Miracle-dz/UniFAR-FTUs huggingface dataset:
A PVT-Tolerant >40-dB IRR, 44% Fractional-Bandwidth Ultra-Wideband mm-Wave Quadrature LO Generator for 5G Networks in 55-nm CMOS

Precise generation of quadrature signals over a wide frequency range is a key function for the next-generation 5G communication systems. In this paper, we present a wideband quadrature generator based on a single-stage polyphase filter (PPF). A phase detector senses the phase error from quadrature signals generated by a single-stage PPF, and a feedback circuit continuously tunes the filter center frequency to the input signal frequency by varying the polyphase resistance of an nMOS device in triode. Transformer-based resonant circuits at the input and output of the PPF ensure wide bandwidth and low loss. Prototypes have been realized in a 55-nm CMOS technology. Tailored to the next-generation 5G systems for cross-network interoperability requirements, the measured quadrature generator shows an image rejection ratio IRR > 40 dB over a bandwidth from 28 to 44 GHz. The power consumption is 36 mW for the PPF and buffers, and 3 mW only for the calibration loop. One key aspect of the proposed solution is its robustness over process, voltage and temperature (PVT), one of the weak aspects of alternatives proposed in the literature. This solution compares favorably with the state of the art and shows the largest fractional bandwidth (44%) among the quadrature generators at frequencies greater than 20 GHz, to authors’ knowledge.
Electric Powertrain: Energy Systems, Power Electronics and Drives for Hybrid, Electric and Fuel Cell Vehicles [Book Review]

Fuel Cell Vehicles to be timely and relevant for automotive engineers and engineering students seeking a deeper appreciation of what vehicle electrification means. The book was years in preparation and reflects the authors’ own wealth of industrial and academic experience. It has been almost three decades since General Motors (GM) displayed the Impact concept car at the Los Angeles Auto Show in January 1990. The Impact inspired the design of the GM EV1, the first modern production electric car, and for California to subsequently issue its zero-emissions mandate. The EV1 became available to lease in 1996 and demonstrated to the public that electric cars were feasible. The authors of this book focus on the inner workings of the electric car. Readers, whether engineers, students, or the interested public, will find this book a treasure trove of knowledge on modern automotive technology.
A Novel Single-Beacon Navigation Method for Group AUVs Based on SIMO Model

Single-beacon navigation for group autonomous underwater vehicles (AUVs) suffers from long navigation period and asynchronization. This paper addresses the problem by introducing the single-input-multiple-output (SIMO) model, where, a single AUV transmits the signal and other AUVs receive the signal. Utilizing the time-delay of signals from the beacon and the sender, the navigation for all AUVs is achieved simultaneously, and the navigation period is dramatically decreased. Moreover, based on the new SIMO model, a tracking method utilizing extended Kalman filter is also put forward to increase the navigation precision and reduce the computational burden. Finally, a coordinate fusion algorithm based on the location, error, and time-delay is presented, which fuses the independent AUV coordinates to be a higher precision holistic coordinate system for group AUVs. Simulation results show the effectiveness of the proposed method.
Selective DF Based Multiple Relayed Cooperative System with M-QAM Signalling

In this paper, we analyze the error rate performance of selective decode-and-forward (DF) based multiple relayed cooperative system for M-QAM (Quadrature Amplitude Modulation) signalling. The analysis is performed for selective DF based cooperative relaying with single as well as multiple relayed system. In particular, an alternate representation of Q-function is used to derive the closed-form expression of asymptotic symbol error rate probability (ASEP) over Rayleigh faded channels with high SNR approximation. Further, the analytical results are corroborated by Monte Carlo simulations for M-QAM MRC cooperative system.
Angular Statistical Resolution Limit of Two Closely-Spaced Point Targets: A GLRT-Based Study

