Miracle-dz/UniFAR-FTUs huggingface dataset:
Response of soil carbon fractions in a Haplic Cambisol to crop rotation systems and residue management practices under no tillage in the Eastern Cape, South Africa

Soil organic carbon (SOC) is a major indicator of soil quality and/or health but its slow short-term response to management changes has prompted the search for more sensitive indicators for monitoring short-term changes in soil organic matter (SOM). This study aimed to investigate the short-term effects of crop rotation systems and residue management practices under no tillage on selected soil carbon fractions of a Haplic Cambisol, using plots from a conservation agriculture (CA) field trial (split plot design replicated in three blocks). The main plots were comprised of the crop rotation systems: maize-fallow-maize (MFM), maize-fallow-soybean (MFS), maize-wheat-maize (MWM) and maize-wheat-soybean (MWS). Residue management practices that included residue removal (R−) and residue retention (R+) were allocated to the sub-plots. The carbon fractions measured were SOC, microbial biomass carbon (MBC), mineralisable carbon (Cmin) and particulate organic matter carbon fractions (POM-C). Crop rotation systems significantly (p < 0.05) influenced Cmin and POM-C fractions whereas residue management practices significantly (p < 0.05) affected all the carbon fractions. Crop residue retention as opposed to its removal enhanced all the carbon pools. The POM-C and Cmin fractions were more sensitive to short-term CA management interventions than SOC and MBC, therefore they can be used for monitoring SOM changes and microbial activities.
Common-Aperture Design of Sub-6 GHz and Millimeter-Wave Antennas for 5G Metal-Rimmed Smartphone

This letter presents a common-aperture design scheme of sub-6GHz and millimeter-wave (mm-wave) antennas for 5G metal-rimmed smartphone. The sub-6GHz antenna can form a high-efficiency antenna covering WWAN/LTE multiple bands by referring to the previous scheme of dual-loop structure composed of metal frame and the ground plane of PCB motherboard. The millimeter-wave antenna consists of a connected slot antenna array (CSAA) etched on the top metal frame and a monopole antenna array (MAA) arranged on the top ground clearance region. Due to the end-fire characteristics of connected slot antenna array and the broad-fire characteristics of monopole antenna array, the proposed millimeter-wave antenna for smartphone has good radiation uniformity in three-dimensional space. In addition, a band stop matching circuit are introduced into sub-6GHz antenna and a high pass filter (HPF) are introduced into millimeter-wave antenna to solve the interference problem of millimeter-wave antenna to sub-6GHz antenna in common-aperture design.
Attention State Classification with In-Ear EEG

Electroencephalography entirely inside the ear (in-ear EEG) opens up exciting avenues for unobtrusive continuous physiological and cognitive state monitoring. This work presents techniques towards precise attention state classification based on data recorded from comfortable, binaural in-ear EEG instruments used during a vigilance task experiment. We recorded both on-scalp and in-ear EEG signals from multiple subjects, and we show that in-ear EEG offers comparable classification accuracy. Our work is the first adaptation of Common-mode spatial filtering techniques applied to signals acquired from sparse electrodes on untethered subjects. We demonstrate 90-95% accuracy (scalp-EEG with 30 electrodes) and 70-75% (in-ear EEG with 5 electrodes inside the ear canal and concha) in classifying attentive and resting states. We also show our approach to be lightweight for low-power on-chip classification with the capacity for few-shot learning. A necessity for wearable, continuous health sensors accommodating resource-constrained applications and adaptation to inter-subject differences and varying environmental conditions. This study suggests the future viability for a System-on-Chip (SoC) integration for user-generic and portable devices capable of closed-loop cognitive state monitoring and neuro-feedback.
Microwave Imaging and Classification of Hand Gestures

