This work investigates the effect of channel estimation error on the average secrecy outage capacity of dual selection in the presence of multiple eavesdroppers. The dual selection selects a transmit antenna of Alice and Bob (i.e., user terminal) which provide the best received signal to noise ratio (SNR) using channel state information from every user terminals. Using Gaussian approximation, this paper obtains the tight analytical expression of the dual selection for the average secrecy outage capacity over channel estimation error and multiple eavesdroppers. Using asymptotic analysis, this work quantifies the high SNR power offset and the high SNR slope for the average secrecy outage capacity at high SNR.

A new software based in-situ training (SBIST) method to achieve high accuracies is proposed for binarized neural networks inference accelerator chips in which measured offsets in sense amplifiers (activation binarizers) are transformed into biases in the training software. To expedite this individual training, the initial values for the weights are taken from results of a common forming training process which is conducted in advance by using the offset fluctuation distribution averaged over the fabrication line. SPICE simulation inference results for the accelerator predict that the accuracy recovers to higher than 90% even when the amplifier offset is as large as 40mV only after a few epochs of the individual training.

This paper presents a novel blind signal separation method for the measurement of pulse waves at multiple body positions using an array radar system. The proposed method is based on a mathematical model of pulse wave propagation. The model relies on three factors: (1) a small displacement approximation, (2) beam pattern orthogonality, and (3) an impulse response model of pulse waves. The separation of radar echoes is formulated as an optimization problem, and the associated objective function is established using the mathematical model. We evaluate the performance of the proposed method using measured radar data from participants lying in a prone position. The accuracy of the proposed method, in terms of estimating the body displacements, is measured using reference data taken from laser displacement sensors. The average estimation errors are found to be 10-21% smaller than those of conventional methods. These results indicate the effectiveness of the proposed method for achieving noncontact measurements of the displacements of multiple body positions.

AONT (All-or-Nothing Transform) is a kind of (n, n)-threshold secret sharing scheme that distributes a message m into a set of n shares such that the message m can be reconstructed if and only if n shares are collected. At CRYPTO 2000, Desai proposed a simple and faster AONT based on the CTR mode of encryption (called CTRT) and proved its security in the ideal cipher model. Though AES-128, whose key length k = 128 and block length l = 128, can be used in CTRT as a block cipher, AES-256 and AES-192 cannot be used due to its intrinsic restriction of k ≤ l. In this paper, we propose an extended CTRT (for short, XCTRT) suitable for AES-256. By thoroughly evaluating all the tricky cases, we prove that XCTRT is secure in the ideal cipher model under the same CTRT security definition. Also, we discuss the security result of XCTRT in concrete parameter settings. For more flexibility of key length, we propose a variant of XCTRT dealing with l

The differential uniformity, the boomerang uniformity, and the extended Walsh spectrum etc are important parameters to evaluate the security of S (substitution)-box. In this paper, we introduce efficient formulas to compute these cryptographic parameters of permutation polynomials of the form xrh(x(2n-1)/d) over a finite field of q=2n elements, where r is a positive integer and d is a positive divisor of 2n-1. The computational cost of those formulas is proportional to d. We investigate differentially 4-uniform permutation polynomials of the form xrh(x(2n-1)/3) and compute the boomerang spectrum and the extended Walsh spectrum of them using the suggested formulas when 6≤n≤12 is even, where d=3 is the smallest nontrivial d for even n. We also investigate the differential uniformity of some permutation polynomials introduced in some recent papers for the case d=2n/2+1.

This paper presents a deep neural network (DNN) based symbol detection and modulation classification detector (SDMCD) for mixed blind signals detection. Unlike conventional methods that employ symbol detection after modulation classification, the proposed SDMCD can perform symbol recovery and modulation identification simultaneously. A cumulant and moment feature vector is presented in conjunction with a low complexity sparse autoencoder architecture to complete mixed signals detection. Numerical results show that SDMCD scheme has remarkable symbol error rate performance and modulation classification accuracy for various modulation formats in AWGN and Rayleigh fading channels. Furthermore, the proposed detector has robust performance under the impact of frequency and phase offsets.

In recent years, there has been an increasing trend of applying artificial intelligence in many different fields, which has a profound and direct impact on human life. Consequently, this raises the need to understand the principles of model making predictions. Since most current high-precision models are black boxes, neither the AI scientist nor the end-user profoundly understands what is happening inside these models. Therefore, many algorithms are studied to explain AI models, especially those in the image classification problem in computer vision such as LIME, CAM, GradCAM. However, these algorithms still have limitations, such as LIME's long execution time and CAM's confusing interpretation of concreteness and clarity. Therefore, in this paper, we will propose a new method called Segmentation - Class Activation Mapping (SeCAM)/ This method combines the advantages of these algorithms above while at simultaneously overcoming their disadvantages. We tested this algorithm with various models, including ResNet50, InceptionV3, and VGG16 from ImageNet Large Scale Visual Recognition Challenge (ILSVRC) data set. Outstanding results were achieved when the algorithm has met all the requirements for a specific explanation in a remarkably short space of time.

