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To effectively monitor a network and verify its performance, it is essential to quickly detect sudden changes in its state, even when the form of such a change is initially unknown. While classical quickest change detection methods are potentially useful, they rely on probing the network state periodically, which in turn, may induce high measurement costs. In this paper, we extend existing frameworks in quickest change detection to allow for both adaptive measurement periods and unknown post-change measurement distribution. In our extended framework, the agent decides both when to raise an alarm and when to take the next measurement (if any), maintaining a trade-off between detection delay, false alarm rate and measurement costs. We evaluate both classical methods with periodic measurements as well as our adaptive scheme, called RoME-QCD (Robust and Measurement Efficient Quickest Change Detection). We demonstrate the latter's superiority analytically and verify this observation via numerical experiments, both using one-way delay data from a 5G testbed and synthetically generated data., QC 20240812

Citizen trust is a central part of an ethical AI ecosystem, and a key part of upcoming AI legislation across the world. However, pockets of society have little awareness of what is meant by AI or its current use by organizations. Therefore, a first step towards building citizen trust is to raise general awareness of what is meant by AI technologies, how data is captured and used in decision making, and existing citizens’ rights to question organizations about the use of their data in automated decision making. This paper describes a mechanism to reach different groups of publics through a Community AI Roadshow. The motivation was to develop a way to reach and engage with traditionally marginalized communities and develop a common language and understanding around AI. An evaluation showed that understanding of AI increased by 33% following the roadshow. A resulting set of recommendations for AI researchers engaging with marginalized communities is given. The methodology presented in this paper has since been adapted for other groups of publics, such as local governmental organizations in the UK.

This paper proposes the application of logic locking over TFHE to protect both user data and algorithms, such as input user data and models in machine learning inference applications. With the proposed secure computation protocol algorithm evaluation can be performed distributively on honest-but-curious user computers while keeping the algorithm secure. To achieve this, we combine conventional logic locking for untrusted foundries with TFHE to enable secure computation. By encrypting the logic locking key using TFHE, the key is secured with the degree of TFHE. We implemented the proposed secure protocols for combinational logic neural networks and decision trees using LUT-based obfuscation. Regarding the security analysis, we subjected them to the SAT attack and evaluated their resistance based on the execution time. We successfully configured the proposed secure protocol to be resistant to the SAT attack in all machine learning benchmarks. Also, the experimental result shows that the proposed secure computation involved almost no TFHE runtime overhead in a test case with thousands of gates.

As the uplink sensing has the advantage of easy implementation it attracts great attention in integrated sensing and communication ISAC system This paper presents an uplink ISAC system based on multi input multi output orthogonal frequency division multiplexing MIMO OFDM technology The mutual information MI is introduced as a unified metric to evaluate the performance of communication and sensing In this paper firstly the upper and lower bounds of communication and sensing MI are derived in details based on the interaction between communication and sensing And the ISAC waveform is optimized by maximizing the weighted sum of sensing and communication MI The Monte Carlo simulation results show that compared with other waveform optimization schemes the proposed ISAC scheme has the best overall performance

This paper presents an overview of the advances in reconfigurable Ygai Uda antenna for smart wireless communications systems In particularly it reports the development of planar Yagi Uda antenna with frequency and pattern reconfiguration Furthermore this paper presents a recent design on a circular array of Yagi Uda monopoles with a full 360 azimuthal beam scanning

3D Object Reconstruction from a single view 2D image has become a promising research field However it remains a crucial and unsolved core issue in AI and Computer Vision research Many scholars think it is the future of Artificial Intelligence and is well deserved at the irreplaceable heart of future AI research In this paper the research history of 3D object reconstruction is introduced and the current state of the art research methods and most novel results are investigated and discussed A prediction for the best research methods and reconstruction model for this field is made This paper also provided the essential clues and trends in 2D images or scenes inverse to 3D sceneries by 3D reconstruction This interdisciplinary research area requires the researchers to have rich knowledge in but not limited to Computer graphics Such as OpenGL deep learning Computer vision like OpenCV and the neurocognitive logics and principles of the cerebral cortex

This paper introduces a parametrized analyzing framework for facilitating power market participation of flexible loads specifically industrial through the aggregation of consumers. By combining flexible loads’ potential, the demand response of the industrial sector can be mobilized and made available, particularly when the qualification limits cannot be met by single industrial plants. To aggregate the flexibility potential of flexible consumers, an analysis framework is necessary to streamline the calculation process for aggregators and plant owners to estimate their potential and prerequisites to participate in various power markets. This paper proposes a simplified approach to categorize the combination of flexible industries into parallel and series configurations. A set of calculations and assumptions is presented to obtain the values for the flexibility parameters of the aggregated industries. The results of this study provide insights into the potential benefits and challenges associated with aggregating industrial consumers in power markets.

