The novel optical path routing architecture named flexible waveband routing networks is reviewed in this paper. The nodes adopt a two-stage path routing scheme where wavelength selective switches (WSSs) bundle optical paths and form a small number of path groups and then optical switches without wavelength selectivity route these groups to desired outputs. Substantial hardware scale reduction can be achieved as the scheme enables us to use small scale WSSs, and even more, share a WSS by multiple input cores/fibers through the use of spatially-joint-switching. Furthermore, path groups distributed over multiple bands can be switched by these optical switches and thus the adaptation to multi-band transmission is straightforward. Network-wide numerical simulations and transmission experiments that assume multi-band transmission demonstrate the validity of flexible waveband routing.

The calculation of cross-correlation between a sequence with good autocorrelation and its decimated sequence is an interesting problem in the field of sequence design. In this letter, we consider a class of ternary sequences with perfect autocorrelation, proposed by Shedd and Sarwate (IEEE Trans. Inf. Theory, 1979, DOI: 10.1109/TIT.1979.1055998), which is generated based on the cross-correlation between m-sequence and its d-decimation sequence. We calculate the cross-correlation distribution between a certain pair of such ternary perfect sequences and show that the cross-correlation takes three different values.

Low confusion coefficient values can make side-channel attacks harder for vector Boolean functions in Block cipher. In this paper, we give new results of confusion coefficient for f ⊞ g, f ⊡ g, f ⊕ g and fg for different Boolean functions f and g, respectively. And we deduce a relationship on the sum-of-squares of the confusion coefficient between one n-variable function and two (n - 1)-variable decomposition functions. Finally, we find that the confusion coefficient of vector Boolean functions is affine invariant.

This letter presents a dual-band load-modulated sequential amplifier (LMSA). The proposed amplifier changed the attenuator terminated at the isolation port of the four-port combiner of the traditional sequential power amplifier (SPA) architecture into a reactance modulation network (RMN) for load modulation. The impedance can be maintained pure resistance by designing RMN, thus realizing high efficiency and a good portion of the output power in the multiple bands. Compared to the dual-band Doherty power amplifier with a complex dual-band load modulation network (LMN), the proposed LMSA has advantages as maintaining high output power back-off (OBO) efficiency, wide bandwidth and simple construction. A 10-watt dual-band LMSA is simulated and measured in 1.7-1.9GHz and 2.4-2.6GHz with saturated efficiencies 61.2-69.9% and 54.4-70.8%, respectively. The corresponding 9dB OBO efficiency is 46.5-57.1% and 46.4-54.4%, respectively.

Person authentication using biometric information has recently become popular among researchers. User management based on biometrics is more reliable than that using conventional methods. To secure private information, it is necessary to build continuous authentication-based user management systems. Brain waves are suitable biometric modalities for continuous authentication. This study is based on biometric authentication using brain waves evoked by invisible visual stimuli. Invisible visual stimulation is considered over visual stimulation to overcome the obstacles faced by a user when using a system. Invisible stimuli are confirmed by changing the intensity of the image and presenting high-speed stimulation. To ensure invisibility, stimuli of different intensities were tested, and the stimuli with an intensity of 5% was confirmed to be invisible. To improve the verification performance, a continuous wavelet transform was introduced over the Fourier transform because it extracts both time and frequency information from the brain wave. The scalogram obtained by the wavelet transform was used as an individual feature and for synchronizing the template and test data. Furthermore, to improve the synchronization performance, the waveband was split based on the power distribution of the scalogram. A performance evaluation using 20 subjects showed an equal error rate of 3.8%.

Most quantum circuits require SWAP gate insertion to run on quantum hardware with limited qubit connectivity. A promising SWAP gate insertion method for blocks of commuting two-qubit gates is a predetermined swap strategy which applies layers of SWAP gates simultaneously executable on the coupling map. A good initial mapping for the swap strategy reduces the number of required swap gates. However, even when a circuit consists of commuting gates, e.g., as in the Quantum Approximate Optimization Algorithm (QAOA) or trotterized simulations of Ising Hamiltonians, finding a good initial mapping is a hard problem. We present a SAT-based approach to find good initial mappings for circuits with commuting gates transpiled to the hardware with swap strategies. Our method achieves a 65% reduction in gate count for random three-regular graphs with 500 nodes. In addition, we present a heuristic approach that combines the SAT formulation with a clustering algorithm to reduce large problems to a manageable size. This approach reduces the number of swap layers by 25% compared to both a trivial and random initial mapping for a random three-regular graph with 1000 nodes. Good initial mappings will therefore enable the study of quantum algorithms, such as QAOA and Ising Hamiltonian simulation applied to sparse problems, on noisy quantum hardware with several hundreds of qubits.

