Phase retrieval, in particular for the operators arising from near-field measurements, is a non-convex task suffering from a severe lack of reliability due to local minima and false solutions. Approaches to tackle this issue, in turn, suffer from strict and often unrealistic oversampling requirements and possibly unfeasible computational complexities---in particular when larger scenarios are considered. In this paper, we analyze the sampling requirements for convex phase retrieval based on partially coherent observations which are captured with multi-probe arrays. We discuss requirements for the orientation and positioning of the probe antennas in the probe arrays. Following these conditions, a recently introduced linearized method is able to reconstruct a unique global solution reliably. The theoretical deliberations are corroborated with simulated near-field data.

Data-driven approaches for molecular diagnostics are emerging as an alternative to perform an accurate and inexpensive multi-pathogen detection. A novel technique called Amplification Curve Analysis (ACA) has been recently developed by coupling machine learning and real-time Polymerase Chain Reaction (qPCR) to enable the simultaneous detection of multiple targets in a single reaction well. However, target classification purely relying on the amplification curve shapes currently faces several challenges, such as distribution discrepancies between different data sources of synthetic DNA and clinical samples (i.e., training vs testing). Optimisation of computational models is required to achieve higher performance of ACA classification in multiplex qPCR through the reduction of those discrepancies. Here, we proposed a novel transformer-based conditional domain adversarial network (T-CDAN) to eliminate data distribution differences between the source domain (synthetic DNA data) and the target domain (clinical isolate data). The labelled training data from the source domain and unlabelled testing data from the target domain are fed into the T-CDAN, which learns both domains’ information simultaneously. After mapping the inputs into a domain-irrelevant space, T-CDAN removes the feature distribution differences and provides a clearer decision boundary for the classifier, resulting in a more accurate pathogen identifi- cation. Evaluation of 198 clinical isolates containing three types of carbapenem-resistant genes (blaNDM , blaIMP and blaOXA-48 ) illustrates a curve-level accuracy of 93.1% and a sample- level accuracy of 97.0% using T-CDAN, showing an accuracy improvement of 20.9% and 4.9% respectively, compared with previous methods. This research emphasises the importance of deep domain adaptation to enable high-level multiplexing in a single qPCR reaction, providing a solid approach to extend qPCR instruments’ capabilities without hardware modification in real- world clinical applications.

Magnetic induction tomography based artificial robotic skin. Showing the imaging results and also quantification of force of touch to skin.

This paper, submitted to IEEE Transactions on Geoscience and Remote Sensing, discusses ways to quantify the light collection by a camera, which is critical for the signal to noise ratio of the imagery. Based on a generalized analysis of a camera from an outside “black box” perspective, a preferred metric for light collection is identified, denoted A*. It is argued that this quantity can serve as a comparative figure of merit for cameras, as well as an element of image metadata. The work addresses conventional cameras as well as hyperspectral cameras. The proposed metric is part of the draft standard for hyperspectral imaging being developed by IEEE SA P4001.

Future private and public transportation will be dominated by Autonomous Vehicles (AV), which are potentially safer than regular vehicles. However, ensuring good performance for the autonomous features requires fast processing of heavy computational tasks. Providing each AV with powerful enough computing resources is certainly a practical solution but may result in increased AV cost and decreased driving range. An alternative solution being explored in research is to install low-power computing hardware on each AV and offload the heavy tasks to powerful nearby edge servers. In this case, the AV’s reaction time depends on how quickly the navigation tasks are completed in the edge server. To reduce task completion latency, the edge servers must be equipped with enough network and computing resources to handle the vehicle demands. However, this demand shows large spatio-temporal variations. Thus, deploying the same amount of resources in different locations may lead to unnecessary resource over-provisioning. Taking these challenges into consideration, in this paper, we discuss the implications of deploying different amounts of resources in different city areas based on real traffic data to sustain peak versus average demand. Because deploying edge resources to handle the average demand leads to lower deployment costs and better system utilization, we then investigate how peak-hour demand affect the safe travel time of AVs and whether current turn-by-turn navigation apps would still provide the fastest travel route. The insights and findings of this paper will inspire new research that can considerably speed up the deployment of edge-assisted AVs in our society.

