Antenna-coupled kinetic inductance detectors (KIDs) have recently shown great promise as microwave detection systems with a large number of channels. However, this technique, still has difficulties in eliminating the radiation loss of the resonator signals. To solve this problem, we propose a design in which the absorption area connected to an antenna is located on the ground-side of a coplanar waveguide. Thereby, radiation loss due to leakage from the resonator to the antenna can be considerably reduced. This simple design also enables the use of a contact aligner for fabrication. We have developed KIDs with this design, named as the ground-side absorption (GSA)-KIDs and demonstrated that they have higher quality factors than those of the existing KIDs, while maintaining a good total sensitivity.

In this paper, we investigate a relationship between many-one-like autoreducibility and completeness for classes of functions computed by polynomial-time nondeterministic Turing transducers. We prove two results. One is that any many-one complete function for these classes is metric many-one autoreducible. The other is that any strict metric many-one complete function for these classes is strict metric many-one autoreducible.

Given a graph G=(V, E), where V and E are vertex and edge sets of G, and a subset VNT of vertices called a non-terminal set, the minimum spanning tree with a non-terminal set VNT, denoted by MSTNT, is a connected and acyclic spanning subgraph of G that contains all vertices of V with the minimum weight where each vertex in a non-terminal set is not a leaf. On general graphs, the problem of finding an MSTNT of G is NP-hard. We show that if G is an outerplanar graph then finding an MSTNT of G is linearly solvable with respect to the number of vertices.

We propose a cache-aware method to accelerate texture-based volume rendering on a graphics processing unit (GPU) that is compatible with the compute unified device architecture. The proposed method extends a previous method such that it can maximize the average rendering performance while rotating the viewing direction around a volume. To realize this, the proposed method performs in-place rotation of volume data, which rearranges the order of voxels to allow consecutive threads (warps) to refer to voxels with the minimum access strides. Experiments indicate that the proposed method replaces the worst texture cache (TC) hit rate of 42% with the best TC hit rate of 93% for a 10243-voxel volume. Thus, the average frame rate increases by a factor of 1.6 in the proposed method compared with that in the previous method. Although the overhead of in-place rotation slightly decreases the frame rate from 2.0 frames per second (fps) to 1.9 fps, this slowdown occurs only with a few viewing directions.

Social Media has already become a new arena of our lives and involved different aspects of our social presence. Users' personal information and activities on social media presumably reveal their personal interests, which offer great opportunities for many e-commerce applications. In this paper, we propose a principled latent variable model to infer user consumption preferences at the category level (e.g. inferring what categories of products a user would like to buy). Our model naturally links users' published content and following relations on microblogs with their consumption behaviors on e-commerce websites. Experimental results show our model outperforms the state-of-the-art methods significantly in inferring a new user's consumption preference. Our model can also learn meaningful consumption-specific topics automatically.

Image-to-sound mapping is a technique that transforms an image to a sound signal, which is subsequently treated as a sound spectrogram. In general, the transformed sound differs from a human speech signal. Herein an efficient image-to-sound mapping method, which provides an understandable speech signal without any training, is proposed. To synthesize such a speech signal, the proposed method utilizes a multi-column image and a speech spectral phase that is obtained from a long-time observation of the speech. The original image can be retrieved from the sound spectrogram of the synthesized speech signal. The synthesized speech and the reconstructed image qualities are evaluated using objective tests.

We consider the problem of two-dimensional (2-D) angles of arrival estimation using a newly proposed structure of nonuniform linear array, referred to as nested coprime array with compressed inter-element spacing (CACIS). By constructing a cross-correlation matrix of the received signals, the nested CACIS exhibits a larger number of degrees of freedom. A two-step weighted l1-norm penalty strategy is proposed to fully utilize these degrees of freedom, where the weight matrices are constructed by MUSIC spectrum function and the threshold function, respectively. The proposed method has several salient advantages over the compared method, including increased resolution and accuracy, estimating many more number of sources and suppressing spurious peaks efficiently. Simulation results validate the superiority of the proposed method.

The impact of intersymbol interference (ISI) on single carrier frequency domain equalization with multiple input multiple output (MIMO-SCFDE) systems is severe. Most existing channel equalization methods fail to solve it completely. In this paper, given the disadvantages of the error propagation and the gap from matched filter bound (MFB), we creatively introduce a decision feedback equalizer with frequency-domain bidirectional noise prediction (DFE-FDBiNP) to tackle intersymbol interference (ISI) in MIMO-SCFDE systems. The equalizer has two-part equalizer, that is the normal mode and the time-reversal mode decision feedback equalization with noise prediction (DFE-NP). Equal-gain combining is used to realize a greatly simplified and low complexity diversity combining. Analysis and simulation results validate the improved performance of the proposed method in quasi-static frequency-selective fading MIMO channel for a typical urban environment.

