Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive LoRaWAN Transmission exploiting Reinforcement Learning: the Industrial Case

Wireless technologies play a key role in the Industrial Internet of Things (IIoT) scenario, for the development of increasingly flexible and interconnected factory systems. A significant opportunity in this context is represented by the advent of Low Power Wide Area Network (LPWAN) wireless technologies, that enable a reliable, secure, and effective transmission of measurement data over long communication ranges and with very low power consumption. Nevertheless, reliability in harsh environments (as typically occurs in the industrial scenario) is a significant issue to deal with. Focusing on LoRaWAN, adaptive strategies can be profitably devised concerning the above tradeoff. To this aim, this paper proposes to exploit Reinforcement Learning (RL) techniques to design an adaptive LoRaWAN strategy for industrial applications. The RL is spreading in many fields since it allows the design of intelligent systems using a stochastic discrete-time system approach. The proposed technique has been implemented within a purposely designed simulator, allowing to draw a preliminary performance assessment in a real-world scenario. A high density of independent nodes per square km has been considered, showing a significant improvement (about 10%) of the overall reliability in terms of data extraction rate (DER) without compromising full compatibility with the standard specifications.
Compact, Highly Efficient, and Controllable Simultaneous 2 × 2 Three-Mode Silicon Photonic Switch in the Continuum Band

Multimode switch (MMS) allows realizing multimode optical communication enabling high-speed communication. However, to develop such an ultrafast switch for simultaneous multimode with a compact size is very challenging. In this paper, we design and demonstrate a compact multimode <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> MMS based on numerical simulation methods using silicon <inline-formula> <tex-math notation="LaTeX">$\Psi $ </tex-math></inline-formula>-junctions and multimode interference (MMI) couplers. The switch is controlled by thermal-optic phase shifters which is able to switch simultaneously states of the optical signal between three quasi-transverse electric modes. The MMS exhibits a low insertion loss from −1.5 dB to −3 dB, low crosstalk below −22 dB, and high extinction ratio larger than 22 dB in 40-nm bandwidth in the third telecom window from 1520 to 1560 nm, respectively. With a compact footprint of <inline-formula> <tex-math notation="LaTeX">$12\,\,\mu \text{m}\,\,\times 1300\,\,\mu \text{m}$ </tex-math></inline-formula>, the MMS exhibits relatively large dimensional tolerances. Besides, the MMS provides total electric power consumption levels smaller than 103 mW at an ultrafast switching time of <inline-formula> <tex-math notation="LaTeX">$4.4~\mu \text{s}$ </tex-math></inline-formula> without the impact of the plasmonic effect. Furthermore, the conceptual principle of the proposed MMS can be reconfigurable and scalable in dimensional multifunctional on-chip mode-division multiplexing optical interconnects and promising potential for photonic large scale integration circuits in the continuum band.
Concise Total Syntheses of (±)-Joubertiamine, (±)-O-Methyljoubertiamine, (±)-3′-Methoxy-4′-O-methyljoubertiamine, (±)-Mesembrane, and (±)-Crinane

Abstract A method to access cis-3a-aryloctahydroindole alkaloids has been developed through a key strategy involving Eschenmoser–Claisen rearrangement of allylalcohol. This approach gives us an opportunity to access the all-carbon quaternary center required for cis-3a-aryloctahydroindole alkaloids. Subsequent simple allylic oxidation of Eschenmoser–Claisen products and synthetic elaborations (reductions/oxidations) enabled the total syntheses of the title compounds to be completed in good yields in a few steps. The strategic viability was further tested in the total syntheses of Amaryllidaceae alkaloids (±)-mesembrane and (±)-crinane. Towards this end, we synthesized advanced intermediate keto-aldehydes from Eschenmoser–Claisen rearrangement products through iodolactonization followed by elaboration involving reduction and oxidation steps.
Camel: Context-Aware Magnetic MIMO Wireless Power Transfer with In-band Communication

