Bit and power loading (BPL) algorithms played a pivotal role in the success of orthogonal frequency division multiplexing (OFDM) in digital transmission, including lightemitting diode (LED) based wireless optical communications. Nevertheless, the conventional BPL algorithms do not distinguish the nonlinear distortion generated in LED transmitters from an additive noise, which leaves room for improvement. This letter presents a novel power loading and two BPL algorithms that maximize the transmission capacity while minimizing the nonlinear distortion generated in LED. The effectiveness of the proposed algorithms is evaluated through simulations and transmission experiments, revealing a throughput increase of up to 10% in comparison to what can be achieved employing classical algorithms.
Late-life balance disorders remain a severe problem with fatal consequences. Perturbation-based balance training (PBT), a form of rehabilitation that intentionally introduces small, unpredictable disruptions to an individual's gait cycle, can improve balance. The Tethered Pelvic Assist Device (TPAD) is a cable-driven robotic trainer that applies perturbations to the user's pelvis during treadmill walking. Earlier work showcased improved gait stability and the first evidence of increased cognition acutely. The mobile Tethered Pelvic Assist Device (mTPAD), a portable version of the TPAD, applies perturbations to a pelvic belt via a posterior walker during overground gait, as opposed to treadmill walking. Forty healthy older adults were randomly assigned to a control group (CG, n = 20) without mTPAD PBT or an experimental group (EG, n = 20) with mTPAD PBT for a two-day study. Day 1 consisted of baseline anthropometrics, vitals, and functional and cognitive measurements. Day 2 consisted of training with the mTPAD and post-interventional cognitive and functional measurements. Results revealed that the EG significantly outperformed the CG in several cognitive (SDMT-C and TMT-B) and functional (BBS and 4-Stage Balance: one-foot stand) measurements while showcasing increased confidence in mobility based on FES-I. To our knowledge, our study is the first randomized, large group (n = 40) clinical study exploring new mobile perturbation-based robotic gait training technology.
We introduce a new construction method of diffusion layers for Substitution Permutation Network (SPN) structures along with its security proofs. The new method can be used in block ciphers, stream ciphers, hash functions, and sponge constructions. Moreover, we define a new stream cipher mode of operation through a fixed pseudorandom permutation and provide its security proofs in the indistinguishability model. We refer to a stream cipher as a Small Internal State Stream (SISS) cipher if its internal state size is less than twice its key size. There are not many studies about how to design and analyze SISS ciphers due to the criterion on the internal state sizes, resulting from the classical tradeoff attacks. We utilize our new mode and diffusion layer construction to design an SISS cipher having two versions, which we call DIZY. We further provide security analyses and hardware implementations Â of DIZY. In terms of area cost, power, and energy consumption, the hardware performance is among the best when compared to some prominent stream ciphers, especially for frame-based encryptions that need frequent initialization. Unlike recent SISS ciphers such as Sprout, Plantlet, LILLE, and Fruit; DIZY does not have a keyed update function, enabling efficient key changing.Â
Affective virtual reality (VR) gaming systems rely on timely physiological data collection, in order to generate personalized responses that enhance the emotional impact of a video game on its user. In this context, we propose a simple policy for timely data collection from wireless sensor nodes placed on the human body. Our policy is applied at each sensor node. Upon each packet arrival we check whether the buffer is full or not. If the buffer is full, then we empty it before adding the packet. In this very simple way, we avoid buffer congestion and impose timeliness. We simulated this aggressive buffer reset policy using a body area network (BAN) model in OMNeT++. By varying the packet generation rate of each node, we showed that our policy outperforms first come first served (FCFS) and last come first served (LCFS) queueing policies in terms of peak age, while packet reception is barely affected. Buffer resets can be easily integrated into existing random access protocols to support timely data collection.
Passive prosthetic legs require undesirable compensations from amputee users to avoid stubbing obstacles and stairsteps. Powered prostheses can reduce those compensations by restoring normative joint biomechanics, but the absence of user proprioception and volitional control combined with the absence of environmental awareness by the prosthesis increases the risk of collisions. This paper presents a novel stub avoidance controller that automatically adjusts prosthetic knee/ankle kinematics based on suprasensory measurements of environmental distance from a small, lightweight, low-power, low-cost ultrasonic sensor mounted above the prosthetic ankle. In a case study with two transfemoral amputee participants, this control method reduced the stub rate during stair ascent by 89.95% and demonstrated an 87.5% avoidance rate for crossing different obstacles on level ground. No thigh kinematic compensation was required to achieve these results. These findings demonstrate a practical perception solution for powered prostheses to avoid collisions with stairs and obstacles while restoring normative biomechanics during daily activities.