Gesture recognition is a key technology in the field of artificial intelligence such as human-computer interaction and smart home. Taking advantage of the all-weather, all-day and penetrating characteristics of microwave, we propose a metamaterials-based gesture recognition method that can solve the shortcomings of optical and video-based methods. We designed two kinds of neural networks inspired by GAN and RNN structure in deep learning for microwave imaging and recognition of hand sign language, and verified them through comprehensive experimental results. Our strategy can pave a new path for intelligent home, human-computer interference, and emotional analysis, and so on.
OCT retinal image-to-image translation: Analysing the use of CycleGAN to improve retinal boundary semantic segmentation

Optical coherence tomography (OCT) images of the posterior eye provide valuable clinical information. To quantify these images and extract appropriate biomarkers, methods to segment the different retinal boundaries are needed. In recent years, deep learning methods have been applied to perform this image analysis task, providing state of the art performance. However, these methods can be affected by image variability, particularly if the network is trained with images that do not match in terms of features to those of the testing dataset. One of the common sources of variability in OCT is speckle noise. In this work, the effect of noise on the semantic segmentation process is investigated and the use of a CycleGAN method as an image-to-image translation to reduce noise and its impact on segmentation are assessed. The results show promising performance and a proof of the potential of this generative adversarial network (GAN) method to positively impact medical image segmentation, obtaining good performance results when compared in terms of Dice coefficient overlap and boundary error metrics. The findings of this work may be translated to other applications such as ‘OCT instrument translation’ to create instrument-agnostic segmentation solutions.
Analog Circuit Design in Nanoscale CMOS Technologies Classic analog designs are being replaced by digital methods, using nanoscale digital devices, for calibrating circuits, overcoming device mismatches, and reducing bias and temperature dependence.

As complementary metal-oxide-semiconductor (CMOS)technologiesarescaleddownintothenanometerrange, a number of major nonidealities must be addressed and overcome to achieve a successful analog and physical circuit design.Thenatureofthesenonidealitieshasbeenwellreported in the technical literature. They include hot carrier injection and time-dependent dielectric breakdown effects limiting supply voltage, stress and lithographic effects limiting matching accuracy, electromigration effects limiting conductor lifetime, leakage and mobility effects limiting device performance, and chip power dissipation limits driving individual circuits to be more energy-efficient. The lack of analog design and simulation tools available to address these problems has become the focus of a significant effort with the electronic design automation industry. Postlayout simulation tools are not useful during the designphase,whiletechnologycomputer-aideddesignphysical simulation tools are slow and not in common use by analog circuit designers. In the nanoscale era of analog CMOS design, an understanding of the physical factors affecting circuit reliability and performance, as well as methods of mitigating or overcoming them, is becoming increasingly important. The first part of the paper presents factors affecting device matching, including those relating to single devices as well as local and long-distance matching effects. Several reliability effects are discussed, including physical design limitations projected for future downscaling. In some cases, it may be helpful to exceed foundry-specified drain-source voltage limits by a few hundred millivolts. Models are presented for achieving this, which include the dependence on the shape of the output waveform. The condition Vsb > 0 is required for cascode circuit configurations. The role of other terminal voltages is discussed, as Vsb > 0 increases both hot and cold carrier damage effects in highly scaled devices. The second part of the paper focuses on trends in device characteristics and how they influence the design of nanoscale analog CMOS circuits. A number of circuit design techniques employed to address the major nonidealities of nanoscale CMOS technologies are discussed. Examples include techniques for establishing on-chip accurate and temperature-insensitive bias currents, digital calibration of analog circuits, and the design of regulator and high-voltage circuits. Achieving high energy efficiency in ICs capable of accommodating 10 9 devices is becoming critically important. This paper also presents a survey of the evolution of figure of merit for analog-to-digital converters.
Design of a Dual-Polarized Crossed Dipole with Broadband and Broad Beam width