Wireless power transfer (e.g., based on RF or magnetic) enables convenient device-charging, and triggers innovative applications that typically call for faster, smarter, economic, and even simultaneous adaptive charging for multiple smart-devices. Designing such a wireless charging system meeting these multi-requirements faces critical challenges, mainly including the better understanding of real-time energy receivers’ status and the power-transferring channels, the limited capability and the smart coordination of the transmitters and receivers. In this work, we devise Camel, a context-aware MIMO MRC-WPT (magnetic resonant coupling-based wireless power transfer) system, which enables adaptive charging of multiple devices simultaneously with a novel context sensing scheme. In Camel, we craft an innovative MIMO WPT channels’ state estimation and collision-aware in-band parallel communication among multiple transmitters and receivers. We design and implement the Camel prototype and conduct extensive experimental studies. The results validate our design and demonstrate that Camel can support simultaneous charging of as many as 10 devices, high-speed context sensing within 50 milliseconds, and efficient parallel communication among transceivers within proximity of ~0.5m.
Neural-Network-Based Nonlinearity Equalizer for 128 GBaud Coherent Transcievers

We propose an efficient neural-network-based equalization jointly compensating fiber and transceiver nonlinearities for high-symbol-rate coherent short-reach links. Providing about 0.9 dB extra SNR gain, it allows achieving experimentally the record single-channel 1.48 Tbps net rate over 240 km G.652 fiber.
Context-Aware Security for Vehicles and Fleets: A Survey

Vehicles are becoming increasingly intelligent and connected. Interfaces for communication with the vehicle, such as WiFi and 5G, enable seamless integration into the user’s life, but also cyber attacks on the vehicle. Therefore, research is working on in-vehicle countermeasures such as authentication, access controls, or intrusion detection. Recently, legal regulations have also become effective that require automobile manufacturers to set up a monitoring system for fleet-wide security analysis. The growing amount of software, networking, and the automation of driving create new challenges for security. Context-awareness, situational understanding, adaptive security, and threat intelligence are necessary to cope with these ever-increasing risks. In-vehicle security should be adaptive to secure the car in an infinite number of (driving) situations. For fleet-wide analysis and alert triage, knowledge and understanding of the circumstances are required. Context-awareness, nonetheless, has been sparsely considered in the field of vehicle security. This work aims to be a precursor to context-aware, adaptive and intelligent security for vehicles and fleets. To this end, we provide a comprehensive literature review that analyzes the vehicular as well as related domains. Our survey is mainly characterized by the detailed analysis of the context information that is relevant for vehicle security in the future.
A Circularly Polarized Antenna in D-Band

In this paper a 140 GHz circularly polarized antenna is studied. The motivation of this work is to propose a suitable antenna configuration to reduce the subsequent manufacturing cost and complexity. The resultant antenna consists of a circular waveguide feed and a dielectric cover which is used to increase antenna directivity. Numerical study shows that the proposed antenna exhibits a 17% relative S11 bandwidth with a maximum gain of 16 dBi. The axial ratio is less than 3 dB ranging from 130 GHz to 155 GHz.
Robust Adaptive Read Scheme for 7nm Configuration SRAMs

This paper presents a new Adaptive Read scheme for Configuration SRAMs with a feedback mechanism to eliminate Read-Disturb, enhancing Read Margin, yield and performance. The proposed scheme is implemented on TSMC 7nm FinFETs for Configuration SRAMs in Xilinx FPGAs, and this idea is applicable to any distributed SRAM structures. The design has an intrinsic dynamic feedback mechanism to adjust the read address voltage effectively, based on the process, voltage and temperature (PVT), by using a special replica memory cell that emulates actual memory cells. The proposed Op-Amp based design with a memory cell emulating circuit in the negative feedback is shown to be working on silicon, which tunes pass-gate (PG) address voltage during read, with 2.7x improvement in read margin, and also avoids read-disturb.
Comparative Study Of CdS Thin Films Fabricated By A Simplified Spray Technique using Two Cationic Precursor Salts With Different Stability Constants