This paper proposes novel nonlinear precoding for XOR-physical layer network coding (XOR-PNC) to improve the performance of bi-directional MIMO relay systems. The proposed precoder comprises a pre-equalizer and a nonlinear filter, which we also propose in the paper. We theoretically analyze the performance of the XOR-PNC with the proposed nonlinear precoding. As a result, it is shown that the proposed pre-equalizer improves the distribution of the received signals at relays, while the nonlinear precoder not only improves the transmission power efficiency but also simplifies the receiver at the relays. The performance is confirmed by computer simulation. The XOR-PNC with the proposed precoding achieves almost the lower bound in BER performance, which is much better than the amplify-and-forward physical layer network coding (AF-PNC).

Doppler lidar (LIght Detection And Ranging) can provide accurate wind velocity vector estimates by processing the time delay and Doppler spectrum of received signals. This system is essential for real-time wind monitoring to assist aircraft taking off and landing. Considering the difficulty of calibration and cost, a single Doppler lidar model is more attractive and practical than a multiple lidar model. In general, it is impossible to estimate two or three dimensional wind vectors from a single lidar model without any prior information, because lidar directly observes only a 1-dimensional (radial direction) velocity component of wind. Although the conventional VAD (Velocity Azimuth Display) and VVP (Velocity Volume Processing) methods have been developed for single lidar model, both of them are inaccurate in the presence of local air turbulence. This paper proposes an accurate wind velocity estimation method based on a parametric approach using typical turbulence models such as tornado, micro-burst and gust front. The results from numerical simulation demonstrate that the proposed method remarkably enhances the accuracy for wind velocity estimation in the assumed modeled turbulence cases, compared with that obtained by the VAD or other conventional method.

Timing fault detection techniques address the problems caused by increased variations on a chip, especially with dynamic voltage and frequency scaling (DVFS). The Razor flip-flop (FF) is a timing fault detection technique that employs double sampling by the main and shadow FFs. In order for the Razor FF to correctly detect a timing fault, not the main FF but the shadow FF must sample the correct value. The application of Razor FFs to static logic relaxes the timing constraints; however, the naive application of Razor FFs to dynamic precharged logic such as SRAM read circuits is not effective. This is because the SRAM precharge cannot start before the shadow FF samples the value; otherwise, the transition of the bitline of the SRAM stops and the value sampled by the shadow FF will be incorrect. Therefore, the detect period cannot overlap the precharge period. This paper proposes a novel application of Razor FFs to SRAM read circuits. Our proposal employs a conditional precharge according to the value of a bitline sampled by the main FF. This enables the detect period to overlap the precharge period, thereby relaxing the timing constraints. The additional circuit required by this method is simple and only needed around the sense amplifier, and there is no need for a clock delayed from the system clock. Consequently, the area overhead of the proposed circuit is negligible. This paper presents SPICE simulations of the proposed circuit. Our proposal reduces the minimum cycle time by 51.5% at a supply voltage of 1.1 V and the minimum voltage by 31.8% at cycle time of 412.5 ps.

This paper addresses the attribute recognition problem, a field of research that is dominated by studies in the visible spectrum. Only a few works are available in the thermal spectrum, which is fundamentally different from the visible one. This research performs recognition specifically on wearable attributes, such as glasses and masks. Usually these attributes are relatively small in size when compared with the human body, on top of a large intra-class variation of the human body itself, therefore recognizing them is not an easy task. Our method utilizes a decomposition framework based on Robust Principal Component Analysis (RPCA) to extract the attribute information for recognition. However, because it is difficult to separate the body and the attributes without any prior knowledge, noise is also extracted along with attributes, hampering the recognition capability. We made use of prior knowledge; namely the location where the attribute is likely to be present. The knowledge is referred to as the Probability Map, incorporated as a weight in the decomposition by RPCA. Using the Probability Map, we achieve an attribute-wise decomposition. The results show a significant improvement with this approach compared to the baseline, and the proposed method achieved the highest performance in average with a 0.83 F-score.

In this paper, we introduce a self-constructive Normalized Gaussian Network (NGnet) for online learning tasks. In online tasks, data samples are received sequentially, and domain knowledge is often limited. Then, we need to employ learning methods to the NGnet that possess robust performance and dynamically select an accurate model size. We revise a previously proposed localized forgetting approach for the NGnet and adapt some unit manipulation mechanisms to it for dynamic model selection. The mechanisms are improved for more robustness in negative interference prone environments, and a new merge manipulation is considered to deal with model redundancies. The effectiveness of the proposed method is compared with the previous localized forgetting approach and an established learning method for the NGnet. Several experiments are conducted for a function approximation and chaotic time series forecasting task. The proposed approach possesses robust and favorable performance in different learning situations over all testbeds.