The study of statistical resolution limit (SRL) of two closely spaced targets has attracted considerable interests in the last decade. Two definitions for SRL have been proposed: one is based on the Cramér–Rao bound (CRB) and the other is based on a decision-based process. In this paper, we focus on the latter one. Different from the existing study, which assumes that the center parameter of interests (POIs) is known a priori, we use a more general model where all the POIs are unknown. We exploit the first-order Taylor expansion of the signals to get an approximate linear model with respect to the tested parameter, namely, the separation of directions of arrival of two sources. Then, we apply the general likelihood rate test to get a closed-form expression of SRL. We consider both the cases with known and unknown noise variance. Moreover, we analyze the impact of some parameters (including the resolution rate, the false-alarm rate, and the waveforms) on the SRL. For comparison, we also derive the CRB-based SRL, which is essentially different with our decision-based counterpart. Numerical simulation results demonstrate the validity of our theoretical results.
Incremental-Precision Based Feature Computation and Multi-Level Classification for Low-Energy Internet-of-Things

This paper presents a novel technique to reduce energy consumption of a machine learning classifier based on incremental-precision feature computation and classification. Specifically, the algorithm starts with features computed using the lowest possible precision. Depending on the classification accuracy, the features of the previous level are combined with features of the incremental-precision to compute the features in higher-precision. This process is continued till a desired accuracy is obtained. A certain threshold that allows many samples to be classified using a low-precision classifier can reduce energy consumption, but increases misclassification error. To implement hardware which provides the required updates in precision, an incremental-precision architecture based on data-path decomposition is proposed. One novel aspect of this work lies in the design of appropriate thresholds for multi-level classification using training data such that a family of designs can be obtained that enable trade-offs between classification accuracy and energy consumption. Another novel aspect involves the design of hardware architectures based on data-path decomposition which can incrementally increase precision upon demand. Using a seizure detection example, it is shown that the proposed incremental-precision-based multi-level classification approach can reduce energy consumption by 35% while maintaining high sensitivity, or by about 50% at the expense of 15% degradation in sensitivity compared with similar approaches to seizure detection in literature. The reduction in energy is achieved at the expense of small area, timing and memory overheads as multiple classification steps are used instead of a single step.
Deep Learning Versus $\ell^{1}$ -Minimization for Compressed Sensing Photoacoustic Tomography

We investigate compressed sensing (CS) techniques for reducing the number of measurements in photoacoustic tomography (PAT). High resolution imaging from CS data requires particular image reconstruction algorithms. The most established reconstruction techniques for that purpose use sparsity and $\ell^{1}$ -minimization. Recently, deep learning appeared as a new paradigm for CS and other inverse problems. In this paper, we compare a recently invented joint $\ell^{1}$ -minimization algorithm with two deep learning methods, namely a residual network and an approximate nullspace network. We present numerical results showing that all developed techniques perform well for deterministic sparse measurements as well as for random Bernoulli measurements. For the deterministic sampling, deep learning shows more accurate results, whereas for Bernoulli measurements the $\ell^{1}$ -minimization algorithm performs best. Comparing the implemented deep learning approaches, we show that the nullspace network uniformly outperforms the residual network in terms of the mean squared error (MSE).
Two-Stage Short-Term Wind Speed Prediction Based on LSTM-LSSVM-CFA

Accurately predicting wind speed is vital to the large scale connection of wind power grids. This paper proposed a two-stage prediction way called LSTM-LSSVM-CFA (LSTMs-long short term memory networks, LSSVM-least squares support vector machine and CFA-chaos firefly algorithm) which may be an effective method to improve the accuracy of short-term wind speed prediction. Integrated learning of wind speed sequence based on LSTMs and LSSVM-CFA is the core technology in this new method. The prediction section include two main procedures. In the first stage, a couple of LSTMs with different hidden layers and different neurons processing guarantee the implied information of wind speed time series can be fully collected and avoid the weak generalization capability of single deep neural network learning. In the second stage, the prediction result of LSTMs-layer is transmitted to the LSSVM-based regression layer and its parameters are optimized by the CFA which has global search capabilities. Finally, the final integrated learning result of prediction is given by the LSSVM-layer. Based on actual historical wind speed data, we made use of single-LSTM, LSTM-LSSVM-PSO and LSTM-LSSVM-CFA to perform short-term wind speed prediction one hour ahead. Compared with single learning and other popular integrated learning models, experimental results show that the proposed LSTM-LSSVM-CFA can perform better.
Research on Early Warning System of Power Network Overloading Under Serverless Architecture

