As sixth-generation (6G) and the internet of things (IoT)-verse push operating frequencies into the millimeter-wave (mm-wave) and terahertz (THz) regimes, conventional planar transmission lines such as microstrip, dielectric, and substrate integrated waveguides (SIW) suffer from prohibitive conductor losses and parasitic radiation. This review provides an analysis of low-loss non-radiative dielectric (NRD) waveguide technology, positioning it as an efficient transmission alternative that maintains radiation-free propagation even at sharp bends. For the first time, this work provides a quantitative performance benchmark, illustrating NRD waveguide ability to achieve significantly lower attenuation and higher power-handling capabilities compared to conventional dielectric and planar transmission line technologies. We critically examine unresolved technical bottlenecks, specifically the suppression of parasitic LSE modes at discontinuities, mode conversion losses at transitions, and the fabrication tolerances required for THz operation. By analyzing insights from over 350 studies, we provide a design analysis for active and passive components, simulation methodologies, optimization approaches, dielectric materials, fabrication techniques, and evaluating recent innovations. Finally, this work provide the evolution of NRD waveguide technology from a basic components to a recent advancement for emerging 6G and IoT system, identifying high-priority research gaps in automated design optimization and multi-layer integration.

Traumatic brain injury (TBI) assessment is crucial for protecting the health of casualties involved in sudden head-impact incidents. However, traditional methods for assessing TBI are primarily based on bulky imaging equipment, which suffers from time lag, leading to misjudgment of the injury and missing the golden opportunity for treatment. Moreover, most existing Internet of Things (IoT)-aided sensor-based head impact detection studies monitor only a single biomechanical parameter, either the external head impact force or the head center-of-mass acceleration, thus offering an incomplete injury profile. This study proposed a method for rapidly acquiring on-site time-series data to support TBI assessment, which employed flexible piezoelectric sensors to detect head impact, whose electromechanical coupling was characterized by an equivalent-impedance model identified with the genetic algorithm (GA). The voltage signal from the sensor was transmitted to mobile devices via Bluetooth to estimate head-impact biomechanics. Specifically, the impact force on the wearer’s head was converted from the voltage using the sequential quadratic programming algorithm (SQP). Based on the impact force data, the acceleration of the center of mass of the head was predicted with a neural network containing the long short-term memory (LSTM) layer. The experiments were carried out on a head-neck dummy subjected to different levels of impact. The resulting models achieved coefficients of determination (R2) of 0.935 for impact force (amplitude of 0–3 kN) and 0.848 for acceleration of the center of mass (amplitude of 0–120 g). The fabricated wireless flexible wearable sensor was installed on the dummy for performance validation, successfully demonstrating collision detection, impact force and acceleration estimation for TBI assessment, which can be conveniently deployed in helmets used in construction, sports, and emergency rescue, leveraging IoT technology for real-time dual-parameter head-impact estimation and medical-data synchronization to safeguard the golden treatment window and hopefully promote the efficient allocation of medical resources.

Flying microrobots have garnered growing research interest owing to their technological intricacies and suitability for various applications leveraging miniaturized size. Electrohydrodynamic (EHD) thrust offers advantages by generating propulsion without moving parts, but real-world use is limited by insufficient thrust generation, manufacturing challenges, fragility, and cost. This work presents the design and development of an optimized ion-propelled flying microrobot that excels in low weight, high thrust-to-weight ratio, and cost efficiency. Regarding design, multiphysics simulations guided structural optimization to increase thrust while decreasing weight. For materials, metal-coated polyethylene terephthalate (PET) film was selected to leverage the combined merits of metal conductivity and polymer flexibility, light weight, and low cost, enabling further weight reduction, easy assembly, robustness, and cost-effectiveness. Various experiments, including voltage-current measurements, ionic wind speed, thrust quantification, and airflow visualization, directed design refinements and validated performance. Through structural optimization, the maximum wind speed attained 2.25 m/s. Flight demonstrations with payloads evidenced the microrobot can stably fly at an inherent 16 mg weight while carrying an additional 72 mg load, achieving a record 5.5 thrust-to-weight ratio. These results open possibilities to incorporate microelectronics, enabling autonomous flight functionality.

