There are demographic biases in current models used for facial recognition (FR). Our Balanced Faces In the Wild (BFW) dataset serves as a proxy to measure bias across ethnicity and gender subgroups, allowing one to characterize FR performances per subgroup. We show performances are non-optimal when a single score threshold is used to determine whether sample pairs are genuine or imposter. Across subgroups, performance ratings vary from the reported across the entire dataset. Thus, claims of specific error rates only hold true for populations matching that of the validation data. We mitigate the imbalanced performances using a novel domain adaptation learning scheme on the facial features extracted using state-of-the-art. Not only does this technique balance performance, but it also boosts the overall performance. A benefit of the proposed is to preserve identity information in facial features while removing demographic knowledge in the lower dimensional features. The removal of demographic knowledge prevents future potential biases from being injected into decision-making. This removal satisfies privacy concerns. We explore why this works qualitatively; we also show quantitatively that subgroup classifiers can no longer learn from the features mapped by the proposed.

Visual servo control uses images that are obtained by a camera for robotic control. This study focuses on the problem of positioning a target object using a robotic manipulator with image-based visual servo (IBVS) control. To perform the positioning task, the image-based visual servoing requires visual features that can be extracted from the appearance of the target object. Therefore, a positioning error tends to increase especially for textureless objects, such as industrial parts. Since it is difficult to extract differences of the visual features between current and goal images. To solve these problems, this paper presents a novel visual servoing named “Active Visual Servoing.” Active Visual Servoing (AVS) projects patterned light onto the target object using a projector. The design of the projection pattern affects the positioning error. AVS uses an optimal pattern which is theoretically derived and maximizes differences between current and goal images. The experimental results show that the proposed active visual servoing method reduces the positioning error by more than 97% compared to conventional image-based visual servoing.

A document by Eiman Kanjo . Click on the document to view its contents.

A study case about ferroresonance in secondary circuits of capacitive voltage transformer

We present novel multi-bit unit-cell topologies for reconfigurable reflective surfaces –RRSs– (e.g., reflectarray antennas) with compact designs for millimeter-wave and terahertz (mmWave/THz) applications. Typically, mmWave/THz RRSs utilize one or multiple single-pole-single-throw (SPST) switches leading to single- or dual-bit modulated surfaces. These surfaces utilize the switches to manipulate the phase of the imping waves, beamforming the reflected waves to the desired direction. As such, RRSs are leveraged either for imaging or wireless communication applications, which typically require the formation of a single beam (no grating lobes) and high gains. The gain and quantization lobe levels of an RRS is strictly related to the number of phase bits utilized in the unit-cell. Explicitly, more phase bits lead to lower quantization errors and better maximum gain/aperture efficiency. However, increasing the number of phase bits requires more SPST switches integrated within the unit-cell, leading to complex designs with high RF losses. Herein, we present, for the first time, RRSs with up to 4 phase quantization bits (16 states) that maintain one switch-per-bit topology thus retaining a low-complexity design. The proposed RRSs is presented alongside a series of analytical and full-wave simulated results.

Multipath TCP (MPTCP) has emerged as a facilitator for harnessing and pooling available bandwidth in wireless/wireline communication networks and in data centers. Existing implementations of MPTCP such as, Linked Increase Algorithm (LIA), Opportunistic LIA (OLIA) and BAlanced LInked Adaptation (BALIA) include separate algorithms for congestion control and packet scheduling, with pre-selected control parameters. We propose a Deep Q-Learning (DQL) based framework for joint congestion control and packet scheduling for MPTCP. At the heart of the solution is an intelligent agent for interface, learning and actuation, which learns from experience optimal congestion control and scheduling mechanism using DQL techniques with policy gradients. We provide a rigorous stability analysis of system dynamics which provides important practical design insights. In addition, the proposed DQL-MPTCPalgorithm utilizes the ‘recurrent neural network’ and integrates it with ‘long short-term memory’ for continuously i) learning dynamic behavior of subflows (paths) and ii) responding promptly to their behavior using prioritized experience replay. With extensive emulations, we show that the proposed DQL-based MPTCP algorithm outperforms MPTCP LIA, OLIA and BALIA algorithms. Moreover, the DQL-MPTCP algorithm is robust to time-varying network characteristics and provides dynamic exploration and exploitation of paths. The revised version is to appear in IEEE Trans. in Mobile Computing soon.

