Miracle-dz/UniFAR-FTUs huggingface dataset:
Accuracy Indoor Localization Based on Fuzzy Transfer Learning Model

Location-based services greatly facilitate people’s daily life, which puts forward higher requirements for the location calculation of target objects in different environments. Since the fingerprint positioning method does not require additional special equipment and easy to implement, it has become one of the most attractive solutions. In order to ensure the positioning accuracy, this method requires complete sampling of the fingerprints of the positioning area, so a lot of sampling costs are required. In particular, when sampling buildings with multiple floors, the labor and time of the entire sampling process will increase dramatically. At the same time, certain floors or rooms may not be allowed to open, so their fingerprints cannot be sampled. In fact, the floor structures of buildings are mostly similar or the same, such as office buildings, hotels. Therefore, this paper proposes a fuzzy transfer learning model and builds the corresponding prototype system FTLoc. On the premise of ensuring the positioning accuracy of different floors, the system greatly reduces the sampling cost of the entire building. First, for the complete fingerprint data (source domain) of a certain floor, we mine the fingerprint features fine-grained to generate a short-term feature set for each sampling point. Then, according to the sparsity and timing of short-term features, we design an optimized SELSTM, and obtain an effective localization model as the PreModel for transfer learning. Finally, fuzzy clustering is used to add category labels to the source domain data and target domain data, and input them into PreModel to realize the localization model transfer, so as to avoid their data distribution differences affecting the transfer effect as much as possible. FTLoc is fully validated in a multi-storey building. According to the experimental results, when using the first floor sampling data as the source domain, the errors of the FTLoc system on the adjacent floor (second floor) are 1.38 meters (sampling rate = 80%) and 2.33 meters (sampling rate = 30%). The average errors in non-adjacent layers (three, four, five) are 1.92 meters (sampling rate = 80%), 2.87 meters (sampling rate = 30%). Compared with traditional migration, the FTLoc system increased by 18.1% and 12.6% respectively. At the same time, the experiment verified that the error jitter multiple of the FTLoc system under different devices and different sampling densities does not exceed 1.5. Therefore, the FTLoc system designed in this paper ensures the transfer learning effect of different floors, and has good robustness and reliability. In actual positioning applications, the system greatly reduces the sampling cost and achieves high-precision positioning at the same time.
Motion-Robust Respiratory Signal Reconstruction Using Smart Glasses

Professional respiration monitors, essential for diagnosing physiological and psychological diseases, are expensive and uncomfortable to wear. The inertial measurement unit (IMU) sensors on smart glasses can detect weak head movement caused by breathing, making them a more affordable and wearable alternative. However, existing methods are mainly evaluated in controlled environments with little motion artifacts and cannot reconstruct the respiratory signal. In this paper, an encoder-decoder block with a cross-resampling structure called Deep-Filter is proposed as the foundation for a generative model to achieve motion-robust respiratory signal reconstruction. Unlike the previous studies, we evaluate the proposed method under the inference of motion artifacts induced by daily activities, including sleeping, talking, typing, walking, and watching videos. In terms of signal reconstruction accuracy, the proposed method achieves an amplitude error (AE) of less than 6.79%, a Spearman correlation coefficient (SRCC) of more than 0.89, and a cosine similarity (CS) of greater than 0.97, demonstrating the potential for motion-robust respiratory signal reconstruction.
In-band Network Telemetry Manipulation Attacks and Countermeasures in Programmable Networks

In-band Network Telemetry (INT) is a widely used monitoring framework in modern large-scale networks that provides fine-grained visibility into network conditions by inserting telemetry data into packets. However, this mechanism also introduces new vulnerabilities that malicious attackers can exploit. In this paper, we present four In-band Network Telemetry Manipulation Attacks that take advantage of INT's weakness, demonstrating that attackers can cause severe damage with little effort by manipulating INT packets. To address this issue, we design SecureINT, a novel INT prototype that ensures confidentiality and integrity for INT packets. To meet the stringent computational requirements of programmable switches, we comprehensively analyze possible attacks on the deployed encryption/hash algorithms and modify them accordingly without compromising their security. According to the experiments, SecureINT can be deployed on programmable switches using a single pipeline, providing encryption and integrity verification for INT packets with minimal overhead.
Engagement Detection of Online Learners Based on Key Frames