Under the background of massive computing resources in power grid, smart grid has become the direction and trend of power industry development. In order to ensure the safe and stable operation of smart grid, the monitoring of the grid load should be strengthened and a reasonable solution should be arranged in time when the grid load exceeded the standard. So early warning system of power network overloading under serverless architecture was proposed. The main advantages of the combination of smart grid and serverless architecture were analyzed in detail, and the technical feasibility and economic feasibility of the early warning system for grid overload under the serverless mode were analyzed by examples.
A Path Planning Algorithm for Smooth Trajectories of Unmanned Aerial Vehicles via Potential Fields

Path planning is of critical importance to Unmanned Aerial Vehicle (UAV) based applications like unmanned delivery and inspection. The operating space of UAVs in urban environments is usually complex and dynamic. Safety and efficiency are hence the major concerns for UAV operations. Traditional path planning algorithms tend to find trajectories for UAVs in terms of safety which are theoretically optimal. However, the objective is usually difficult to achieve in practice given the complex operating environments and limited UAV capabilities. Thus, a path planning algorithm for UAVs that finds feasible trajectories is needed for UAV-based operations. In this paper, an Artificial Potential Field (APF) based algorithm is proposed for UAV's trajectory planning considerings concerns of safety, efficiency and kinetic constraints of UAVs. Simulation results show an improvement of 23% on efficiency and 89.5% on smoothness of trajectories over existing heuristic based methods. Performance metrics of safety, efficiency and smoothness of trajectories are analyzed based on simulation results. Parameter values in obtaining the good performance metrics have been suggested in the end of the paper.
Grid State Estimation Over Unreliable Channel Using IoT Networks

This paper designs a distributed state estimation scheme considering cyber attacks using the internet of things (IoT) technologies. The IoT sensors are utilised to get synchronous generator information. After locally estimating the system states, the attack occurs during transmission of sensors information to the remote estimator. In the fusion center, the convex optimization problem is developed to estimate the generator states. Lastly, the feedback controller is proposed to regulate the generator states. Numerical results demonstrate that the developed algorithm can properly estimate and regulate the generator states.
A Comparitive Study of On-Chip Clock Generators Using a-IGZO TFTs for Flexible Electronic Systems

This paper presents a comparitive study of ring oscillators (RO) using amorphous Indium Gallium Zinc Oxide (a-IGZO) thin-film transistors (TFTs) to implement on-chip clock generator for flexible electronic systems. A five-stage RO has been implemented with different inverter topologies using IGZO TFTs, which includes Diode connected load, Capacitive bootstrapping (BS), Pseudo-CMOS and Pseudo-CMOS bootstrapping architectures. These topologies have been simulated using in-house IGZO TFT models under similar conditions using different power supplies (10 V, 15 V and 20 V) in cadence environment. Among all architechtures Capacitive bootstrapping RO has ensured highest frequency of operation in the order of MHz and an output swing of 82% of VDD. Whereas, Pseudo-CMOS based RO provides the lowest power consumption in the order of μW with an output swing of 57% of VDD. On the other hand, the combination of Pseudo CMOS and bootstrapping has ensured highest voltage swing of 95% of VDD. In terms of power delay product (PDP) BS RO is superior with respect to other topologies. This work provides a clear insight to the designer to choose a particular topology for given application, mainly for on-chip clock generation for flexible electronic systems based on the requirements.
Diode Reverse Recovery Process and Reduction of a Half-Wave Series Cockcroft–Walton Voltage Multiplier for High-Frequency High-Voltage Generator Applications

