A document by Siamak Azadiabad . Click on the document to view its contents.

Proxy re-encryption (PRE), as a promising cryptographic primitive for secure data sharing in cloud, has been widely studied for decades. PRE allows the proxies to use the re-encryption keys to convert ciphertexts computed under the delegator’s public key into ones that can be decrypted using the delegatees’ secret keys, without knowing anything about the underlying plaintext. This delegable property of decryption rights gives rise to an important issue: if some proxies reveal their re-encryption keys, or collude with some delegatees to create a pirate decoder, then anyone who gains access to the pirate decoder can decrypt all ciphertexts computed under the delegator’s public key without the delegator’s permission. Several works have provided potential solutions to this issue by designing tracing mechanisms on PRE, where proxies that reveal their re-encryption keys can be identifified by the delegator. However,  these solutions perform poorly in terms of the sizes of the public, the secret and the re-encryption keys, and support neither multi-hop nor public traceability. This paper advances the research of tracing mechanisms on PRE and proposes the fifirst public trace-and-revoke PRE system, where the malicious delegatees involved in the generation of a pirate decoder can be identifified by anyone who gains access to the pirate decoder, and their decryption capabilities can subsequently be revoked by the content distributor. Our construction is multi-hop, supports user revocation and public (black-box) traceability, and achieves signifificant effificiency advantages over previous constructions. Our construction is a generic transformation from inner-product functional PRE (IPFPRE) that we introduce to trace-and-revoke PRE. In addition, we instantiate our generic construction of trace-and-revoke PRE from the Learning with Errors (LWE) assumption, which was widely believed to be quantum-resistant. This is achieved by proposing the fifirst LWE-based IPFPRE scheme, which may be of independent interest.

This paper examines the feasibility of blockchain solutions for national and transnational business-to-business and business-to-government (B2B/B2G) compliance frameworks, namely a trust-less, de-centralised, self-regulating distributed ledger. In particular, the paper examines whether blockchain platforms scale to support national and transnational e-business trading.

A tradeoff between the Quality of Control of a controlled plant and the thermal stress suffered by the controlling cyber platform is modeled under conditions allowing a small fraction of control update overruns. This paper has been submitted for possible publication to an IEEE journal.

This paper starts by reviewing demand response models in MISO’s energy management and market systems. It then discusses a recent enhancement that improves the ability of demand response to participate in MISO energy and ancillary service markets. Some future enhancements are also discussed.

Artificial Intelligence of Things (AIoT) which integrates artificial intelligence (AI) and the Internet of Things (IoT), has attracted increasing attention recently. With the remarkable development of AI, deep neural networks (DNNs) have achieved great success from research to deployment in many applications. However, deploying complex and state-of-the-art (SOTA) AI models on edge applications is increasingly a big challenge. This paper focuses on the convergence of AIoT-based edge devices, lightweight DNNs, and neural network compression. We provide a comprehensive analysis of them and many practical suggestions for researchers: how to obtain/design lightweight DNNs, how to select suitable AI edge devices, and how to compress and deploy them in practice. Finally, future trends and opportunities are presented, including trustworthy AI, federated learning, and robust deployment.

Compressive sensing (CS) is quite appealing as a low-complexity method for the compression of ECG data in resource-limited wearable devices. We propose a codec architecture comprising adaptive quantization and asymmetric numeral systems (ANS) based entropy coding of compressive measurements that can boost the compression ratio without sacrificing reconstruction performance. The quantized Gaussian entropy model for the compressive measurements is estimated directly from the data and is adapted dynamically to achieve better compression. We have tested our encoder with Block Sparse Bayesian learning as well as CS-NET sparse recovery algorithms on the MIT-BIH Arrhythmia database. Our encoder can achieve 5-25\% of additional space savings over compressive sensing. The software code implementing this codec and all scripts for the experimental studies conducted in this work have been released as opensource software on GitHub.

In this study, a deep neural network (DNN) for a massive multiple-input-multiple-output (mMIMO) nonorthogonal multiple access (NOMA) system’s channel estimation and detection is presented. The proposed network applies deep learning (DL) to a discrete wavelet transform (DWT) based orthogonal frequency-division-multiplexing (OFDM) to reduce the noise and inter-channel interference (ICI) while maintaining compatibility with the existing networks. The transmission overhead is reduced because the presented network is trained to estimate and detect symbols with and without pilot signals. The results of this study demonstrate that our provided DL-based successive interference cancellation (SIC) method performs better than the conventional SIC-based signal detection in the mMIMONOMA network. The suggested wavelet-based mMIMO-NOMA is also compared to the traditional fast-Fourier-transform (FFT) based mMIMO-NOMA, particularly in terms of symbol error rate (SER).