In this paper, a dual-polarized crossed dipole with broadband and broad beam width is proposed. The crossed dipole fed by microstrip baluns consists of two printed dipoles which are placed vertically across the metal pedestal. With this configuration, the simulated and measured impedance bandwidth of the proposed antenna covers 8 GHz to 12 GHz ($\text{VSWR} \leqslant 3.0$). By setting the phase difference between the two ports to 90°, left circularly polarized pattern and right circularly polarized pattern can be obtained. Within the bandwidth, the proposed antenna has wide beam width ($\geqslant 130^{\circ}$) in H-plane. Measured results and simulated results are also obtained and discussed at the end of this paper.
Miniaturized Wideband 90° Coupler Based on Shorted 4-line Coupled-Line Sections

In this paper, a miniaturized wideband 90° coupler is proposed. Without defected ground planes and a multilayer process, a wider bandwidth and compact size are obtained by using shorted 4-line coupled-line sections. For demonstration, a miniaturized wideband 90° coupler operating at 3.5 GHz is designed, simulated, and measured. The measured fractional bandwidths for 20-dB $\vert S_{11}\vert$, 22-dB isolation, phase difference (±5°) and amplitude imbalance (±1.6 dB) are 62.45%, 62.01%, 72.18%, and 55.23%, respectively, which sufficiently validate the proposed methodology.
Demo Proposal: Tele-Operated Support over 4G/5G Mobile Communications

This demonstration presents the design and implementation of a remote driving (tele-operated) support service over mobile communications with an actual vehicle, in the framework of the 5G-HEART project. Also, it evaluates the performance of existing cellular technologies, which is essential for better understanding and planning remote driving through 5G.
Semantic Attribute Enriched Storytelling from a Sequence of Images

Visual storytelling (VST) pertains to the task of generating story-based sentences from an ordered sequence of images. Contemporary techniques suffer from several limitations such as inadequate encapsulation of visual variance and context capturing among the input sequence. Consequently, generated story from such techniques often lacks coherence, context and semantic information. In this research, we devise a ‘Semantic Attribute Enriched Storytelling’ (SAES) framework to mitigate these issues. To that end, we first extract the visual features of input image sequence and the noun entities present in the visual input by employing an off-the-shelf object detector. The two features are concatenated to encapsulate the visual variance of the input sequence. The features are then passed through a Bidirectional-LSTM sequence encoder to capture the past and future context of the input image sequence followed by attention mechanism to enhance the discriminality of the input to language model i.e., mogrifier-LSTM. Additionally, we incorporate semantic attributes e.g., nouns to complement the semantic context in the generated story. Detailed experimental and human evaluations are performed to establish competitive performance of proposed technique. We achieve up 1.4% improvement on BLEU metric over the recent state-of-art methods.
A Broadband Microstrip-to-Microstrip Transition for Flip-Chip Interconnects

This work presented a solution of microstrip-to-microstrip (MS-to-MS) transition for flip-chip interconnects between a silicon die and an LTCC/PCB substrate at 60 GHz. The presented transition structure utilizes a V-shaped slotted ground to suppress resonances induced from the coupling between the microstrip line on LTCC/PCB substrate and the on-chip ground. The transition structure is compatible with different sizes of bump balls employed in flip-chip interconnects. Two transition designs, as examples, are implemented with bump balls whose diameters are $50\ \mu \mathrm{m}$ and $100\ \mu \mathrm{m}$, respectively. They exhibit a similar broadband transition performance with the return loss better than 20 dB and the insert loss less than 1 dB at 45 ∼ 65 GHz. This transition is also flexible for other frequencies at the millimetre-wave spectrum.
Improving the flexibility of 5G heterogeneous networks by slicing

5G and beyond-5G networks need to support all the traffic pressure by extensive Internet of Things networks. In this document, we review how to improve the flexibility of 5G networks by conducting slicing. We describe the four main pillars for this transition: mass adoption of Software Defined Networking, collocation of aggregators at Multi-access Edge Computing platforms, virtualization of infrastructure and removal of Application Functions through distributed responsibilities over the network. We will present an overview of the efforts being conducted for each of these points and the vision for beyond-5G networks.
Banshee: A Fast LLVM-Based RISC-V Binary Translator