Power Hardware in the Loop (P-HIL) systems and Battery Energy Storage Systems (BESS) are essential tools in the transition to a more sustainable and efficient energy matrix. These systems work together to analyze the integration of intermittent renewable energy sources to improve the stability and reliability of electrical grids. This, in turn, contributes to the reduction of greenhouse gas emissions and the development of a low-carbon economy. This paper presents an experimental working platform and real-time simulation based on P-HIL technology and scaled power electronics prototypes. The platform allows the analysis of the interaction of BESS devices in electrical distribution grids. A case study is presented to demonstrate the combination of two main qualities of a BESS: improving voltage stability and reducing peak demand. To this end, an experimental 1 kW BESS consisting of a Dual Active Bridge (DAB) and a Voltage Source Converter (VSC) is connected to a 13-bus IEEE distribution network. The attained results demonstrate the ability of the platform to bridge two areas of electrical engineering and highlight its significant advantages.

The high-tech equipment industry is adopting approaches like Model-Based Systems Engineering (MBSE) and Scaled Agile Framework (SAFe) to improve their practices. Instantiations of these approaches do not necessarily align across disciplines. Given the importance of software aspects in systems, the interplay between software and other engineering disciplines should be carefully addressed. Decision makers are interested in mitigating any risks in the collaboration of specialists in different disciplines. This short positioning paper proposes an applied research approach to identify such risks for later mitigation. The approach is inspired by grounded theory (we constructed concepts from interviews) and systems thinking. Work still to do is to cross-check identified concepts with literature. This research is in progress and we welcome discussions and critical opinions.

Electrifying the energy system with heat pumps and electric vehicles is a strategy of many countries to reduce CO2 emissions. Large electrification, however, poses several new challenges for the electricity system, particularly in combination with a simultaneous substitution of nuclear power plants by volatile renewables such as photovoltaics. The increasing consumption of electricity and the growing number of installed photovoltaic systems are pushing the existing electrical grid to its limits. Today’s grid must be expanded with smart controllers and their components regulated. For this purpose, an understanding of flexible participants such as boilers, heat pumps and electric vehicles, and new production plants such as photovoltaic systems must be built up. The aim of this paper is to present the flexibility potential of the municipality and the proposed energy scheduler for flexible shifting of loads to the optimal location considering defined constraints. The results are used to calculate the new parameters for the locally installed energy management systems, which control appliances according to the scheduler setpoints, to utilise flexibilities for peak shaving optimally.

The electricity supply system of Stadtwerk Winterthur is undergoing modernisation and improvement, including the voltage increase of the medium voltage network (NE5) from 11 kV to 22 kV to enhance transfer capacity. However, this increase requires reinforcement of the existing cable lines in the Bruderhaus area, which are designed for 6 kV. Grid reinforcements are costly and alternative solutions must be considered to resolve or delay the problem. This paper aim is to show investigated and evaluated alternative solutions to ensure a continuous and reliable power supply for the Bruderhaus area in Winterthur. The results showed the technical and economic feasibility of various alternatives and propose suitable solutions for radial medium voltage grid.

As the number of base stations keeps increasing due to ever-growing wireless services and pervasive connectivity, the cost of operation for network service providers rises proportionally. In addition, with the evolution of wireless technology and development of next generation networks such as 5G and 6G, network operators are expected to encounter more complex challenges soon. One such network issue is the Passive Inter-modulation (PIM) problem that is observed in both 4G and 5G networks. Although there is a significant body of work regarding PIM detection and cancellation methods, majority of such studies depend on hardware solutions and manual investigation by network engineers, which is costly in terms of time and labor. In this paper, we propose a time-series based anomaly detection method, for identifying PIM problems in network sites. The proposed solution utilizes a set of Key Performance Indicator (KPI) data of base stations, obtained from network management systems for a significantly long time interval, and detects possible PIM problems in a site without the need for a human-in-the-loop. We measure the performance of our solution, with the guidance of experienced network engineers, on our collected dataset.

