As the size of the silver generation grows, the demographic shift poses both challenges and opportunities in embracing innovation and technology. This study delves into the challenges and barriers faced by the silver generation when using digital platforms. It focuses on a collective intelligence platform, the Digital Silver Hub, and two age groups of older adults within the silver generation: adults aged 55 to 65 and those aged 65 and over. Semi-structured interviews were conducted with representatives of both age groups from the Baltic Sea Region. Thematic analysis results revealed challenges faced by each group, their expectations from the Digital Silver Hub, the importance of a user-friendly interface, concerns about data privacy and security, and the potential impacts of the platform. The research findings emphasize the importance of adopting a user-centric evaluation when designing the platform. The recommendations generated from this study can guide the development of user-friendly solutions, empowering and enriching the lives of the silver generation in our increasingly digitalized world.

As communication systems shift towards ever higher frequency bands, the propagation of signal between a user device and an infrastructure becomes more susceptible to nearby obstacles, including other users. As an extreme case, we consider such proximity-induced channel impairments in indoor optical wireless communication (OWC) systems. We set up a model, where the achievable OWC data rate depends not only on the relative position between a user device and an infrastructure access point, but also on the location of other users modeled as proximal interferers. We use a reinforcement learning (RL) approach to enable users to find suitable positions, both relative to the access point and to each other, that maximise the sum-rate capacity of the system. Our initial results demonstrate a feasibility of RL-based approach that enables indoor OWC users to find suitable balance between establishing high-rate direct link while remaining distant from proximal interferers.

A comprehensive review of the applications of the biomechanical gait models in the development of lower-limb exoskeletons.

In this paper, a newly developed 300 GHz channel sounder is presented followed by a detailed description of test measurements and the subsequent results obtained to validate its working. An elucidation of the high-resolution double-directional channel measurement in a typical conference room scenario precedes the comparison of the results obtained for the current campaign to that obtained from an earlier campaign at 60 GHz for a similar setup. It is observed that a similar number of clusters for both the bands under investigation for all the transmitter (Tx) and receiver (Rx) positions are obtained from the generated power spectra. Any deviation is theorized to be caused either due to the usage of lower measurement bandwidth in the 60 GHz campaign or due to limited elevation expanse in the 300 GHz measurement. To identify the interacting objects (IOs) causing the clusters, environment-embedded angular power spectra (APS) and ray tracing simulations are used. The large-scale parameters (LSPs) are also evaluated for both campaigns. It is observed that signal propagation in the terahertz (THz) band is dominated more by the line-of-sight (LoS) path compared to the millimeter wave (mm-wave) band (below 100 GHz). The results are also compared with other similar results from the literature.