Deployment of machine-type communications (MTCs) over the current cellular network could lead to severe overloading of the radio access network of Long Term Evolution (LTE)-based systems. This paper proposes a slotted access-based solution, called the Slotted Access For Group Paging (SAFGP), to cope with the paging-induced MTC traffic. The proposed SAFGP splits paged devices into multiple access groups, and each group is then allocated separate radio resources on the LTE's Physical Random Access Channel (PRACH) in a periodic manner during the paging interval. To support the proposed scheme, a new adaptive barring algorithm is proposed to stabilize the number of successful devices in each dedicated access slot. The objective is to let as few devices transmitting preambles in an access slot as possible while ensuring that the number of preambles selected by exactly one device approximates the maximum number of uplink grants that can be allocated by the eNB for an access slot. Analysis and simulation results demonstrate that, given the same amount of time-frequency resources, the proposed method significantly improves the access success and resource utilization rates at the cost of slightly increasing the access delay compared to state-of-the-art methods.

In this letter, a ray tracing (RT) acceleration method based on rank minimization is proposed. RT is a general tool used to simulate wireless communication environments. However, the simulation is time consuming because of the large number of ray calculations. This letter focuses on radio map interpolation as an acceleration approach. In the conventional methods cannot appropriately estimate short-span variation caused by multipath fading. To overcome the shortage of the conventional methods, we adopt rank minimization based interpolation. A computational simulation using commercial RT software revealed that the interpolation accuracy of the proposed method was higher than those of other radio map interpolation methods and that RT simulation can be accelerated approximate five times faster with the missing rate of 0.8.

This paper details the design of a plate that controls the beam direction in an aperture array excited by a waveguide 2-plane hybrid coupler. The beam direction can be controlled in the range of ±15-32deg. in the quasi H-plane, and ±26-54deg. in the quasi E-plane at the design frequency of 66.425GHz. Inductive irises are introduced into tapered waveguides in the plate and the reflection is suppressed by narrow apertures. A plate that has a larger tilt angle in the quasi E-plane and another plate with conventional rectangular waveguide ports as a reference are fabricated and measured. The measured values agree well with the simulation results.

The final exponentiation affects the efficiency of pairing computations especially on pairing-friendly curves with high embedding degree. We propose an efficient method for computing the hard part of the final exponentiation on the KSS18 curve at the 192-bit security level. Implementations indicate that the computation of the final exponentiation is 8.74% faster than the previously fastest result.

While social media is now used by many people and plays a role in distributing information, it has recently created an unexpected problem: the actual shrinkage of information sources. This is mainly due to the ease of connecting people with similar opinions and the recommendation system. Biased information distribution promotes polarization that divides people into multiple groups with opposing views. Also, people may receive only the seemingly positive information that they prefer, or may trigger them into holding onto their opinions more strongly when they encounter opposing views. This, combined with the characteristics of social media, is accelerating the polarization of opinions and eventually social division. In this paper, we propose a model of opinion formation on social media to simulate polarization. While based on the idea that opinion neutrality is only relative, this model provides new techniques for dealing with polarization.

Person re-identification (Re-ID) aims to match the same pedestrain identity images across different camera views. Because pedestrians will change clothes frequently for a relatively long time, while many current methods rely heavily on color appearance information or only focus on the person biometric features, these methods make the performance dropped apparently when it is applied to Clohting-Changing. To relieve this dilemma, we proposed a novel Multi Feature Fusion Attention Network (MFFAN), which learns the fine-grained local features. Then we introduced a Clothing Adaptive Attention (CAA) module, which can integrate multiple granularity features to guide model to learn pedestrain's biometric feature. Meanwhile, in order to fully verify the performance of our method on clothing-changing Re-ID problem, we designed a Clothing Generation Network (CGN), which can generate multiple pictures of the same identity wearing different clothes. Finally, experimental results show that our method exceeds the current best method by over 5% and 6% on the VCcloth and PRCC datasets respectively.