Abstract Information Extraction (IE) systems assist analysts to assimilate information from electronic documents. This paper focuses on IE tasks designed to support information discovery applications. Since information discovery implies examining large volumes of heterogeneous documents for situations that cannot be anticipated a priori, they require IE systems to have breadth as well as depth. This implies the need for a domain-independent IE system that can easily be customized for specific domains: end users must be given tools to customize the system on their own. It also implies the need for defining new intermediate level IE tasks that are richer than the subject-verb-object (SVO) triples produced by shallow systems, yet not as complex as the domain-specific scenarios defined by the Message Understanding Conference (MUC). This paper describes InfoXtract, a robust, scalable, intermediate-level IE engine that can be ported to various domains. It describes new IE tasks such as synthesis of entity profiles, and extraction of concept-based general events which represent realistic near-term goals focused on deriving useful, actionable information. Entity profiles consolidate information about a person/organization/location etc. within a document and across documents into a single template; this takes into account aliases and anaphoric references as well as key relationships and events pertaining to that entity. Concept-based events attempt to normalize information such as time expressions (e.g., yesterday) as well as ambiguous location references (e.g., Buffalo). These new tasks facilitate the correlation of output from an IE engine with structured data to enable text mining. InfoXtract's hybrid architecture comprised of grammatical processing and machine learning is described in detail. Benchmarking results for the core engine and applications utilizing the engine are presented.

It is fairly common that different people are associated with the same name.In tracking person entities in a large document pool, it is important to determine whether multiple mentions of the same name across documents refer to the same entity or not.Previous approach to this problem involves measuring context similarity only based on co-occurring words.This paper presents a new algorithm using information extraction support in addition to co-occurring words.A learning scheme with minimal supervision is developed within the Bayesian framework.Maximum entropy modeling is then used to represent the probability distribution of context similarities based on heterogeneous features.Statistical annealing is applied to derive the final entity coreference chains by globally fitting the pairwise context similarities.Benchmarking shows that our new approach significantly outperforms the existing algorithm by 25 percentage points in overall F-measure.

Phrasal Verbs are an important feature of the English language. Properly identifying them provides the basis for an English parser to decode the related structures. Phrasal verbs have been a challenge to Natural Language Processing (NLP) because they sit at the borderline between lexicon and syntax. Traditional NLP frameworks that separate the lexicon module from the parser make it difficult to handle this problem properly. This paper presents a finite state approach that integrates a phrasal verb expert lexicon between shallow parsing and deep parsing to handle morpho-syntactic interaction. With precision/recall combined performance benchmarked consistently at 95.8%-97.5%, the Phrasal Verb identification problem has basically been solved with the presented method.

This paper presents a new bootstrapping approach to named entity (NE) classification. This approach only requires a few common noun/pronoun seeds that correspond to the concept for the target NE type, e.g. he/she/man/woman for PERSON NE. The entire bootstrapping procedure is implemented as training two successive learners: (i) a decision list is used to learn the parsing-based high precision NE rules; (ii) a Hidden Markov Model is then trained to learn string sequence-based NE patterns. The second learner uses the training corpus automatically tagged by the first learner. The resulting NE system approaches supervised NE performance for some NE types. The system also demonstrates intuitive support for tagging user-defined NE types. The differences of this approach from the co-training-based NE bootstrapping are also discussed.

This paper describes a novel approach to named entity (NE) tagging on degraded documents. NE tagging is the process of identifying salient text strings in unstructured text, corresponding to names of people, places, organizations, times/dates, etc. Although NE tagging is typically part of a larger information extraction process, it has other applications, such as improving search in an information retrieval system, and post-processing the results of an OCR system. We focus on degraded documents, i.e. case insensitive documents that lack orthographic information. Examples include output of speech recognition systems, as well as e-mail. The traditional approach involves retraining an NE tagger on degraded text, a cumbersome operation. This paper describes an approach whereby text is first “restored ” to its implicit case sensitive form, and subsequently processed by the original NE tagger. Results show that this new approach leads to far less precision loss in NE tagging of degraded documents. 1.