System simulators are essential for the exploration, evaluation, and verification of manycore processors and are vital for writing software and developing programming models in conjunction with architecture design. A promising approach to fast, scalable, and instruction-accurate simulation is binary translation. In this paper, we present Banshee, an instruction-accurate full-system RISC-V multi-core simulator based on LLVM-powered ahead-of-time binary translation that can simulate systems with thousands of cores. Banshee supports the RV32IMAFD instruction set. It also models peripherals, custom ISA extensions, and a multi-level, actively-managed memory hierarchy used in existing multi-cluster systems. Banshee is agnostic to the host architecture, fully open-source, and easily extensible to facilitate the exploration and evaluation of new ISA extensions. As a key novelty with respect to existing binary translation approaches, Banshee supports performance estimation through a lightweight extension, modeling the effect of architectural latencies with an average deviation of only 2 % from their actual impact. We evaluate Banshee by simulating various compute-intensive workloads on two large-scale open-source RISC-V manycore systems, Manticore and MemPool (with 4096 and 256 cores, respectively). We achieve simulation speeds of up to 618 MIPS per core or 72 GIPS for complete systems, exhibiting almost perfect scaling, competitive single-core performance, and leading multi-core performance. We demonstrate Banshee's extensibility by implementing multiple custom RISC-V ISA extensions.
Resonant Converter based DC Transformer Operation with Magnetic Control

DC transformers (DCXs) have attracted more and more attention in applications such as microgrids, data centers, and renewable energy sources. A DCX is adopted to interface between various energy sources with high power conversion efficiency. An LLC resonant converter operating at resonant frequency with a fixed voltage gain that is independent of load is a promising candidate for a DCX application. However, resonant frequency operation cannot be ensured based on the following three main reasons: 1) tolerances exist for resonant components, which makes the practical resonant frequency deviate from the theoretical one; 2) both capacitance and inductance are variables in terms of temperature and other operating conditions. Thus, under different circuit operating conditions and environments, the corresponding resonant frequency may different; 3) lastly, aging affects will change the components values. The magnetic control/variable inductor is utilized to compensate the variations of resonant frequency in this article. The proposed method is simple, effective, and low-cost. The feasibility and effectiveness of the proposed method are proved under different scenarios.
Ultra-Wideband 3-Way Equal-Power Divider based on Multi-Section Chebyshev Matching Method

In this paper, an ultra-wideband (UWB) 3-way power divider is proposed. It employs a three-section Chebyshev matching transformer to enhance the bandwidth. In addition, three pairs of isolation resistors connecting the three branches are utilized to improve the port isolations. The simulation results show that it operates in the frequency range from 1.0 to 6.3 GHz, i.e., a fractional bandwidth of 630%. In its operating band, the insertion losses of the three ways are within 0.8 dB variation compared with the theoretical values, and the transmission phase fluctuations between the output ports are less than 5°. The isolations between the three output ports are greater than 10 dB. These advantageous performance characteristics make it an alternative candidate as a network feed with both constant-amplitude and in-phase transmission characteristics for UWB array antennas.
Business Value and Social Acceptance for the Validation of 5G Technology

User experience, Social disruptiveness, Value impact, and Trust are fundamental dimensions to be considered for a disruptive technology as 5G promises to be. Indeed, while the race of 5G is moving towards the deployment all over the world, more and more opinion of citizens on this technology is demonstrating lack of awareness in terms of benefits (as well as risks) on 5G technology, and often this results in raising barriers and hindering the local deployment. This definitely impacts on development and business plans as well. To this extent, it plays a main role in how these four dimensions are defined, evaluated with the relevant stakeholders, prioritized and finally validated. This paper describes the on-going experience that the 5G-SOLUTONS project is performing with respect to the business validation of 5G technology, by adopting and improving a structured and holistic methodology, where the Social Acceptance of Technology (SAT) provides a conceptual framework for modelling social concerns related to technology acceptability. This novel approach overcomes limits of current methodologies that usually focus on users’ behaviour and acceptability on the user-experience side, without considering the systematic aspects of social impact.
The PGRMC1 Antagonist AG-205 Inhibits Synthesis of Galactosylceramide and Sulfatide

