In modern cryptographic systems, physical unclonable functions (PUFs) are efficient mechanisms for many security applications, which extract intrinsic random physical variations to generate secret keys. The classical PUFs mainly exhibit static challenge-response behaviors and generate static keys, while many practical cryptographic systems need reconfigurable PUFs which allow dynamic keys derived from the same circuit. In this paper, the concept of reconfigurable multi-port PUFs (RM-PUFs) is proposed. RM-PUFs not only allow updating the keys without physically replacement, but also generate multiple keys from different ports in one clock cycle. A practical RM-PUFs construction is designed based on asynchronous clock and fabricated in TSMC low-power 65 nm CMOS process. The area of test chip is 1.1 mm2, and the maximum clock frequency is 0.8 GHz at 1.2 V. The average power consumption is 27.6 mW at 27. Finally, test results show that the RM-PUFs generate four reconfigurable 128-bit secret keys, and the keys are secure and reliable over a range of environmental variations such as supply voltage and temperature.

The equilibrium optimizer (EO) is a novel physics-based meta-heuristic optimization algorithm that is inspired by estimating dynamics and equilibrium states in controlled volume mass balance models. As a stochastic optimization algorithm, EO inevitably produces duplicated solutions, which is wasteful of valuable evaluation opportunities. In addition, an excessive number of duplicated solutions can increase the risk of the algorithm getting trapped in local optima. In this paper, an improved EO algorithm with a bis-population-based non-revisiting (BNR) mechanism is proposed, namely BEO. It aims to eliminate duplicate solutions generated by the population during iterations, thus avoiding wasted evaluation opportunities. Furthermore, when a revisited solution is detected, the BNR mechanism activates its unique archive population learning mechanism to assist the algorithm in generating a high-quality solution using the excellent genes in the historical information, which not only improves the algorithm's population diversity but also helps the algorithm get out of the local optimum dilemma. Experimental findings with the IEEE CEC2017 benchmark demonstrate that the proposed BEO algorithm outperforms other seven representative meta-heuristic optimization techniques, including the original EO algorithm.

Many optimisation algorithms improve the algorithm from the perspective of population structure. However, most improvement methods simply add hierarchical structure to the original population structure, which fails to fundamentally change its structure. In this paper, we propose an umbrellalike hierarchical artificial bee colony algorithm (UHABC). For the first time, a historical information layer is added to the artificial bee colony algorithm (ABC), and this information layer is allowed to interact with other layers to generate information. To verify the effectiveness of the proposed algorithm, we compare it with the original artificial bee colony algorithm and five representative meta-heuristic algorithms on the IEEE CEC2017. The experimental results and statistical analysis show that the umbrellalike mechanism effectively improves the performance of ABC.

Visual quality evaluation is crucially important for various video and image processing systems. Traditionally, subjective image quality assessment (IQA) given by the judgments of people can be perfectly consistent with human visual system (HVS). However, subjective IQA metrics are cumbersome and easily affected by experimental environment. These problems further limits its applications of evaluating massive pictures. Therefore, objective IQA metrics are desired which can be incorporated into machines and automatically evaluate image quality. Effective objective IQA methods should predict accurate quality in accord with the subjective evaluation. Motivated by observations that HVS is highly adapted to extract irregularity information of textures in a scene, we introduce multifractal formalism into an image quality assessment scheme in this paper. Based on multifractal analysis, statistical complexity features of nature images are extracted robustly. Then a novel framework for image quality assessment is further proposed by quantifying the discrepancies between multifractal spectrums of images. A total of 982 images are used to validate the proposed algorithm, including five type of distortions: JPEG2000 compression, JPEG compression, white noise, Gaussian blur, and Fast Fading. Experimental results demonstrate that the proposed metric is highly effective for evaluating perceived image quality and it outperforms many state-of-the-art methods.