Young’s double slit experiment is the mystery of the quantum mechanics (Feynman). To explore the mystery, the double slit experiments have been extended to cross double slit experiments. cross double slit L = 20 L = 50 mm L = 130 mm L = 250 mm L = 1200 mm blob:https://www.techrxiv.org/d533330f-0c43-46d8-ac99-4d5a6dc5b8eb Exp. A.1 blob:https://www.techrxiv.org/d41e4f20-99c9-420a-9def-a4b75d2249ca blob:https://www.techrxiv.org/5671fee2-ce44-4c20-8eb9-1bcbe2f3871f blob:https://www.techrxiv.org/e3f05315-2d96-48fa-a69c-3c37e5500d4f blob:https://www.techrxiv.org/bd5db6a3-32ea-4251-902a-e69318f8166d blob:https://www.techrxiv.org/792dba5f-0e15-40cc-a632-a68241130f9f blob:https://www.techrxiv.org/7ce9f14c-535c-4354-b6be-fb59839abc63 Exp. A.2 blob:https://www.techrxiv.org/dc981908-4e00-4401-b5fa-962d736545c4 blob:https://www.techrxiv.org/138522db-39e3-4743-99ce-239ab567d22f blob:https://www.techrxiv.org/5db31062-cbbd-4d60-b02d-b8c1ef97a199 blob:https://www.techrxiv.org/8c50243c-a8d0-4802-9649-c27e84b28a93 blob:https://www.techrxiv.org/5435dae5-62b6-4caf-95c2-537f9da3b331 blob:https://www.techrxiv.org/f35daacf-d3c7-4b2d-a02b-49e037274e71 Exp. A.3 blob:https://www.techrxiv.org/1250c99b-c90c-4674-acc4-49a4df935b87 blob:https://www.techrxiv.org/d73d9882-eb6f-491b-bbd4-7d20e3033981 blob:https://www.techrxiv.org/8512a804-a4ac-4a50-9d82-606f19c4d5d3 blob:https://www.techrxiv.org/43df7de8-7128-4cdf-a747-236be95af0dd blob:https://www.techrxiv.org/5c100403-cbc9-44cc-98b8-b8932d2568ff blob:https://www.techrxiv.org/62875629-fffa-4eeb-8b28-d7f8b3efafc4 Exp-A.4 blob:https://www.techrxiv.org/18efd2c4-4edb-4f17-8417-abebac6c94fd blob:https://www.techrxiv.org/7d719fce-d72d-40a7-946e-a10f1d77016d blob:https://www.techrxiv.org/3f2aa1ae-ad41-4c3f-a629-01a912ebc7bf blob:https://www.techrxiv.org/b66d470d-b7f3-4594-a256-9fb62a3f0135 blob:https://www.techrxiv.org/ff751b95-d82f-4eb8-ab39-aabbf32f3ac4 blob:https://www.techrxiv.org/7d428565-2e5a-49a2-9c57-b391b6cc6547 Exp. A.5 blob:https://www.techrxiv.org/4918b2c5-3e99-4d30-a0d4-a02362a84425 blob:https://www.techrxiv.org/9d3d0aa9-f2fc-4e3f-b0e3-422079d161a4 blob:https://www.techrxiv.org/ae8bf297-d381-47bb-a226-9ca68a55d079 blob:https://www.techrxiv.org/06bf31a9-b484-42df-87d3-ce95df1d0408 blob:https://www.techrxiv.org/69d04751-e85b-422b-bdce-044a4479db2b blob:https://www.techrxiv.org/6c264a07-bf8f-402c-ac11-9dec72d5e6d9 Exp A.6 blob:https://www.techrxiv.org/05c986f3-aa01-49a8-aab8-7f2d61177de8 blob:https://www.techrxiv.org/8f5cb559-f29e-42a9-af22-2e9c2c1ce67c blob:https://www.techrxiv.org/2b3e4e54-a310-4339-a71e-d228aa6fa985 blob:https://www.techrxiv.org/fe73c811-b026-4988-bcfb-072e87efd5a5 blob:https://www.techrxiv.org/e41147e0-5dcf-4676-b97a-4487598a570a In this article, we utilize the fact that each slit of the cross double slit (a double slit crossing a double slit) is divided into three segments by other two slits crossing, and experimentally show the function of each segment in producing both non-interference patterns (including Pre-Particle patterns, Particle patterns and Transition Patterns) and the Interference patterns by eliminate the segment and then, compare with the patterns of the standard cross double slit experiment. Although the non-interference patterns of different modified cross double slits are obviously different, the final interference patterns are surprisingly similar. This phenomenon needs a consistent theoretical interpretation, which would motivate the further development of optics. When applying the interference patterns of the cross double slit diaphragms, this phenomenon allows a tolerance on the defect of making the cross double slit diaphragm.

This study aims to enhance data consistency in NoSQL databases, traditionally designed with BASE properties, as opposed to the strong consistency guaranteed by ACID principles in RDBMS. We introduce a comprehensive four-stage server-side model engineered explicitly for MongoDB. This model covers transaction management, bifurcation of read and write transactions, assessment of transaction readiness, and transaction execution via a specialized locking algorithm. Utilizing the Yahoo Cloud Services Benchmark (YCSB), particularly for update-heavy workloads (A, B, and F), our model exhibited significant improvements. Specifically, the average throughput, read, and update latencies improved to 2864.726 ms, 32806.275 ms, and 51845.629 ms, respectively, from the baseline metrics of 2914.110 ms, 26510.930 ms, and 32457.662 ms. These results demonstrate the efficacy of our proposed model in enhancing consistency not only in document-based NoSQL databases like MongoDB but also in other NoSQL database variants, including key-value, graph, and wide-column stores.

