Miracle-dz/UniFAR-FTUs huggingface dataset:
Work-in-Progress: Synchronous Intersection Management Protocol for Mixed Traffic Flows

Urban traffic management (UTM) is responsible for planning and controlling traffic on road infrastructures, including lane closures, full freeway closures, and pedestrian access. An essential element in UTM is the Intersection Management (IM) that deals with traffic control and is vulnerable to traffic congestion and accidents. In this paper, we propose an intelligent intersection management architecture along with the synchronous intersection management protocol (SIMP) instantiated in two versions. Simulation results show the advantages of SIMP-M (one of the versions) over the well known TraCI IM protocol, in terms of both worst-case and average vehicle speed passing through one intersection.
Characterizing Spatiotemporal Pattern of Vegetation Greenness Breakpoints on Tibetan Plateau Using GIMMS NDVI3g Dataset

Due to the harsh natural environment on the Tibetan Plateau (TP), its vegetation is sensitive to climate change. Therefore, it is essential to characterize long-term vegetation shifts for understanding of land surface processes across the TP. Gradual greening or browning growth in vegetation greenness is detectable while the alternating process between greening and browning, its timing, and type remain unclear. In this paper, breakpoint in time series of a satellite-derived vegetation index was detected at pixel-level during 1982-2012. The long-term growth procedure of vegetation was then characterized by combining the greening/browning trend for the two sub-periods, on each side of the breakpoint. The combinations of greening/browning status were classified into three categories (monotonic, interrupted and growth reversal). Possible causes for abrupt vegetation changes are discussed in the content of climate change and grassland management. Results show that breakpoints are temporally widely distributed and have significant spatial heterogeneity. About 21% (11%) of the vegetated area showed significant greening (browning) trends. Vegetation in central and eastern TP has tended to be greening. Browning trends were particularly evident in the southern and northeastern TP and were scarce in the west. About 32% of the vegetation was found to change significantly in this analysis. Greening trends occurred more often than browning trends and exhibited both a monotonic and an interrupted growing process. Trend reversal in vegetation was dominated by declining trends. Breakpoints in monotonic and interrupted trends were concentrated in some time points, but those with reversal trends were discretely distributed over time span. Among different ecosystem types, desert ecosystems presented the most significant greening trends, accounting for 53% of plant-covered desert. Conspicuous degraded trends were identified on alpine sparse vegetated area. Statistically, breakpoints in precipitation and air temperature are not consistent with those in vegetation greenness index. And grazing projects have not posed a significant effect on abrupt shifts in vegetation greenness.
Simple and Easy: Transfer Learning-Based Attacks to Text CAPTCHA

CAPTCHA, or Completely Automated Public Turing Tests to Tell Computers and Humans Apart, is a common mechanism used to protect commercial accounts from malicious computer bots, and the most widely used scheme is text-based CAPTCHA. In recent years, newly emerged deep learning techniques have achieved high accuracy and speed in attacking text-based CAPTCHAs. However, most of the existing attacks have various disadvantages, the attack process made high complexity or manually collecting and labeling a large number of samples to train a deep learning recognition model is time-consuming and expensive. In this paper, we propose a transfer learning-based approach that greatly reduces the attack complexity and the cost of labeling samples, specifically, by pre-training the model with randomly generated samples and fine-tuning the pre-trained model with a small number of real-world samples. To evaluate our attack, we tested 25 online CAPTCHAs achieving success rates ranging from 36.3% to 96.9%. To further explore the effect of the training sample characteristics on the attack accuracy, we elaborately imitate some samples and apply a generative adversarial network to refine the samples, sequentially we use these two kinds of generated samples to pre-train the models, respectively. The experimental results demonstrate that the similarity between randomly generated samples and elaborately imitated samples has a negligible impact on the attack accuracy. Instead, transfer learning is the key factor; it reduces the cost of data preparation while preserving the model’s attack accuracy.
Secrecy at Physical Layer in NOMA with Cooperative Jamming

