature and humidity measurements. 31 Conslusions and Discussion. 32 General conclusions, pertaining to study hypotheses. 32 Effects of datasets and scale. 32 Overarching parameter effects. 34 Species-specific responses. 36 Ecological implications and future directions. 39 Closing remarks. 42 Works Cited. 43 Tables. 52 Table 1. 52 Table 2. 53 Tables 3A-J. . 54 Tables 4A-J. . 57 Tables 5A-D. 63
Calculation of Breakdown Voltage of Gas Gaps With Weakly Nonuniform Field: Sphere and Donut Gaps

Breakdown voltage (BDV) is calculated for two electrode structures, namely, sphere and donut gaps, operating in air at atmospheric pressure. The calculation is based on the streamer BD criterion. COMSOL software package is used for field analysis and calculation of critical number of electrons by assessing integrals of the efficient ionization coefficient along field lines. Thus, also lengths of critical avalanches that can propagate from both electrodes were obtained. First, simulations were performed for sphere gaps at conditions standardized by IEEE to derive critical number of electrons for highly reliable experimental data. It was shown that these numbers deviate greatly from the widely accepted 108 number, and that clear tendencies for the deviations exist depending on the gap to sphere diameter ratio. This assisted us in the second part of this work, which is a description of a spark gap (SG) formed by two donuts. Such gaps are compact and can serve as closing switches, and as a crude HV measurement means. Experimental results and comparison to calculated BDVs are reported. These two examples illustrate a simple and effective method of BDV calculation in arbitrary geometries with weakly nonuniform field for well-characterized gases at moderate pressures; it can be used with industry-standard software in HV practice.
Sensor Data Compression for the Normal Reaction Predictor Training for Walking Robots

In this paper, the problem of compression and feature extraction for walking robot sensor data is studied. The aim is to facilitate the process of building a machine learning-based predictor for the ground reaction forces, which should allow to simplify generating control systems for walking robots, capable of working in changing contact interaction scenarios. In particular, the paper proposes use of undercomplete and overcomplete autoencoders for an automated feature extraction, with the aim of using the encoders as a preprocessing step in the machine learning (ML)-based ground reaction predictor, implemented by solving a regression problem. Papers shows trends in the mean squared error (MSE) on reconstruction tasks for different depths and structures of the encoders, demonstrates the trade-off between complexity and the quality of the reconstruction.
DewROS: A Platform for Informed Dew Robotics in ROS

In recent years Cloud Robotics technology has been proposed to overcome the constraints imposed by the resources of standalone robots. We can imagine that in near future robots will be very present in everyday life and interact with humans, so it is necessary to guarantee that robots could make decisions even if the connection to the cloud is unavailable. It is then important to move the critical tasks on the edge devices in order to make them always accessible, not following the Cloud Robotics paradigm but the Dew Robotics one instead. In this paper we propose DewROS, a platform for Dew Robotics that uses monitoring entities to monitor the system status in order to adapt the application operating conditions. In particular in this work we describe the DewROS platform and its application in the case of video analysis in a surveillance scenario. The results provided in this paper demonstrate how DewROS allows us to exploit at their best the limited resources of our robots.
Joint Spectrum and Power Allocation in 5G Integrated Access and Backhaul Networks at mmWave Band

The millimeter wave (mmWave) band has been considered as an effective way to meet the increasing traffic demand by sufficient bandwidth and high spatial reuse. The harsh propagation experienced at such high frequencies requires a dense base station deployment, which is not feasible to provide wired backhaul due to the unavailability of fiber drops. To address this issue, the integrated access and backhaul (IAB) architecture is proposed by the third generation partnership project (3GPP). In this paper, we investigate the spectrum and power allocation for IAB mmWave enabled cellular network to maximize the network capacity with considering the data rate requirements. A novel resource allocation scheme based on the sequential convex programming approach (RASCPA) is proposed. The simulation results show that our proposed scheme is superior to other schemes. The proposed scheme can increase the network capacity while satisfying the user data rate requirements.
Game Theoretic Algorithm for Energy Efficient Mobile Edge Computing with Multiple Access Points

This paper considers a Mobile Edge Computing scenario with multiple mobile units (MUs), multiple access points (APs) and one cloudlet server. The MUs have to decide whether offloading their computation tasks to the cloudlet is energy wise beneficial. As there are multiple APs available to connect the MUs to the cloudlet and communication and computation resources have to be shared among all MUs, each MU also has to choose the AP for transmission that minimizes its offloading energy under the given fraction of the overall resources. The problem is formulated as a energy minimization problem with a maximum offloading time constraint. MUs not only need to consider the energy required for local computation or offloading, but simultaneously avoid an overlong processing time of offloaded computation. This joint offloading decision and resource allocation is divided into two subproblems in the proposed approach. The resource allocation problem is reformulated by using Lagrange multipliers and closed-forms for the calculation of the shared resources are found. These results can be integrated into the proposed game theoretic algorithm for the offloading decision problem. The algorithm is based on a potential game and therefore, can be proven to converge to a Nash equilibrium. Numerical results show a benefit of the proposed resource allocation strategy, a performance of the proposed game algorithm near the optimal solution and a fast algorithm execution time that can even be significantly improved by proposed sorting metrics. mobile edge computing, joint optimization, resource allocation strategy, game theory
A Soft Graph Attention Reinforcement Learning for Multi-Agent Cooperation

The multi-agent reinforcement learning (MARL) suffers from several issues when it is applied to large-scale environments. Specifically, the communication among the agents is limited by the communication distance or bandwidth. Besides, the interactions among the agents are complex in large-scale environments, which makes each agent hard to take different influences of the other agents into consideration and to learn a stable policy. To address these issues, a soft graph attention reinforcement learning (SGA-RL) is proposed. By taking the advantage of the chain propagation characteristics of graph neural networks, stacked graph convolution layers can overcome the limitation of the communication and enlarge the agents’ receptive field to promote the cooperation behavior among the agents. Moreover, unlike traditional multi-head attention mechanism which takes all the heads into consideration equally, a soft attention mechanism is designed to learn each attention head’s importance, which means that each agent can learn how to treat the other agents’ influence more effectively during large-scale environments. The results of the simulations indicate that the agents can learn stable and complicated cooperative strategies with SGA-RL in large-scale environments.