The study investigates the impact on cortical activation during Single-leg Stance (SLS) and Dual-leg Stance (DLS) and its connection to skier balance performance, utilizing functional nearinfrared spectroscopy (fNIRS). In this experiment, nine young skiers participated, performing both SLS and DLS on challenging surfaces while being monitored with fNIRS. We introduced an alpinespecific balance test called the Modified Hop Balance Test (MHBT), designed to assess the balance proficiency of skierâ\euro™s ability to control the upper bodyâ\euro™s vertical orientation during SLS. We employed a statistical model based on a functional mixed-effects framework to analyze hemodynamic response changes across various conditions. The results from the MHBT with open eyes (tests 1 and 2) revealed variations in stability between the left and right foot when moving forward, compared to lateral movements with the same foot (tests 5 and 7). Interestingly, skiers exhibited slightly better overall scores in lateral movements compared to straight-line ones, indicating their proficiency in maintaining stability during sideways skiing. SLS exhibited significantly higher cortical activation than DLS, with no significant difference observed between left and right-leg stances. It appears that tasks demanding greater motor control or higher complexity lead to increased cortical activation. For future research, a longitudinal study is needed to observe how training and corresponding neuromuscular efforts influence cortical activation over time. Furthermore, we hypothesize that as athletes improve their balance skills, the level of cortical activity will become more optimized and similar in both SLS and DLS conditions. Therefore, considering lateralized patterns of cortical activity could be valuable when interpreting postural deficiencies in cases of SLS.

This paper presents a novel localization technique—Multipath-radio tomographic imaging (RTI)—based on millimeterwave (mm-wave) radio systems, such as 5G mobile and WiGig LAN systems. Because of their high delay and angle resolutions, mm-wave radio systems can realize highly accurate device-free localization (DFL) for smart home/smart building applications. They can thus easily separate the multipath components. The Multipath-RTI can drastically reduce the number of anchor nodes using virtual nodes created by multipaths. This paper established the signal processing procedure for RTI image generation and position estimation using Multipath-RTI. Then, a practical signal processing scheme, ray-tracing-assisted Multipath-RTI, is presented, and the DFL results obtained by multilink double-directional channel sounding measurements are shown. Here, the position estimation errors in the measurements were less than 0:5 m for all target positions. Furthermore, the empirical position estimation errors for a single target under various conditions obtained by simulations were less than 0:5 m in 96.2 % and 88.0 % of the areas in an L-shaped room and a room with an obstacle in the center, respectively. The position estimation accuracy for multiple targets was also evaluated, where, for example, the errors for three targets were less than 0.1 m at 50 % probability with failure rates of less than 20 %.

The article focuses on the problem of detecting transmitted data over an additive Gaussian noise channel. We propose an integration of various deep-learning architectures with the classical Viterbi algorithm for channel estimation and equalization, to improve the estimation performance. The goal is to design communication schemes that are oblivious to the channel coefficients. The study utilizes various deep-learning architectures to address the challenges of signal detection and data reconstruction over AWGN channels in communication systems, by learning the log-likelihood ratio (LLR) /priors of the channel as a function of the channel memory. We draw inspiration from the ViterbiNetmodel-based architecture and explore the implementation of novel deep neural network (DNN) architectures for channel estimation. These architectures include Convolutional Neural Networks (CNN), Transformer Encoder, Residual Convolutional Networks as well as SIONNA (by Nvidia) architecture. The performance of these architectures was evaluated by bit-error-rate (BER) across different channel conditions by signal-noise-ratio (SNR). The results show an improvement of up to 1dB in signal-to-noise ratio performance in the moderate and high signal-to-noise ratio regions compared to previously proposed learning-based solutions. The experiments demonstrate the potential of DNN model-based architectures for symbol detection and channel equalization in communication systems. Our work details the methodology used in the experiments, including the details of the DNN architecture implementation, data-set generation, channel model, training process, evaluation metrics, and semi-supervised online training method. The experiments demonstrate the performance of the proposed architectures and compare them to the classical Viterbi algorithm (with known CSI RS) and online Viterbi-Net pervious deep-learning-based architecture. In conclusion, the article presents the integration of different model-based deep-learning architectures into the Viterbi algorithm for signal reconstruction of transmitted data over an additive Gaussian noise channel. The proposed architectures show promising results in improving the signal-to-noise ratio performance compared to existing learning-based solutions. These findings contribute to the advancement of communication systems and highlight the potential for further improvements in performance and robustness.

In modern Electrical Power Distribution System (EPDS), power electronics devices are gaining more prominence. Three-phase ac/dc converters provide a fully-controlled interface between the onboard ac and dc electrical equipment. In the context of utility ac electrical power systems, impedance-based stability analysis under Synchronous Reference Frame (SRF), or dq impedance has been established as a powerful tool to evaluate stability and optimize control design. However, precise measurement of the dq impedance is challenging in practice especially for high-capacity or high ac fundamental frequency systems. In this paper, a hardware-in-the-loop (HiL) platform is proposed for measuring dq impedance. An aircraft ac EPDS bus is built with a grid forming (GFM) converter and an active front end (AFE) converter to verify the proposed scheme. A comparison is made between the HiL measurement results, the experimental results and the analytical model, validating that dq-impedance measurement with the proposed setup provides a precise and efficient solution that obviates the need for complete hardware experiments.