Moving to a multi-cloud environment and service-based architecture, 5G and future 6G networks require additional defensive mechanisms to protect virtualized network resources. This paper presents MERLINS, a novel architecture generating optimal Moving Target Defense (MTD) policies for proactive and reactive security of network slices. By formally modeling telecommunication networks compliant with Network Function Virtualization (NFV) into a multi-objective Markov Decision Process (MOMDP), MERLINS uses deep Reinforcement Learning (deep-RL) to optimize the MTD strategy that considers security, network performance, and service level requirements. Practical experiments on a 5G testbed showcase the feasibility as well as restrictions of MTD operations and the effectiveness in mitigating malware infections. It is observed that multi-objective RL (MORL) algorithms outperform state-of-the-art deep-RL algorithms that scalarize the reward vector of the MOMDP. This improvement by a factor of two leads to a better MTD policy than the baseline static counterpart used for the evaluation.

Predicting sea ice concentration is an important task in climate analysis. The recently proposed deep learning system IceNet is the state of the art sea ice prediction model. IceNet takes high-dimensional climate simulations and observational data as input features and forecasts sea ice concentration for the next six months over a spatial grid over the northern hemisphere. The model has proven to be particularly good at predicting extreme sea ice events compared to previous dynamical models, but lacks interpretability. In the original IceNet paper, a permute-and-predict approach was taken for assessing feature importance. However, this approach is not capable of revealing whether a feature contributes positively or negatively to the final prediction, nor can it reveal the importance of features over the spatial grid of predictions. In this paper, we take steps to instead interrogate the effect of the IceNet input feature with a gradient-based analysis, taking advantage of developments within the deep learning literature to open the so-called black box. Our analysis focuses on the unusually large sea ice extent event in September 2013 and indicates that IceNet places a strong emphasis on previous observations of sea ice concentration, linear trends, and seasonal components when making predictions. In our analysis, we identify which input features that are most influential for the prediction, and also at which spatial location these measurements are particularly influential.

The US FAA regulations and regulations from other global Civil Aviation Authorities impose significant limitations on secure Broadcast Remote ID strategies for Unmanned Aircraft Systems (UAS). One of the most challenging constraints is configuring messaging within Bluetooth 4 broadcast frames, seemingly limiting the capacity for a reliable Remote ID and trust in UAS communications. However, the Internet Engineering Task Force (IETF) Drone Remote ID Protocol (DRIP) workgroup has adeptly navigated these constraints. They have developed a provably secure Remote ID that authenticates all its messages and can be independently validated without needing an external, internet-based verification service. Remarkably, they achieve this within the stringent message length confines set by Broadcast Remote ID regulations and American Society for Testing and Materials (ASTM) standards. This novel solution, the DRIP Entity Tag (DET), doubles as a legitimate yet non-routable IPv6 address. Beyond its primary application, the DET has promising potential for adoption in broader mobile device ecosystems, fostering interdevice trust. In this paper, we explain the DRIP initiative and its implementation in real-world scenarios and examine its potential use on various mobile devices.

This paper presents the ongoing research effort related to the design of the Data Management Infrastructure (DMI) to support experimental research on digital technologies with application to the ESFRI SLICES scientific instrument. We consider the experiment documentation and data collection across the whole continuum of access network, IoT, edge, cloud, and data processing workflow. The paper includes the requirements analysis for DMI to enable research reproducibility of complex and large-scale experimentation. We provide an analysis of data collected and processed in SLICES and explain approaches and solutions used in SLICES for experimental research reproducibility, primarily based on the plain orchestration service and supported by metadata collection tools. The proposed multi-layer DMI includes: data (storage) access, data processing, data ingest, experiment management, and virtual research environment. The paper also provides recommendations for the selection of existing standards and tools for data and metadata management, in particular those developed by EOSC and supported by the RDA community to ensure wide compatibility and integration.