Temperature is an important factor for evaluating the seismic response of deep reservoirs. We develop an amplitude variation with offset (AVO) approximation based on the Lord-Shulman (LS) thermoelasticity theory. The model predicts two compressional (P and T) waves (the second is a thermal mode) and a shear (S) wave. The T mode is due to the coupling between the elastic and heat equations. In the thermoelastic case, the approximation is more accurate than in the elastic case. Its accuracy is veried by comparison with the exact equations calculated in terms of potential functions. We examine two reservoir models with high temperatures and compute synthetic seismograms that illustrate the reliability of the approximation. Moreover, we consider real data to build a model, and show that the approximate equation not only simplies the calculations, but is accurate enough and can be used to evaluate the temperature-dependent elastic properties, providing a basis for further application of the thermoelasticity theory, such as geothermal exploration, thermal enhanced oil recovery, and ultra-deep oil and gas resources subject to high temperatures. Temperature is an important factor for evaluating the seismic response of deep reservoirs. We develop an amplitude variation with offset (AVO) approximation based on the Lord-Shulman (LS) thermoelasticity theory. The model predicts two compressional (P and T) waves (the second is a thermal mode) and a shear (S) wave. The T mode is due to the coupling between the elastic and heat equations. In the thermoelastic case, the approximation is more accurate than in the elastic case. Its accuracy is veried by comparison with the exact equations calculated in terms of potential functions. We examine two reservoir models with high temperatures and compute synthetic seismograms that illustrate the reliability of the approximation. Moreover, we consider real data to build a model, and show that the approximate equation not only simplies the calculations, but is accurate enough and can be used to evaluate the temperature-dependent elastic properties, providing a basis for further application of the thermoelasticity theory, such as geothermal exploration, thermal enhanced oil recovery, and ultra-deep oil and gas resources subject to high temperatures.