Cadmium sulphide (CdS) thin films were prepared by a low cost simplified spray technique using perfume atomizer on glass substrates at 325oC using two different cationic precursors: cadmium acetate and cadmium chloride. The effect of cationic precursors on the structural, morphological, optical and electrical properties was studied. Structural studies reveal that the films are polycrystalline in nature with a mixture of both cubic and hexagonal phases with hexagonal phase being predominant irrespective of the precursor used. The preferential orientation factor f(h k l) of CdCl2 based film is high compared to that of Cd(CH3COO)2 based film. Grain size value were found to be equal to 21.33 nm and 32.63 nm for Cd(CH3COO)2 and CdCl2 based CdS films respectively. The optical transmittance in the visible range was found to be > 85 % for both the films. The specular reflectance values for both the films are very low. High transmittance and low reflectance values obtained for the films makes them suitable as antireflecting coating materials for thin film solar cells.
Research on Function Test Method of Intelligent High-speed Railway Centralized Traffic Control System

With the successive launch of multiple intelligent high-speed railways, the functions of the CTC have continued to expand. Intelligent adjustment, big data processing, and multifunctional interaction have become the new outstanding features of the CTC. In order to ensure the stability and reliability of the new generation of intelligent CTC, the testing methods of system functions need to be continuously improved and innovated. CTC functional testing can be divided into two aspects: test case design and test environment design. Comprehensively considering the characteristics of CTC, this paper introduced the design method of CTC functional test cases in detail, from the aspects of functional logic forward verification, functional logic boundary protection, performance testing, compatibility testing, business scenario applications, etc. At the same time, an environmental structure that is convenient for simulating test input conditions was designed according to the test function category, including mirror structure test environment, parallel structure test environment, information recharge test environment, dynamic simulation test environment, etc., and the characteristics and principles of the structure were analyzed one by one.
Current Trends and Approaches in Synonyms Extraction: Potential Adaptation to Arabic

Extracting synonyms from dictionaries or corpora is gaining a special attention as synonyms play an important role in improving NLP application performance. This paper presents a survey of the different approaches and trends used in automatically extracting the synonyms. These approaches can be divided into four main categories. The first approach is to find the Synonyms using a translation graph, The second approach is to discover new transition pairs such as (Arabic – English) (English – France) then (Arabic- France). The third approach is to construct new WordNets by exploring synonymy graphs, and the fourth approach is to find similar words from corpora using Deelp Learning methods, such as word embeddings and recently BERT models. The papers also presents comparative analysis between these approoaches, and highlights potential adaptation to generate synonyms automatically in the Arabic language as future work.
An Optimization of Fractal Microstrip Patch Antenna with Partial Ground using Genetic Algorithm Method

UWB is increasingly advancing as a high data rate wireless technology after the Federal Communication Commission announced the bandwidth of 7.5 GHz (from 3.1 GHz to 10.6 GHz) for ultra-wideband (UWB) applications. Furthermore, designing a UWB antenna faces more difficulties than designing a narrow band antenna. A suitable UWB antenna should be able to work over the Federal Communication Commission (FCC) of ultra-wide bandwidth allocation. Furthermore, good radiation properties across the entire frequency spectrum are needed. This paper outlines an optimization of fractal square microstrip patch antenna with the partial ground using a genetic algorithm at 3.5 GHz and 6 GHz. The optimized antenna design shows improved results compared to the non-optimized design. This design is optimized using a genetic algorithm and simulated using CST simulation software. The size of the optimized design is reduced by cutting the edges and the center of the patch. The optimized results reported, and concentrated on the rerun loss, VSWR and gain. The results indicate a significant enhancement as is illustrated in Table II. Thus, the optimized design is suitable for S-band and C-band applications.
Byzantine Fault-Tolerance in Decentralized Optimization under 2f-Redundancy