Repetitive, individual exercises can improve the functional ability of stroke survivors over the long term. With the aim of providing extra motivation to adhere to repetitive, individual rehabilitation, this paper presents a robotic coach for stroke rehabilitation. Our system uses the Pepper robot and performs one of twelve data-driven coaching policies. The policies were learned from human-human observations of professional stroke physiotherapists and provide high-level personalisation based on user information and training context. A within subjects evaluation of the system was conducted in-person involving short interactions with 3 stroke survivors. The system was able to engage the target end users and there were indications that decreased workload could be possible when using the system compared to exercising alone., Interactive Intelligence

Using HRI theory to inform robot development is an important, but difficult, endeavor. This paper explores the relationship between HRI theory and HRI practice through a design project on the development of design guidelines for human-robot communication together with a dairy farming robot manufacturer. The design guidelines, a type of intermediate-level knowledge, were intended to enrich the specialized knowledge of the company on farming context with relevant academic knowledge. In this process, we identified that HRI theories were used as a frame, a tool, best practices, and a reference; while the HRI practice provided a context, a reference, and validation for the theories. Our intended contribution is to propose a means to facilitate exchanges both ways between HRI theory and practice and add to the emerging repertoire of designerly ways of producing knowledge in HRI., Human Information Communication Design, Industrial Design Engineering

Accurate camera relocalisation is a fundamental technology for extended reality (XR), facilitating the seamless integration and persistence of digital content within the real world. Benchmark datasets that measure camera pose accuracy have driven progress in visual re-localisation research. Despite notable progress in this field, there is a limited availability of datasets incorporating Visual Inertial Odometry (VIO) data from typical mobile AR frameworks such as ARKit or ARCore. This paper presents a new dataset, MARViN, comprising diverse indoor and outdoor scenes captured using heterogeneous mobile consumer devices. The dataset includes camera images, ARCore or ARKit VIO data, and raw sensor data for several mobile devices, together with the corresponding ground-truth poses. MARViN allows us to demonstrate the capability of ARKit and ARCore to provide relative pose estimates that closely approximate ground truth within a short timeframe. Subsequently, we evaluate the performance of mobile VIO data in enhancing absolute pose estimations in both desktop simulation and user study. MARViN is available at https://github.com/XRIM-Lab/MarViN.

This paper exploits ubiquitous desktop interaction data as an input source for generating virtual reality (VR) interaction data, which can benefit tasks like user behavior analysis and experience enhancement. Time-varying stroke gestures are selected as the primary focus because of their prevalence across various applications and their diverse patterns. The commonalities (e.g., features like velocity and curvature) between desktop and VR strokes allow the generation of additional dimensions (e.g., z vectors) in VR strokes. However, distribution shifts exist between different interaction environments (i.e., desktop vs. VR), and within the same interaction environment for different strokes by various users, making it challenging to build models capable of generalizing to unseen distributions. To address the challenges, we formulate the problem of generating VR strokes from desktop strokes as a conditional time series generation problem, aiming to learn representations that are capable of handling out-of-distribution data. We propose a novel architecture based on conditional generative adversarial networks, with the generator encompassing three steps: discretizing the output space, characterizing latent distributions, and learning conditional domain-invariant representations. We evaluate the effectiveness of our methods by comparing them with state-of-the-art time series generation models and conducting ablation studies. We further illustrate the applicability of the enriched VR datasets through two applications: VR stroke classification and stroke prediction.

In recent years, machine learning has demonstrated its potential in many challenging problems. In this paper, we extend its use to hardware formal property verification and propose DeepIC3, a method that takes advantage of graph learning in the classic IC3/PDR algorithm. In DeepIC3, graph neural networks are integrated to improve the result of local inductive generalization. This helps provide a global view of the state transition system and can potentially lead the algorithm out of local optima in the search of inductive invariants. Our experiments demonstrate that DeepIC3 accelerates the vanilla algorithm in nontrivial test cases of hardware model checking competition benchmarks (HWMCC2020) with up to 10. 8x speed-up. The proposed machine-learning integration preserves soundness and is universally applicable to various IC3/PDR implementations.

