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Tweezers: A Framework for Security Event Detection via Event Attribution-centric Tweet Embedding
No full textTwitter is recognized as a crucial platform for the
dissemination and gathering of Cyber Threat Intelligence (CTI).
Its capability to provide real-time, actionable intelligence makes
it an indispensable tool for detecting security events, helping
security professionals cope with ever-growing threats. However,
the large volume of tweets and inherent noises of humancrafted tweets pose significant challenges in accurately identifying
security events. While many studies tried to filter out eventrelated tweets based on keywords, they are not effective due
to their limitation in understanding the semantics of tweets.
Another challenge in security event detection from Twitter is
the comprehensive coverage of security events. Previous studies
emphasized the importance of early detection of security events,
but they overlooked the importance of event coverage. To cope
with these challenges, in our study, we introduce a novel event
attribution-centric tweet embedding method to enable the high
precision and coverage of events. Our experiment result shows
that the proposed method outperforms existing text and graphbased tweet embedding methods in identifying security events.
Leveraging this novel embedding approach, we have developed
and implemented a framework, Tweezers, that is applicable to
security event detection from Twitter for CTI gathering. This
framework has demonstrated its effectiveness, detecting twice
as many events compared to established baselines. Additionally,
we have showcased two applications, built on Tweezers for the
integration and inspection of security events, i.e., security event
trend analysis and informative security user identification
A Wireless Power and Data Transfer System for Medical Implants Using a Miniaturized Inductive Link With Frequency-Splitting Enhancement
No full textThis article presents an enhanced-frequency-splitting-based wireless power and data transfer (EFS-WPDT) system that simultaneously delivers power and forward data over a compact inductive link. For data transmission, the proposed system employs frequency-shift keying (FSK) based on frequency-splitting enhancement (FSE), which is enabled by dynamic link-load isolation (LLI) and time-interleaved resonance. This approach effectively addresses the conventional tradeoffs among power delivered to the load (PDL), data rate (DR), and power transfer efficiency (PTE). The dynamic LLI decouples the load during each resonance phase, which is critical for enabling FSE, and is implemented using a quasi-resonant boost converter (QRBC) that provides a boosted and regulated output voltage. For time-interleaved operation, reliable peak detection is achieved by a body-tuned peak detector (BTPD), which maintains accurate timing across varying link conditions. A frequency-to-amplitude converter enhances sensitivity by amplifying envelope differences, enabling robust data demodulation even in the miniaturized link. The presented ICs, fabricated in a 180-nm bipolar-CMOS-DMOS (BCD) process, simultaneously achieve 60.2% overall PTE, 43.4-mW PDL, and 1-Mb/s DR with a sub-centimeter receiver (RX) coil. As a result, the figure of merit (FoM) for data transmission is improved to a level comparable to previous works using centimeter-scale links, while the FoM for power delivery is improved by compared to prior state-of-the-art systems, employing a single inductive link.
Time-lens-based optical phased array LiDAR for ranging and accuracy enhancement
No full textLight Detection and Ranging (LiDAR) has been widely applied to a variety of fields. Among various approaches, the time-of-flight (ToF) ranging cooperated with silicon optical phased array (OPA) has gained intense interest because of simple operation, all solid-state structure, and precise beam control. However, the long-range ToF LiDAR usually requires high optical power that is unsustainable for human eyes safety and even for the chip-scale devices. Besides, the nanosecond-width optical pulse used in the ToF LiDAR limits the ranging accuracy and requires complex processing for time discrimination. Hence, the millimeter-order accuracy is still challenging for the ToF LiDAR. Here, we propose a novel design of a time-lens-based OPA system that can improve the detection range and accuracy of a ToF LiDAR simultaneously. By virtue of a time-lens system, the target echo signal can be translated into a train of compressed pulses with boosted peak power prior to the light detection. In a proof-of-concept demonstration, a 10-ns width echo signal has been compressed into a series of 22-ps subpulses with 2.74 dB SNR improvement at a repetition rate of 4.8 GHz. In addition, the time-lens system enables the ToF LiDAR to detect the noise-overwhelming echo signals through coherent amplification. The ultra-narrow pulse width of the subpulses can improve the accuracy of time discrimination. Consequently, the ranging precision and accuracy can be improved to 6 mm and 1.5 cm, respectively. Last but not least, the minimum location shift that the system can distinguish is 5.6 mm.
Predicted lane-based time to collision: A novel surrogate safety measure for pedestrian potential risk evaluation at non-signalized intersections
No full textPedestrian crossing safety at non-signalized intersections remains a critical challenge. Proactive pedestrian protection systems, such as pedestrian warning systems, have emerged as reliable alternatives to crash data-dependent analyses. These systems use real-time sensor data to evaluate potential risk and enable timely interventions. Prediction-based Surrogate Safety Measures (SSMs), which project current states to estimate future conflicts, are widely adopted for risk evaluation. However, due to the complexity of pedestrian behavior, existing prediction-based SSMs are limited in accurately estimating conflict locations and arrival times, making long-distance risk evaluation particularly challenging. To address these limitations, this study proposes Predicted Lane-based Time to Collision (PLTTC), a novel surrogate safety measure for evaluating pedestrian risks at non-signalized intersections. PLTTC extends Lane-based PET by shifting from precise conflict points to a broader area-based approach and removes rigid kinematic assumptions by employing deep learning for arrival time prediction. Experiments with real-world CCTV data show that: (1) PLTTC outperforms existing SSMs in risk evaluation, particularly in long-distance scenarios, achieving an AUC of 0.92 on the Precision-Recall curve; (2) PLTTC demonstrates superior performance for road users with high variability in velocity, such as kids; and (3) PLTTC shows generalizability through cross-site validation, maintaining consistent performance across different intersection. By addressing long-distance scenarios, capturing high-variability behaviors, and demonstrating generalizability, PLTTC can enhance pedestrian warning systems and improves safety at non-signalized intersections.
Unveiling Bidentate Nitrile-Driven Structural Degradation in Ultra-High-Nickel Cathodes
No full textUltra-high-Ni layered oxides are promising cathodes for high-energy lithium–ion batteries (LIBs), however, their stability is compromised by interfacial degradation. Understanding molecular-level interactions at the cathode–electrolyte interface, especially involving transition-metal ions, is crucial. Here, we investigate dinitrile additives, focusing on succinonitrile (CN4) and its effects on Ni3+ ions in LiNi0.91Co0.08Al0.01O2 (NCA91). CN4 coordinates with Ni3+ via rigid bidentate binding through its two −CN groups, inducing ligand-field-driven electronic reconfiguration and Jahn–Teller distortion. This interaction enhances lattice strain, promotes π* back-donation, reduces surface electron density, and accelerates electrolyte oxidation, causing rapid deterioration in NCA91|graphite–Si full-cells. By contrast, longer-chain dinitriles (CN–[CH2]x–CN, x ≥ 3) exhibit weaker interactions due to internal backbone strain, which limits bidentate coordination. These findings explain the limited effectiveness of current dinitrile additives in mitigating structural degradation of ultra-high-Ni cathodes and provide molecular-level design principles for next-generation electrolyte additives to stabilize these materials.