Let $\mathcal A$ be the classical, singly-graded Steenrod algebra over the prime order field $\mathbb F_2$ and let $P^{\otimes h}: = \mathbb F_2[t_1, \ldots, t_h]$ denote the polynomial algebra on $h$ generators, each of degree $1.$ Write $GL_h$ for the usual general linear group of rank $h$ over $\mathbb F_2.$ Then, $P^{\otimes h}$ is an $\mathcal A[GL_h]$-module. As is well known, for all homological degrees $h \geq 6$, the cohomology groups ${\rm Ext}_{\mathcal A}^{h, h+\bullet}(\mathbb F_2, \mathbb F_2)$ of the algebra $\mathcal A$ are still shrouded in mystery. The algebraic transfer $Tr_h^{\mathcal A}: (\mathbb F_2\otimes_{GL_h}{\rm Ann}_{\overline{\mathcal A}}[P^{\otimes h}]^{*})_{\bullet}\longrightarrow {\rm Ext}_{\mathcal A}^{h, h+\bullet}(\mathbb F_2, \mathbb F_2)$ of rank $h,$ constructed by W. Singer [Math. Z. \textbf{202} (1989), 493-523], is a beneficial technique for describing the Ext groups. Singer’s conjecture about this transfer states that \textit{it is always a one-to-one map}. Despite significant effort, neither a complete proof nor a counterexample has been found to date.  The unresolved nature of the conjecture makes it an interesting topic of research in Algebraic topology in general and in  homotopy theory in particular. \medskip The objective of this paper is to investigate Singer’s conjecture, with a focus on all $h\geq 1$ in degrees $n\leq 10 = 6(2^{0}-1) + 10\cdot 2^{0}$ and for $h=6$ in the general degree $n:=n_s=6(2^{s}-1) + 10\cdot 2^{s},\, s\geq 0.$ Our methodology relies on the hit problem techniques for the polynomial algebra $P^{\otimes h}$, which allows us to investigate the Singer conjecture in the specified degrees. Our work is a continuation of the work presented by Mothebe et al. [J. Math. Res. \textbf{8} (2016), 92-100] with regard to the hit problem for $P^{\otimes 6}$ in degree $n_s$, expanding upon their results and providing novel contributions to this subject. More generally, for $h\geq 6,$ we show that the dimension of the cohit module $\mathbb F_2\otimes_{\mathcal A}P^{\otimes h}$ in degrees $2^{s+4}-h$ is equal to the order of the factor group of $GL_{h-1}$ by the Borel subgroup $B_{h-1}$ for every $s\geq h-5.$ Especially, for the Galois field $\mathbb F_{q}$ ($q$ denoting the power of a prime number), based on Hai’s recent work [C. R. Math. Acad. Sci. Paris \textbf{360} (2022), 1009-1026], we claim that the dimension of the space of the indecomposable elements of $\mathbb F_q[t_1, \ldots t_h]$ in general degree $q^{h-1}-h$ is equal to the order of the factor group of $GL_{h-1}(\mathbb F_q)$ by a subgroup of the Borel group $B_{h-1}(\mathbb F_q).$ As applications, we establish the dimension result for the cohit module $\mathbb F_2\otimes_{\mathcal A}P^{\otimes 7}$ in degrees $n_{s+5},\, s > 0.$ Simultaneously, we demonstrate that the non-zero elements $h_2^{2}g_1 = h_4Ph_2\in {\rm Ext}_{\mathcal A}^{6, 6+n_1}(\mathbb F_2, \mathbb F_2)$ and $D_2\in {\rm Ext}_{\mathcal A}^{6, 6+n_2}(\mathbb F_2, \mathbb F_2)$ do not belong to the image of the sixth Singer algebraic transfer, $Tr_6^{\mathcal A}.$ This discovery holds significant implications for Singer’s conjecture concerning algebraic transfers. We further deliberate on the correlation between these conjectures and antecedent studies, thus furnishing a comprehensive analysis of their implications.

Fully Homomorphic Encryption (FHE) is a powerful encryption system in cloud computing that allows homomorphic computations on encrypted data without decrypting them. Multi-key fully homomorphic encryption (MFHE), as an extension to FHE, allows homomorphic computations on ciphertexts encrypted under different keys.   However, most MFHE schemes require a Common Random\slash Reference String (CRS), while the few that do not are based on the Learning With Errors (LWE) problem, which means that they can only deal with single bit plaintext.  Consequently, MFHE schemes based on the Ring Learning With Errors (RLWE) problem are more desirable, as they can handle polynomial plaintext.  Requiring the CRS seems to weaken the semantic definition of MFHE, where all users generate their own keys independently. In this paper, we study the RLWE-based MFHE in the CRS model and propose the first RLWE-based MFHE without CRS.   To this end, we remove the CRS by designing a new relinearization algorithm.  Like previous MFHE schemes, our RLWE-based MFHE without CRS has a simple 1-round threshold decryption, which implies a $3$-round secure MPC protocol in the plain model from the RLWE assumption.

This paper presents an H-plane SIW horn antenna whose directivity is enhanced using holey unit cells along the horn flaring. By wisely drilling the horn antenna, it is possible to reduce the phase error in the aperture which is a common problem in horn antennas if the optimum dimensions are not employed. An analysis of the distribution of the unit cells along the horn antenna has been carried out to achieve the desired equivalent refractive indices. By changing the hole radius, different equivalent refractive indices can be tuned with a wideband performance. This fact enables the implementation of a collimation zone inside the horn antenna which transforms the pseudo-circular wavefront into a quasi-planar one in the radiating aperture. The produced directivity is similar to the horn antenna with the optimum dimensions but a longitudinal reduction of 53.7% and a higher realized gain are achieved. A holey SIW horn antenna is designed and manufactured. The measured results show an impedance bandwidth performance below -10 dB from 34.3 GHz to 44.5 GHz (25.9%) with a realized gain above 10 dBi. The gain difference regarding a SIW horn antenna without the collimation zone is about 2-3 dBi in the operating frequency range.