Spectrum sensing technology is a crucial aspect of modern communication technology, serving as one of the essential techniques for efficiently utilizing scarce information resources in tight frequency bands. This paper first introduces three common logical circuit decision criteria in hard decisions and analyzes their decision rigor. Building upon hard decisions, the paper further introduces a method for multi-user spectrum sensing based on soft decisions. Then the paper simulates the false alarm probability and detection probability curves corresponding to the three criteria. The simulated results of multi-user collaborative sensing indicate that the simulation process significantly reduces false alarm probability and enhances detection probability. This approach effectively detects spectrum resources unoccupied during idle periods, leveraging the concept of time-division multiplexing and rationalizing the redistribution of information resources. The entire computation process relies on the calculation principles of power spectral density in communication theory, involving threshold decision detection for noise power and the sum of noise and signal power. It provides a secondary decision detection, reflecting the perceptual decision performance of logical detection methods with relative accuracy.
Efficiently transferring image-based object detectors to the domain of video remains challenging under resource constraints. Previous efforts used feature propagation to avoid recomputing unchanged features. However, the overhead is significant when working with very slowly changing scenes, such as in surveillance applications. In this paper, we propose temporal early exits to reduce the computational complexity of video object detection. Multiple temporal early exit modules with low computational overhead are inserted at early layers of the backbone network to identify the semantic differences between consecutive frames. Full computation is only required if the frame is identified as having a semantic change to previous frames; otherwise, detection results from previous frames are reused. Experiments on ImangeNet VID and TVnet show that the approach can accelerate video object detection by 1.7x compared to SOTA, with a reduction of only <1% in mAP.
We present a novel high-accuracy rotary magnetic sensor system composed of a two-track coded multi-pole magnet and a dual-spot multi-axes magnetic sensor. The novelty is the measurement of two orthogonal magnetic field components in each of the two sensing spots of the sensor, one associated with each magnet track. As the two orthogonal signals in each spot are naturally in quadrature, i.e. they represent a sine and a cosine signal, the measurement principle is virtually independent of the magnet pole size and pitch. We can therefore design a magnet with much larger poles which in turn generate stronger magnetic flux, allowing for an increased air gap between sensor and magnet. The calibrated encoder system was characterized to deliver 13 bits of absolute accuracy and 17 bits of resolution over the full 360° range. Article ACCEPTED for publication in 2023 IEEE Sensors.
Passive intermodulation (PIM) by metal contacts limits the bandwidth and capacity of radio links used in mobile and satellite communications. In this work, we investigate the effect of nonlinearities of metal-to-metal contacts and their effects on PIM generation. An analytical expression is obtained for the tunneling current density which has an error of ~1.6% in the case of a very thin insulator and low voltages in MIM (Metal-InsulatorMetal) junctions. The presented analytical model of the contact surfaces with the fractal geometry is applied to simulate PIM products of third-order (PIM3) and fifth-order (PIM5) versus the contact resistance and applied pressure. The simulation results are validated experimentally by an open-ended rectangular coaxial structure with a slotted enclosure. The measurement results demonstrate that the presented model predicts the PIM with a mean error of about 4.8 dB when the contact pressure varies from 0.5 MPa to 1.7 MPa.
This experimental research aims to investigate the potential benefits of integrating augmented reality (AR) technology into the classroom setting. The study hypothesizes that the use of AR technology will enhance student engagement and lead to improved learning outcomes. A sample of participants from a local high school will be involved in this research. The research employs a pre-test/post-test design to assess the impact of AR technology on student engagement and learning outcomes. Data will be collected and analysed to determine the effectiveness of AR technology in enhancing classroom education.
Depression severity can be classified into distinct phases based on the Beck depression inventory (BDI) test scores, a subjective questionnaire. However, quantitative assessment of depression may be attained through the examination and categorization of electroencephalography (EEG) signals. Spiking neural networks (SNNs), as the third generation of neural networks, incorporate biologically realistic algorithms, making them ideal for mimicking internal brain activities while processing EEG signals. This study introduces a novel framework that for the first time, combines an SNN architecture and a long short-term memory (LSTM) structure to model the brainâ\euro™s underlying structures during different stages of depression and effectively classify individual depression levels using raw EEG signals. By employing a brain-inspired SNN model, our research provides fresh perspectives and advances knowledge of the neurological mechanisms underlying different levels of depression. The methodology employed in this study includes the utilization of the synaptic time dependent plasticity (STDP) learning rule within a 3-dimensional braintemplate structured SNN model. Furthermore, it encompasses the tasks of classifying and predicting individual outcomes, visually representing the structural alterations in the brain linked to the anticipated outcomes, and offering interpretations of the findings. Notably, our method achieves exceptional accuracy in classification, with average rates of 98% and 96% for eyes-closed and eyes-open states, respectively. These results significantly outperform state-of-the-art deep learning methods.