We have proposed and analyzed secrecy performance of a network architecture which contains a source, two destinations, a friendly jammer (FJ) and an eavesdropper (Eve). To jam the Eve in order to secure the confidential information of both users, the FJ sends a jamming signal while the source sends the combined information signal using non-orthogonal multiple access (NOMA) technique to both the users. A known jamming is considered which is known by both the users. Both the users subtract the known jamming from the received signal and then decode the information signal as per the NOMA technique. We analyzed the physical layer security of the network in terms of the secrecy outage probability (SOP) where the two users have different target secrecy rates. A closed form expression of the SOP is developed. We have analyzed the SOP under the impact of several numerical parameters such as: source transmit signal to noise ratio (SNR), FJ transmit SNR, fraction of source transmit power, and different target secrecy rates. For a fixed transmit SNR of the FJ, we obtain an optimal value of the source transmit SNR at which SOP is minimized. These analyzed results are also supported by the MATLAB based simulation results.
Using Metallic Coil to Optimize the Heating Efficiency for Tumor Hyperthermia

Instead of using magnetic nanoparticles in magnetically mediated hyperthermia for tumor therapy, metallic loops are adopted to avoid particles diffusion. In order to effectively heat the tumor to a desired temperature while keeping the healthy tissue at the normal temperature, the geometrical parameters of the power transmitter and receiver coils and the operating frequency are optimized to improve the heating efficiency, under the restriction of biocompatible materials for implanted metal coils. The advantage of the proposed method is verified through a design example. The heating efficiency can be improved from 1% of the existing design to more than 4% of the proposed design for a 0.5-mm radius implanted single-turn titanium coil at 5-MHz operating frequency.
Monolithic MEMS Filter Banks on RFSOI Front-End Module

This work is the first demonstration of a monolithic multiband RF front-end module (RF-FEM), integrating MEMS Lamb-wave filters and switches on 200mm RF silicon-on-insulator (RFSOI) foundry technology. Multiple MEMS filters with photolithography-defined frequencies coexist with RF components in the same wafer. This technology enables vertical integration of RF-FEM components for more compact System-on-Chip (SoC) architectures. The resulting RF-FEMs will then integrate in the same process multi-frequency filter banks, low noise amplifiers (LNAs), and switches, with a footprint reduction up to 50% compared to system-in-package (SiP) modules. The SoC architecture also simplifies the design of interconnection lines and impedance matching networks.
Direct Binary Encoding of Displacements on the Nano-Scale

We present a passive force and displacement sensor based on a compliant mechanical amplifier combined with a micromechanical analog-to-digital converter that allows to amplify and convert a displacement signal in the nanometer range directly into an electrically readable binary code without the need of electrical energy for the conversion itself. The presented compliant amplifier achieves a displacement amplification ratio of about 100 within an input range of $1.3\ \mu \mathrm{m}$, which enables a resolution of 40 nm steps by using a 5-bit mechanical analog-to-digital converter. Furthermore, a method is proposed to implement a pre-defined transfer function into the mechanical A/D converter in order to linearize or benchmark a non-linear primary displacement signal.
A Review on Deep Learning Approaches for 3D Data Representations in Retrieval and Classifications

Deep learning approach has been used extensively in image analysis tasks. However, implementing the methods in 3D data is a bit complex because most of the previously designed deep learning architectures used 1D or 2D as input. In this work, the performance of deep learning methods on different 3D data representations has been reviewed. Based on the categorization of the different 3D data representations proposed in this paper, the importance of choosing a suitable 3D data representation which depends on simplicity, usability, and efficiency has been highlighted. Furthermore, the origin and contents of the major 3D datasets were discussed in detail. Due to growing interest in 3D object retrieval and classification tasks, the performance of different 3D object retrieval and classification on ModelNet40 dataset were compared. According to the findings in this work, multi views methods surpass voxel-based methods and with increased layers and enough data augmentation the performance can still be increased. Therefore, it can be concluded that deep learning together with a suitable 3D data representation gives an effective approach for improving the performance of 3D shape analysis. Finally, some possible directions for future researches were suggested.
Multimodal Recognition System Based on High-Resolution Palmprints