Geometry-based point cloud compression (G-PCC) has been rapidly evolving in the context of international standards. Despite the inherent scalability of octree-based geometry description, current G-PCC attribute compression techniques prevent full scalability for compressed point clouds. In this paper, we present a solution to add scalability to attributes compressed using the region-adaptive hierarchical transform (RAHT), enabling the reconstruction of the point cloud using only a portion of the original bitstream. Without the full geometry information, one cannot compute the weights in which the RAHT relies on to calculate its coefficients for further levels of detail. In order to overcome this problem, we propose a linear relationship approximation relating the downsampled point cloud to the truncated inverse RAHT coefficients at that same level. The linear relationship parameters are sent as side information. After truncating the bitstream at a point corresponding to a given octree level, we can, then, recreate the attributes at that level. Tests were carried out and results attest the good approximation quality of the proposed technique.

Ultra high-throughput satellite systems are expected to play an essential role in future beyond 5G and 6G networks. These systems must remain as flexible as possible to adapt to heterogeneous traffic demands, while also delivering the highest possible rate for dedicated services. Satellites flexible payloads are increasingly employing wideband output multiplexers. In this context, it is now more important than ever to evaluate frequency-dependent degradations on multicarrier signals. In particular, it is critical to characterize the distortions entailed by the output multiplexers filters. In this paper, models are presented and novel formulas are derived to determine the carrier-to-interference ratio resulting from these distortions. Derivations are oriented towards the applicability of either high-accuracy (e.g., for link budget) or low-complexity calculations (e.g., for real-time carrier allocation). The influence of key parameters such as the optimal decision instant, symbol rate and roll-off factor is thoroughly analyzed. Furthermore, formulas are evaluated in a practical scenario: the dynamic carrier allocation optimization. They are combined with efficient optimization algorithms to obtain the best performance based on user fairness. Relevant metrics such as accuracy, complexity and allocation gain are also investigated. In the end, the application of the proposed formulas and algorithms leads to a significant allocation gain that is increasing with the number of carriers. The feasibility of real-time dynamic carrier allocation to further increase the capacity of the next generation of satellite systems is emphasized.

Hamidreza. Amiri

A document by Yasmina Zaky . Click on the document to view its contents.

Currently, remote monitoring systems have evolvedto respond to the peculiar needs in the healthcare industry, suchas Cardiovascular diseases, which is an essential foundation in themodern concept of the smart city. We propose a system to monitorpatient current health conditions as a healthcare system basedon the widely spread available low-cost technologies, namely,GSM and Arduino platform. Statistics show that hypertensiveheart disease and blood pressure are risk factors for the highdeath rate. To decrease it, preventive measures should be appliedproviding a health monitoring system to save the patient life atan acceptable time. The objective of this paper is to developa prototype of a low-cost system, that is capable of performingremote monitoring of a patient’s heart rate and body temperaturein real-time and stores the data on the memory card using low-cost hardware such as Arduino, and also, using the servicesprovided by the GSM network, to enable communication witha health specialists cell phone upon request or if there areanomalies in the values measured by the sensors. The studyfocuses on heartbeat rate using the Difference Operation Method(DOM), described by Yeh & Wang, and body temperature, thus,in case of emergency, a short call is done, and then an SMS issent to the health specialist’s mobile containing measured values.Using this algorithm and after some tests using data from theMIT BIH Database distribution, we manage to find the bestsample frequency and eliminate the distortion of the signal. sample frequency and eliminate the distortion of the signal.

The proposed paper addresses how Support Vector Data Description (SVDD) can be used to detect safety regions with zero statistical error. It provides a detailed methodology for the applicability of SVDD in real-life applications, such as Vehicle Platooning, by addressing common machine learning problems such as parameter tuning and handling large data sets. Also, intelligible analytics for knowledge extraction with rules is presented: it is targeted to understand safety regions of system parameters. Results are shown by feeding data through simulation to the train of different rule extraction mechanisms.