This paper presents a compact fully-differential distributed amplifier using a coupled inductor. Differential distributed amplifiers are widely required in optical communication systems. Most of the distributed amplifiers reported in the past are single-ended or pseudo-differential topologies. In addition, the differential distributed amplifiers require many inductors, which increases the silicon cost. In this study, we use differentially coupled inductors to reduce the chip area to less than half and eliminate the difficulties in layout design. The challenge in using coupled inductors is the capacitive parasitic coupling that degrades the flatness of frequency response. To address this challenge, the odd-mode image parameters of a differential artificial transmission line are derived using a simple loss-less model. Based on the analytical results, we optimize the dimensions of the inductor with the gradient descent algorithm to achieve accurate impedance matching and phase matching. The amplifier was fabricated in 0.18-µm CMOS technology. The core area of the amplifier is 0.27 mm2, which is 57% smaller than the previous work. Besides, we demonstrated a small group delay variation of ±2.7 ps thanks to the optimization. the amplifier successfully performed 30-Gbps NRZ and PAM4 transmissions with superior jitter performance. The proposed technique will promote the high-density integration of differential traveling wave devices.

This paper presents a quantized gradient descent algorithm for distributed nonconvex optimization in multiagent systems that takes into account the bandwidth limitation of communication channels. Each agent encodes its estimation variable using a zoom-in parameter and sends the quantized intermediate variable to the neighboring agents. Then, each agent updates the estimation by decoding the received information. In this paper, we show that all agents achieve consensus and their estimated variables converge to a critical point in the optimization problem. A numerical example of a nonconvex logistic regression shows that there is a trade-off between the convergence rate of the estimation and the communication bandwidth.

Electrophysiology, which is the study of the electrical properties of biological tissues and cells, has become indispensable in modern clinical research, diagnostics, disease monitoring and therapeutics. In this paper we present a brief history of this discipline and how integrated circuit design shaped electrophysiology in the last few decades. We will discuss how biopotential amplifier design has evolved from the classical three-opamp architecture to more advanced high-performance circuits enabling long-term wearable monitoring of the autonomous and central nervous system. We will also discuss how these integrated circuits evolved to measure in-vivo neural circuits. This paper targets readers who are new to the domain of biopotential recording and want to get a brief historical overview and get up to speed on the main circuit design concepts for both wearable and in-vivo biopotential recording.

In this research, we investigated the threshold voltage (VTH) control by partial polarization of metal-ferroelectric-semiconductor field-effect transistors (MFSFETs) with 5 nm-thick nondoped HfO2 gate insulator utilizing Kr-plasma sputtering for Pt gate electrode deposition. The remnant polarization (2Pr) of 7.2 μC/cm2 was realized by Kr-plasma sputtering for Pt gate electrode deposition. The memory window (MW) of 0.58 V was realized by the pulse amplitude and width of -5/5 V, 100 ms. Furthermore, the VTH of MFSFET was controllable by program/erase (P/E) input pulse even with the pulse width below 100 ns which may be caused by the reduction of leakage current with decreasing plasma damage.

High power dissipation during scan test often causes undue yield loss, especially for low-power circuits. One major reason is that the resulting IR-drop in shift mode may corrupt test data. A common approach to solving this problem is partial-shift, in which multiple scan chains are formed and only one group of scan chains is shifted at a time. However, existing partial-shift based methods suffer from two major problems: (1) their IR-drop estimation is not accurate enough or computationally too expensive to be done for each shift cycle; (2) partial-shift is hence applied to all shift cycles, resulting in long test time. This paper addresses these two problems with a novel IR-drop-aware scan shift method, featuring: (1) Cycle-based IR-Drop Estimation (CIDE) supported by a GPU-accelerated dynamic power simulator to quickly find potential shift cycles with excessive peak IR-drop; (2) a scan shift scheduling method that generates a scan chain grouping targeted for each considered shift cycle to reduce the impact on test time. Experiments on ITC'99 benchmark circuits show that: (1) the CIDE is computationally feasible; (2) the proposed scan shift schedule can achieve a global peak IR-drop reduction of up to 47%. Its scheduling efficiency is 58.4% higher than that of an existing typical method on average, which means our method has less test time.