The 2x-thru calibration has emerged as an attractive alternative to the classical TRL for S parameter measurement of printed circuit board and packaging devices, due to its simplicity that only one THRU standard suffices to fully characterize the test fixtures, thus eliminating the LINE measurement in TRL and also overcoming the bandwidth limit. Although various 2x-thru calibration tools are available nowadays, there are still some issues that have not been studied in detail. This paper addresses the following two fundamental questions: (i) what is the reference impedance (Zref) of the S parameters after 2x-thru calibration? (ii) what is the time-domain reflectometry (TDR) response of the THRU for high conductor-loss transmission lines, and how to estimate Zref given such TDR? For problem (i), we rigorously show that the Zref is equal to the characteristic impedance of the center transmission line in the THRU standard, and provide the important interpretation for the S parameters in terms of the traveling-wave and pseudowave concepts. For problem (ii), we show that the TDR impedance is a monotonically increasing function instead of constant, and we propose a new method to estimate the characteristic impedance by fitting the TDR with a causal impedance model. Simulation and measurement examples are provided to validate the proposed theory and techniques. In the last example, we also compare five commercial/open-source 2x-thru calibration tools; the results reveal that with the proposed method of impedance estimation, better accuracy can be achieved.

This paper proposes an inflow scenario reduction framework applied in long-term hydro-thermal scheduling. The strategic management of limited stored hydro energy in interaction with the electricity system is defined in long-term hydro-thermal. The Scenario Fan Problem (SFP) is selected as a long-term hydro-thermal scheduling approach. This approach is suitable to represent the disaggregated physical representation of the hydro stations  and captures the short-term flexibility of individual hydropower plants in the long-term scheduling. We develop a shape-based feature extraction machine learning method to capture the hydro inflow features. To highlight the performance, we compare the shape-based  method with five alternative clustering methods via the validation process on a hydro-thermal test case, using in-sample and out-of-sample data analysis. Our results show that the proposed framework can significantly reduce the computational time of long-term hydro-thermal scheduling by around 90\%. Results reveal that the shape-based feature extraction performs better regarding the computational time and the expected reservoir volumes. Moreover, this study demonstrates the possibility of performing long-term hydro-thermal scheduling at a disaggregated level with detailed topology information and a small time scale. This provides more opportunities to deal with the penetration of renewable energy resources in long-term hydro-thermal scheduling.

This paper presents a new design of diverse frequency time-modulation for linear frequency modulated (LFM) radar passive false target spoofing. To realize the passive diverse frequency time-modulation, the modulated metasurface is designed accordingly, which is composed by a set of elements, each of which is independently time-modulated to realize specific false targets in the high-resolution range profile (HRRP) of the radar system. Thanks to the control of the modulation scheme in each element and the combined response of the overall surface, we demonstrate the capability to realize several false target patterns consisting of single and multiple targets symmetrically, or asymmetrically, distributed around the actual target. A HRRP general expression of the time modulated diverse frequency metasurface at different observation angles is given to show the relation between the generated false targets and frequency components. Since having more flexible capacity of reflected signal manipulation comparing to false target spoofing only through time modulation with consistent modulation frequency, the proposed method has enhanced performance on control of false targets at different observation angles. Both simulated and measured results demonstrate the validity of false targets manipulation, demonstrating its effectiveness as electromagnetic countermeasure for achieving radar camouflaging and deception.

With the usage of machine learning (ML) algorithms in modern-day network intrusion detection systems (NIDS), contemporary network communications are efficiently protected from cyber threats. However, these ML algorithms are starting to be compromised by adversarial attacks that ambush the ML pipeline. In this paper, we demonstrate the feasibility of an adversarial attack called the Cosine Similarity Label Manipulation (CSLM), which is geared toward compromising training labels for ML-based NIDS, and how they can affect ML pipelines. We demonstrate the efficacy of the attacks towards both single and multi-controller software-defined network (SDN) setups. Results indicate that the proposed attacks provide substantial deterioration of classifier performance in single SDNs, specifically, those that utilize RF, which deteriorates  ~50% under Min-CSLM attacks, and SVMs, which undergo ~60% deterioration from a Max-CSLM attack. We also note that RF, SVM, and MLP classifiers are also extensively vulnerable to these attacks in MSDNs as they incur the most observed utility deterioration. MLP-based uniform multi-controller setups (MSDN) incur the most deterioration under both proposed CSLM attacks with ~28% decrease in performance, while SVM and RF-based variable MSDNs incur the most deterioration under both CSLM attacks with ~30% and ~35% decrease in performance, respectively.