We investigate a problem of finding the minimum, in which each user has a real value, and we want to estimate the minimum of these values under the local differential privacy constraint. We reveal that this problem is fundamentally difficult, and we cannot construct a consistent mechanism in the worst case. Instead of considering the worst case, we aim to construct a private mechanism whose error rate is adaptive to the easiness of estimation of the minimum. As a measure of easiness, we introduce a parameter α that characterizes the fatness of the minimum-side tail of the user data distribution. As a result, we reveal that the mechanism can achieve O((ln6N/ε2N)1/2α) error without knowledge of α and the error rate is near-optimal in the sense that any mechanism incurs Ω((1/ε2N)1/2α) error. Furthermore, we demonstrate that our mechanism outperforms a naive mechanism by empirical evaluations on synthetic datasets. Also, we conducted experiments on the MovieLens dataset and a purchase history dataset and demonstrate that our algorithm achieves Õ((1/N)1/2α) error adaptively to α.

Currently, wireless power transmission technology is being developed for capsule endoscopes. By removing the battery, the capsule endoscope is miniaturized, the number of images that can be taken increases, and the risk of harmful substances leaking from the battery when it is damaged inside the body is avoided. Furthermore, diagnostic accuracy is improved by adjusting the directivity of radio waves according to the position of the capsule endoscope to improve efficiency and adjusting the number of images to be taken according to position by real-time position estimation. In this study, we report the result of position estimation in a high-definition numerical human body model and in an experiment on an electromagnetic phantom.

This letter presents a new framework for synchronous remote online exams. This framework proposes new monitoring of notebooks in remote locations and limited messaging only enabled between students and their instructor during online exams. This framework was evaluated by students as highly effective in minimizing cheating during online exams.

In this paper, an algorithm based on Taylor series expansion is proposed to calculate the logarithm (log2x) of IEEE754 binary32 accuracy floating-point number by a multi-domain partitioning method. The general mantissa (1≤x<2) is multiplied by 2, 4, 8, … (or equivalently left-shifted by 1, 2, 3, … bits), the regions of (2≤x<4), (4≤x<8), (8≤x<16),… are considered, and Taylor-series expansion is applied. In those regions, the slope of f(x)=log2 x with respect to x is gentle compared to the region of (1≤x<2), which reduces the required number of terms. We also consider the trade-offs among the numbers of additions, subtractions, and multiplications and Look-Up Table (LUT) size in hardware to select the best algorithm for the engineer's design and build the best hardware device.

This paper presents robust optimization models for minimizing the required backup capacity while providing probabilistic protection against multiple simultaneous failures of physical machines under uncertain virtual machine capacities in a cloud provider. If random failures occur, the required capacities for virtual machines are allocated to the dedicated backup physical machines, which are determined in advance. We consider two uncertainties: failure event and virtual machine capacity. By adopting a robust optimization technique, we formulate six mixed integer linear programming problems. Numerical results show that for a small size problem, our presented models are applicable to the case that virtual machine capacities are uncertain, and by using these models, we can obtain the optimal solution of the allocation of virtual machines under the uncertainty. A simulated annealing heuristic is presented to solve large size problems. By using this heuristic, an approximate solution is obtained for a large size problem.

This paper proposes a novel multi-layer substrate integrated waveguide (SIW) resonator loaded with asymmetric E-shaped slot-lines and shows a tri-band band-pass filter (BPF) using the proposed structure. In the previous literature, various SIW resonators have been proposed to simultaneously solve the problems of large area and high insertion loss. Although these SIWs have a lower insertion loss than planar-type resonators using a printed circuit board, the size of these structures tends to be larger. A multi-layer SIW resonator loaded with asymmetric E-shaped slot-lines can solve the above problems and realize a tri-band BPF without increasing the size to realize further miniaturization. The theoretical design method and the structural design are shown. Moreover, the configured structure is fabricated and measured for showing the validity of the design method in this paper.

Optical phase conjugation (OPC) is an all-optical signal processing technique for mitigating fiber nonlinearity and is promising for building cost-efficient fiber networks with few optic-electric-optic conversions and long amplification spacing. In lumped amplified systems, OPC has a little nonlinearity mitigation efficiency for nonlinear distortion induced by cross-phase modulation (XPM) due to the asymmetry of power and chromatic dispersion (CD) maps during propagation in transmission fiber. In addition, the walk-off of XPM-induced noise becomes small due to the CD compensation effect of OPC, so the deterministic nonlinear distortion increases. Therefore, lumped amplified transmission systems with OPC are more sensitive to channel spacing than conventional systems. In this paper, we show the channel spacing dependence of NZ-DSF transmission using amplification repeater with OPC. Numerical simulations show comprehensive characteristics between channel spacing and CD in a 100-Gbps/λ WDM signal. An experimental verification using periodically poled LiNbO3-based OPC is also performed. These results suggest that channel spacing design is more important in OPC-assisted systems than in conventional dispersion-unmanaged systems.