Orthogonal Time Frequency Space (OTFS) modulation has established itself as a dependable  protocol for high-speed vehicular communication. This pioneering technique operates within a novel two-dimensional (2D) delay-Doppler domain waveform. When juxtaposed with conventional modulation methods like orthogonal frequency division multiplexing (OFDM), OTFS demonstrates superior performance enhancements in scenarios involving rapidly moving wireless channels. This paper begins by initially unveiling the input-output association of the OTFS signal within the delay-time domain. A comprehensive comparison with the established OFDM waveform highlights the potential of OTFS for achieving notably lower bit error rate (BER) under various conditions, which has been obtained by using the Minimum Mean Square Equalizer (MMSE) equalization technique. Finally, we have proposed novel and low complexity Very-large-scale integration (VLSI) architecture for the OTFS transmitter and the receiver by using the LU decomposition technique for the first time in state-of-the-art. We have shown comparison of computational complexity of both direct implementation and LU decomposition techniques, followed by resource utilization, timing analysis, and functionality testing of the proposed architecture.

All people have a fingerprint that is unique to them and persistent throughout life. Similarly, we propose that people have a gaitprint, a persistent walking pattern that contains unique information about an individual. To provide evidence of a unique gaitprint, we aimed to identify individuals based on basic spatiotemporal variables. Healthy young adults were recruited to walk overground on an indoor track at their own pace for four minutes wearing inertial measurement units. A total of 18 trials per participant were completed between two days, one week apart. Four methods of pattern analysis, a) Euclidean distance, b) cosine similarity, c) random forest, and d) support vector machine, were applied to our basic spatiotemporal variables such as step and stride lengths to accurately identify people. Our best accuracy (99.38%) was achieved by the support vector machine and by the top 5 and top 10 most similar trials from cosine similarity. Our results clearly demonstrate a persistent walking pattern with sufficient information about the individual to make them identifiable, suggesting the existence of a gaitprint.

This study investigates the cortical activation effects of the Infinity Walk and examines the influence of the foot’s overpronation and footwear on motor control. Functional near-infrared spectroscopy (fNIRS), a portable and user-friendly neuroimaging technique, was used to measure hemodynamical changes in six individuals with non-critical pronation degrees. Participants perform the Infinity Walk under various footwear conditions while wearing a fNIRS portable imaging device. Results indicate a consistent hemodynamic pattern in both hemispheres during the Infinity Walk, with no significant differences observed across subjects and footwear conditions in the prefrontal cortex (PFC), pre-motor area, the supplementary motor cortex (PMA & SMC), the primary motor cortex (PMC), and Wernicke’s area (WA). The impact of pronation and footwear on motor control remains inconclusive due to inconsistent hemodynamic patterns. Notably, the activation patterns in Broca’s area (BA) and the temporal gyrus (TG) differ significantly from other brain regions. The balanced hemodynamic responses in the bilateral hemispheres may be attributed to the Infinity Walk’s inherent walking pattern. These findings suggest that the Infinity Walk shows promise as a motor rehabilitation tool, as it promotes positive hemodynamic activation and exhibits similar activation patterns in both hemispheres.

The feasibility of a quantum-classical hybrid network using multicore fiber is explored and key performance metrics of the quantum channel with respect to co-propagating classical light are examined. Multiple commercial time bin quantum key distribution systems from ID Quantique are characterized using direct injection techniques to determine optimal operational parameters for the classical optical channels in order to minimize the quantum bit error rate and maximize secret key generation. A phenomenological model that determines the expected quantum bit error rate for a wide range of injected input wavelengths and powers is utilized to evaluate the quantum key distribution system performance. Analysis shows that the performance limitations exhibited by the tested commercial quantum key distribution systems in a hybrid network based on multicore fiber could be overcome by a strategic choice of the classical channel parameters.