In recent years, more and more students are willing to choose courses on online learning platforms, such as MOOC. However, the online learning platform cannot capture the learning status of students, and cannot give real-time feedback when students’ attention is out of learning. The lack of feedback mechanisms mainly leads to a staggering dropout rate of MOOC. Therefore, engagement detection is very important for online learning platforms. Nowadays, many researchers have focused on engagement detection which are based on pictures. This method of selecting pictures is through random selection or repeated selection after short interval of time. However, both of these methods may lead to the problem of inaccurate samples or heavy identification workload. In this paper, we propose to introduce Key Frames that can more accurately represent the engagement level of the online learning personal video in the sample selection stage. Experimental results show that our proposed method has better results.
A Three-Dimensional Cyclic Multiplication - Convolution Neural Network for Hyperspectral Image Classification

Hyperspectral images have a large amount of spectral and spatial information, and play a significant role in remote sensing. However, the traditional image classification techniques are no longer applicable to the developing hyperspectral remote sensing images, and how to reasonably utilize the rich feature information of hyperspectral images to obtain high-accuracy classification results has become one of the most important research topics at present. This paper proposes a new 3D convolutional neural network to solve the problem of feature wastage in hyperspectral images. The framework cycles the convolutional kernel size, and the number of convolutional kernels increases in a multiplicative relationship for hyperspectral image classification (CM-CNN). Compared with existing neural network frameworks on different datasets, CMCNN achieves the highest Kappa coefficient and confusion matrix accuracy, which are stable at above 95%. From the classification results, there are almost no obvious classification errors.
SVFF: An Automated Framework for SR-IOV Virtual Function Management in FPGA Accelerated Virtualized Environments

FPGA accelerator devices have emerged as a powerful platform for implementing high-performance and scalable solutions in a wide range of industries, leveraging their reconfigurability and virtualization capabilities. Virtualization, in particular, offers several benefits including improved security by resource isolation and sharing, and SR-IOV is the main solution for enabling it on FPGAs. This paper introduces the SR-IOV Virtual Function Frame-work (SVFF), a solution that aims to simplify and enhance the management of Virtual Functions (VFs) on PCIe-attached FPGA devices in Linux and QEMU/KVM environments, solving the lack of SR-IOV re-configuration support on guests. The framework leverages the SR-IOV support in the Xilinx Queue-based Direct Memory Access (QDMA) to automate the creation, attachment, detachment, and reconfiguration of VFs to different Virtual Machines (VMs). A novel pause functionality for the VFIO device has been implemented in QEMU to enable the detachment of VFs from the host without detaching them from the guest, making reconfiguration of VFs transparent for guests that already have a VF attached to them without any performance loss. The proposed solution offers the ability to automatically and seamlessly assign a set of VFs to different VMs and adjust the configuration on the fly. Thanks to the pause functionality, it also offers the ability to attach additional VFs to new VMs without affecting devices already attached to other VMs.
Sarcasm Detection of Dual Multimodal Contrastive Attention Networks

Sarcasm is a rhetorical method that is commonly used on social media platforms or in daily life to communicate a speaker’s feelings of irritation, anger, or mocking, and it often presented in implicit and exaggeration ways. Early sarcasm detection relied on unimodal text identification; however, sarcastic conversations cannot be precisely identified by unimodal model in domains like dialogues system. Most current multimodal studies focus on feature fusion or modality comparison. This method ignored dealing with out-of-vocabulary (OOV) words. For example, this sentence, ”All right, Amy’s in charge of pricing and being seventy-five. ” which using the number seventy-five to represent outdated clothing tastes, an obvious sarcasm that would be taken as a neutral word and lead to inconsistencies across different modalities being ignored. We call such words out of vocabulary (OOV) words. In this paper, we propose a dual multimodal contrastive attention network (DMCAN) for sarcasm detection, which consists of two sub-networks containing contrastive attention mechanisms. The first of these networks scores the word polarity of OOV words (including neutral words with sarcasm meaning) and is called the polarity scoring network, while the second network detects sarcasm by inconsistency between different modalities and is called the sarcasm detection network. Our experiments on MUStARD (Multimodal Sarcasm Detection Dataset) prove the effectiveness of the model.
Formal Verification of Safety Architectures for Automated Driving