This paper considers the problem of exact Byzantine fault-tolerance in multi-agent decentralized optimization. We consider a complete peer-to-peer network of $n$ agents; each agent has a local cost function, however, up to $f$ of the agents are Byzantine faulty. Such faulty agents may not follow a prescribed algorithm correctly, and may share arbitrary incorrect information about their costs. The goal of the exact fault-tolerance problem is to design a decentralized algorithm that allows all the non-faulty agents to compute a common minimum point of only non-faulty agents' aggregate cost function. We propose the first-ever decentralized algorithm with provable exact fault-tolerance against a bounded fraction of faulty agents, provided the non-faulty agents have the necessary property named 2f-redundancy, defined later in the paper.
Demonstration of Asynchronous Successive Interference Canceller for Uplink NOMA-PON

This paper proposes a successive interference canceller (SIC) with carrier frequency offset (CFO) compensator for uplink transmission in a non-orthogonal multiple access (NOMA)-based coherent PON. We experimentally confirmed SIC’s error-free operation when multiplexing 2 ONUs.
GPU-Ether: GPU-native Packet I/O for GPU Applications on Commodity Ethernet

Despite the advent of various network enhancement technologies, it is yet a challenge to provide high-performance networking for GPU-accelerated applications on commodity Ethernet. Kernel-bypass I/O, such as DPDK or netmap, which is normally optimized for host memory-based CPU applications, has limitations on improving the performance of GPU-accelerated applications due to the data transfer overhead between host and GPU. In this paper, we propose GPU-Ether, GPU-native packet I/O on commodity Ethernet, which enables direct network access from GPU via dedicated persistent kernel threads. We implement GPU-Ether prototype on a commodity Ethernet NIC and perform extensive testing to evaluate it. The results show that GPU-Ether can provide high throughput and low latency for GPU applications.
Edge computing optimization method. Analyzed task: crowd counting

The exponential increase in the amount of data that IoT systems generate poses serious problems to cloud and data centers, such as data management and processing scalability, power overheads, network traffic management and security. Embedded systems provided with data processing may partially mitigate such problems. Their employment is very convenient in low latency applications, in reducing traffic flows towards the cloud, in minimizing storage and processing requirements, and in providing an effective solution to several security issues. Moreover, even if with limitations, such devices allow the implementation of artificial intelligence algorithms opening to their widespread use with and without cloud integration. This work focused on the neural networks assessment on an embedded system by following a specific approach, based on edge limitations and both computational and performance parameters. The nets purpose was to detect crowded and uncrowded states. Some of these networks were then ported on an embedded platform, the STM32F767ZIT6U Nucleo board, and the trade-off between classification and computational performance was thoroughly addressed. This study shows the ability of embedded systems to run complex artificial intelligence neural networks with limited classification performance reduction.
Performance Evaluation of LoRa Networks in an Open Field Cultivation Scenario

The employment of Internet of Things (IoT) technology in agriculture could be beneficial in managing the cultivation production in a highly-customizable way. LoRa (Long Range) is one of the most important technologies in cultivation fields mainly thanks to its ability to provide long-range transmission and low power consumption. In this paper, we evaluate the performance of LoRa networks in an open field cultivation scenario via simulations using FLoRa, an open-source framework in OMNeT++. The number of nodes, the number of gateways, the antenna gain, and the size of the deployment area have a considerable impact on both the data extraction rate and the energy consumption of a LoRa network. Our results show that the optimization of the parameters that affect the performance of a LoRa network could transform traditional agriculture into a new perspective of smart cultivation. Finally, we evaluate the impact of the density and the geometric characteristics of three types of crop (tomatoes, grapes, apples) on the number of intersections that were caused by the insertion of physical objects-obstacles in a LoRa network.
Tunable U-Net: Controlling Image-to-Image Outputs Using a Tunable Scalar Value