A class of multimodulus algorithms (MMA(p)) optimized by an optimal step-size (OS) for blind equalization are firstly investigated in this letter. The multimodulus (MM) criterion is essentially a split cost function that separately implements the real and imaginary part of the signal, hence the phase can be recovered jointly with equalization. More importantly, the step-size leading to the minimum of the MM criterion along the search direction can be obtained algebraically among the roots of a higher-order polynomial at each iteration, thus a robust optimal step-size multimodulus algorithm (OS-MMA(p)) is developed. Experimental results demonstrate improved performance of the proposed algorithm in mitigating the inter-symbol interference (ISI) compared with the OS constant modulus algorithm (OS-CMA). Besides, the computational complexity can be reduced by the proposed OS-MMA(2) algorithm.

Magnetic Anomaly Detection (MAD) is a passive method for the detection of ferromagnetic objects. Currently, the performance of a MAD system is limited by the magnetic background noise that is non-stationary and shows self-similarity and long-range correlation. In this paper, we propose an empirical mode decomposition (EMD) trend filtering based energy detector for adaptively detecting the magnetic anomaly signal from the background noise. The input data is first detrended adaptively with the energy-ratio trend filtering approach. Then, the magnetic anomaly signal is detected using an energy detector. The proposed detector does not need any a priori knowledge about the target or assumptions regarding the background noise. Experiments also prove that the proposed detector shows a more stable performance than the existing undecimated discrete wavelet transform (UDWT) based energy detector.

In this letter, a new semi-blind approach incorporating the bounded nature of communication sources with the distance between the equalizer outputs and the training sequence is proposed. By utilizing the sparsity property of l1-norm cost function, the proposed algorithm can outperform the semi-blind method based on higher-order statistics (HOS) criterion especially for transmitting sources with non-constant modulus. Experimental results demonstrate that the proposed method shows superior performance over the HOS based semi-blind method and the classical training-based method for QPSK and 16QAM sources equalization. While for 64QAM signal inputs, the proposed algorithm exhibits its superiority in low signal-to-noise-ratio (SNR) conditions compared with the training-based method.

This paper analyzes the effect of error propagation of decision feedback equalizer (DFE) for PAM4 based 400Gb/s Ethernet. First, an analytic model for the error propagation is proposed to estimate the probability of different burst error length due to error propagation for PAM4 link system with multi-tap TX FFE (Feed Forward Equalizer) + RX DFE architecture. After calculating the symbol error rate (SER) and bit error rate (BER) based on the probability model, the theoretical analysis about the impact of different equalizer configurations on BER is compared with the simulation results, and then BER performance with FEC (Forward Error Correction) is analyzed to evaluate the effect of DFE error propagation on PAM4 link. Finally, two FEC interleaving schemes, symbol and bit interleaving, are employed in order to reduce BER further and then the theoretical analysis and the simulation result of their performance improvement are also evaluated. Simulation results show that at most 0.52dB interleaving gain can be achieved compared with non-interleaving scheme just at a little cost in storing memory and latency. And between the two interleaving methods, symbol interleaving performs better compared with the other one from the view of tradeoff between the interleaving gain and the cost and can be applied for 400Gb/s Ethernet.

Optical navigation (OPNAV) is the use of the on-board imaging data to provide a direct measurement of the image coordinates of the target as navigation information. Among the optical observables in deep-space, the edge of the celestial body is an important feature that can be utilized for locating the planet centroid. However, traditional edge detection algorithms like Canny algorithm cannot be applied directly for OPNAV due to the noise edges caused by surface markings. Moreover, due to the constrained computation and energy capacity on-board, light-weight image-processing algorithms with less computational complexity are desirable for real-time processing. Thus, to fast and accurately extract the edge of the celestial body from high-resolution satellite imageries, this paper presents an algorithm-hardware co-design of real-time edge detection for OPNAV. First, a light-weight edge detection algorithm is proposed to efficiently detect the edge of the celestial body while suppressing the noise edges caused by surface markings. Then, we further present an FPGA implementation of the proposed algorithm with an optimized real-time performance and resource efficiency. Experimental results show that, compared with the traditional edge detection algorithms, our proposed one enables more accurate celestial body edge detection, while simplifying the hardware implementation.