Safety architectures play a crucial role in the safety assurance of automated driving vehicles (ADVs). They can be used as safety envelopes of black-box ADV controllers, and for graceful degradation from one ODD to another. Building on our previous work on the formalization of responsibility-sensitive safety (RSS), we introduce a novel program logic that accommodates assume-guarantee reasoning and fallback-like constructs. This allows us to formally define and prove the safety of existing and novel safety architectures. We apply the logic to a pull over scenario and experimentally evaluate the resulting safety architecture.
A 74.8-88.8 GHz Wideband CMOS LNA Achieving +4.73 dBm OP1dB and 6.39 dB Minimum NF

This paper presents a 74.8-88.8 GHz low-noise amplifier (LNA) in a 55-nm CMOS technology. The LNA employs one common-gate (CG) stage, one common-source (CS) stage, and two cascode stages. A hybrid broadband interstage network (HBIN) is developed to extend the amplifier bandwidth. An inductor-feedback common-gate-shorting (IFCGS) technique is proposed to improve the gain and output 1dB compression point (OP1dB). An out-of-phase-dual-coupling (OPDC) transformer structure is also developed to achieve gm-boost and reduce the noise of the CG stage. Benefiting from the proposed techniques, the LNA achieves a measured -3dB bandwidth of 14 GHz, a 17.1 dB peak gain, a minimum noise figure of 6.39 dB, and 4.73 dBm OP1dB at 80 GHz while consuming 72.4 mW of power.
5.8 GHz band 10 W rectenna with GaAs E-pHEMT gated anode diode on the aluminum nitride antenna for thermal dispersion

In this paper, the 5.8 GHz band 10 W rectenna is demonstrated. The GaAs bridge rectifier IC with GaAs E-pHEMT gated anode diodes (GADs) is employed to obtain input power of 10 W. The IC is directly connected with the inductive high-impedance antenna to reduce circuits’ loss. Furthermore, the antenna is implemented on the aluminum nitride (AlN) substrate for thermal dispersion. With migrated circuit functionalities, simulated radiation efficiency of the antenna is 99.2 %. Measured rectification efficiency of the rectifier is 83.7 % at input power of 10 W. This is the top performance among 10 W class rectifies
A Real-Time Simulation Framework for Powerline Communications in More-Electric Aircraft and Spacecraft

Aeronautical research is pushing toward the electrification of aircraft to achieve more electric aircraft and all-electric aircraft. The same trend is reflected into aerospace research for all-electric spacecraft. Nevertheless, aircraft and spacecraft electrification requires additional data networks and cables, increasing the complexity and the weight of wiring. Powerline communications (PLCs) solve such wiring issue by exploiting the vehicle power network for data transmission in order to remove the onboard data network. Within the ORCHESTRA project, PLCs are one of the building blocks of a “much more electric aircraft”. An accurate PLC channel model and the related software simulator allow: an early prediction of data transmission performance; more adequate adaptation to avoid impedance mismatches. This work proposes the development of a real-time simulation framework for PLC channels encompassing two phases: evaluation of the channel frequency response of a PLC network topology; performance evaluation of PLC receivers by means of the Software-Defined Radio (SDR) paradigm. The framework may be applied for PLC networks in both aircraft and spacecraft.
A 30-680-MHz GaAs pHEMT Power Amplifier with Guanella-type Transformers Integrated on a Flexible Printed Circuit Board

This paper presents a 30-680 MHz flexible power amplifier based on a 0.25-μm GaAs pHEMT process with off-chip Guanella transformer based matching networks integrated on a lightweight flexible substrate. The fabricated prototype achieves 22 dB gain, 33 dBm OP1dB with 48% peak PAE with flexibility. Both simulations and measurements demonstrate stable gain in the full VHF and part UHF bands. This flexible power amplifier design can be conveniently applied to large complex radio frequency transmission communication systems like flexible active phased arrays to provide the desired conformal capability.
SBFT Tool Competition 2023 - Cyber-Physical Systems Track