Recent research has shown that the class of rotation symmetric Boolean functions is beneficial to cryptographics. In this paper, for an odd prime p, two sufficient conditions for p-variable rotation symmetric Boolean functions to be 1-resilient are obtained, and then several concrete constructions satisfying the conditions are presented. This is the first time that resilient rotation symmetric Boolean functions have been systematically constructed. In particular, we construct a class of 2-resilient rotation symmetric Boolean functions when p=2m+1 for m ≥ 4. Moreover, several classes of 1-order correlation immune rotation symmetric Boolean functions are also got.

An all-digital fully-synthesizable PVT-tolerant clock data recovery (CDR) architecture for wireline chip-to-chip interconnects is presented. The proposed architecture enables the co-synthesis of the CDR with the digital core. By eliminating the resource hungry manual layout and interfacing steps, which are necessary for conventional CDR topologies, the design process and the time-to-market can be drastically improved. Besides, the proposed CDR architecture enables the re-usability of majority of the sub-systems which enables easy migration to different process nodes. The proposed CDR is also equipped with a self-calibration scheme for ensuring tolerence over PVT. The proposed fully-syntehsizable CDR was implemented in 28nm FDSOI. The system achieves a maximum data rate of 10.06Gbps while consuming a power of 16.1mW from a 1V power supply.

Permutation polynomials over Zpn are useful in the design of cryptographic algorithms. In this paper, we obtain an equivalent condition for polynomial functions over Zpn to be permutations, and this equivalent condition can help us to analysis the randomness of such functions. Our results provide a method to distinguish permutation polynomials from random functions. We also introduce how to improve the randomness of permutation polynomials over Zpn.

In this paper, we study the problem of noise with regard to the perfect reconstruction of non-bandlimited signals, the class of signals having a finite number of degrees of freedom per unit time. The finite rate of innovation (FRI) method provides a means of recovering a non-bandlimited signal through using of appropriate kernels. In the presence of noise, however, the reconstruction function of this scheme may become ill-conditioned. Further, the reduced sampling rates afforded by this scheme can be accompanied by increased error sensitivity. In this paper, to obtain improved noise robustness, we propose the matrix pencil (MP) method for sample signal reconstruction, which is based on principal component analysis (PCA). Through the selection of an adaptive eigenvalue, a non-bandlimited signal can be perfectly reconstructed via a stable solution of the Yule-Walker equation. The proposed method can obtain a high signal-to-noise-ratio (SNR) for the reconstruction results. Herein, the method is applied to certain non-bandlimited signals, such as a stream of Diracs and nonuniform splines. The simulation results demonstrate that the MP and PCA are more effective than the FRI method in suppressing noise. The FRI method can be used in many applications, including those related to bioimaging, radar, and ultrasound imaging.

The differential fault analysis of SOSEMNAUK was presented in Africacrypt in 2011. In this paper, we improve previous work with algebraic techniques which can result in a considerable reduction not only in the number of fault injections but also in time complexity. First, we propose an enhanced method to determine the fault position with a success rate up to 99% based on the single-word fault model. Then, instead of following the design of SOSEMANUK at word levels, we view SOSEMANUK at bit levels during the fault analysis and calculate most components of SOSEMANUK as bit-oriented. We show how to build algebraic equations for SOSEMANUK and how to represent the injected faults in bit-level. Finally, an SAT solver is exploited to solve the combined equations to recover the secret inner state. The results of simulations on a PC show that the full 384 bits initial inner state of SOSEMANUK can be recovered with only 15 fault injections in 3.97h.

Given an undirected graph G, an edge dominating set is a subset F of edges such that each edge not in F is adjacent to some edge in F, and computing the minimum size of an edge dominating set is known to be NP-hard. Since the size of any edge dominating set is at least half of the maximum size µ(G) of a matching in G, we study the problem of testing whether a given graph G has an edge dominating set of size ⌈µ(G)/2⌉ or not. In this paper, we prove that the problem is NP-complete, whereas we design an O*(2.0801µ(G)/2)-time and polynomial-space algorithm to the problem.

In this paper we study a recently proposed variant of the facility location problem, called the r-gathering problem. Given an integer r, a set C of customers, a set F of facilities, and a connecting cost co(c, f) for each pair of c ∈ C and f ∈ F, an r-gathering of customers C to facilities F is an assignment A of C to open facilities F' ⊆ F such that at least r customers are assigned to each open facility. We give an algorithm to find an r-gathering with the minimum cost, where the cost is maxc ∈ C{co(c, A(c))}, when all C and F are on the real line.