Recent years have seen significant improvement in absolute camera pose estimation, paving the way for pervasive markerless Augmented Reality (AR). However, accurate absolute pose estimation techniques are computation- and storage-heavy, requiring computation offloading. As such, AR systems rely on visual-inertial odometry (VIO) to track the device’s relative pose between requests to the server. However, VIO suffers from drift, requiring frequent absolute repositioning. This paper introduces MobileARLoc, a new framework for on-device large-scale markerless mobile AR that combines an absolute pose regressor (APR) with a local VIO tracking system. Absolute pose regressors (APRs) provide fast on-device pose estimation at the cost of reduced accuracy. To address APR accuracy and reduce VIO drift, MobileARLoc creates a feedback loop where VIO pose estimations refine the APR predictions. The VIO system identifies reliable predictions of APR, which are then used to compensate for the VIO drift. We comprehensively evaluate MobileARLoc through dataset simulations. MobileARLoc halves the error compared to the underlying APR and achieves fast (80ms) on-device inference speed.

Modern datacenter applications widely exhibit multicast communication patterns. Meanwhile, RDMA is emerging as the de-facto networking architecture to meet the stringent performance requirement of applications. However, existing multicast approaches fail to efficiently collaborate multicast with commodity RDMA transport, either causing inefficient multicast traffic transmission or being trapped in the insufficient end-host transport protocol. In this paper, we propose Cepheus, which delivers performance gains from both multicast (i.e., traffic reduction and transmission hop minimization) and RDMA transport (i.e., ultra-low latency, high throughput and low CPU overhead). Cepheus reuses RoCE as its transport layer and provides a RoCE-capable multicast primitive via in-network assistance. At its core, Cepheus builds on and goes beyond the native multicast architecture by exploiting more switch functionalities to tackle the incompatibilities between multicast flow structure and RoCE processing logic. We prototype Cepheus on an FPGA board, as a building block attached to an Ethernet switch. Extensive experiments demonstrate Cepheus inter-operates with commodity RoCE protocol and outperforms existing RDMA multicast schemes, e.g., 5.2 × faster multicast communication and 2.7 × higher replication throughput for distributed storage.

This paper reports on the demonstration of a 6.2 GHz acoustic filter with an over-1 GHz bandwidth, a 30 dB out-of-band rejection, and a sharp roll-off. The demonstrated filter employs the first-order antisymmetric (A1) lamb wave in Y-128° cut lithium niobate thin film to maximize the electromechanical coupling (k2t=48%). Amorphous Si is utilized to decrease the parasitic conductor effect and feedthrough for better out-of-band rejection of the filter. The ultrawide passband proves the promising potential of lithium niobate thin films in WIFI 7 and 6G applications.

This paper reports a novel high aspect ratio (height/width) vertical serpentine conductor with superior performance. The conductor was developed and manufactured through silicon-based MEMS fabrication technology. For the first time, the serpentine structure can be fully stretched into straight status while maintaining structural integrity and electrical performance. The experiment results show the vertical serpentine conductors possess up to 4000% stretchability, and the relative resistance varies within 1.3% under 3500% applied strain. In addition, the superior durability and stability of the conductor have been verified after 20,000 cyclic stretching and releasing at 3000% applied strain, where the relative resistance only changes within 4.6%.

Power-efficient ADCs are widely in demand in multi-channel high-resolution analog frontends (e.g., medical imaging), where the SAR architecture is highly favored due to its Nyquist-rate nature, excellent power efficiency, and compatibility with process scaling [1]-[5]. However, the kT/C noise becomes a key bottleneck in high-resolution SAR ADCs (>q 16b). To achieve lower kT/C noise, [2]-[5] increase the total capacitance (e.g., > 10pF for a 16b noise level with a 6.6Vpp swing) of the CDAC at the price of increased area and higher power. A kT/C noise cancelation technique is presented in [6] to reduce the kT/C noise with a small DAC. As shown in Fig. 1 (top-left), In this scheme, the input is connected to the DAC during autozeroing (AZ), requiring a short Taz to prevent the changing input from saturating the amplifier. A short Taz requires a high AZ bandwidth for proper settling, which inevitably aliases the amplifier's wideband noise, requiring a power-hungry low-noise pre-amplifier for high-resolution ADCs. This paper proposes a 16b SAR ADC with the split sampling (SS) that decouples the sampling and conversion operations to effectively address the above tradeoff, enabling a low AZ bandwidth and achieving low noise and low power. © 2024 IEEE.