High-resolution palmprint recognition is a challenging problem due to deficiencies in images, such as poor quality, skin distortion, and unallocated images. Considering the importance of high-resolution palmprints in forensic applications, this study proposes a novel multimodal palmprint scheme that combines the left and right palmprints using feature-level fusion by exploiting the similarity between the left and right palmprints using high-resolution images. The proposed system accepts as input palmprints that were captured at 500 ppi, which is the standard in forensic applications. The system is implemented by employing a statistical gray-level co-occurrence matrix (GLCM) as the texture feature extraction algorithm. Then, the features are ranked based on their probability distribution functions (PDFs) to select the most significant features. Finally, an enhanced probabilistic neural network (PNN) is used to estimate the recognition system. The benchmark THUPALMLAB database is used to conduct experiments, the results of which demonstrate that the proposed method can yield satisfactory results.
Energy Optimized Virtual Network Embedding With Location Constraint in the Enterprise Network

To satisfy enterprise demands of analyzing and dealing with the large scale of data with lower costs, an effective method is to integrate the servers and computers and use virtualization technology to construct an enterprise network. Prior studies on network virtualization have mainly been executed in the cloud; however, these studies may not be appropriate for enterprise networks for two reasons: i) the goal of most of them is to generate more revenues for cloud providers, but focus less on saving costs; ii) the physical machines are relatively concentrated in the cloud platform but dispersed over different geographic locations in enterprise networks. In this paper, we solve the problem of energy-optimized virtual network embedding with location constraints (EO-VNE). First, the node and link capabilities in enterprise networks are defined in the form of complex number theory, unifying computers and virtual requests. Second, the normalized method of computing and storage capabilities are proposed to identify the node capability. Third, an energy model of the enterprise network is built, and using this model, EO-VNE is shown to be NP-complete. Finally, an energy-optimized virtual network embedding with a location constraint algorithm (EOLC) is proposed to minimize the energy consumption under the constraint of node position. The experiments show that EOLC consumes less energy compared with the algorithm of energy-aware virtual network embedding with dynamic demands (EAD). It also has better performance than the location constraint algorithm based on bisection (GLC).
Error performance of OTFS in the presence of IQI and PA Nonlinearity

Orthogonal time frequency space (OTFS) modulation, which was proposed recently is a two dimensional modulation technique designed in the delay-Doppler (DD) domain unlike OFDM which was designed in the time-frequency (TF) domain. It is suited for doubly dispersive fading wireless channels. Some transformations are done at the transmitter and receiver of the conventional multicarrier modulation. In this paper, we pre- distort frequency dependent transmitter (Tx) IQ imbalance (IQI) and power amplifier (PA) nonlinearity in two steps. The bit error rate performance analysis of OTFS in the presence of receiver (Rx) IQI along with residual Tx IQI is analyzed. We develop an input-output relation of OTFS in the DD domain, with impairments in the presence of time varying (TV) channel. A message passing algorithm is used to detect the OTFS signal with impairments. We show that compensating IQI at the Tx and PA non-linearity has improved the BER performance.
A Preemptive Hybrid Approach to Minimum Spanning Tree Restoration in Large Mesh SDN Networks