Database performance is one of the main components in supporting the sustainability of a system in this case is Student’s Activity Record System (STARS). One way to improve the performance of the database is to use the concept of a partition table. Thus, in this study, the design and evaluation of the partition table will be carried out which will then be applied to the Satya Wacana Christian University (SWCU) STARS database. The results of this study indicate that partition tables have better performance than non-partitioned tables seen from several SQL syntaxes like update, delete, and select. Meanwhile, insert performs poorly for partitioned tables compared to non-partitioned tables.

The evolution of communication system to the next generation e.g. B5G and 6G demands an ultra-reliable performance regardless of weather conditions. Such ultra-reliable system design will require that that the effects of short-term adverse weather events on communication system has to be modelled more accurately so that system design should be dynamically adaptive to such events. The performance of satellite link is severely affected by dynamic rain attenuation and thus an accurate and reliable modelling of performance parameters is essential for dynamic fade countermeasures especially above 10 GHz. In this work, we model the energy per bit to noise spectral density ratio ($E_b/N_0$) using Gaussian Mixture (GM) model during rainy events. The developed mathematical expression is used to accurately model the bit error rate (BER), outage probability and channel capacity of the link. The average BER, upper bound on BER and average ergodic capacity of an M-ary phase shift keying scheme (MPSK) using the GM model of $E_b/N_0$ is derived to evaluate the performance of the link under such short-term weather impairments. We then show the numerical results and analysis using the GM model of the measured $E_b/N_0$ data obtained with AMoS-7 satellite at a site located in Israel.

The Deutsch-Jozsa problem is for a database of size N, where, according to their claim, the deterministic solution of the problem on a classical Turing computer requires O(N) computational complexity. They produced the famous Deutsch-Jozsa quantum algorithm that offered an exponential speedup over the envisioned classical computer, namely O[log(N)] complexity in a quantum computer. In the present paper, the Deutsch-Jozsa problem is implemented by two different algorithms on a Turing machine. It is shown that, similarly to the quantum algorithm, the Deutsch-Jozsa problem can deterministically be solved with O[log(N)] complexity on a classical computer with these algorithms.

Spectroelectrochemical (SEC) measurements are widely applied in analytical chemistry. Transparent or semitransparent electrodes are typically used for optical analysis of the electrochemical (EC) processes, where mainly EC readout indicates the state of the electrode while changes in transmitted optical spectrum help to identify products of the EC reactions. In this work, we propose SEC measurements enhanced by additional optical monitoring of the electrode. In this setup, an optical fiber sensor or rather a section of the polymer-clad silica multimode fiber core coated with a thin conductive oxide, i.e., indium tin oxide (ITO), acts also as the electrode. The properties of the film were appropriately tailored to give rise to the lossy-mode resonance (LMR) phenomenon and therefore, except for the SEC readouts, optical monitoring of the electrode was simultaneously possible. The LMR response depends on the properties of the ITO and the optical properties of the surrounding medium. Thus, in the proposed setup, three interrogation readouts may be received, i.e., EC, optical spectrum corresponding to the volume of the analyte as for standard SEC, and the LMR spectrum dependent on the state of the sensor/electrode surface. It must be noted that the optical measurements are performed in two, separate, perpendicularly arranged, spectrophotometric paths with the EC activation (S2EC). In this work, the three readouts have been correlated and compared to the application of other working electrodes applied in standard SEC, such as platinum mesh and ITO-coated glass slide. First, every interrogation path was investigated individually during the cyclic voltammetry (CV) with two metal ion oxidation-reduction reaction (redox) probes, such as potassium ferricyanide and methylene blue. Then simultaneous measurements during chronoamperometry (CA) were conducted with the sensor and cross-correlation between the readouts had been discussed. By combining the sensing techniques an enhancement in the amount of gathered information about the analyte has been achieved.