This paper presents the ongoing research effort related to the design of the Data Management Infrastructure (DMI) to support experimental research on digital technologies with application to the ESFRI SLICES scientific instrument. We consider the experiment documentation and data collection across the whole continuum of access network, IoT, edge, cloud, and data processing workflow. The paper includes the requirements analysis for DMI to enable research reproducibility of complex and large-scale experimentation. We provide an analysis of data collected and processed in SLICES and explain approaches and solutions used in SLICES for experimental research reproducibility, primarily based on the plain orchestration service and supported by metadata collection tools. The proposed multi-layer DMI includes: data (storage) access, data processing, data ingest, experiment management, and virtual research environment. The paper also provides recommendations for the selection of existing standards and tools for data and metadata management, in particular those developed by EOSC and supported by the RDA community to ensure wide compatibility and integration.

Integrating variable energy resources plays a vital role in achieving energy sustainability and transitioning away from fossil fuels for a greener environment However numerous challenges hinder the widespread utilization of these resources A prominent challenge lies in their interface with the grid through power electronic devices necessitating intricate control systems to emulate conventional synchronous generators This paper addresses the integration challenges identifies current limitations in control methods and highlights the critical functionalities of virtual impedance and virtual synchronous generators Additionally the paper presents design considerations for virtual impedance and virtual synchronous machines aiming to identify the gaps in general guidelines and diverse approaches for implementing these technologies Finally the paper outlines future directions for the design of virtual impedance and virtual synchronous generators

DC microgrids have emerged as an efficient power system design approach in academia and industry offering the potential to integrate Distributed Energy Resources DERs even in challenging locations such as offshore platforms and rural villages The intermittent nature of DERs poses integration challenges for conventional power systems In contrast DC microgrids can accommodate these uncontrollable behaviours This paper provides a comprehensive overview of controlling techniques for DC microgrids focusing on centralized decentralized and distributed controlling methods Each controlling approach possesses unique advantages and disadvantages that must be carefully evaluated when selecting an appropriate control strategy This paper aims to provide researchers with a comprehensive understanding of the available controlling methods in terms of the communication architecture in DC microgrids facilitating informed decisionmaking on their respective research procedures

This paper presents the 6th edition of the Drone vs Bird Detection Grand Challenge organized within the 48th IEEE International Conference on Acoustics Speech and Signal Processing ICASSP Taking video samples recorded by commercial RGB cameras as input the challenge stimulates the design of advanced approaches capable of detecting the presence of small drones flying in a given area under surveillance Successful methods should ensure high detection rates while limiting the number of false alarms erroneously triggered in presence of very similar false targets birds The paper summarizes the novel methods proposed by the four participating teams that succeeded in providing satisfactory detection performance on the 2023 challenge dataset

With the increasing congestion in lower frequency bands of satellite spectrum the integration of cognitive radio technology is expected to become essential for efficient spectrum sharing in both Geostationary and Low Earth Orbit satellite systems Intelligent spectrum sharing strategies enable more efficient and effective satellite communications leading to increased capacity and improved performance In this paper we provide an overview of incorporating cognitive radio technology into dual satellite communications applications Moreover this paper presents a measurement apparatus for identifying spectral opportunities and presents preliminary results for such opportunities in a portion of the L band used by the Inmarsat Broadband Global Area Network downlink Numerous opportunities in both the time and frequency domains were identified for the spectrum surveyed The results revealed that almost half of the spectrum was available for over 99 of the capture duration with only 8 being available less than 1 of the time

We demonstrate ROX, a run-time optimizer of XQueries, that focuses on finding the best execution order of XPath steps and relational joins in an XQuery. The problem of join ordering has been extensively researched, but the proposed techniques are still unsatisfying. These either rely on a cost model which might result in inaccurate estimations, or explore only a restrictive number of plans from the search space. ROX is developed to tackle these problems. ROX does not need any cost model, and defers query optimization to run-time intertwining optimization and execution steps. In every optimization step, sampling techniques are used to estimate the cardinality of unexecuted steps and joins to make a decision which sequence of operators to process next. Consequently, each execution step will provide updated and accurate knowledge about intermediate results, which will be used during the next optimization round. This demonstration will focus on: (i) illustrating the steps that ROX follows and the decisions it makes to choose a good join order, (ii) showing ROX’s robustness in the face of data with different degree of correlation, (iii) comparing the performance of the plan chosen by ROX to different plans picked from the search space, (iv) proving that the run-time overhead needed by ROX is restricted to a small fraction of the execution time.