This paper investigates the diode reverse recovery process and reduction of a half-wave (HW) series Cockcroft–Walton (CW) voltage multiplier based on the steady-state analysis for high-frequency high-voltage (HV) generator applications. The diode reverse recovery process for a multistage voltage multiplier is analyzed after the introduction of steady-state operation. The diode reverse recovery problem is the bottleneck to further increase the circuit operation switching frequency for achieving high power density and short HV pulse rise and decay times. The diode reverse recovery problem is mainly caused by the diodes in the first-stage voltage multiplier. It is suggested that the most effective and economic way to alleviate the diode reverse recovery problem is by employing diodes without reverse recovery such as silicon carbide Schottky diodes in the first stage only. The silicon carbide Schottky diode without reverse recovery needs to be used only in the first stage of the voltage multiplier to effectively mitigate the reverse recovery problems at high frequency. The 300 kHz switching frequency three-stage voltage multiplier circuit hardware prototype experimental results finally validate the analysis. A technology demonstrator of a 300 kHz 8 kW 160 kV HV generator based on the proposed hybrid silicon carbide and silicon diode solution for the HW series CW voltage multiplier is provided finally.
Numerical and Experimental Investigation of an Improved Flux Path Brushless-DC Machine for Variable Speed Applications

In this paper, analysis of an improved axially flux path brushless-dc machine (IAFP-BLDM) with the enhanced housing including its operation principle and particular structure is presented. The proposed configuration efficiently improves the magnetic performance by reducing the reluctance and leakage flux. Moreover, unlike the conventional brushless-dc generators, the proposed configuration of this paper utilizes an assisted dc field coil replaced with the permanent-magnet excitation. As the main salient feature of including the dc field coil which increases the controllability of the flux, providing a regulated terminal voltage in a very wide range appropriate for variable speed applications can be addressed. In this paper, by employing the finite-element method, the 3-D magnetic field analysis of the IAFP-BLDM is carried out by using the MagNet CAD package (Infolytica Corporation Ltd.). In order to verify the actual performance of the proposed configuration, a 1.5 kW, 3600 rpm, 9–6 salient poles and two layers are manufactured and tested followed by a discussion on the results. The output results were in good agreement with the analysis results, and also the output performance was verified by the experiment set.
Transporting Microobjects Using a Magnetic Microrobot at Water Surfaces*

Microobject transporting is an important task in numerous micromanipulation applications. In this paper, we propose a novel method to transport microobjects using a magnetically actuated microrobot at water surfaces. The microrobot is a pentagram-shaped magnetized sheet with typical diameter of $800\boldsymbol{\mu}\mathbf{m}$. By applying magnetic gradient generated by electromagnetic coils, the microrobot can move controllably at water surfaces. Microobjects at water surfaces can be trapped by the microrobot due to the capillary attraction. Microobject transporting is realized by direct push of the microrobot. A releasing strategy by accelerating the microrobot is also presented to separate the microobject from the microrobot. The experimental results show that the maximum transporting speed for solder balls achieved 3.3mm/s with a magnetic gradient of 79mT/m.
Ariotivity: Provisioning Smart Objects with Augmented Reality and Open Standards

Smart objects are computationally enhanced everyday objects such as watches, keychains and more. As these devices become ubiquitous, its seamless and secure interaction becomes critical. Due to the fragmented landscape of the Internet of Things (IoT) and the difficulty of allowing a secure exchange of resources among objects, the provisioning and interoperability of smart objects is still an open problem in IoT. With this goal in mind, we developed the Ariotivity framework: an Augmented Reality enhancement for the Iotivity framework. This framework is a preliminary solution to provision and interact with smart objects by means of a mobile device. It allows users to reconfigure and control smart objects through an augmented reality application and a backend that uses open standards developed for the IoT industry. This article presents the design of our first prototype, describes its functionality and reports the ways in which it could be enhanced. Preliminary results show its feasibility and proof-of-concept through example devices, but further development is needed to be deployed in real environments.
Synthesis of Point Memory-Based Adaptive Gain Robust Controllers with Guaranteed $\mathcal{L}_{2}$ Gain Performance for a Class of Uncertain Time-Delay Systems