Multicast/broadcast delivery is a critical challenge of future sixth-generation (6G) mobile networks where massive internet of things deployment and extended reality multimedia such as teleportation are target application scenarios. Non-terrestrial networks (NTNs) are considered essential for the success of 6G, which aims to provide true “global” services by extending mobile access worldwide, thus overcoming the coverage limit of current terrestrial networks (TNs). This paper discusses how the main distinguishing features of NTNs can be effectively exploited for 6G multicasting. Furthermore, in line with the evolution of future 6G networks toward softwarized systems, we evaluate the potential of using the softwarization paradigm in the heterogeneous TN/NTN architecture to deliver multicast services.

In order to accurately compute scattered and radiated fields in the presence of arbitrary excitations, a low-frequency stable discretization of the right-hand side (RHS) of a quasi-Helmholtz preconditioned electric field integral equation (EFIE) on multiply-connected geometries is introduced, which avoids an ad-hoc extraction of the static contribution of the RHS when tested with solenoidal functions. To obtain an excitation agnostic approach, our approach generalizes a technique to multiply-connected geometries where the testing of the RHS with loop functions is replaced by a testing of the normal component of the magnetic field with a scalar function. To this end, we leverage orientable global loop functions that are formed by a chain of Rao-Wilton-Glisson (RWG) functions around the holes and handles of the geometry, for which we introduce cap surfaces that allow to uniquely define a suitable scalar function. We show that this approach works with open and closed, orientable and non-orientable geometries. The numerical results demonstrate the effectiveness of this approach.

As renewable energy penetration on the grid increases, requirements are being placed on PV owners and operators to limit power ramp rates. PV power ramping is an issue for grid stability since generation-load balance must be continually met, and when a large PV resource significantly increases or decreases, another resource must compensate to ensure matching. Traditional dispatchable resources have a limited ability to respond quickly. Therefore, to aid in grid stability, ramp rate limitations have been imposed on PV plants. This work addresses the question of how much fast-responding storage is needed to mitigate high ramp rates of PV plants, and how much benefit is there from short-term power forecasting in terms of reducing the storage requirement. The results provide a baseline estimate for system planners and designers. Furthermore, the storage controller design and optimization are given, along with the open-source code, such that others can tailor the simulation to their specific plant and weather profile. results from studying a 100 MW PV plant power production profile show a reduction in ramp-rate violations from 10% of yearly intervals to below 1% with 12 minutes of storage. With forecasting, the same level of smoothing is achieved with a 5-minute rated storage. A sensitivity analysis shows the impacts of varying constraints, such as storage power rating, PV system size for geographic smoothing, forecast window length, and the ramp-rate limit magnitude.

In this work, we propose using deep neural 2D networks, which have renowned generalization performance on unseen data. It specifically explains the use of Novel Augmentation technique for SMI Fringe detection and classification. Overall, it highlights the importance of data augmentation in computer vision algorithms. The results prove that computer vision can be used to cater the problems in signal processing and photoelectronics domain.

A central aspect of the Android platform is Inter-Component Communication (ICC), which enables the reuse of functionality across apps and components via message passing. While a powerful feature, ICC still constitutes a serious attack surface. This paper addresses the issue of generating exploits for a subset of Android ICC vulnerabilities (i.e., IDOS, XAS, and FI) through static analysis, Deep Reinforcement Learning-based dynamic analysis and software instrumentation. Our approach, called RONIN, achieves better results than state-of-the-art and baseline tools, in the number of exploited vulnerabilities.