With the advances in Information Technology (IT) there has been an increase in threats to the communication systems and its assets. One of the most important issues of all IP networks that integrate wireless and wired technologies is the applicability and performance of the electronic identification and authentication methods. These schemes employ a variety of technologies of different degrees of security. Biometrics technology nowadays is typically considered a security necessity, tightly coupled with the foundation of highly secure identification and authentication solutions. This article provides a short survey on different types of authentication and identity management systems. It attempts to describe a summary of related works including some of the latest schemes in face and fingerprint recognition. With a better understanding of electronic authentication techniques any organization can properly select and utilize the technologies which meet its needs.

With the advances in Information Technology (IT) there has been an increase in threats to the communication systems and its assets. One of the most important issues of all IP networks that integrate wireless and wired technologies is the applicability and performance of the electronic identification and authentication methods. These schemes employ a variety of technologies of different degrees of security. Biometrics technology nowadays is typically considered a security necessity, tightly coupled with the foundation of highly secure identification and authentication solutions. This article provides a short survey on different types of authentication and identity management systems. It attempts to describe a summary of related works including some of the latest schemes in face and fingerprint recognition. With a better understanding of electronic authentication techniques any organization can properly select and utilize the technologies which meet its needs.

This research paper explores the potential of multisensory interaction design to enhance emotional engagement in virtual reality (VR) cinematic experiences. With VR technology becoming increasingly prevalent, there is a growing interest in creating immersive narratives that evoke powerful emotional responses. The challenge lies in effectively leveraging the multisensory capabilities of VR to deepen emotional engagement. This paper proposes a framework that combines theoretical foundations from emotional psychology, human-computer interaction, and sensory design to guide the development of multi-sensory interactions in VR cinematic experiences. This research aims to provide insights into the design principles, techniques, and considerations for creating emotionally impactful VR cinematic experiences through a comprehensive review of existing literature and case studies. © 2023 IEEE.

At present, the more widely used and mature neural network is Back Propagation (BP) neural network, which is a typical feed-forward network in artificial neural network, especially suitable for dealing with system nonlinearity, uncertainty and the identification function of approximating system or structural characteristics. Neural network-based control methods are widely used in various intelligent control systems and have a very broad development prospect. The application of neural networks in aerospace vehicles can effectively control their stability, anti-interference and convergence speed. The system performance is better adapted to different scenarios and the control accuracy is continuously improved. Therefore, this paper proposes a neural network-based adaptive control method for quadcopter, and the design of neural network-based adaptive control system is carried out based on the elaboration of neural network related theories. After simulation experiments, it is confirmed that the design in this paper has good tracking performance and small tracking error. And the application effect is good. © 2023 IEEE.

We propose a new blind watermarking algorithm for three-dimensional (3D) printed objects that has applications in metadata embedding, robotic grasping, counterfeit prevention, and crime investigation. Our method can be used on fused deposition modeling (FDM) 3D printers and works by modifying the printed layer thickness on small patches of the surface of an object. These patches can be applied to multiple regions of the object, thereby making it resistant to various attacks such as cropping, local deformation, local surface degradation, or printing errors. The novelties of our method are the use of the thickness of printed layers as a one-dimensional carrier signal to embed data, the minimization of distortion by only modifying the layers locally, and one-shot detection using a common paper scanner. To correct encoding or decoding errors, our method combines multiple patches and uses a two-dimensional (2D) parity check to estimate the error probability of each bit to obtain a higher correction rate than a naive majority vote. The parity bits included in the patches have a double purpose because, in addition to error detection, they are also used to identify the orientation of the patches. In our experiments, we successfully embedded a watermark into flat surfaces of 3D objects with various filament colors using a standard FDM 3D printer, extracted it using a common 2D paper scanner and evaluated the sensitivity to surface degradation and signal amplitude.