Aims: we propose a sociotechnical taxonomy for the analysis of socio-economic disruptions caused by technological innovations. Methodology: a transdisciplinary principled approach is used to build the taxonomy through categorization and characterization of technologies using concepts and definitions originating from cybernetics, occupational science, and economics. The sociotechnical taxonomy is then used, with the help of logical propositions, to connect the characteristics of different categories of technologies to their socio-economic effects, for example their externalities. Results: we offer concrete illustrations of concepts and uses, and an Industry 5.0 case study as an application of the taxonomy. We suggest that the taxonomy can inform the analysis of opportunities and risks related to technological disruptions, specially of those that result from the rise of cognitive machines.
Developing aerial robots that can both safely navigate and execute assigned mission without any human intervention â\euro“ i.e., fully autonomous aerial mobility of passengers and goods â\euro“ is the larger vision that guides the research, design, and development efforts in the aerial autonomy space. However, it is highly challenging to concurrently operationalize all types of aerial vehicles that are operating fully autonomously sharing the airspace. Full autonomy of the aerial transportation sector includes several aspects, such as design of the technology that powers the vehicles, operations of multi-agent fleets, and process of certification that meets stringent safety requirements of aviation sector. Thereby, Autonomous Advanced Aerial Mobility is still a vague term and its consequences for researchers and professionals are ambiguous. To address this gap, we present a comprehensive perspective on the emerging field of autonomous advanced aerial mobility, which involves the use of unmanned aerial vehicles (UAVs) and electric vertical takeoff and landing (eVTOL) aircraft for various applications, such as urban air mobility, package delivery, and surveillance. The article proposes a scalable and extensible autonomy framework consisting of four main blocks: sensing, perception, planning, and controls. Furthermore, the article discusses the challenges and opportunities in multi-agent fleet operations and management, as well as the testing, validation, and certification aspects of autonomous aerial systems. Finally, the article explores the potential of monolithic models for aerial autonomy and analyzes their advantages and limitations. The perspective aims to provide a holistic picture of the autonomous advanced aerial mobility field and its future directions.
In this letter, we consider a downlink multi-user (MU) non-orthogonal multiple access (NOMA) network. We demonstrate that utilizing knowledge of the channel gains to the users to determine the NOMA power allocation coefficients can dramatically improve throughput performance. Considering practical imperfect successive interference cancellation, expressions are derived for the optimum power allocation that ensures the minimum outage probability for the signalling scheme. The level of successive interference cancellation at each user and the decoding order are specified. It is shown that the proposed decoding order and the power allocations result in the maximum throughput. Expressions are derived for the throughput with these power allocations. Channel knowledge is exploited to determine the minimum power required for non-outage, and an expression is derived for the average value of this minimum power requirement. Computer simulations validate the derived expressions.
In distance education programs with a large number of students, the organization and facilitation of collaborative writing projects are particularly challenging. Teachers must specify the didactical design and group formation, supervise and support distributed groups, grade, and finally evaluate the learning experiences. Distributed student groups need their own workspace including both, support for a structured writing process including necessary instructions and materials as well as tools for collaborative text editing, communication, coordination, and providing formative and summative feedback. Current approaches to support collaborative writing in education are mostly based on the use of Web 2.0 applications, such as Wikis and Weblogs, or Collaborative real-time text editors, failing to support teachers and students appropriately. As a consequence, teachers often refrain from implementing collaborative writing projects in large scale distance learning courses. We introduce a process model of a collaborative writing project aimed at creating a summary of a research paper and present the architecture and implementation of a Collaborative Learning Platform implementing Collaborative Writing Activities by an extension of a Learning Management System and integrating it with a collaboration environment. The platform supports the phases and activities of the process model and provides distributed teachers and students with integrated support throughout the collaborative writing project lifecycle. Our experience shows that the platform provides a scalable, responsive, and robust environment for collaborative writing and is accepted by teachers and students. It provides the basis for the analysis of collaborative writing behavior and further research.
COVID-19 is an acute respiratory disease that has become a pandemic worldwide. Many studies have been conducted to enhance our understanding of COVID-19. However, the abundance of information obtained from these studies has resulted in information overload. In this study, we purposed a simple COVID-19 Knowledge Graph in Bahasa Indonesia as a way to reconstruct knowledge to combat this information overload. We used Bahasa Indonesia in our study to explore its potential for constructing a Knowledge Graph (KG). The construction of our KG involved manual curation of medical literatures and annotation of entities and relationships by the domain experts. The KG was implemented using Neo4J version 5. We successfully demonstrated our COVID-19 KG, which consists of 240 nodes and 276 relationships with 15 and 14 node and relationship labels respectively. Accessing the information within the KG is made effortless through the use of Cypher queries in Neo4J. Further research is still needed to develop the KG into a larger and better one. However, our COVID-19 KG can serve as a basis for further development.