We report on the organization and results of the third edition of the Cyber-Physical Systems tool competition, held as part of the SBFT workshop. Six tools (i.e., CRAG, EvoMBT, RIGAA, RoadSign, Spirale, and WOGAN) competed with the aim of triggering failures of two autonomous driving agents.We evaluated the effectiveness of the tools in exposing failures as well as the diversity of the generated failures. This report describes our methodology, the competitors, the results, and the challenges we faced while running the competition experiments.
Tunability of room-temperature ferromagnetism in spintronic semiconductors through nonmagnetic atoms

The implementation and control of room temperature ferromagnetism (RTFM) by adding magnetic atoms to a semiconductor's lattice has been one of the most important problems in solid state state physics in the last decade. Herein we report for the first time, to our knowledge, on the mechanism that allows RTFM to be tuned by the inclusion of \emph{non-magnetic} aluminum in nickel ferrite. This material, NiFe$_{2-x}$Al$_x$O$_4$ (x=0, 0.5, 1.5), has already shown much promise for magnetic semiconductor technologies, and we are able to add to its versatility technological viability with our results. The site occupancies and valencies of Fe atoms (Fe$^{3+}$ T$_d$, Fe$^{2+}$ O$_h$, and Fe$^{3+}$ O$_h$) can be methodically controlled by including aluminum. Using the fact that aluminum strongly prefers a 3+ octahedral environment, we can selectively fill iron sites with aluminum atoms, and hence specifically tune the magnetic contributions for each of the iron sites, and therefore the bulk material as well. Interestingly, the influence of the aluminum is weak on the electronic structure (supplemental material), allowing one to retain the desirable electronic properties while achieving desirable magnetic properties.
Cross Dataset Analysis and Network Architecture Repair for Autonomous Car Lane Detection*

Transfer Learning has become one of the standard methods to solve problems to overcome the isolated learning paradigm by utilizing knowledge acquired for one task to solve another related one. However, research needs to be done, to identify the initial steps before inducing transfer learning to applications for further verification and explainablity. In this research, we have performed cross dataset analysis and network architecture repair for the lane detection application in autonomous vehicles. Lane detection is an important aspect of autonomous vehicles’ driving assistance system. In most circumstances, modern deep-learning-based lane recognition systems are successful, but they struggle with lanes with complex topologies. The proposed architecture, ERF-CondLaneNet is an enhancement to the CondlaneNet used for lane identification framework to solve the difficulty of detecting lane lines with complex topologies like dense, curved and fork lines. The newly proposed technique was tested on two common lane detecting benchmarks, CULane and CurveLanes respectively, and two different backbones, ResNet and ERFNet. The researched technique with ERF-CondLaneNet, exhibited similar performance in comparison to Resnet-CondLaneNet, while using 33% less features, resulting in a reduction of model size by 46%.
A Monolithic 0.8-to-18 GHz Ultra-Wideband Reconfigurable Dual-Mode Transceiver Front-End in 0.15 μm GaAs Technology

This paper presents a monolithic 0.8-to-18GHz reconfigurable transceiver with dual-mode of direction and conversion functions. In the direction mode, the transceiver processes the signals from 0.8 to 5GHz. While in the conversion mode, it up-converts or down-converts the signals in a frequency from 4.5 to 18GHz. An overlap of 0.5GHz exists in the two modes, so that the transceiver will be flexible in frequencies from 4.5 to 5GHz. In the RX, the two modes share the LNA, tunable LPF, and VGA. Similarly, the two modes of TX share the VGA and reconfigurable PA. The proposed transceiver was prototyped using a 0.15-µm E-mode GaAs pHEMT process. The RX exhibits gain range of 7.3 to 41.9dB, gain step of 1dB, tunable instantaneous bandwidth from 1 to 4.2GHz. The TX demonstrates gain ranging from -2.1 to 30.4dB and output 1dB compression point of 18.8dBm. The chip occupies 79.8-mm2 area including padframe.
A 2.4-GHz MEMS-Based Oscillator with Phase Noise of -138 dBc/Hz at 100 kHz Offset and 226 dBc/Hz FoM