This paper proposes a point memory-based adaptive gain robust controller with $\mathcal{L}_{2}$ gain performance for a class of uncertain linear systems with state delays. The point memory adaptive gain robust controller presented in this paper consists of a fixed gain controller and a adaptive gain one. In this paper, we show that LMI-based sufficient conditions for the existence of the proposed adaptive gain robust controller are presented. Finally, a simple illustrative example is included to show the effectiveness of the proposed robust control system.
Removing Atmospheric Turbulence Effects in Unified Complex Steerable Pyramid Framework

Simultaneously removing atmospheric turbulence-induced geometric distortion and blurry degradation is a challenging task. In this paper, we propose an effective method to remove or at least reduce turbulence effects in unified complex steerable pyramid (CSP) framework. The proposed method first decomposes the degraded image sequence by CSP. Then, the local motion and the energy information of the image sequence can be represented by multiscale and multidirectional phases and amplitudes. To mitigate turbulence-induced random oscillation, we use temporal average phase as the initial reference phase. Then, the reference phase is iteratively corrected, using the proposed phase correction method which is capable of correcting the large displacement. To reduce blurry degradation, optimal amplitude selection and fusion methods are proposed to reduce blur variation and CSP reconstruction errors. Finally, the corrected phase and fused amplitude can be synthesized to generate a reconstructed image. To further enhance the image quality, a blind deconvolution approach is adopted to deblur the reconstructed image. Through a variety of experiments on the simulated and real data, experimental results show that the proposed method can effectively alleviate the turbulence effects, recover image details, and significantenhance visual quality.
Deformable User Interfaces: Using Flexible Electronics for Human Computer Interaction

Deformable inputs offer users the ability to physically manipulate a device for system interaction. We combine flexible electronic technologies with human computer interaction to study how changing the form factor of digital devices can offer new interaction techniques to users. We introduce our research on deformable user interfaces by discussing bend gestures as a source of input, presenting our process to rapidly prototype flexible devices, and demonstrate three deformable user interfaces: bend passwords, bend for mobile games, and bend for vision impaired users. We show how flexible devices and deformable interactions can improve how we interact with our devices.
AVB-Aware Routing and Scheduling of Time-Triggered Traffic for TSN

IEEE 802.1 time-sensitive networking (TSN) is a set of amendments to the IEEE 802.1 standard that enable safety-critical and real-time behavior over Ethernet for the industrial automation and automotive domains. Selected TSN mechanisms offer the possibility to emulate the well-known traffic classes found in mixed-criticality distributed systems: Time-triggered (TT) communication with low jitter and bounded end-to-end latency, audio-video-bridging (AVB) streams with bounded end-to-end latency, and general best-effort messages, which have no timing guarantees. Critical traffic is guaranteed via the global network schedule which is stored in so-called gate control lists (GCLs) and controls the timely behavior of frames for each queue of an egress port. Although researchers have started to propose approaches for the routing and scheduling (i.e., GCL synthesis) of TT traffic, all previous research has ignored lower priority real-time traffic, such as AVB, resulting in TT configurations that may increase the worst-case delays of AVB traffic, rendering it unschedulable. In this paper, we propose a joint routing and scheduling approach for TT traffic, which takes into account the AVB traffic, such that both TT and the AVB traffic are schedulable. We extensively evaluate our approach on a number of synthetic and realistic test cases.
How do ALOHA and Listen Before Talk Coexist in LoRaWAN?

In this work we address the analysis of a LoRaWAN network where some devices access the channel according to the standard-compliant ALOHA protocol, while other devices transmit according to a Listen Before Talk paradigm based on the CSMA/CA mechanism. To analyze this scenario, we propose a mathematical model to obtain the Data Extraction Rate both for CSMA/CA and ALOHA devices, as well as the average delay experienced by messages transmitted by CSMA/CA devices. Simulation results show the accuracy of our model, as well as the benefits of introducing CSMA/CA devices into the network, even when not all the devices implement this mechanism and must coexist with ALOHA devices.