This paper introduces significant improvements over the existing cryptanalysis approaches on Salsa20 and ChaCha stream ciphers. For the first time, we reduced the attack complexity on Salsa20/8 to the lowest possible margin. We introduced an attack on ChaCha7.25. It is the first attack of its type on ChaCha7.25/20. In our approach, we studied differential cryptanalysis of the Salsa20 and ChaCha stream ciphers based on a comprehensive analysis of probabilistic neutral bits (PNBs). The existing differential cryptanalysis approaches on Salsa20 and ChaCha stream ciphers first study the differential bias at specific input and output differential positions and then search for probabilistic neutral bits. However, the differential bias and the set of PNBs obtained in this method are not always the ideal combination to conduct the attack against the ciphers. The researchers have not focused on the comprehensive analysis of the probabilistic neutrality measure of all key bits concerning all possible output difference positions at all possible internal rounds of Salsa20 and ChaCha stream ciphers. Moreover, the relationship between the neutrality measure and the number of inverse quarter rounds has not been scrutinized yet. To address these study gaps, we study the differential cryptanalysis based on the comprehensive analysis of probabilistic neutral bits on the reduced-round Salsa20 and ChaCha. At first, we comprehensively analyze the neutrality measure of 256 key bits positions. Afterward, we select the output difference bit position with the best average neutrality measure and look for the corresponding input differential with the best differential bias. Considering all aspects, we present an attack on Salsa20/8 with a time complexity of 2241.62 and data complexity of 231.5, which is the best-known single bit differential attack on Salsa20/8 and then, we introduced an attack on ChaCha7.25 rounds with a time complexity of 2254.011 and data complexity of 251.81.

In frequency hopping communication, time delay and Doppler shift incur interference. With the escalating upgrading of complicated interference, in this paper, the time-frequency two-dimensional (TFTD) partial Hamming correlation (PHC) properties of wide-gap frequency-hopping sequences (WGFHSs) with frequency shift are discussed. A bound on the maximum TFTD partial Hamming auto-correlation (PHAC) and two bounds on the maximum TFTD PHC of WGFHSs are got. Li-Fan-Yang bounds are the particular cases of new bounds for frequency shift is zero.

In CRYPTO 2019, Gohr first introduced the deep learning method to cryptanalysis for SPECK32/64. A differential-neural distinguisher was obtained using ResNet neural network. Zhang et al. used multiple parallel convolutional layers with different kernel sizes to capture information from multiple dimensions, thus improving the accuracy or obtaining a more round of distinguisher for SPECK32/64 and SIMON32/64. Inspired by Zhang's work, we apply the network structure to other ciphers. We not only improve the accuracy of the distinguisher, but also increase the number of rounds of the distinguisher, that is, distinguish more rounds of ciphertext and random number for DES, Chaskey and PRESENT.

The selection of mutation strategy greatly affects the performance of differential evolution algorithm (DE). For different types of optimization problems, different mutation strategies should be selected. How to choose a suitable mutation strategy for different problems is a challenging task. To deal with this challenge, this paper proposes a novel DE algorithm based on local fitness landscape, called FLIDE. In the proposed method, fitness landscape information is obtained to guide the selection of mutation operators. In this way, different problems can be solved with proper evolutionary mechanisms. Moreover, a population adjustment method is used to balance the search ability and population diversity. On one hand, the diversity of the population in the early stage is enhanced with a relative large population. One the other hand, the computational cost is reduced in the later stage with a relative small population. The evolutionary information is utilized as much as possible to guide the search direction. The proposed method is compared with five popular algorithms on 30 test functions with different characteristics. Experimental results show that the proposed FLIDE is more effective on problems with high dimensions.

Differential privacy is a confidentiality metric and quantitatively guarantees the confidentiality of individuals. A noise criterion, called sensitivity, must be calculated when constructing a probabilistic disturbance mechanism that satisfies differential privacy. Depending on the statistical process, the sensitivity may be very large or even impossible to compute. As a result, the usefulness of the constructed mechanism may be significantly low; it might even be impossible to directly construct it. In this paper, we first discuss situations in which sensitivity is difficult to calculate, and then propose a differential privacy with additional dummy data as a countermeasure. When the sensitivity in the conventional differential privacy is calculable, a mechanism that satisfies the proposed metric satisfies the conventional differential privacy at the same time, and it is possible to evaluate the relationship between the respective privacy parameters. Next, we derive sensitivity by focusing on correlation coefficients as a case study of a statistical process for which sensitivity is difficult to calculate, and propose a probabilistic disturbing mechanism that satisfies the proposed metric. Finally, we experimentally evaluate the effect of noise on the sensitivity of the proposed and direct methods. Experiments show that privacy-preserving correlation coefficients can be derived with less noise compared to using direct methods.