Surrogate-assisted evolutionary algorithms (SAEAs) are a promising approach for solving expensive multiobjective optimization problems, but they often cannot address high-dimensional problems. Although one common approach to this challenge is to construct reliable surrogates, their accuracy inevitably deteriorates in a high-dimensional search space. Thus, this paper presents a novel SAEA based on scalarization function approximation, which is designed to strengthen its robustness against this deterioration. The proposed algorithm constructs an approximation model for each scalarization function defined in a decomposition-based framework. Each decomposed problem is then solved using multiple independent models trained for its neighbor problems. The intent is to decrease the risk of search performance degradations caused by unreliable approximations and retain the redundancy of the surrogate-assisted search to hedge the risk of over-fitting. Furthermore, each approximation model is adapted to a promising region of its corresponding decomposed problem to reduce the complexity of model fitting given a limited number of training samples. Experimental results show that the proposed algorithm is competitive with state-of-the-art SAEAs adapted for high-dimensional problems.

A bio-inspired novel CAT-leap parkour rolling mechanism (CPRM) is designed and developed for mobile robots. It was inspired by cat-leap jumping and monkey rolling motion. It enhanced the mobility of the mobile robot. Using CPRM, the robot can climb and cross unknown obstacles as well as perform locomotion. This mechanism protects the robot from unexpected impact forces on the robot during climbing and landing. It also can cross obstacles having double robot height. We developed a CPRM with a six-wheeled triangle-shaped mobile robot having two cat-leap arms for parkour rolling motion, allows a soft landing. We would like to share our experience working with CPRM mechanism and design from the inception to the realization, which includes design and iterations, prototype development, feasibility, functioning steps, advantages, and future applications.

Augmented reality-based brain-computer interface (AR-BCI) system is one of the important ways to promote BCI technology outside of the laboratory due to its portability and mobility, but its performance in real-world scenarios has not been fully studied. In current study, we first investigated the effect of ambient brightness on AR-BCI performance. 5 different light intensities were set as experimental conditions to simulate typical brightness in real scenes, while the same steady-state visual evoked potentials (SSVEP) stimulus was displayed in the AR glass. The data analysis results showed that SSVEP can be evoked under all 5 light intensities, but the response intensity became weaker when the brightness increased. The recognition accuracies of SSVEP were negatively correlated to light intensity, the highest accuracies were 89.35% with FBCCA and 83.33% with CCA under 0 lux light intensity, while they decreased to 62.53% and 49.24% under 1200 lux. In addition, we designed a new SSVEP recognition algorithm, named ensemble online adaptive CCA (eOACCA), to solve the accuracy loss problem in high ambient brightness. The main strategy of eOACCA is to provide initial filters for high-intensity data by iteratively learning low-light-intensity SSVEP data. The experimental results showed that the eOACCA algorithm had significant advantages under higher light intensities (>600 lux). Compared with FBCCA, the accuracies of eOACCA under 1200 lux was increased by 12.83%. In conclusion, current study contributed to the in-depth understanding of the performance variations of AR-BCI under different lighting conditions, and was helpful to promote the AR-BCI application in complex lighting environments.

A document by Carlos Baena . Click on the document to view its contents.

The manuscript explains how an error in synchronization can create wrong length and wrong time interval observations and describes with an example how the same gets corrected when the error in sychronization is removed by analysis. After the error is corrected, the wrong observations disappear and the correct obsrvations are observable.

When developing deep learning models for melody extraction, MedleyDB is a must-have dataset owing to its scale, diverse genres, and quality. However, it has no ground-truth labels for vocal melody. At the same time, none of existing methods for vocal melody labeling is able to accurately label vocal melody. In this paper we propose a simple, correct method for this task. Through extensive experiment, we evaluate the influence of labeling on the performance of models for vocal melody extraction, and find that in most cases the proposed method can boost the performance. The proposed method can accelerate research in relevant areas.