In Positron Emission Tomography (PET) reconstruction, utilizing Time of Flight (TOF) information can significantly enhance the signal-to-noise (SNR) ratio, posing a greater challenge for the precision of TOF. To address this, we employed two distinct waveform datasets for training our developed network. One dataset comprises simulated waveform data obtained through a comprehensive simulation process established using Geant4 and GosSip. The other dataset consists of real waveform data collected from lutetium yttrium orthosilicate (LYSO) scintillators and silicon photomultiplier (SiPM) detectors placed at various positions.  Our network, a combination of Transformer and Convolutional Neural Network (CNN), was developed for predicting the TOF of coincidence events based on waveform data from PET detectors. Our network achieved average full width at half maximum (FWHM) of 189 ps, with reductions of 82 ps and 13 ps compared to constant fraction discriminator (CFD) and CNN, across multiple positions.  Additionally, there was an average bias reduction of 10.3 ps compared to CNN. We visualized the attention map, revealing the remarkable enhancement of Transformer on the rising edge of waveforms. We also demonstrated the robustness of our proposed network by including waveforms with scattered events in the real training dataset.  Data augmentation through translation and flip was investigated and resulted in an improvement of 5 ps.  Furthermore, we analyzed the characteristic differences between real and simulated waveform data, providing valuable insights for generating more realistic simulated data in the future. Our network improved the average FWHM and bias, leading to enhanced SNR and clearer imaging. Data augmentation effectively expanded the dataset and facilitated the data collection process.

The majority of technological advancements associated with the electrical grid, and particularly the increasing use of non-linear loads and power electronics in the distribution network, a weak network, challenge the fundamental assumption that voltages and currents exhibit sinusoidal waveforms. As extensively discussed by numerous authors, the electrical grid undergoes changes for which there are no conceptual foundations, measurement techniques, or practical validation strategies to address basic tasks such as energy consumption measurement, control of distributed generators, or the effective implementation of electrical protections in the non-sinusoidal environment. While the IEEE 1459 standard exists for such cases, it encounters conceptual complications regarding reactive power, as well as practical limitations requiring a complete spectral decomposition that can only be performed offline, preventing its use in real-time processes. Therefore, this study scrutinizes available techniques for online electrical power measurement in typical electrical disturbance scenarios, comparing their accuracy, dynamic response, and effectiveness in terms of the aforementioned standard.

Generative linguistic steganography aims to embed information into natural language texts with imperceptibility to achieve covert transmission, while segmentation ambiguity in unsegmented languages could induce decoding errors. Existing disambiguating ways are based on removing tokens of candidate pools, which could degrade statistical imperceptibility, potentially incurring safety risks. To avoid damaging imperceptibility, we focus on keeping candidate pools lossless meanwhile tackling segmentation ambiguity. In this paper, we propose SegFree, a segmentation-free generative linguistic steganographic approach for unsegmented languages. First, we present an adaptive-checksum verification method to filter errors caused by segmentation ambiguity. The sender appends adaptive-length checksum to the original covert message and embeds the whole message into steganographic texts, and correspondingly the receiver extracts and verifies all potential results through our proposed all-case extraction. The checksum length is adaptive and perceptible in both sides, to minimize checksum overhead and adapt to various cases. Further, in order to transmit large-scale messages and deter knowledgeable adversaries from extracting messages, we present a key-based and anti-extraction steganographic mode where the input parameters for each grouped message can be only accessed by both sides. Experiments show that SegFree with lossless candidate pools achieves about 40% higher imperceptibility and over 30% higher embedding capacity compared to linguistic steganography equipped with the existing disambiguating approach. Besides, SegFree obtains more than 99.60% success rate of preventing adversaries’ extraction.

Chest radiography is a commonly utilized imaging technique for acquiring Chest X-Ray (CXR) images due to its cost-effectiveness and its role in diagnosing lung?related disorders. Nevertheless, interpreting CXR images can be challenging, and the process of separating the lung field from CXR images can be a valuable tool for assessing and diagnosing lung diseases. While various segmentation methods exist, this study primarily focuses on META’s latest Segment Anything Model (SAM). SAM is an Artificial Intelligence (AI) model designed to segment objects within an image. This research aims to harness SAM’s capabilities for segmenting CXR images. Additionally, we explore the potential of another novel model called Grounding DINO. Grounding DINO is a zero-shot object detection model that utilizes a Swin (Shifted Windows) transformer for extracting image features and BERT (Bidirectional Encoder Representations from Transformers) for extracting textual information. It is primarily employed to detect objects in an image based on a provided text prompt, creating bounding boxes around the objects when certain text and box thresholds are met. These bounding boxes are then used as prompts for SAM to generate segmentation masks. The proposed framework has been assessed on CXRs obtained from patients at Emory Hospital in Atlanta, Georgia, USA and further evaluated using NIH clinical center’s CXR image dataset.