Sulfatide synthesis in the human renal cancer cell line SMKT-R3 was strongly inhibited in the presence of low µM concentrations of AG-205, a progesterone receptor membrane component 1 (PGRMC1) antagonist. This was also the case in Chinese hamster ovary (CHO) cells stably transfected with UDP-galactose: ceramide galactosyltransferase and cerebroside sulfotransferase, the two enzymes required for sulfatide synthesis. In CHO cells synthesizing galactosylceramide but not sulfatide, galactosylceramide was also strongly reduced, suggesting an effect at the level of galactolipid synthesis. Notably, AG-205 inhibited galactosylceramide synthesis to a similar extent in wild type CHO cells and cells that lack PGRMC1 and/or PGRMC2. In vitro enzyme activity assays showed that AG-205 is an inhibitor of UDP-galactose: ceramide galactosyltransferase, but not cerebroside sulfotransferase. This study shows that PGRMC1 is only one of several targets of AG-205 and should be used with caution, especially in studies using cells synthesizing galactosylceramide and sulfatide.
The Secret to Better AI and Better Software (Is Requirements Engineering)

Recently, practitioners and researchers met to discuss the role of requirements, and AI and SE. We offer here notes on that fascinating discussion. Also, have you considered writing for this column? This “SE for AI” column publishes commentaries on the growing field of SE for AI. Submissions are welcomed and encouraged (1,000–2,400 words, each figure and table counts as 250 words, try to use fewer than 12 references, and keep the discussion practitioner focused). Please submit your ideas to me at timm@ieee.org. —Tim Menzies
Residual Learning Attention CNN for Motion Intention Recognition Based on EEG Data

Brain computer interfaces (BCIs) recognize users' intentions to directly control the external environment or devices. Thus, accuracy and robustness of decoding algorithms have an essential effect on the performance of BCIs. Traditional algorithms depend on manually extracting features and updating parameters in various experimental conditions, which limit BCI paradigm application in reality. Nowadays, inspired by the success of deep learning in computer vision and speech recognition applications, scientific researchers take advantages of convolutional networks to recognize EEG motion intention with superior performance. Although the accuracy of methods based on convolutional networks can reach or exceed that of traditional machine learning methods, it is still not high enough to guarantee widespread application, especially in complex tasks, like multi-dimension motion intention recognition. In this paper, we propose a novel motor imagery EEG decoding method based on Residual Learning Attention Convolutional Neural Network (RLA-CNN). First, RLA-CNN architecture is demonstrated based on the formulated motion intention recognition problem. The basic idea of RLA-CNN is to learn attention weights of EEG channel, time points, and feature maps automatically, which can simplify the decoding algorithms and working electrodes in BCIs. Second, Compared with previous work in four, three, and two classes tasks on the same dataset, the average validation accuracy of the proposed method is 75.85%, 84.81%, and 84.69% respectively, which outperforms the state-of-the-art by 5.65%, 10.06%, and 7.41%. Finally, the attention weights learned by RLA-CNN are visualized to explain the effectiveness of RLA-CNN.
Multiple Grid-Forming Inverters in Black-Start: The Challenges

Black-start of multiple grid-forming inverters is one of the major challenges toward implementing grid of microgrids. In this article, the technical challenges for the black-start of multiple grid-forming inverters are reported. Specifically, this article discusses inverter synchronization, voltage and frequency regulations, and power-sharing between multiple grid-forming inverters. Furthermore, a control scheme for black-start operation is developed for grid-forming inverters in this work. Some experiment results have been provided to support the proposed controller performance and some technical challenges. The experiments have been performed using a 208V, 60Hz, 30kVA islanded small-scale nanogrid powered by multiple inverters.