Achieving high levels of network resiliency has long been a major objective for data centre and service providers. Recently, Software Defined Networking (SDN) has arrived with the ability to extend centralised programmability, control and management to all aspects of networking. For traditional TCP/IP networks, due to the close coupling of the control and data plane on network devices, protocols that provide recovery and restoration operate effectively in a distributed manner. In practice a programmed SDN controller is at the heart of any SDN deployment. SDN controllers offers opportunities for algorithms to operate at a logically global or centralised level. Shifting distributed protocols away from localised switches or routers to a centralised controller effectively creates a level playing field for algorithms. A global view of the entire network is now available not only to the network programmer or manager, but also to all the algorithms programmed at the controller. Although a central view may be an advantage, little is known about the behaviour of different restoration algorithms and their recovery in a centralised controller when operating under a worst case environment such a scaled wired mesh SDN topology. This paper presents the SDN implementation and analysis of a range of Minimum Spanning Tree (MST) algorithms, their key performance metrics are evaluated and based on these results a preemptive approach to path restoration is proposed. Initial results of the approach show considerable time savings in the initial stages of building an MST.
A Systematic Method to Design Efficient Ternary High Performance CNTFET-Based Logic Cells

The huge quantity of nodes and interconnections in modern binary circuits leads to extremely high levels of energy consumption. The interconnection complexity and other issues of binary circuits encourage researchers to consider multiple-valued logic (MVL) alternatives. Features of Carbon Nanotube Field-Effect Transistors (CNTFETs) make this technology a potential candidate to implement MVL circuits. In this article, a new systematic methodology is proposed to design ternary logic block circuits based on CNTFETs. The methodology is applied to the design of two basic logic circuits, a half adder and a 1-digit multiplier, which are evaluated through HSPICE simulations. Simulation results indicate improvements over current equivalents in transistor count and PDP mean with the half adder version of 19.2%, and 74.07% respectively, and with the 1-digit multiplier of 24.67% and 81.12% respectively.
GaN Schottky Barrier Diode for Sub-Terahertz Rectenna

We developed GaN schottky barrier diode (SBD) for sub-terahertz rectenna to improve the RF-DC conversion efficiency and DC output power. Designed a GaN SBD using the device simulator and fabricated prototype. The similar 1-V curve was obtained between measurement and simulation. Furthermore, calculated the theoretical RF-DC conversion efficiency on the rectenna. As a result, diodes with series resistance 3.6 Ω, junction capacitance 0.035 pF and breakdown voltage 46.3 V were calculated. The RF-DC conversion efficiency of 78% was theoretically obtained at 303 GHz. This work showed RF-DC conversion efficiency can be greatly improved using GaN SBD for rectenna in sub-terahertz (herein 100 GHz ~ 700 GHz).
A Sparse-Adaptive CNN Processor with Area/Performance balanced N-Way Set-Associate PE Arrays Assisted by a Collision-Aware Scheduler

Convolutional Neural Networks give heavy storage and computation burden to accelerators, whose energy efficiency can be improved by leveraging their sparsity. However, using sparsity in networks will introduce large overhead especially when networks have various sparse situations and multiple quantization. This work named STICKER-II firstly proposes an area/performance balanced chip with simultaneously sparsi-ty/quantization adaptive capability, enabled by multi-sparsity and multi-quantization compatible storage and computation circuits. Further more, N-way set-associate PE architecture is explored to trade off its performance and area with the collision-aware hardware scheduler. This chip achieves 4.64 TOPS/W energy efficiency on Alexnet, 1.65x better than the state-of-the-art sparse processors, along with 19.4% PE area reduction.
Improved Graph Model for Interdependent Gas and Electricity Critical Infrastructures

Interdependence between gas and electricity transmission networks is a subject of concern due to the expanding use of gas for electricity generation in combined-cycle power plants around the world. This paper proposes a novel and much more accurate representation of natural gas and electrical networks based on graph theory, which includes all the assets of both systems and their couplings and offers a more realistic topological model of the two coupled networks. The representation is proposed as a scale-free graph and is mathematically validated in test networks, finding that the representations maintain the same characteristics of traditional graphs, but with more topological detail of the infrastructures.
A Comparison of Empirical and Ray-Tracing Models for Indoor Radio-Wave Propagation [Wireless Corner]