Quantum entanglement is a primary feature of quantum mechanics and probably results from the quantum state's conservation and the quantum state's transition with the matter wave's phase velocity at the fast-than-light speed. The quantum state transition of entangled particles proceeds with the phase velocity, while the observer measures the process with the electromagnetic or the light speed. This speed difference makes the causality law no longer fully valid except in certain areas

Identification of static nonlinear elements (i.e., nonlinear elements whose outputs depend only on the present value of inputs) is crucial for the success of system identification tasks. Identification of static nonlinear elements though can pose several challenges. Two of the main challenges are: (1) mathematical models describing the elements being unknown and thus requiring black-box identification; and (2) collection of sufficiently informative measurements. With the aim of addressing the two challenges, we propose in this paper a method of predetermining informative measurement points offline (i.e., prior to conducting experiments or seeing any measured data), and using those measurements for online model calibration. Since we deal with an unknown model structure scenario, a high order polynomial model is assumed. Over fit and under fit avoidance are achieved via checking model convergence via an iterative means. Model dependent information maximization is done via a D-optimal design of experiments strategy. Due to experiments being designed offline and being designed prior to conducting measurements, this method eases off the computation burden at the point of conducting measurements. The need for in-the-loop information maximization while conducting measurements is avoided. We conclude by comparing the proposed D-optimal design method with a method of in-the-loop information maximization and point out the pros and cons. The method is demonstrated for the single-input-single-output (SISO) static nonlinear element case. The method can be extended to MISO systems as well.

Cross-chain exchange swaps assets among different blockchains, which facilitates cooperation among blockchains. A cross-chain exchange contains several transactions from different blockchains. It requires to synchronize asset transfers in those associated blockchains to avoid partial transfers. Current cross-chain methods are divided into two main types. One locks the assets first and transfers the frozen asset to receivers later. The second one is the two-phase or three-phase transaction commit protocol. Those methods require at least two steps, which makes those blockchains coherent to synchronize at different time(steps). Serialization is required as the second step has to wait for the completion of the first step, and even some steps in the same stage are required to be serialized. Meanwhile, their implements are either by smart contracts or special blockchain structures/roles. Smart contract based methods require to pre-deploy a smart contract and cannot change dynamically. Special structure or role-based methods force associated blockchains to have those special requirements. In this paper, we propose a new cross-chain exchange model based on the dependence of associated transactions, which is expressed inside a transaction and can change when sending transactions. It saves the step to lock the asset or perform a pre-commit and has no special requirements for blockchains or pre-deployment of smart contracts. The simulation results show the proposed model exchanges asset effectively among different blockchains.

The paper presents a mixed method approach for the analysis of power systems in augmented uncertainty scenarios, related to the increasing penetration of variable renewable energy and country specific constraints to be found in fragile states. In the formulated methodology, both deterministic and probabilistic load flow have their own specific, necessary and interactive role. To establish the soundness of the methodology, the analysis is conducted for a real case study, along with wind speed measurements (eleven-month duration), visual model validations, statistical and load flow analysis. The probabilistic simulations are based on Monte Carlo (MC) analysis. Synthetic data are created from probabilistic distribution functions (PDF) calculated on original measured samples, operational constraints, and load uncertainties. These data are processed by load flow simulations and the results consolidated and analyzed. To facilitate the implementation of the proposed methods, scripts developed in Python programming language have been created for the analysis of statistical data, sample generation, post processing, data visualization and the interaction with conventional software for load flow analysis. The scripts are made public and available for download. The proposed methodology of analysis, conceptualized for developing and fragile states, may also be used as a basis for all power system planning where the number of uncertainties is no longer negligible, and the use of deterministic methods alone would provide inadequate results.

we characterize the network structure of data such that federated mulit-task learning is possible.

The ability to perceive prosthetic grasping may enable amputees to better interact with external objects. This may require customized coding of multiple sensory feedback for each amputee. This study developed a protocol to determine optimal modulation ranges of sensations elicited by transcutaneous electrical nerve stimulation (TENS). These sensations that were referred to the lost fingers provided the possibility for restoring multi-modalities of sensory feedback for amputees with evoked tactile sensation (ETS) non-invasively. To match the restricted projected finger map area, smaller electrodes must be used to deliver electrical stimulation for multi-channel sensory information, which resulted in fewer types of sensations. Our protocol provided comprehensive information for optimal selection of amplitude and frequency in a personalized, pulse-width encoding paradigm. The good sensitivity for vibration and buzz in both able-bodied and amputee subjects suggested that perceptual intensity can be effectively modulated to convey sensory information via either of the sensations. The efficacy of this protocol in sensory coding for forearm amputees was demonstrated in finger-specific identification experiment. This protocol may allow customization of ETS-based sensory feedback with an optimal encoding strategy for individual amputees.