A 2.4-GHz MEMS-based oscillator is presented with gm-boosting technique. A novel Darlington cell with a dynamic self-body-biasing scheme is proposed to boost the transconductance and to shorten the start-up time without introducing additional parasitics to the resonant tank. A 2.4-GHz MEMS resonator is used for high Q resonant tank. The proposed oscillator core is fabricated in a 180-nm CMOS technology. Measurement result shows that the oscillator achieves a phase noise of -138.23 dBc/Hz at 100 kHz offset and a figure-of-merit (FoM) of 226 dBc/Hz. The DC power consumption is 0.98-mW from a 0.7-V supply voltage. The total chip area is 0.65 × 0.57 mm2 including the pads.
220-to-320-GHz Fundamental Mixer in 60-nm InP HEMT Technology Achieving 120/152/168-Gbps Data Transmission in Three Bands

A 300-GHz-band fundamental mixer was designed and fabricated in indium phosphide (InP) high electron mobility transistor (HEMT) technology for 6G wireless communications. To achieve a wide bandwidth, we proposed a resistive mixer with a widely split frequency matching network. The fabricated mixer IC achieved a conversion gain of -15 dB, a -3-dB RF bandwidth of 60 GHz (235-295 GHz), and a -6-dB RF bandwidth of 100 GHz (220-320 GHz), the widest ever reported. We implemented the mixer IC as a WR3.4 waveguide mixer module and used a single mixer module in the TX and in the RX in a back-to-back data transmission experiment in three different frequency bands in the range from 220 GHz to 320 GHz. Data rates of 120, 152 and 168 Gbps were achieved in the three frequency bands, with 168 Gbps being the highest data rate achieved so far by a single mixer.
A Wideband Patch Antenna with Symmetrical Notches at 3.5 GHz: Design and Achievement

A compact patch antenna with a notched contour is proposed, analyzed, fabricated and used for receiving 5G mobile communication signals in the FR1 range. The bandwidth at a voltage standing ware ratio $(\text{VSWR}) < 1.5$ is 340 MHz, the resonance frequency is 3.55 GHz and its total efficiency is higher than 73% over the whole band. The maximum gain in the band is 3.42 dBi, while the antenna is slightly directive with the main beam as large as 87.1 degrees. The measured versus simulated VSWR is not significantly different. With these characteristics the antenna is mitigating interference from other unwanted signals in the 5G band, especially when time division duplexing system is in use. After its fabrication, the antenna's versatility is checked in a receiving situation nearby a targeted mobile phone in real life usage conditions. It is demonstrated that its response conforms the initial established requirements.
Soil Roughness Estimation Using Fractal Analysis on Digital Images of Soil Surface

Irregularities of soil are defined by the term soil surface roughness and various factors affect it such as tillage operations, land management, soil texture, etc. Soil roughness impacts water infiltration and surface storage level, as well as wind and water erosion. We used two classical methods for soil roughness estimation based on chain and pinboard and tested their effectiveness in lab and in situ measurements. However, we concluded that even though these two methods are perfectly correlated when they are aligned on the same line over the sample surface, in-field results showed the opposite. Thus, we propose a new soil surface roughness measurement method based on fractal analysis of digital images of the soil surface, acquired using a camera obscura-based technique. We show that the 2D fractal analysis gives more pertinent results compared to the other methods designed for 1D measurements.
mmIDs Enter the 3rd Dimension: A Camera Inspired Broadbeam High-Gain Retrodirective Backscatter Tag

For the first time, the authors propose a mm-Wave (28 GHz) retrodirective backscatter system capable of large coverage in both azimuth and elevation directions. The structure, inspired by optical cameras, features a 3D-lens backed by an array of individual backscattering elements (pixels). First, the concept is presented. Then, the components of the system are described in detail, including the PTFE lens and the backscatter pixels array. Subsequently, the entire system is assembled and its combination of large azimuth/elevation coverage and differential Radar Cross Section (RCS) is measured, demonstrated, and compared to a lens-less system. Finally, the mmID is tested up to a range of 80 m to demonstrate its long-range potential. The mmID displays a −10 dB coverage of 0.736 sr about boresight, improving the angular coverage by 62 %, relative to the state of the art. This approach unlocks the deployment of dense battery-less 5G-connected mmID sensor swarms in smart city environments.