In recent years, concerns about location privacy are increasing with the spread of location-based services (LBSs). Many methods to protect location privacy have been proposed in the past decades. Especially, perturbation methods based on Geo-Indistinguishability (GeoI), which randomly perturb a true location to a pseudolocation, are getting attention due to its strong privacy guarantee inherited from differential privacy. However, GeoI is based on the Euclidean plane even though many LBSs are based on road networks (e.g. ride-sharing services). This causes unnecessary noise and thus an insufficient tradeoff between utility and privacy for LBSs on road networks. To address this issue, we propose a new privacy notion, Geo-Graph-Indistinguishability (GeoGI), for locations on a road network to achieve a better tradeoff. We propose Graph-Exponential Mechanism (GEM), which satisfies GeoGI. Moreover, we formalize the optimization problem to find the optimal GEM in terms of the tradeoff. However, the computational complexity of a naive method to find the optimal solution is prohibitive, so we propose a greedy algorithm to find an approximate solution in an acceptable amount of time. Finally, our experiments show that our proposed mechanism outperforms GeoI mechanisms, including optimal GeoI mechanism, with respect to the tradeoff.

Convolutional Neural Networks (CNNs) have recently demonstrated outstanding performance in image retrieval tasks. Local convolutional features extracted by CNNs, in particular, show exceptional capability in discrimination. Recent research in this field has concentrated on pooling methods that incorporate local features into global features and assess the global similarity of two images. However, the pooling methods sacrifice the image's local region information and spatial relationships, which are precisely known as the keys to the robustness against occlusion and viewpoint changes. In this paper, instead of pooling methods, we propose an alternative method based on local similarity, determined by directly using local convolutional features. Specifically, we first define three forms of local similarity tensors (LSTs), which take into account information about local regions as well as spatial relationships between them. We then construct a similarity CNN model (SCNN) based on LSTs to assess the similarity between the query and gallery images. The ideal configuration of our method is sought through thorough experiments from three perspectives: local region size, local region content, and spatial relationships between local regions. The experimental results on a modified open dataset (where query images are limited to occluded ones) confirm that the proposed method outperforms the pooling methods because of robustness enhancement. Furthermore, testing on three public retrieval datasets shows that combining LSTs with conventional pooling methods achieves the best results.

High gain antennas with narrow-beamforming are required to compensate for the high propagation loss expected in high frequency bands such as the millimeter wave and sub-terahertz wave bands, which are promising for achieving extremely high speeds and capacity. However using narrow-beamforming for initial access (IA) beam search in all directions incurs an excessive overhead. Using wide-beamforming can reduce the overhead for IA but it also shrinks the coverage area due to the lower beamforming gain. Here, it is assumed that there are some situations in which the required coverage distance differs depending on the direction from the antenna. For example, the distance to an floor for a ceiling-mounted antenna varies depending on the direction, and the distance to the obstruction becomes the required coverage distance for an antenna installation design that assumes line-of-sight. In this paper, we propose a novel IA beam search scheme with adaptive beam width control based on the distance to shield obstacles in each direction. Simulations and experiments show that the proposed method reduces the overhead by 20%-50% without shrinking the coverage area in shield environments compared to exhaustive beam search with narrow-beamforming.

Influential user detection is critical in supporting the human facilitator-based facilitation in the online forum. Traditional approaches to detect influential users in the online forum focus on the statistical activity information such as the number of posts. However, statistical activity information cannot fully reflect the influence that users bring to the online forum. In this paper, we propose to detect the influencers from the influence propagation perspective and focus on the influential maximization (IM) problem which aims at choosing a set of users that maximize the influence propagation from the entire social network. An online forum influence propagation network (OFIPN) is proposed to model the influence from an individual user perspective and influence propagation between users, and a heuristic algorithm that is proposed to find influential users in OFIPN. Experiments are conducted by simulations with a real-world social network. Our empirical results show the effectiveness of the proposed algorithm.