This paper proposes a novel method for generating arbitrary stereoscopic view from multiple omni directional image sequences. Although conventional methods for arbitrary view generation with an image-based rendering approach can create binocular views, positions and directions of viewpoints for stereoscopic vision are limited to a small range. In this research, we attempt to generate arbitrary stereoscopic views from omni directional image sequences that are captured in various multiple paths. To generate a high-quality stereoscopic view from a number of images captured at various viewpoints, appropriate ray information needs to be selected. In this paper, appropriate ray information is selected from a number of omni directional images using a penalty function expressed as ray similarity. In experiments, we show the validity of this penalty function by generating stereoscopic view from multiple real image sequences., ICPR 2010 : 20th International Conference on Pattern Recognition , Aug 23-26, 2010 , Istanbul, Turkey

In this paper, a barge-in free spoken dialogue system using sound field control and microphone array is proposed. In the conventional spoken dialogue system using an acoustic echo canceller, it is indispensable to estimate and update the room transfer function, especially when the transfer function is changed by various interferences. However the estimation process for the transfer function prevents the user from speaking freely and simultaneously with speech responses from the system. In order to resolve the problem, we have already proposed a barge-in free spoken dialogue system that controls a sound field using multiple loudspeakers. In this paper, a microphone array for acquisition of user's speech is newly introduced in the previously proposed system. By introducing the microphone array, we can reduce the number of loudspeakers to be required in the system, and make the interface for spoken dialogue system more robust against the change of room transfer functions., ICASSP2003: IEEE International Conference on Acoustics, Speech, and Signal Processing, April 6-10, 2003, Hong Kong, China.

This paper describes a new blind signal separation method using the directivity patterns of a microphone array. In this method, to deal with the arriving lags among each microphone, the inverses of the mixing matrices are calculated in the frequency domain so that the separated signals are mutually independent. Since the calculations are carried out in each frequency independently, the following problems arise: (1) permutation of each sound source, (2) arbitrariness of each source gain. In this paper, we propose a new solution that directivity patterns are explicitly used to estimate each sound source direction. As the results of signal separation experiments, it is shown that the proposed method improves the SNR of degraded speech by about 16 dB under non-reverberant condition. Also, the proposed method improves the SNR by 8.7 dB when the reverberation time is 184 ms, and by 5.1 dB when the reverberation time is 322 ms, ICASSP2000: IEEE International Conference on Acoustics, Speech, and Signal Processing, June 5-9, 2000, Istanbul, Turkey.

Deals with the recognition of distant talking speech and, particularly, with the simultaneous recognition of multiple sound sources. A problem that must be solved in the recognition of distant talking speech is talker localization. In some approaches, the talker is localized by using short- and long-term power. The 3-D Viterbi search based method proposed by Yamada et al.(1998), integrates talker localization and speech recognition. This method provides high recognition rates but its application is restricted to the presence of one talker. In order to deal with multiple talkers, we extended the 3-D Viterbi search method to a 3-D N-best search method enabling the recognition of multiple sound sources. The paper describes our baseline 3-D N-best search-based system and two additional techniques, namely, a likelihood normalization technique and a path distance-based clustering technique. The paper also describes experiments carried out in order to evaluate the performance of the system, ICASSP2001: IEEE International Conference on Acoustics, Speech and Signal Processing, May 7-11, 2001, Salt Lake City, Utah, US.

This paper describes a method for determining the vocal tract spectrum from articulatory movements using an hidden Markov models (HMMs). In the proposed system, articulatory parameters are generated from a TTS system and converted to acoustic features to be synthesized. Comparing with conventional GMM-based systems, the proposed system has two additional properties: 1) phonetic information given input texts is available for the conversion, 2) the use of HMMs allows us to utilize the temporal structure of speech. In this paper, we investigate the optimal structure of HMMs for the conversion. Experimental results show that using phonetic and temporal information can improve the mapping accuracy in a spectral distortion measure, ICASSP2006: IEEE International Conference on Acoustics, Speech, and Signal Processing, May 14-19, 2006, Toulouse, France.