Both empirical and ray-tracing models are powerful tools for studying propagation in indoor environments. Although the empirical models perform well in many cases, their physical significance is not clearly understood. Ray tracing is based on physics fundamentals, and its application as well as the interpretation of its results is more general. We compare the performance of both models for indoor environments. We found that the path-loss (PL) prediction error of the ray-tracing model is comparable to the error of the linear regression fit of the PL in a site-specific empirical model. Those empirical models provide some advantages when making estimations in the same environment in which they were trained, but their performance degrades in other environments. Ray-tracing models obtain better results when a wider range of application environments is sought. Ray tracing also provides a good insight into the specific propagation phenomena that take place in the wideband radio channel, so the relevant contributions can be identified.
Speciation in the "Várzéa" flooded forest: a new Mabuya (Squamata, Scincidae) from Western Amazonia

Mabuya altamazonica sp. nov. is described from Peruvian Amazonia. This species is sympatric with M. nigropunctata. Despite their superficial similarity, those two Amazonian species could be differentiated both by cephalic scalation characters and molecular results (12S rRNA). Mabuya altamazonica sp. nov. differs from all Amazonian and Andean species of Mabuya by the combined presence of (1) paired prefrontals and frontoparietals, (2) a single pair of nuchals, (3) four supraoculars, (4) five subequal supraciliaries, (5) seven supralabials with the fifth being the largest and placed under the eye, (6) parietals in contact behind the interparietal and (7) dark palms and soles.
Micromachining flexibility by tunable ultrashort pulse duration, pulse-on-demand, and hybrid processing from single pulse to GHz burst with TruMicro Series 2000

Ultrafast micromachining has found broad applications in a variety of scientific and industrial fields. Different materials and competing customer requirements (surface quality vs. processing speed vs. surface structure etc.) call for parameter studies prior to volume production as well as pulse parameter flexibility during operation. Up to now, often a nonoptimized point of operation for either best speed or quality had to be chosen due to limited laser source flexibility. TruMicro Series 2000 introduces true inter- and intra-process flexibility for pulse parameters such as pulse duration, pulse energy and pulse spacing up to GHz bursts. As of now, switching the pulse duration is possible within 300 fs and 20 ps in less than 600 ms without affecting beam pointing or energy stability. Therefore, intra-process pulse parameter changes allow maximization of the ablation-volume efficiency in one step and surface-quality optimization in a second, finalizing step. Additionally, inter-process pulse parameter changes enable material changes in between workpieces. In this contribution, we show how this novel flexibility for the first time leads to comprehensive and automated parameter studies that allow for next-generation process understanding and the clear selection of enhanced points of operation. We demonstrate how ablation of various materials can be increased by employing bursts on a nanosecond timescale where a simple increase in fluence would result in cone-like protrusions. Choosing the suitable timescale for energy deposition can either maximize energy efficiency of ablation or optimize ablation quality. With the TruMicro Series 2000, both optima can be combined to one efficient, high-quality process.
Widely-tunable femtosecond source tunable between 770-1180 nm for two-photon fluorescence microscopy

We have demonstrated a widely-tunable femtosecond fiber source between 770-1180 nm enabled by self-phase modulation, and the wide spectral coverage is suitable for most of two-photon fluorescence microscopy applications. Based on femtosecond Yb:fiber laser, we also compared spectral broadening in different dispersion regimes using different photonic crystal fibers. We managed to maximize the self-phase modulated feature from the broadened spectra while avoiding unwanted nonlinear temporal trapping. The optimization of fiber selection and laser input conditions led to a wide tunability down to below 800nm region, which is the most commonly used two-photon excitation wavelength for many intrinsic fluorescent labels in biological tissues. We believe this fiber-based femtosecond source can be a relatively cost-effective and robust solution for most of two-photon fluorescence microscopy applications.