Image-to-image conversion tasks are more accurate and sophisticated than ever thanks to advances in deep learning. However, since typical deep learning models are trained to perform only one task, multiple trained models are required to perform each task even if they are related to each other. For example, the popular image-to-image convolutional neural network, U-Net, is normally trained for a single task. Based on U-Net, this study proposes a model that outputs variable results using only one trained model. The proposed method produces a continuously changing output by setting an external parameter. We confirm the robustness of our proposed model by evaluating it on binarization and background blurring. According to these evaluations, we confirmed that the proposed model can generate well-predicted outputs by using un-trained tuning parameters as well as the outputs by using trained tuning parameters. Furthermore, the proposed model can generate extrapolated outputs outside the learning range.
Efficient Association of Wi-Fi Probe Requests under MAC Address Randomization

Wi-Fi-enabled devices such as smartphones periodically search for available networks by broadcasting probe requests which encapsulate MAC addresses as the device identifiers. To protect privacy (user identity and location), modern devices embed random MAC addresses in their probe frames, the so-called MAC address randomization. Such randomization greatly hampers statistical analysis such as people counting and trajectory inference. To mitigate its impact while respecting privacy, we propose Espresso, a simple, novel and efficient approach which establishes probe request association under MAC address randomization. Espresso models the frame association as a flow network, with frames as nodes and frame correlation as edge cost. To estimate the correlation between any two frames, it considers the multimodality of request frames, including information elements, sequence numbers and received signal strength. It then associates frames with minimum-cost flow optimization. To the best of our knowledge, this is the first piece of work that formulates the probe request association problem as network flow optimization using frame correlation. We have implemented Espresso and conducted extensive experiments in a leading shopping mall. Our results show that Espresso outperforms the state-of-the-art schemes in terms of discrimination accuracy (> 80%) and V-measure scores (> 0.85).
Anisotropic Disturbance Rejection for Kinematically Redundant Systems With Applications on an UVMS

Systems with a manipulator and a mobile base, such as in aerial and underwater applications, are susceptible to disturbances which create difficulties in maintaining a desired end effector pose. However, kinematically redundant vehicle manipulator systems can make use of the continuous space of configurations to solve an inverse kinematics problem that can be exploited to improve disturbance rejection. In this letter, we explore disturbance rejection along a given direction for both a single pose, and along a trajectory. Simulated validation results are provided on an underwater vehicle manipulator system subject to environmental disturbances.
Backscatter analysis in UWB chipless RFID based on UWB-IR

A novel technique is applied in this work to estimate the resonance characteristics of a chipless RFID tag built on a multi-patch backscatter. To monitor the backscatter, the RFID system uses the reader antenna and the chipless tag. Thus, the resonators can be detected in real environments. A chipless RFID tag consisting of four resonators, which is relying on a triangular patch antenna is also proposed. Moreover, we develop an RFID reader antenna based on ultra-wideband pulse (UWB-IR) which is able to interrogate the chipless tag through a UWB pulse. Experimental measurements obtained to validate the simulation results in an anechoic chamber environment
MOZART: Masking Outputs with Zeros for Architectural Robustness and Testing of DNN Accelerators

Deep Neural Networks (DNNs) are increasingly used in safety critical autonomous systems. In this paper, we present MOZART, a DNN accelerator architecture which provides fault detection and fault tolerance. MOZART is a systolic architecture based on the Output Stationary (OS) variant, as it is the one that inherently limits fault propagation. In addition, MOZART achieves fault detection with on-line functional testing of the Processing Elements (PEs). Faulty PEs are swiftly taken off-line with minimal classification impact. The implementation of our approach on Squeezenet results in a loss of accuracy of less than 3% in the presence of a single faulty PE, compared to 15–33% without mitigation. The area overhead for the test logic does not exceed 8%. Dropout during training further improves fault tolerance, without a priori knowledge of the faults.